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Science, health, and public trust.

September 8, 2021

Explaining How Research Works

We’ve heard “follow the science” a lot during the pandemic. But it seems science has taken us on a long and winding road filled with twists and turns, even changing directions at times. That’s led some people to feel they can’t trust science. But when what we know changes, it often means science is working.

Explaining the scientific process may be one way that science communicators can help maintain public trust in science. Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle.

Questions about how the world works are often investigated on many different levels. For example, scientists can look at the different atoms in a molecule, cells in a tissue, or how different tissues or systems affect each other. Researchers often must choose one or a finite number of ways to investigate a question. It can take many different studies using different approaches to start piecing the whole picture together.

Sometimes it might seem like research results contradict each other. But often, studies are just looking at different aspects of the same problem. Researchers can also investigate a question using different techniques or timeframes. That may lead them to arrive at different conclusions from the same data.

Using the data available at the time of their study, scientists develop different explanations, or models. New information may mean that a novel model needs to be developed to account for it. The models that prevail are those that can withstand the test of time and incorporate new information. Science is a constantly evolving and self-correcting process.

Scientists gain more confidence about a model through the scientific process. They replicate each other’s work. They present at conferences. And papers undergo peer review, in which experts in the field review the work before it can be published in scientific journals. This helps ensure that the study is up to current scientific standards and maintains a level of integrity. Peer reviewers may find problems with the experiments or think different experiments are needed to justify the conclusions. They might even offer new ways to interpret the data.

It’s important for science communicators to consider which stage a study is at in the scientific process when deciding whether to cover it. Some studies are posted on preprint servers for other scientists to start weighing in on and haven’t yet been fully vetted. Results that haven't yet been subjected to scientific scrutiny should be reported on with care and context to avoid confusion or frustration from readers.

We’ve developed a one-page guide, "How Research Works: Understanding the Process of Science" to help communicators put the process of science into perspective. We hope it can serve as a useful resource to help explain why science changes—and why it’s important to expect that change. Please take a look and share your thoughts with us by sending an email to  [email protected].

Below are some additional resources:

• Discoveries in Basic Science: A Perfectly Imperfect Process
• When Clinical Research Is in the News
• What is Basic Science and Why is it Important?
• ​ What is a Research Organism?
• What Are Clinical Trials and Studies?
• Basic Research – Digital Media Kit
• Decoding Science: How Does Science Know What It Knows? (NAS)
• Can Science Help People Make Decisions ? (NAS)

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Biomedical Beat Blog – National Institute of General Medical Sciences

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How research works: understanding the process of science.

Have you ever wondered how research works? How scientists make discoveries about our health and the world around us? Whether they’re studying plants, animals, humans, or something else in our world, they follow the scientific method. But this method isn’t always—or even usually—a straight line, and often the answers are unexpected and lead to more questions. Let’s dive in to see how it all works.

The Question Scientists start with a question about something they observe in the world. They develop a hypothesis, which is a testable prediction of what the answer to their question will be. Often their predictions turn out to be correct, but sometimes searching for the answer leads to unexpected outcomes.

The Techniques To test their hypotheses, scientists conduct experiments. They use many different tools and techniques, and sometimes they need to invent a new tool to fully answer their question. They may also work with one or more scientists with different areas of expertise to approach the question from other angles and get a more complete answer to their question.

The Evidence Throughout their experiments, scientists collect and analyze their data. They reach conclusions based on those analyses and determine whether their results match the predictions from their hypothesis. Often these conclusions trigger new questions and new hypotheses to test.

Researchers share their findings with one another by publishing papers in scientific journals and giving presentations at meetings. Data sharing is very important for the scientific field, and although some results may seem insignificant, each finding is often a small piece of a larger puzzle. That small piece may spark a new question and ultimately lead to new findings.

Sometimes research results seem to contradict each other, but this doesn’t necessarily mean that the results are wrong. Instead, it often means that the researchers used different tools, methods, or timeframes to obtain their results. The results of a single study are usually unable to fully explain the complex systems in the world around us. We must consider how results from many research studies fit together. This perspective gives us a more complete picture of what’s really happening.

Even if the scientific process doesn’t answer the original question, the knowledge gained may help provide other answers that lead to new hypotheses and discoveries.

Learn more about the importance of communicating how this process works in the NIH News in Health article, “ Explaining How Research Works .”

This post is a great supplement to Pathways: The Basic Science Careers Issue.

Pathways introduces the important role that scientists play in understanding the world around us, and all scientists use the scientific method as they make discoveries—which is explained in this post.

Learn more in our Educator’s Corner .

2 Replies to “How Research Works: Understanding the Process of Science”

Nice basic explanation. I believe informing the lay public on how science works, how parts of the body interact, etc. is a worthwhile endeavor. You all Rock! Now, we need to spread the word ‼️❗️‼️ Maybe eith a unique app. And one day, with VR and incentives to read & answer a couple questions.

As you know, the importance of an informed population is what will keep democracy alive. Plus it will improve peoples overall wellness & life outcomes.

Thanks for this clear explanation for the person who does not know science. Without getting too technical or advanced, it might be helpful to follow your explanation of replication with a reference to meta-analysis. You might say something as simple as, “Meta-analysis is a method for doing research on all the best research; meta-analytic research confirms the overall trend in results, even when the best studies show different results.”

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How to Conduct Responsible Research: A Guide for Graduate Students

Alison l. antes.

1 Department of Medicine, Division of General Medical Sciences, Washington University School of Medicine, St. Louis, Missouri, 314-362-6006

Leonard B. Maggi, Jr.

2 Department of Medicine, Division of Molecular Oncology, Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri, 314-362-4102

Researchers must conduct research responsibly for it to have an impact and to safeguard trust in science. Essential responsibilities of researchers include using rigorous, reproducible research methods, reporting findings in a trustworthy manner, and giving the researchers who contributed appropriate authorship credit. This “how-to” guide covers strategies and practices for doing reproducible research and being a responsible author. The article also covers how to utilize decision-making strategies when uncertain about the best way to proceed in a challenging situation. The advice focuses especially on graduate students but is appropriate for undergraduates and experienced researchers. The article begins with an overview of the responsible conduct of research, research misconduct, and ethical behavior in the scientific workplace. The takeaway message is that responsible conduct of research requires a thoughtful approach to doing research to ensure trustworthy results and conclusions and that researchers receive fair credit.

INTRODUCTION

Doing research is stimulating and fulfilling work. Scientists make discoveries to build knowledge and solve problems, and they work with other dedicated researchers. Research is a highly complex activity, so it takes years for beginning researchers to learn everything they need to know to do science well. Part of this large body of knowledge is learning how to do research responsibly. Our purpose in this article is to provide graduate students a guide for how to perform responsible research. Our advice is also relevant to undergraduate researchers and for principal investigators (PIs), postdocs, or other researchers who mentor beginning researchers and wish to share our advice.

We begin by introducing some fundamentals about the responsible conduct of research (RCR), research misconduct, and ethical behavior. We focus on how to do reproducible science and be a responsible author. We provide practical advice for these topics and present scenarios to practice thinking through challenges in research. Our article concludes with decision-making strategies for addressing complex problems.

What is the responsible conduct of research?

To be committed to RCR means upholding the highest standards of honesty, accuracy, efficiency, and objectivity ( Steneck, 2007 ). Each day, RCR requires engaging in research in a conscientious, intentional fashion that yields the best science possible ( “Research Integrity is Much More Than Misconduct,” 2019 ). We adopt a practical, “how-to” approach, discussing the behaviors and habits that yield responsible research. However, some background knowledge about RCR is helpful to frame our discussion.

The scientific community uses many terms to refer to ethical and responsible behavior in research: responsible conduct of research, research integrity, scientific integrity, and research ethics ( National Academies of Science, 2009 ; National Academies of Sciences Engineering and Medicine, 2017 ; Steneck, 2007 ). A helpful way to think about these concepts is “doing good science in a good manner” ( DuBois & Antes, 2018 ). This means that the way researchers do their work, from experimental procedures to data analysis and interpretation, research reporting, and so on, leads to trustworthy research findings and conclusions. It also includes respectful interactions among researchers both within research teams (e.g., between peers, mentors and trainees, and collaborators) and with researchers external to the team (e.g., peer reviewers). We expand on trainee-mentor relationships and interpersonal dynamics with labmates in a companion article ( Antes & Maggi, 2021 ). When research involves human or animal research subjects, RCR includes protecting the well-being of research subjects.

We do not cover all potential RCR topics but focus on what we consider fundamentals for graduate students. Common topics covered in texts and courses on RCR include the following: authorship and publication; collaboration; conflicts of interest; data management, sharing, and ownership; intellectual property; mentor and trainee responsibilities; peer review; protecting human subjects; protecting animal subjects; research misconduct; the role of researchers in society; and laboratory safety. A number of topics prominently discussed among the scientific community in recent years are also relevant to RCR. These include the reproducibility of research ( Baker, 2016 ; Barba, 2016 ; Winchester, 2018 ), diversity and inclusion in science ( Asplund & Welle, 2018 ; Hofstra et al., 2020 ; Meyers, Brown, Moneta-Koehler, & Chalkley, 2018 ; National Academies of Sciences Engineering and Medicine, 2018a ; Roper, 2019 ), harassment and bullying ( Else, 2018 ; National Academies of Sciences Engineering and Medicine, 2018b ; “ No Place for Bullies in Science,” 2018 ), healthy research work environments ( Norris, Dirnagl, Zigmond, Thompson-Peer, & Chow, 2018 ; “ Research Institutions Must Put the Health of Labs First,” 2018 ), and the mental health of graduate students ( Evans, Bira, Gastelum, Weiss, & Vanderford, 2018 ).

The National Institutes of Health (NIH) ( National Institutes of Health, 2009 ) and the National Science Foundation ( National Science Foundation, 2017 ) have formal policies indicating research trainees must receive education in RCR. Researchers are accountable to these funding agencies and the public which supports research through billions in tax dollars annually. The public stands to benefit from, or be harmed by, research. For example, the public may be harmed if medical treatments or social policies are based on untrustworthy research findings. Funding for research, participation in research, and utilization of the fruits of research all rely on public trust ( Resnik, 2011 ). Trustworthy findings are also essential for good stewardship of scarce resources ( Emanuel, Wendler, & Grady, 2000 ). Researchers are further accountable to their peers, colleagues, and scientists more broadly. Trust in the work of other researchers is essential for science to advance. Finally, researchers are accountable for complying with the rules and policies of their universities or research institutions, such as rules about laboratory safety, bullying and harassment, and the treatment of animal research subjects.

What is research misconduct?

When researchers intentionally misrepresent or manipulate their results, these cases of scientific fraud often make the news headlines ( Chappell, 2019 ; O’Connor, 2018 ; Park, 2012 ), and they can seriously undermine public trust in research. These cases also harm trust within the scientific community.

The U.S. defines research misconduct as fabrication, falsification, and plagiarism (FFP) ( Department of Health and Human Services, 2005 ). FFP violate the fundamental ethical principle of honesty. Fabrication is making up data, and falsification is manipulating or changing data or results so they are no longer truthful. Plagiarism is a form of dishonesty because it includes using someone’s words or ideas and portraying them as your own. When brought to light, misconduct involves lengthy investigations and serious consequences, such as ineligibility to receive federal research funding, loss of employment, paper retractions, and, for students, withdrawal of graduate degrees.

One aspect of responsible behavior includes addressing misconduct if you observe it. We suggest a guide titled “Responding to Research Wrongdoing: A User-Friendly Guide” that provides advice for thinking about your options if you think you have observed misconduct ( Keith-Spiegel, Sieber, & Koocher, 2010 ). Your university will have written policies and procedures for investigating allegations of misconduct. Making an allegation is very serious. As Keith-Spiegel et al.’s guide indicates, it is important to know the evidence that supports your claim, and what to expect in the process. We encourage, if possible, talking to the persons involved first. For example, one of us knew of a graduate student who reported to a journal editor their suspicion of falsified data in a manuscript. It turned out that the student was incorrect. Going above the PI directly to the editor ultimately led to the PI leaving the university, and the student had a difficult time finding a new lab to complete their degree. If the student had first spoken to the PI and lab members, they could have learned that their assumptions about the data in the paper were wrong. In turn, they could have avoided accusing the PI of a serious form of scientific misconduct—making up data—and harming everyone’s scientific career.

What shapes ethical behavior in the scientific workplace?

Responsible conduct of research and research misconduct are two sides of a continuum of behavior—RCR upholds the ideals of research and research misconduct violates them. Problematic practices that fall in the middle but are not defined formally as research misconduct have been labeled as detrimental research practices ( National Academies of Sciences Engineering and Medicine, 2017 ). Researchers conducting misleading statistical analyses or PIs providing inadequate supervision are examples of the latter. Research suggests that characteristics of individual researchers and research environments explain (un)ethical behavior in the scientific workplace ( Antes et al., 2007 ; Antes, English, Baldwin, & DuBois, 2018 ; Davis, Riske-Morris, & Diaz, 2007 ; DuBois et al., 2013 ).

These two influences on ethical behavior are helpful to keep in mind when thinking about your behavior. When people think about their ethical behavior, they think about their personal values and integrity and tend to overlook the influence of their environment. While “being a good person” and having the right intentions are essential to ethical behavior, the environment also has an influence. In addition, knowledge of standards for ethical research is important for ethical behavior, and graduate students new to research do not yet know everything they need to. They also have not fully refined their ethical decision-making skills for solving professional problems. We discuss strategies for ethical decision-making in the final section of this article ( McIntosh, Antes, & DuBois, 2020 ).

The research environment influences ethical behavior in a number of ways. For example, if a research group explicitly discusses high standards for research, people will be more likely to prioritize these ideals in their behavior ( Plemmons et al., 2020 ). A mentor who sets a good example is another important factor ( Anderson et al., 2007 ). Research labs must also provide individuals with adequate training, supervision and feedback, opportunities to discuss data, and the psychological safety to feel comfortable communicating about problems, including mistakes ( Antes, Kuykendall, & DuBois, 2019a , 2019b ). On the other hand, unfair research environments, inadequate supervision, poor communication, and severe stress and anxiety may undermine ethical decision-making and behavior; particularly when many of these factors exist together. Thus, (un)ethical behavior is a complex interplay of individual factors (e.g., personality, stress, decision-making skills) and the environment.

For graduate students, it is important to attend to what you are learning and how the environment around you might influence your behavior. You do not know what you do not know, and you necessarily rely on others to teach you responsible practices. So, it is important to be aware. Ultimately, you are accountable for your behavior. You cannot just say “I didn’t know.” Rather, just like you are curious about your scientific questions, maintain a curiosity about responsible behavior as a researcher. If you feel uncomfortable with something, pay attention to that feeling, speak to someone you trust, and seek out information about how to handle the situation. In what follows, we cover key tips for responsible behavior in the areas of reproducibility and authorship that we hope will help you as you begin.

HOW TO DO REPRODUCIBLE SCIENCE

The foremost responsibility of scientists is to ensure they conduct research in such a manner that the findings are trustworthy. Reproducibility is the ability to duplicate results ( Goodman, Fanelli, & Ioannidis, 2016 ). The scientific community has called for greater openness, transparency, and rigor as key remedies for lack of reproducibility ( Munafò et al., 2017 ). As a graduate student, essential to fostering reproducibility is the rigor of your approach to doing experiments and handling data. We discuss how to utilize research protocols, document experiments in a lab notebook, and handle data responsibly.

Utilize research protocols

1. learn and utilize the lab’s protocols.

Research protocols describe the step-by-step procedures for doing an experiment. They are critical for the quality and reproducibility of experiments. Lab members must learn and follow the lab’s protocols with the understanding that they may need to make adjustments based on the requirements of a specific experiment.

Also, it is important to distinguish between the experiment you are performing and analyzing the data from that experiment. For example, the experiment you want to perform might be to determine if loss of a gene blocks cell growth. Several protocols, each with pros and cons, will allow you to examine “cell growth.” Using the wrong experimental protocol can produce data that leads to muddled conclusions. In this example, the gene does block cell growth, but the experiment used to produce the data that you analyze to understand cell growth is wrong, thus giving a result that is a false negative.

When first joining a lab, it is essential to commit to learning the protocols necessary for your assigned research project. Researchers must ensure they are proficient in executing a protocol and can perform their experiments reliably. If you do not feel confident with a protocol, you should do practice runs if possible. Repetition is the best way to work through difficulties with protocols. Often it takes several attempts to work through the steps of a protocol before you will be comfortable performing it. Asking to watch another lab member perform the protocol is also helpful. Be sure to watch closely how steps are performed, as often there are minor steps taken that are not written down. Also, experienced lab members may do things as second nature and not think to explicitly mention them when working through the protocol. Ask questions of other lab members so that you can improve your knowledge and gain confidence with a protocol. It is better to ask a question than potentially ruin a valuable or hard-to-get sample.

Be cautious of differences in the standing protocols in the lab and how you actually perform the experiment. Even the most minor deviations can seriously impact the results and reproducibility of an experiment. As mentioned above, often there are minor things that are done that might not be listed in the protocol. Paying attention and asking questions are the best ways to learn, in addition to adding notes to the protocol if you find minor details are missing.

2. Develop your own protocols

Often you will find that a project requires a protocol that has not been performed in the lab. If performing a new experiment in the lab and no protocol exists, find a protocol and try it. Protocols can be obtained from many different sources. A great source is other labs on campus, as you can speak directly to the person who performs the experiment. There are many journal sources as well, such as Current Protocols, Nature Protocols, Nature Methods, and Cell STAR Methods . These methods journals provide the most detailed protocols for experiments often with troubleshooting tips. Scientific papers are the most common source of protocols. However, keep in mind that due to the common brevity of methods sections, they often omit crucial details or reference other papers that may not contain a complete description of the protocol.

3. Handle mistakes or problems promptly

At some point, everyone encounters problems with a protocol, or realizes they made a mistake. You should be prepared to handle this situation by being able to detail exactly how you performed the experiment. Did you skip a step? Shorten or lengthen a time point? Did you have to make a new buffer or borrow a labmate’s buffer? There are too many ways an experiment can go wrong to list here but being able to recount all the steps you performed in detail will help you work through the problem. Keep in mind that often the best way to understand how to perform an experiment is learning from when something goes wrong. This situation requires you to critically think through what was done and understand the steps taken. When everything works perfectly, it is easy to pay less attention to the details, which can lead to problems down the line.

It is up to you to be attentive and meticulous in the lab. Paying attention to the details may feel like a pain at first, or even seem overwhelming. Practice and repetition will help this focus on details become a natural part of your lab work. Ultimately, this skill will be essential to being a responsible scientist.

Document experiments in a lab notebook

1. recognize the importance of a lab notebook.

Maintaining detailed documentation in a lab notebook allows researchers to keep track of their experiments and generation of data. This detailed documentation helps you communicate about your research with others in the lab, and serves as a basis for preparing publications. It also provides a lasting record for the lab that exists beyond your time in the lab. After graduate students leave the lab, sometimes it is necessary to go back to the results of older experiments. A complete and detailed notebook is essential, or all of the time, effort, and resources are lost.

2. Learn the note-keeping practices in your lab

When you enter a new lab, it is important to understand how the lab keeps notebooks and the expectations for documentation. Being conscientious about documentation will make you a better scientist. In some labs, the PI might routinely examine your notebook, while in other labs you may be expected to maintain a notebook, but it may not be regularly viewed by others. It is tempting to become relaxed in documentation if you think your notebook may not be reviewed. Avoid this temptation; documentation of your ideas and process will improve your ability to think critically about research. Further, even if the PI or lab members do not physically view your notebook, you will need to communicate with them about your experiments. This documentation is necessary to communicate effectively about your work.

3. Organize your lab notebook

Different labs use different formats; some use electronic notebooks while others handwritten notebooks. The contents of a good notebook include the purpose of the experiment, the details of the experimental procedure, the data, and thoughts about the results. To effectively document your experiment, there are 5 critical questions that the information you record should be able to answer.

• Why I am doing this experiment? (purpose)
• What did I do to perform the experiment? (protocol)
• What are the results of what I did? (data, graphs)
• What do I think about the results?
• What do I think are the next steps?

We also recommend a table of contents. It will make the information more useful to you and the lab in the future. The table of contents should list the title of the experiment, the date(s) it was performed, and the page numbers on which it is recorded. Also, make sure that you write clearly and provide a legend or explanation of any shorthand or non-standard abbreviation you use. Often labs will have a combination of written lab notebooks and electronic data. It is important to reference where electronic data are located that go with each experiment. The idea is to make it as easy as possible to understand what you did and where to find all the data (electronic and hard copy) that accompanies your experiment.

Keeping a lab notebook becomes easier with practice. It can be thought of almost like journaling about your experiment. Sometimes people think of it as just a place to paste their protocol and a graph or data. We strongly encourage you to include your thoughts about why you made the decisions you made when conducting the experiment and to document your thoughts about next steps.

4. Commit to doing it the right way

A common reason to become lax in documentation is feeling rushed for time. Although documentation takes time, it saves time in the long-run and fosters good science. Without good notes, you will waste time trying to recall precisely what you did, reproduce your findings, and remember what you thought would be important next steps. The lab notebook helps you think about your research critically and keep your thoughts together. It can also save you time later when writing up results for publication. Further, well-documented data will help you draft a cogent and rigorous dissertation.

Handle data responsibly

1. keep all data.

Data are the product of research. Data include raw data, processed data, analyzed data, figures, and tables. Many data today are electronic, but not all. Generating data requires a lot of time and resources and researchers must treat data with care. The first essential tip is to keep all data. Do not discard data just because the experiment did not turn out as expected. A lot of experiments do not turn out to yield publishable data, but the results are still important for informing next steps.

Always keep the original, raw data. That is, as you process and analyze data, always maintain an unprocessed version of the original data.

Universities and funding agencies have data retention policies. These policies specify the number of years beyond a grant that data must be kept. Some policies also indicate researchers need to retain original data that served as the basis for a publication for a certain number of years. Therefore, your data will be important well beyond your time in graduate school. Most labs require you to keep samples for reanalysis until a paper is published, then the analyzed data are enough. If you leave a lab before a paper is accepted for publication, you are responsible for ensuring your data and original samples are well documented for others to find and use.

2. Document all data

In addition to keeping all data, data must be well-organized and documented. This means that no matter the way you keep your data (e.g., electronic or in written lab notebooks), there is a clear guide—in your lab notebook, a binder, or on a lab hard drive—to finding the data for a particular experiment. For example, it must be clear which data produced a particular graph. Version control of data is also critical. Your documentation should include “metadata” (data about your data) that tracks versions of the data. For example, as you edit data for a table, you should save separate versions of the tables, name the files sequentially, and note the changes that were made to each version.

3. Backup your data

You should backup electronic data regularly. Ideally, your lab has a shared server or cloud storage to backup data. If you are supposed to put your data there, make sure you do it! When you leave the lab, it must be possible to find your data.

4. Perform data analysis honestly and competently

Inappropriate use of statistics is a major concern in the scientific community, as the results and conclusions will be misleading if done incorrectly ( DeMets, 1999 ). Some practices are clearly an abuse of statistics, while other inappropriate practices stem from lack of knowledge. For example, a practice called “p-hacking” describes when researchers “collect or select data or statistical analyses until nonsignificant results become significant” ( Head, Holman, Lanfear, Kahn, & Jennions, 2015 ). In addition to avoiding such misbehavior, it is essential to be proficient with statistics to ensure you do statistical procedures appropriately. Learning statistical procedures and analyzing data takes many years of practice, and your statistics courses may only cover the basics. You will need to know when to consult others for help. In addition to consulting members in your lab or your PI, your university may have statistical experts who can provide consultations.

5. Master pressure to obtain favored results

When you conduct an experiment, the results are the results. As a beginning researcher, it is important to be prepared to manage the frustration of experiments not turning out as expected. It is also important to manage the real or perceived pressure to produce favored results. Investigators can become wedded to a hypothesis, and they can have a difficult time accepting the results. Sometimes you may feel this pressure coming from yourself; for example, if you want to please your PI, or if you want to get results for a certain publication. It is important to always follow the data no matter where it leads.

If you do feel pressure, this situation can be uncomfortable and stressful. If you have been meticulous and followed the above recommendations, this can be one great safeguard. You will be better able to confidently communicate your results to the PI because of your detailed documentation, and you will be more confident in your procedures if the possibility of error is suggested. Typically, with enough evidence that the unexpected results are real, the PI will concede. We recommend seeking the support of friends or colleagues to vent and cope with stress. In the rare case that the PI does not relent, you could turn to an advisor outside the lab if you need advice about how to proceed. They can help you look at the data objectively and also help you think about the interpersonal aspects of navigating this situation.

6. Communicate about your data in the lab

A critical element of reproducible research is communication in the lab. Ideally, there are weekly or bi-weekly meetings to discuss data. You need to develop your communication skills for writing and speaking about data. Often you and your labmates will discuss experimental issues and results informally during the course of daily work. This is an excellent way to hone critical thinking and communication skills about data.

Scenario 1 – The Protocol is Not Working

At the beginning of a rotation during their first year, a graduate student is handed a lab notebook and a pen and is told to keep track of their work. There does not appear to be a specific format to follow. There are standard lab protocols that everyone follows, but minor tweaks to the protocols do not seem to be tracked from experiment to experiment in the standard lab protocol nor in other lab notebooks. After two weeks of trying to follow one of the standard lab protocols, the student still cannot get the experiment to work. The student has included the appropriate positive and negative controls which are failing, making the experiment uninterpretable. After asking others in the lab for help, the graduate student learns that no one currently in the lab has performed this particular experiment. The former lab member who had performed the experiment only lists the standard protocol in their lab notebook.

How should the graduate student start to solve the problem?

Speaking to the PI would be the next logical step. As a first-year student in a lab rotation, the PI should expect this type of situation and provide additional troubleshooting guidance. It is possible that the PI may want to see how the new graduate student thinks critically and handles adversity in the lab. Rather than giving an answer, the PI might ask the student to work through the problem. The PI should give guidance, but it may not be an immediate fix for the problem. If the PI’s suggestions fail to correct the problem, asking a labmate or the PI for the contact information of the former lab member who most recently performed the experiment would be a reasonable next step. The graduate student’s conversations with the PI and labmates in this situation will help them learn a lot about how the people in the lab interact.

Most of the answers for these types of problems will require you as a graduate student to take the initiative to answer. They will require your effort and ingenuity to talk to other lab members, other labs at the university, and even scour the literature for alternatives. While labs have standard protocols, there are multiple ways to do many experiments, and working out an alternative will teach you more than when everything works. Having to troubleshoot problems will result in better standard protocols in the lab and better science.

HOW TO BE A RESPONSIBLE AUTHOR

Researchers communicate their findings via peer-reviewed publications, and publications are important for advancing in a research career. Many graduate students will first author or co-author publications in graduate school. For good advice on how to write a research manuscript, consult the Current Protocols article “How to write a research manuscript” ( Frank, 2018 ). We focus on the issues of assigning authors and reporting your findings responsibly. First, we describe some important basics: journal impact factors, predatory journals, and peer review.

What are journal impact factors?

It is helpful to understand journal impact factors. There is criticism about an overemphasis on impact factors for evaluating the quality or importance of researchers’ work ( DePellegrin & Johnston, 2015 ), but they remain common for this purpose. Journal impact factors reflect the average number of times articles in a journal were cited in the last two years. Higher impact factors place journals at a higher rank. Approximately 2% of journals have an impact factor of 10 or higher. For example, Cell, Science, and Nature have impact factors of approximately 39, 42, and 43, respectively. Journals can be great journals but have lower impact factors; often this is because they focus on a smaller specialty field. For example, Journal of Immunology and Oncogene are respected journals, but their impact factors are about 4 and 7, respectively.

Research trainees often want to publish in journals with the highest possible impact factor because they expect this to be viewed favorably when applying to future positions. We encourage you to bear in mind that many different journals publish excellent science and focus on publishing where your work will reach the desired audience. Also, keep in mind that while a high impact factor can direct you to respectable, high-impact science, it does not guarantee that the science in the paper is good or even correct. You must critically evaluate all papers you read no matter the impact factor.

What are predatory journals?

Predatory journals have flourished over the past few years as publishing science has moved online. An international panel defined predatory journals as follows ( Grudniewicz et al., 2019 ):

Predatory journals and publishers are entities that prioritize self-interest at the expense of scholarship and are characterized by false or misleading information, deviation from best editorial and publication practices, a lack of transparency, and/or the use of aggressive and indiscriminate solicitation practices. (p. 211)

Often young researchers receive emails soliciting them to submit their work to a journal. There are typically small fees (around $99 US) requested but these fees will be much lower than open access fees of reputable journals (often around $2000 US). A warning sign of a predatory journal is outlandish promises, such as 24-hour peer review or immediate publication. You can find a list of predatory journals created by a postdoc in Europe at BeallsList.net ( “Beall’s List of Potential Predatory Journals and Publishers,” 2020 ).

What is peer review?

Peer reviewers are other scientists who have the expertise to evaluate a manuscript. Typically 2 or 3 reviewers evaluate a manuscript. First, an editor performs an initial screen of the manuscript to ensure its appropriateness for the journal and that it meets basic quality standards. At this stage, an editor can decide to reject the manuscript and not send it to review. Not sending a paper for peer review is common in the highest impact journals that receive more submissions per year than can be reviewed and published. For average-impact journals and specialty journals, typically your paper will be sent for peer review.

In general, peer review focuses on three aspects of a manuscript: research design and methods, validity of the data and conclusions, and significance. Peer reviewers assess the merit and rigor of the research design and methodology, and they evaluate the overall validity of the results, interpretations, and conclusions. Essentially, reviewers want to ensure that the data support the claims. Additionally, reviewers evaluate the overall significance, or contribution, of the findings, which involves the novelty of the research and the likelihood that the findings will advance the field. Significance standards vary between journals. Some journals are open to publishing findings that are incremental advancements in a field, while others want to publish only what they deem as major advancements. This feature can distinguish the highest impact journals which seek the most significant advancements and other journals that tend to consider a broader range of work as long as it is scientifically sound. It is important to keep in mind that determining at the stage of review and publication whether a paper is “high impact” is quite subjective. In reality, this can only really be determined in retrospect.

The key ethical issues in peer review are fairness, objectivity, and confidentiality ( Shamoo & Resnik, 2015 ). Peer reviewers are to evaluate the manuscript on its merits and not based on biases related to the authors or the science itself. If reviewers have a conflict of interest, this should be disclosed to the editor. Confidentiality of peer review means that the reviewers should keep private the information; they should not share the information with others or use it to their benefit. Reviewers can ultimately recommend that the manuscript is rejected, revised, and resubmitted (major or minor revisions), or accepted. The editor evaluates the reviewers’ feedback and makes a judgment about rejecting, accepting, or requesting a revision. Sometimes PIs will ask experienced graduate students to assist with peer reviewing a manuscript. This is a good learning opportunity. The PI should disclose to the editor that they included a trainee in preparing the review.

Assign authorship fairly

Authorship gives credit to the people who contributed to the research. This includes thinking of the ideas, designing and performing experiments, interpreting the results, and writing the paper. Two key questions regarding authorship include: 1 - Who will be an author? 2 - What will be the order in which authors are listed? These seem simple on the surface but can get quite complex.

1. Know authorship guidelines

Authorship guidelines published by journals, professional societies, and universities communicate key principles of authorship and standards for earning authorship. The core ethical principle of assigning authorship is fairness in who receives credit for the work. The people who contributed to the work should get credit for it. This seems simply enough, but determining authorship can (and often does) create conflict.

Many universities have authorship guidelines, and you should know the policies at your university. The International Committee of Medical Journal Editors (ICMJE) provides four criteria for determining who should be an author ( International Committee of Medical Journal Editors, 2020 ). These criteria indicate that an author should do all of the following: 1) make “substantial contributions” to the development of the idea or research design, or to acquiring, analyzing, or interpreting the data, 2) write the manuscript or revise it a substantive way, 3) give approval of the final manuscript (i.e., before it is submitted for review, and after it is revised, if necessary), and 4) agree to be responsible for any questions about the accuracy or integrity of the research.

Several types of authorship violate these guidelines and should be avoided. Guest authorship is when respected researchers are added out of appreciation, or to have the manuscript be perceived more favorably to get it published or increase its impact. Gift authorship is giving authorship to reward an individual, or as a favor. Ghost authorship is when someone made significant contributions to the paper but is not listed as an author. To increase transparency, some journals require authors to indicate how each individual contributed to the research and manuscript.

2. Apply the guidelines

Conflicts often arise from disagreements about how much people contributed to the research and whether those contributions merit authorship. The best approach is an open, honest, and ongoing discussion about authorship, which we discuss in #3 below. To have effective, informed conversations about authorship, you must understand how to apply the guidelines to your specific situation. The following is a simple rule of thumb that indicates there are three components of authorship. We do not list giving final approval of the manuscript and agreeing to be accountable, but we do consider these essentials of authorship.

• Thinking – this means contributing to the ideas leading to the hypothesis of the work, designing experiments to address the hypothesis, and/or analyzing the results in the larger context of the literature in the field.
• Doing – this means performing and analyzing the experiments.
• Writing – this means editing a draft, or writing the entire paper. The first author often writes the entire first draft.

In our experience, a first author would typically do all three. They also usually coordinate the writing and editing process. Co-authors are typically very involved in at least two of the three, and are somewhat involved in the other. The PI, who oversees and contributes to all three, is often the last, or “senior author.” The “senior author” is typically the “corresponding author”—the person listed as the individual to contact about the paper. The other co-authors are listed between the first and senior author either alphabetically, or more commonly, in order from the largest to smallest contribution.

Problems in assigning authorship typically arise due to people’s interpretations of #1 (thinking) and #2 (doing)—what and how much each individual contributed to a project’s design, execution, and analysis. Different fields or PIs may have their own slight variations on these guidelines. The potential conflicts associated with assigning authorship lead to the most common recommendation for responsibly assigning authorship: discuss authorship expectations early and revisit them during the project.

3. Discuss authorship with your collaborators

Publications are important for career advancement, so you can see why people might be worried about fairness in assigning authorship. If the problem arises from a lack of a shared understanding about contributions to the research, the only way to clarify this is an open discussion. This discussion should ideally take place very early at the beginning of a project, and should be ongoing. Hopefully you work in a laboratory that makes these discussions a natural part of the research process; this makes it much easier to understand the expectations upfront.

We encourage you to speak up about your interest in making a contribution that would merit authorship, especially if you want to earn first authorship. Sometimes norms about authoring papers in a lab make it clear you are expected to first and co-author publications, but it is best to communicate your interest in earning authorship. If the project is not yours, but you wish to collaborate, you can inquire what you may be able to contribute that would merit authorship.

If it is not a norm in your lab to discuss authorship throughout the life of projects, then as a graduate student you may feel reluctant to speak up. You could initiate a conversation with a more senior graduate student, a postdoc, or your PI, depending on the dynamics in the group. You could ask generally about how the lab approaches assignment of authorship, but discussing a specific project and paper may be best. It may feel awkward to ask, but asking early is less uncomfortable than waiting until the end of the project. If the group is already drafting a manuscript and you are told that your contribution is insufficient for authorship, this situation is much more discouraging than if you had asked earlier about what is expected to earn authorship.

How to report findings responsibly

The most significant responsibility of authors is to present their research accurately and honestly. Deliberately presenting misleading information is clearly unethical, but there are significant judgment calls about how to present your research findings. For example, an author can mislead by overstating the conclusions given what the data support.

1. Commit to presenting your findings honestly

Any good scientific manuscript writer will tell you that you need to “tell a good story.” This means that your paper is organized and framed to draw the reader into the research and convince them of the importance of the findings. But, this story must be sound and justified by the data. Other authors are presenting their findings in the best, most “publishable” light, so it is a balancing act to be persuasive but also responsible in presenting your findings in a trustworthy manner. To present your findings honestly, you must be conscious of how you interpret your data and present your conclusions so that they are accurate and not overstated.

One misbehavior known as “HARKing,” Hypothesis After the Results are Known, occurs when hypotheses are created after seeing the results of an experiment, but they are presented as if they were defined prior to collecting the data ( Munafò et al., 2017 ). This practice should be avoided. HARKing may be driven, in part, by a concern in scientific publishing known as publication bias. This bias is a preference that reviewers, editors, and researchers have for papers describing positive findings instead of negative findings ( Carroll, Toumpakari, Johnson, & Betts, 2017 ). This preference can lead to manipulating one’s practices, such as by HARKing, so that positive findings can be reported.

It is important to note that in addition to avoiding misbehaviors such as HARKing, all researchers are susceptible to a number of more subtle traps in judgment. Even the most well-intentioned researcher may jump to conclusions, discount alternative explanations, or accept results that seem correct without further scrutiny ( Nuzzo, 2015 ). Therefore, researchers must not only commit to presenting their findings honestly but consider how they can counteract such traps by slowing down and increasing their skepticism towards their findings.

2. Provide an appropriate amount of detail

Providing enough detail in a manuscript can be a challenge with the word limits imposed by most journals. Therefore, you will need to determine what details to include and which to exclude, or potentially include in the supplemental materials. Methods sections can be long and are often the first to be shortened, but complete methods are important for others to evaluate the research and to repeat the methods in other studies. Even more significant is making decisions about what experimental data to include and potentially exclude from the manuscript. Researchers must determine what data is required to create a complete scientific story that supports the central hypothesis of the paper. On the other hand, it is not necessary or helpful to include so much data in the manuscript, or in supplemental material, that the central point of the paper is difficult to discern. It is a tricky balance.

3. Follow proper citation practices

Of course, responsible authorship requires avoiding plagiarism. Many researchers think that plagiarism is not a concern for them because they assume it is always done intentionally by “copying and pasting” someone else’s words and claiming them as your own. Sometimes poor writing practices, such as taking notes from references without distinguishing between direct quotes and paraphrased material, can lead to including material that is not quoted properly. More broadly, proper citation practices include accurately and completely referencing prior studies to provide appropriate context for your manuscript.

4. Attend to the other important details

The journal will require several pieces of additional information, such as disclosure of sources of funding and potential conflicts of interest. Typically, graduate students do not have relationships that constitute conflicts of interest, but a PI who is a co-author may. In submitting a manuscript, also make sure to acknowledge individuals not listed as authors but who contributed to the work.

5. Share data and promote transparency

Data sharing is a key facet of promoting transparency in science ( Nosek et al., 2015 ). It will be important to know the expectations of the journals in which you wish to publish. Many top journals now require data sharing; for example, sharing your data files in an online repository so others have access to the data for secondary use. Funding agencies like NIH also increasingly require data sharing. To further foster transparency and public trust in research, researchers must deposit their final peer-reviewed manuscripts that report on research funded by NIH to PubMed Central. PubMed makes biomedical and life science research publicly accessible in a free, online database.

Scenario 2 – Authors In Conflict

To prepare a manuscript for publication, a postdoc’s data is added to a graduate student’s thesis project. After working together to combine the data and write the paper, the postdoc requests co-first authorship on the paper. The graduate student balks at this request on the basis that it is their thesis project. In a weekly meeting with the lab’s PI to discuss the status of the paper, the graduate student states that they should divide the data between the authors as a way to prove that the graduate student should be the sole first author. The PI agrees to this attempt to quantify how much data each person contributed to the manuscript. All parties agree the writing and thinking were equally shared between them. After this assessment, the graduate student sees that the postdoc actually contributed more than half of the data presented in the paper. The graduate student and a second graduate student contributed the remaining data; this means the graduate student contributed much less than half of the data in the paper. However, the graduate student is still adamant that they must be the sole first author of the paper because it is their thesis project.

Is the graduate student correct in insisting that it is their project, so they are entitled to be the sole first author?

Co-first authorship became popular about 10 years ago as a way to acknowledge shared contributions to a paper in which authors worked together and contributed equally. If the postdoc contributed half of the data and worked with the graduate student to combine their interpretations and write the first draft of the paper, then the postdoc did make a substantial contribution. If the graduate student wrote much of the first draft of the paper, contributed significantly to the second half of data, and played a major role in the thesis concept and design, this is also a major contribution. We summarized authorship requirements as contributing to thinking, doing, and writing, and we noted that a first author usually contributes to all of these. The graduate student has met all 3 elements to claim first authorship. However, it appears that the postdoc has also met these 3 requirements. Thus, it is at least reasonable for the postdoc to ask about co-first authorship.

The best way to move forward is to discuss their perspectives openly. Both the graduate student and postdoc want first authorship on papers to advance their careers. The postdoc feels they contributed more to the overall concept and design than the graduate student is recognizing, and the postdoc did contribute half of the data. This is likely frustrating and upsetting for the postdoc. On the other hand, perhaps the postdoc is forgetting how much a thesis becomes like “your baby,” so to speak. The work is the graduate student’s thesis, so it is easy to see why the graduate student would feel a sense of ownership of it. Given this fact, it may be hard for the graduate student to accept the idea that they would share first-author recognition for the work. Yet, the graduate student should consider that the manuscript would not be possible without the postdoc’s contribution. Further, if the postdoc was truly being unreasonable, then the postdoc could make the case for sole first authorship based on contributing the most data to the paper, in addition to contributing ideas and writing the paper. The graduate student should consider that the postdoc may be suggesting co-first authorship in good faith.

As with any interpersonal conflict, clear communication is key. While it might be temporarily uncomfortable to voice their views and address this disagreement, it is critical to avoiding permanent damage to their working relationship. The pair should consider each other’s perspectives and potential alternatives. For example, if the graduate student is first author and the postdoc second, at a minimum they could include an author note in the manuscript that describes the contribution of each author. This would make it clear the scope of the postdoc’s contribution, if they decided not to go with co-first authorship. Also, the graduate student should consider their assumptions about co-first authorship. Maybe they assume it makes it appear they contributed less, but instead, perhaps co-first authorship highlights their collaborative approach to science. Collaboration is a desirable quality many (although arguably not all) research organizations look for when they are hiring.

They will also need to speak with others for advice. The pair should definitely speak with the PI who could provide input about how these cases have been handled in the past. Ultimately, if they cannot reach an agreement, the PI, who is likely to be the last or “senior” author, may make the final decision. They should also speak to the other graduate student who is an author.

If either individual is upset with the situation, they will want to discuss it when they have had time to cool down. This might mean taking a day before discussing, or speaking with someone outside of the lab for support. Ideally, all authors on this paper would have initiated this conversation earlier, and the standards in the lab for first authorship would be discussed routinely. Clear communication may have avoided the conflict.

HOW TO USE DECISION-MAKING STRATEGIES TO NAVIGATE CHALLENGES

We have provided advice on some specific challenges you might encounter in research. This final section covers our overarching recommendation that you adopt a set of ethical decision-making strategies. These strategies help researchers address challenges by helping them think through a problem and possible alternatives ( McIntosh et al., 2020 ). The strategies encourage you to gather information, examine possible outcomes, consider your assumptions, and address emotional reactions before acting. They are especially helpful when you are uncertain how to proceed, face a new problem, or when the consequences of a decision could negatively impact you or others. The strategies also help people be honest with themselves, such as when they are discounting important factors or have competing goals, by encouraging them to identify outside perspectives and test their motivations. You can remember the strategies using the acronym SMART .

1. S eek Help

Obtain input from others who can be objective and that you trust. They can assist you with assessing the situation, predicting possible outcomes, and identifying potential options. They can also provide you with support. Individuals to consult may be peers, other faculty, or people in your personal life. It is important that you trust the people you talk with, but it is also good when they challenge your perspective, or encourage you to think in a new way about a problem. Keep in mind that people such as program directors and university ombudsmen are often available for confidential, objective advice.

2. M anage Emotions

Consider your emotional reaction to the situation and how it might influence your assessment of the situation, and your potential decisions and actions. In particular, identify negative emotions, like frustration, anxiety, fear, and anger, as they particularly tend to diminish decision-making and the quality of interactions with others. Take time to address these emotions before acting, for example, by exercising, listening to music, or simply taking a day before responding.

3. A nticipate Consequences

Think about how the situation could turn out. This includes for you, for the research team, and anyone else involved. Consider the short, middle-term, and longer-term impacts of the problem and your potential approach to addressing the situation. Ideally, it is possible to identify win-win outcomes. Often, however, in tough professional situations, you may need to select the best option from among several that are not ideal.

4. R ecognize Rules and Context

Determine if any ethical principles, professional policies, or rules apply that might help guide your choices. For instance, if the problem involves an authorship dispute, consider the authorship guidelines that apply. Recognizing the context means considering the situational factors that could impact your options and how you proceed. For example, factors such as the reality that ultimately the PI may have the final decision about authorship.

5. T est Assumptions and Motives

Examine your beliefs about the situation and whether any of your thoughts may not be justified. This includes critically examining the personal motivations and goals that are driving your interpretation of the problem and thoughts about how to resolve it.

These strategies do not have to be engaged in order, and they are interrelated. For example, seeking help can help you manage emotions, test assumptions, and anticipate consequences. Go back to the scenarios and our advice throughout this article, and you will see many of our suggestions align with these strategies. Practice applying SMART strategies when you encounter a problem and they will become more natural.

Learning practices for responsible research will be the foundation for your success in graduate school and your career. We encourage you to be reflective and intentional as you learn and hope that our advice helps you along the way.

ACKNOWLEDGEMENTS

This work was supported by the National Human Genome Research Institute (Antes, K01HG008990) and the National Center for Advancing Translational Sciences (UL1 TR002345).

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

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• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

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Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

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Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Weisner, T. S. (Ed.). (2005). Discovering successful pathways in children’s development: Mixed methods in the study of childhood and family life . University of Chicago Press.

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Discovering my identity as a scientist

by Tyler A. Allen | NC State University, College of Veterinary Medicine | 

Although most of my life I was drawn to science, I did have doubts about whether this was the right choice for me.

I was quite the inquisitive child, to the point where some found it agonizing how inquisitive I was. However, this same trait helps me succeed in my work today. This is because science is all about asking questions and critically thinking to test these queries, often leading to amazing discoveries.

Tyler Allen

Although neither of my parents were scientists by training, I was taught to formulate questions effectively, especially by my mother. She would always encourage my brothers and me to think about problems and questions as tangible creations that could be manipulated and uncovered with the power of our minds. This type of encouragement led to a mindset that perfectly complimented my innate interest for science. In high school, excelled in my biology courses and quickly realized this was the field I wanted to pursue.

Although most of my life I was drawn to science, I did have doubts about whether this was the right choice for me. Growing up in a low-income single-parent household, my exposure to scientists was limited. I often wondered why most scientists I saw on television and movies looked nothing like me. Even as I entered college and began to develop as a budding scientist I was still insecure about my self-image in science. I eventually started participating in research in a laboratory, but would vehemently deny my status as a scientist. Anytime my family or friends would refer to me as a scientist (even jokingly), I would quickly deny this and brush it off with nervous laughter. In my mind, I wasn’t really a scientist, I was just playing dress-up and putting on a lab coat. The researchers I worked for and other members in the lab were scientists. But not me.  A combination of not having any scientists in my family nor many science role models that looked like me had tainted the image of myself as a scientist.

A combination of not having any scientists in my family nor many science role models that looked like me had tainted the image of myself as a scientist.

This all changed during my junior year as I joined a National Institutes of Health (NIH)-funded program at my university. This program was called the Initiative for Maximizing Student Diversity (IMSD), and its purpose was to increase the number of students from underrepresented backgrounds who go on to earn PhDs. This program changed the way I thought about not only science and research, but it also helped redefine how I viewed scientists. Through this program, I was exposed to a plethora of successful scientists who looked like me, and I began to discover my identity. My thoughts truly came to a turning point during an IMSD program which focused on a concept known as the impostor syndrome. This syndrome occurs when high-achieving people doubt their talents and fail to internalize their accomplishments, ultimately feeling like an impostor. We had a senior scientist, who was a woman of color, come and tell us about her journey and how she too once had doubts about her image as a scientist. She went on to explain that she overcame these doubts by realizing her potential and accepting that her accomplishments as a scientist were due to her brilliance.

Hearing this story from a successful academic scientist gave me the courage to redefine the image I had of myself. After struggling with my identity as a scientist for years, I was finally determined to accept that I was indeed a scientist, and a talented one at that. I began to change the way I saw myself, and my success in research only further strengthened my newfound scientific confidence. I hope that I can serve as a role model for other young students, so that they can embrace their potential in science, or whichever field they are drawn to. Now when I am asked by students, family members, and friends: Tyler, what are you? I confidently answer I am a scholar, a biologist, a scientist, and you can be too!

Tyler Allen is currently a 3rd Year PhD Student in Comparative Biomedical Science at NC State University, College of Veterinary Medicine. 

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How to build a scientific career

Early-career scientists face difficult decisions when building their career in research, and indeed about whether a career in research is right for them at all. to help with these decisions, the  nobel prize inspiration initiative  gives young scientists the opportunity to question nobel laureates about career paths. the laureate’s answers are varied, insightful, and sometimes unexpected..

How do I choose a scientific career path?

The first step towards a scientific career can be dauting, but the advice from laureates can be quite simple. For example, they are often keen to stress the importance of doing something you enjoy . Picking a good mentor is a common theme, as is confidence: don’t think that research is for someone else .

Bruce Beutler advises people not to agonise over their career, and to simply take forks in the road as they reach them. Likewise, Paul Nurse says you can’t map out your career , and suggests that young scientists should simply go for opportunities that interest them. In fact, Michael Brown believes there’s only one ‘make or break’ decision in your whole life: who your spouse is (if you have one at all)!

Ultimately, in the words of Roger Kornberg , if you are sure that a research career is for you “there will be no obstacle that you can’t overcome”.

In this video, Chemistry Laureate Martin Chalfie questions the common advice of ‘follow your passion’. Speaking from his own experience, he didn’t identify what his passion was until it appeared in front of him.

Watch more laureate videos about choosing a career path .

How should I choose a research topic?

The choice of research topic is an important challenge that scientists are faced with early on in their careers. Laureates have wide-ranging advice, and some of it can be surprising.

Tim Hunt advises people to choose a research problem they can solve in their lifetime . It should be difficult but doable in a reasonable timeframe, particularly because funding depends on demonstrating your progress.

You don’t instantly need to be able to see the application of you research though, and laureates often speak about the importance of curiosity-driven research. Barry Marshall, for example, encourages people to tackle a problem that interests them , and who knows what this new knowledge might lead to.

It’s also important to think about how much you will enjoy doing your research, and what you will learn. Brian Kobilka therefore tells people to choose projects that will expose them to new techniques and skills .

Not everyone will agree with your choice of research topic, in fact Michael Brown advises people to choose a topic that others think is boring . In this video, Chemistry Laureate Fraser Stoddart warns scientists to be prepared for criticism and suggests that early criticism in an indicator they are onto something creative.

Watch more laureate videos about choosing a research topic .

Should I stay in research?

Only a very small proportion of graduate students will go on to become professors, and there are many exciting careers available for those who choose not to stay in academia. Laureates have advice about what careers are available outside research, and how to decide whether research is for you.

A scientific training equips you with a valuable skill set. For example, scientists learn how to analyse problems and rely on evidence, as Paul Nurse points out .

There are endless  opportunities for putting a knowledge of science together with other kinds of careers, and examples include: public policy, patent law, publishing, education and teaching, medical writing, journalism, venture capital, and museum work.

There is certainly no reason for young scientists to feel restricted to an academic career path, and the biggest challenge is perhaps choosing which route to follow. In this video, Medicine Laureate May-Britt Moser gives advice on how to decide whether to follow a research career, and on the next step towards that goal.

Watch more laureate videos about career opportunities for scientists.

Should I work in academia or industry?

One common alternative to academic research is a career as a scientist in industry – there are many opportunities to do experimental work in a commercial setting.

Barry Marshall has experience of both academia and industry, creating commercial products from diagnostic tests and treatments for Helicobacter. He finds it a different experience to work in a commercial company compared to a university or research institute, particularly because success is judged in a different way. Whereas universities are looking for people who have brought in lots of funding, companies are looking for people who have produced a product, ideally with as little funding as possible. The product is your main focus , unlike in universities where you can switch between different ideas.

Randy Schekman is keen to point out the advantages of working in industry. Industry may be a better setting for people who just want to focus on research, for example, because scientists in universities spend a lot of time on teaching, grant writing and administration.

In this video, Medicine Laureate Michael Young stresses that the choice depends entirely on the preference of the scientist. He believes it is inevitable and important that different people are attracted to different career paths.

Watch more laureate videos about working in industry .

These videos were filmed at Nobel Prize Inspiration Initiative events delivered in partnership with AstraZeneca.

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How to become a successful researcher at every stage of your career

November 16, 2020

By Sneha Mittal Sachdeva

Steps to building a successful research career – with a JACC webinar for physician-scientists

Pursuing a career in research can be daunting. Regardless of your field, it can be highly competitive, with challenges at every stage. These include the uncertainty of grants and fellowships, maintaining work-life balance, and  publishing in premium, high-impact journals opens in new tab/window .

For physician-scientists, the success rates for securing research grants has declined from 33 percent to 19 percent, while the number of grant applications has increased by 72 percent. However, with a roadmap for success, the path can provide personal and professional fulfillment and dynamism.

In this article – based on our webinar the  Journal of the American College of Cardiology opens in new tab/window  (JACC) – Dr. Valentin Fuster and Dr. Harlan Krumholtz share best practices to become a successful researcher at three stages of your career: early, mid-career and senior. While their advice is for physician-scientists, it can apply to people in all fields of research.

Webinar: How to become a successful researcher at every stage of your career

How to Become a Successful Researcher At Every Stage of Your Career (brighttalk.com) opens in new tab/window Join this  free webinar opens in new tab/window  with Valentin Fuster, MD, PhD, MACC, Editor-in-Chief of the  Journal of the American College of Cardiology (JACC) opens in new tab/window , and Harlan M. Krumholz, MD, SM, FACC, Director of the  Yale Center for Outcomes Research and Evaluation opens in new tab/window  at the Yale School of Medicine. They discuss how to maintain a successful physician-scientist career at three stages: early, mid-career and senior. They also suggest tips for grant receipts and talk about the importance of the mentor/mentee relationship and the need for creativity in grant submissions.

1. Identify the right research project

As a researcher, irrespective of the career stage, understand that you’re in constant competition to continue your research. To ensure that you’re working in the right direction, you can follow this step-by-step approach:

Identify your skills and resources: Identify the range of skills you currently have and your available resources. But don’t be afraid to think big!

Recognize the requirements: Next, recognize what kind of projects are you willing to do. Ask yourself if you are flexible, if you’re willing to take risks and if you can really choose and afford to be entrepreneurial in terms of the available opportunities for your project.

Research the topic: Read and learn from the existing literature around your research topic, demonstrate the rationale for selecting the topic and ensure you’ve completed the background research before finalizing your research topic.

Improve the likelihood of success: Identify what resources, skills, individuals and support can enhance the likelihood of your success.

Prioritize time: Estimate the amount of time required to complete the project vs your available time. Allocate your time carefully to important projects, and don’t underestimate the time, efforts and energy required for each project. If you’re a senior researcher, identify the opportunities for networking, learning and future opportunities, but take a calculated approach before taking on a new project.

Understanding the current scenario: Understand what projects your sponsors, funding teams or organization will pay you to do. Ask yourself if you can leverage the available opportunities to find a balance between what you want to do, what the world is interested in and the support you can expect to receive for the project.

Make a strong case: Do you understand what you’re doing and why you’re doing this? In a short description, try to write key compelling reasons why you should take the project, and only take on the project if the reasons are convincing.

Once you have clarity on the research project topic, ensure you put your energy and efforts toward making the project a success. Then take all your learnings to your next project.

2. Develop and nurture qualities of being a successful researcher.

Is a researcher born or created with dedication and hard work? Or is it a combination of both? The best researchers are curious by nature. Here are a few other qualities that predispose them for success:

Courage: The top quality of any successful researcher is the courage to ask the right questions, seek answers from peers, experts as well as literature and questioning how their project will make an impact. A successful researcher will fight the fall into the comfort zone and will understand the rewards of a life in science which can help him/her contribute to the world.

Persistence: When thinking about your research career growth, envision the position you would like to achieve and the journey you would like to take to reach that position. Even though sometimes the journey might not quite suit you, don’t quit, learn and improve as you go.

Determination & Resilience in the face of challenges: Everyone among the top successful researchers have faced challenges at one or more junctures of their life. Everyone faces difficult times when people don’t believe in them or doubt their capabilities. However, what made them stand apart was the resilience they displayed in the face of challenges. When times are hard, don’t quit easily because success only comes to those who work hard.

Self-motivation: Surround yourself by an environment where you see examples of success, where you see people you admire, people who inspire us to think about what we might aspire to be, who we want to be and how do we want to get there. Find colleagues who’re asking questions, trying to seek knowledge to improve people lives and don’t limit this search to people just in front of you, but look for opportunities across institutions and across borders.

3. Find a mentor for every stage of your life.

A mentor is someone who can provide guidance and support, accommodate and suit your individual needs and requirements, understand your aspirations and become an anchor for you at difficult stages of life. Regardless of the stage of your career, the role of a mentor is critically important in steering your interests and contributing to your growth.

You can have several mentors in your life based on your career stage; for example, a mentor to guide your thesis, a mentor who supports your career growth and a mentor who is an anchor for your life. A great mentor-mentee relationship is one where you have good chemistry and comfort. Mentorship doesn’t necessarily mean a mentor is supposed to tell you what needs to be done, but it’s a relationship where you can always seek guidance and supporting advice.

If you’re in early or middle stages of your career, find a mentor who is welcoming, supportive, encouraging and helps create or discover opportunities for your growth.

If you’re a senior researcher, contribute to society by discovering people with talent and encouraging them. Find the right triggers, understand talent, and support the people who have the right ingredients to become successful in their life.

4. Understand your talent and enhance it.

Understand your talents, skills and interests, and spend time enhancing these. You can ask yourself these key questions to help you grow in the right direction:

Self-discovery questions:

What are you trying to achieve in the next 5 years?

What are your strengths and weaknesses?

What projects keep me excited

Which strengths would you like to cultivate in the upcoming years?

Self-motivation:

What happens when things don’t work?

How can I keep myself motivated?

What are my contributions to the society?

What is the journey I would like to go through?

How can I achieve that big win?

How do I connect with people?

How do I motivate others around me?

How do others around me keep me motivated?

Research area of interest:

Do you enjoy working on new ground-breaking research or does your strength lie in enhancing the existing research?

How can you add value to your institution with your research?

Parting wisdom

At every stage of your career, remember to be a bold and creative problem solver. Ensure you thank the people who have made your journey important and memorable. Be satisfied with what you do, understand your talent and invest in them continuously. Begin with the end in mind. Your research is not the money, but the contribution you have made to the society and the impact you have had on your team. And most importantly don’t forget to enjoy each stage of your journey, learning lessons and striving towards becoming a better version of yourself each day.

Related stories and resources

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Contributor

Sneha mittal sachdeva.

How To Become A Research Scientist: What To Know

Updated: Feb 29, 2024, 1:40pm

Research is at the center of everything we know and discover, whether it’s food science, engineering, wildlife or the climate. Behind these discoveries, a research scientist conducts experiments, collects data, and shares their findings with the world.

Research and development scientist, or R&D scientist, is a broad career term that encompasses numerous types of scientists, from geologists to historians. Still, every research scientist has the same goal of furthering their field through experimentation and data analysis.

Browse this guide to discover how to become a research scientist and learn about this role, responsibilities and career outlook.

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What Does a Research Scientist Do?

Research scientists design and conduct research projects and experiments to collect and interpret relevant data. Many research scientists work in laboratory settings for universities, private businesses or government agencies.

These professionals are key players in many industries, from healthcare to marine biology . For instance, a chemist may test various materials for future upgrades to a medical device, while a wildlife research scientist might conduct long-term studies on a species’s breeding patterns.

The typical duties of a research scientist, regardless of their industry and position, include:

• Identifying research needs
• Collaborating with other professionals in a project
• Conducting research and experiments
• Writing laboratory reports
• Writing grant proposals
• Analyzing data
• Presenting research to appropriate audiences
• Developing research-related plans or projects

Research scientists may face challenges throughout their careers, like securing research funding or staying updated with policy changes and technologies. Additionally, to become involved in high-level research projects, research scientists usually need a doctoral degree, requiring substantial time and financial commitment.

How To Become a Research Scientist

The path to becoming a research scientist depends on your desired type of work.

For example, if you plan to become a research scientist for a hospital’s oncology department, you’ll likely need a doctoral degree and postdoctoral research experience. However, a product development researcher may only need a bachelor’s or master’s degree.

The following steps outline the general path needed for many research scientist positions.

Earn a Bachelor’s Degree

Research scientists can start by pursuing a bachelor’s degree in a field relevant to the research they want to conduct. For instance, an undergraduate degree in natural resources is helpful to become a wildlife biologist, while a prospective forensic scientist can pursue a degree in forensics.

If you’re undecided about your post-graduate goals, you can pursue a general major like chemistry, biology or physics before choosing a more field-specific master’s or doctoral degree.

Complete a Master’s Degree

Many higher-level research jobs require a master’s degree in a relevant field. Pursuing a master’s degree lets you gain work experience before beginning a doctorate, sets you apart from other doctoral candidates and qualifies you for advanced research positions.

However, you can skip a master’s degree and enter a doctoral program. Many doctoral programs only require a bachelor’s degree for admission, so you could save time and money by choosing that route rather than earning a master’s.

Get a Doctoral Degree

Doctorates require students to hone their research skills while mastering their field of interest, making these degrees the gold standard for research scientists.

A doctorate can take four to six years to complete. Research scientists should opt for the most relevant doctorate for their career path, like clinical research, bioscience or developmental science.

Pursue a Research Fellowship

Some jobs for research scientists require candidates to have experience in their field, making a research fellowship beneficial. In a research fellowship, students execute research projects under the mentorship of an industry expert, often a researcher within the student’s college or university.

Students can sometimes complete a fellowship while pursuing their doctoral degree, but other fellowships are only available to doctoral graduates.

Research Scientist Salary and Job Outlook

Payscale reports the average research scientist earns about $87,800 per year as of February 2024. However, research scientist salaries can vary significantly depending on the field and the scientist’s experience level.

For example, Payscale reports that entry-level research scientists earn about $84,000 annually, but those with 20 or more years of experience average approximately $106,000 as of February 2024.

The U.S. Bureau of Labor Statistics (BLS) reports salary data for several types of research scientist careers. For example, a geoscientist earns a median wage of about $87,000, while the median wage of a physicist is around $139,000 as of May 2022.

As salaries vary based on research science positions, so does demand. To illustrate, the BLS projects the need for chemists and materials scientists to grow by 6% from 2022 to 2032 but projects medical scientist jobs to increase by 10% in the same timeframe. Both projections demonstrate above-average career growth, however.

Research Scientist Specializations

A research scientist can work in many industries, so it’s crucial to understand your options before beginning your studies. Pinpointing a few areas of interest can help you find the right educational path for your future career.

Research scientists can specialize in life, physical or earth sciences.

Life science researchers like botanists, biologists and geneticists study living things and their environments. Physical research scientists, like chemists and physicists, explore non-living things and their interactions with an environment. Earth science researchers like meteorologists and geologists study Earth and its features.

Frequently Asked Questions (FAQs) About Becoming a Research Scientist

What degree does a research scientist need.

Research scientist education requirements vary by specialization, but entry-level research positions require at least a bachelor’s degree in a relevant field. Some employers prefer a master’s or doctoral degree, as advanced degrees demonstrate specialized knowledge and research experience.

How do I start a career in scientific research?

Research scientists need at least a bachelor’s degree. Many graduates pursue a master’s or doctoral degree while gaining experience with an entry-level position, internship or fellowship.

Does being a research scientist pay well?

Research scientist careers generally pay well; some specializations pay more than others. For example, the BLS reports a median salary of about $67,000 for zoologists and wildlife biologists as of May 2022, but physicists and astronomers earn just over $139,000 annually.

How many years does it take to become a research scientist?

It can take up to 10 years to become a doctorate-prepared research scientist, plus another one to five years to complete a postdoctoral fellowship. Entry-level research scientist roles may only require a four-year bachelor’s degree or a master’s degree, which takes one to two years.

Do you need a Ph.D. to be a research scientist?

No, not all research scientists need a Ph.D. Entry-level roles like forensic scientist technicians may only need a bachelor’s degree, and sociologists and economists usually need a master’s. Some research scientist roles, like physicists and medical scientists, require a doctoral degree.

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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McCombes, S. (2023, October 19). 10 Research Question Examples to Guide your Research Project. Scribbr. Retrieved August 21, 2024, from https://www.scribbr.com/research-process/research-question-examples/

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30 Scientific Researcher Interview Questions and Answers

Common Scientific Researcher interview questions, how to answer them, and example answers from a certified career coach.

Embarking on a career as a scientific researcher is an exhilarating journey into the unknown. You know better than anyone that discovery and innovation are born from curiosity, critical thinking, and meticulous attention to detail. As you stand on the threshold of your next career step, it’s time to prepare for one of the most important stages in your job pursuit: the interview.

In this article, we’ll delve into some typical questions asked during interviews for scientific researchers. Along with our expert tips and sample answers, these insights will equip you to articulate your skills, experiences, and passion for scientific exploration compellingly and convincingly.

1. What is your experience with statistical analysis and data reconfiguration?

A key component of scientific research is the ability to analyze and interpret data. The interviewer wants to gauge your experience and proficiency with statistical analysis and data reconfiguration. These skills are essential in the research process and in drawing accurate conclusions from the data. Understanding your competency in these areas helps the interviewer determine if you’re equipped to handle the quantitative aspects of the research role.

Example: “I have a strong background in statistical analysis and data reconfiguration. During my PhD, I extensively used these skills to analyze large datasets for my research projects.

My proficiency lies in using software like R, Python, and SPSS for data manipulation and interpretation. I am also adept at hypothesis testing, regression analysis, and predictive modeling.

In terms of data reconfiguration, I’ve worked on transforming raw data into a more suitable format for analysis. This includes handling missing values, outliers, and normalizing variables. My experience allows me to efficiently clean, manage, and interpret complex datasets, providing valuable insights for scientific research.”

2. How have you ensured the ethical handling of test subjects in previous research projects?

The query is intended to gauge your understanding and adherence to ethical guidelines in scientific research. Ethical considerations are paramount in any research, especially when it involves human or animal subjects. Your answer will let the interviewers assess your commitment to maintaining dignity, rights, safety, and well-being of the subjects, which is a critical aspect in the field of scientific research.

Example: “In my previous research projects, I’ve always adhered to the ethical guidelines outlined by the Declaration of Helsinki. This included obtaining informed consent from all test subjects and ensuring their anonymity in data presentation.

I emphasized transparency about the purpose, benefits, and potential risks of the study. Moreover, I made sure that participation was voluntary, with participants having the right to withdraw at any time without penalty.

To ensure fair treatment, I used a non-discriminatory selection process for test subjects. Regular audits were conducted to maintain compliance with these ethical standards.”

3. Which software tools or technologies do you use for data management and why?

In a field as data-driven as scientific research, the tools and technologies you use to manage and analyze your data can have a huge impact on your work. The interviewer wants to understand your familiarity with these tools, and how you use them to ensure accuracy, efficiency, and integrity in your research. This gives them a sense of your technical skills and your approach to the critical task of data management.

Example: “I primarily use SQL for data management due to its efficiency in handling large datasets. It allows me to query, manipulate and structure the data effectively.

For statistical analysis and modeling, I prefer R or Python as they have robust libraries like Pandas and NumPy that make data manipulation and cleaning easier.

Moreover, I utilize Tableau for data visualization because it provides interactive dashboards which are essential for understanding complex data patterns.

In terms of managing workflows and pipelines, I find Apache Airflow useful. It helps automate, schedule and monitor complex workflows, ensuring data integrity and consistency.”

4. Describe a time when you had to adjust your research methodology mid-project.

Adaptability is key in the world of scientific research. Sometimes, experiments don’t go as planned, new information becomes available, or resources change in some way. Interviewers want to know that you can handle these curveballs and continue to make progress on your research. They are interested in your problem-solving skills and your ability to innovate under pressure.

Example: “During a project on studying the effects of certain chemicals on plant growth, our initial methodology involved using a control group and an experimental group. However, we noticed that the results were inconsistent and didn’t align with our hypothesis.

Upon reviewing our process, we realized the inconsistency was due to varying sunlight exposure across different parts of our greenhouse. To rectify this, we adjusted our methodology by standardizing light conditions for all plants through artificial lighting systems.

This change not only improved the reliability of our data but also highlighted the importance of considering all environmental factors in research design. This experience has strengthened my ability to critically evaluate methodologies and make necessary adjustments for accurate results.”

5. In what ways have you contributed towards scientific literature in your field?

In the field of scientific research, contributing to the existing body of knowledge is of paramount importance. It’s not just about conducting experiments and gathering data, but also about sharing your findings with the wider scientific community. By asking this question, hiring managers are looking to gauge your experience, your dedication to enriching the field, and your ability to communicate complex ideas effectively.

Example: “I have contributed to scientific literature through publishing research papers in peer-reviewed journals. My work primarily focuses on molecular biology and genetics, where I’ve explored the role of certain genes in disease development.

Moreover, I’ve also reviewed articles for several high-impact factor journals, providing critical feedback to enhance the quality of published works. This not only contributes to the body of knowledge but also ensures that accurate and reliable information is disseminated within our field.

Additionally, I’ve presented my findings at international conferences, engaging with other researchers and fostering collaborative efforts towards advancements in our field.”

6. How would you handle disagreements with team members regarding research findings?

Diving into the world of scientific research, disagreements and debates are part and parcel of the process. They help refine theories, improve models, and lead to new discoveries. Hiring managers ask this question to gauge your ability to handle conflicts professionally, your capacity for open-mindedness, and your potential to work collaboratively, even in the face of differing perspectives. They want to ensure you can navigate these challenges while maintaining a productive and respectful work environment.

Example: “Disagreements in research findings are common and can often lead to deeper understanding. If such a situation arises, my first step would be to ensure that we all understand each other’s perspectives thoroughly.

Next, I’d suggest revisiting the data collectively, as it’s possible that different interpretations could stem from how the data is analyzed or presented.

If disagreements persist, seeking an external opinion, perhaps through peer review, might provide new insights. It’s crucial to remember that the goal isn’t to prove who is right, but rather to arrive at the most accurate conclusion based on our collective knowledge and expertise.”

7. What steps do you take to ensure accuracy and reproducibility in your experiments?

Accuracy and reproducibility are the bedrock of scientific research. They help maintain the integrity of the scientific process and ensure that findings are robust, reliable, and can be trusted. This question allows hiring managers to assess your understanding and application of good scientific methodology and your commitment to producing high-quality, dependable results.

Example: “To ensure accuracy in my experiments, I meticulously follow standard operating procedures and protocols. I also use calibrated equipment and validated methods.

For reproducibility, I maintain detailed lab notebooks that outline each step of the experiment. This includes data collection methods, observations, deviations, and results.

I perform tests in triplicate to confirm consistency of findings. To minimize bias, I incorporate controls and blind testing when applicable.

Furthermore, peer review is a key part of maintaining quality. I invite colleagues to critique my methodology and results, providing an additional layer of scrutiny.

Lastly, I stay updated with latest research practices and guidelines for ethical conduct in scientific research.”

8. Detail how you’ve used computational models in your past research.

As a scientific researcher, you’re expected to be at the forefront of innovation and technology. Computational models are powerful tools that can greatly enhance research, offering insights and predictions that might not be readily apparent. By asking about your experience with these models, interviewers are gauging your technical skills, your ability to apply advanced techniques to your research, and your capacity for innovative thinking. They want to know if you’re comfortable using these tools and if you understand their potential impact on your work.

Example: “In my previous research, I utilized computational models to analyze large data sets and predict outcomes. For instance, during a project on climate change, I used these models to simulate the effects of various environmental factors on global temperatures.

The model allowed us to manipulate variables such as CO2 levels or solar radiation in order to see their potential impact. This not only helped us understand current trends but also forecast future scenarios based on different interventions.

This approach was instrumental in providing insights that were otherwise difficult to obtain through traditional methods. The ability to test hypotheses virtually before implementing them in real-world situations proved invaluable.”

9. Have you ever encountered unexpected results during your research? How did you respond?

Unanticipated outcomes are part and parcel of scientific research. This question is designed to assess your problem-solving skills, your adaptability, and your ability to think critically. It is also a test of your resilience and determination, as research often involves unexpected twists and turns. The interviewer wants to see how you handle setbacks, how you interpret data, and how you can adjust your plans to move forward when things don’t go as expected.

Example: “Yes, encountering unexpected results is a common occurrence in scientific research. In one instance, my team was conducting an experiment to understand the effects of certain compounds on cell growth. The initial results were contrary to our hypothesis.

Instead of disregarding these findings, we decided to reanalyze our approach and data. We performed additional tests to rule out experimental error and revisited literature for any overlooked information.

This process led us to identify gaps in our original understanding and helped refine our hypothesis. Although it extended our timeline, this experience reinforced the importance of critical analysis and flexibility in research.”

10. How do you keep up-to-date with the latest advancements in your specific field of study?

The world of scientific research is dynamic and ever-evolving. Staying ahead of the curve is absolutely essential, not only to produce valid and relevant research but also to ensure your knowledge base isn’t obsolete. This question helps hiring managers gauge your passion for your field, your commitment to continuous learning, and your proactive nature in ensuring your work contributes to cutting-edge discoveries.

Example: “Staying updated in the field of scientific research is crucial. I regularly read peer-reviewed journals such as Nature and Science, which publish cutting-edge research. Attending conferences also keeps me abreast with new developments and provides networking opportunities.

Online platforms like ResearchGate are valuable for discussions and insights from fellow researchers globally. Webinars and online courses help me gain deeper understanding of complex topics.

Moreover, participating in collaborative projects exposes me to diverse perspectives and novel methodologies. This multi-pronged approach ensures that I remain at the forefront of my field.”

11. What’s your approach to managing multiple research projects simultaneously?

In the dynamic world of scientific research, multitasking is a must-have skill. Researchers often juggle between different projects, each with its unique timelines, objectives, and complexities. Therefore, the question aims to gauge your project management skills, ability to prioritize, and how well you handle pressure without compromising the quality of your research.

Example: “Managing multiple research projects simultaneously requires a strategic approach. I prioritize tasks based on deadlines and project importance, ensuring the most critical work is addressed first.

Utilizing project management tools helps me keep track of each project’s progress and stay organized. These platforms provide visibility into timelines, resources, and milestones which are crucial for successful execution.

Regular communication with team members and stakeholders is also key. This ensures everyone is aligned, aware of their responsibilities, and any issues are identified early.

Lastly, I always allocate time for unexpected challenges or delays. This flexibility allows me to adapt quickly when necessary, maintaining productivity without compromising the quality of the research.”

12. Could you share an instance where you had to secure funding for your research project?

Securing research funding is a key aspect of a scientific researcher’s role. It’s not just about having brilliant ideas, but also about convincing others of their worth. Therefore, hiring managers want to understand your abilities in writing compelling grant proposals, your creativity in finding alternative funding sources, and your resilience in the face of potential rejections. Your experiences and success in securing research funding can be a strong indicator of your capability to sustain and progress in your research career.

Example: “In one of my previous projects, we were studying the effects of certain compounds on cell regeneration. However, due to the high costs associated with procuring these compounds, we needed additional funding.

I took the initiative and drafted a proposal detailing our research objectives, potential impact, and budget requirements. I also included preliminary data to demonstrate the feasibility of our project.

We submitted this proposal to various scientific grant organizations and managed to secure sufficient funding from two sources. This not only allowed us to continue our research but also led to significant findings that were later published in a renowned scientific journal.”

13. How do you manage the balance between detail-oriented work and big picture strategy in research?

Research is a meticulous process, often requiring painstaking attention to detail. However, it’s equally important to keep a clear vision of the overarching objectives and goals. With this question, employers want to gauge your ability to maneuver between these two aspects of the job. They want to ensure you can concentrate on the minutiae without losing sight of the larger strategic picture.

Example: “Balancing detail-oriented work and big picture strategy in research requires a structured approach. I usually start with the end goal, identifying key objectives and milestones. This provides a clear vision of what needs to be achieved.

From there, I break down each milestone into smaller tasks, focusing on the details necessary for their completion. This ensures accuracy and thoroughness in the data collection and analysis process.

To maintain balance, I regularly revisit the overarching goals. This helps to align my detailed work with the larger objective and adjust course if needed. Regular communication with team members also plays a crucial role in maintaining this equilibrium.”

14. Share an example of a complex scientific concept you had to explain to a non-scientific audience.

Being able to communicate complex scientific concepts in a way that non-scientists can understand is a critical part of being a researcher. It’s not just about doing the research, but also about sharing the findings and their implications with the world. This could mean speaking to journalists, policymakers, funders, or the general public. Demonstrating this skill in an interview can show that you’re not just a great scientist, but also a great communicator.

Example: “During a community outreach event, I was tasked with explaining the concept of genetic modification to a non-scientific audience. I used the analogy of a recipe book, where each gene in our DNA is like a recipe for a specific trait. Genetic modification, then, is like swapping out one recipe for another to achieve a desired outcome – such as creating crops that are more resistant to pests or disease. This made the process relatable and easier to understand for those unfamiliar with scientific jargon.”

15. How familiar are you with patent applications related to your research?

In the landscape of scientific research, it’s not only about making discoveries, but also protecting intellectual property. If you’ve been involved in novel research, it’s likely that there will be patentable aspects. Hiring managers need to know if you are comfortable with this process, as it is a critical component in turning research outputs into viable products or technologies. This question helps assess your experience with, and understanding of, the patent application process, which can be a major asset to research institutions and companies.

Example: “I am quite familiar with patent applications in the context of scientific research. Understanding intellectual property rights is crucial when developing new technologies or methodologies.

During my PhD, I was involved in a project that led to a patent application. This process gave me firsthand experience on how to draft a patent document, conduct prior art search and respond to office actions.

Moreover, as part of my ongoing professional development, I regularly attend webinars and workshops on IP management. This helps me stay updated about changes in patent laws and regulations.”

16. Tell us about a time when your initial hypothesis was proven wrong.

Science is all about discovery and learning. Sometimes, this means admitting that your initial hypothesis was incorrect. By asking this question, hiring managers want to see that you are open-minded, flexible, and not afraid to admit when you are wrong. They want to know that you have the ability to adapt and learn from your mistakes, which are critical traits for any successful scientific researcher.

Example: “During my PhD, I was investigating a potential new drug for treating Alzheimer’s disease. My initial hypothesis was that the drug would reduce neuroinflammation and improve cognitive function in mice.

However, after conducting several experiments, the data showed no significant reduction in inflammation or improvement in cognition. This was unexpected and initially disappointing.

But this failure led to further exploration. We discovered that the drug had other beneficial effects, such as reducing oxidative stress in brain cells. It was an important lesson about adaptability in scientific research: hypotheses can guide us, but they shouldn’t limit our ability to observe and learn from the data we collect.”

17. What strategies do you employ to maintain meticulous record-keeping for future reference?

As a scientific researcher, keeping meticulous records is not just a good habit, it’s a fundamental requirement. This is because research is a methodical process, with every step needing to be documented in detail. This allows for the replication of studies and for others to understand and build upon your work. Therefore, interviewers ask this question to gauge your organization skills and your understanding of the importance of record-keeping in scientific research.

Example: “I utilize digital tools for effective record-keeping. For instance, I use cloud-based platforms such as Google Drive and Dropbox to store data securely and ensure easy access from anywhere.

Moreover, I employ project management software like Trello or Asana to track the progress of different tasks and experiments. This helps in maintaining an organized workflow.

Furthermore, I adhere strictly to labelling conventions when storing physical records. It’s crucial to have a systematic approach towards this to avoid confusion later on.

Lastly, regular audits are conducted to check for any discrepancies or missing information. This ensures that our records remain accurate and up-to-date.”

18. How proficient are you in using laboratory equipment relevant to our research?

Your ability to navigate around a lab is essential to carrying out experiments and procedures smoothly and safely. Familiarity with relevant lab equipment doesn’t just mean you can do the job efficiently—it also means you can do it safely. Plus, it’s an indicator of your overall experience and knowledge in the field, which is something every hiring manager wants to see.

Example: “I have extensive experience using various laboratory equipment, including spectrophotometers, centrifuges, and microscopes. My proficiency extends to more specialized apparatus like flow cytometers and chromatography systems.

During my PhD research, I regularly utilized these tools for data collection and analysis. This hands-on experience has equipped me with the necessary skills to operate, troubleshoot, and maintain such equipment effectively.

Moreover, I have a strong understanding of lab safety protocols and good laboratory practices. I believe this combination of practical skills and theoretical knowledge makes me adept at handling any laboratory equipment relevant to your research.”

19. Describe any innovative ideas you’ve implemented in your previous research.

Innovation is the lifeblood of scientific progress. Hiring managers want to understand your ability to think outside the box, to challenge existing norms, and to develop new solutions or novel approaches in your research. Your answer will shed light on your creative thinking skills, problem-solving abilities, and capacity to contribute positively to the team’s research objectives.

Example: “In my previous research, I developed a novel method for analyzing the genetic structure of bacteria. Traditional methods were time-consuming and often resulted in incomplete data.

I implemented machine learning algorithms to analyze genomic sequences more efficiently. This approach not only reduced analysis time by 50%, but also increased the accuracy of our findings.

This innovation led to new insights into bacterial evolution and antibiotic resistance, contributing significantly to our field.”

20. How do you prioritize safety while conducting potentially hazardous experiments?

Safety is paramount in any laboratory setting, especially when hazardous materials or procedures are involved. This question is designed to gauge your understanding of safety protocols, risk management, and your ability to prioritize these elements while conducting experiments. It’s also a chance to demonstrate your commitment to maintaining a safe and secure workplace for yourself and your colleagues.

Example: “Safety is paramount in any experimental setup. I prioritize it by:

1. Thoroughly understanding the potential hazards of each experiment before starting, and ensuring all team members are aware.

2. Implementing strict adherence to safety protocols and guidelines, including the use of appropriate personal protective equipment (PPE).

3. Regularly maintaining and checking equipment to prevent malfunctions that could lead to accidents.

4. Having an emergency response plan in place for quick action if something goes wrong.

5. Encouraging a culture of safety where everyone feels responsible and empowered to report unsafe conditions or practices.”

21. What process do you follow for peer review of your research papers?

The essence of science lies in the validity and reproducibility of research. Peer reviews are a critical part of scientific research, ensuring that the studies and conclusions are sound, unbiased, and contribute meaningfully to the field. By asking this question, interviewers want to gauge how well you understand and value the peer review process, and how diligent you are in ensuring your work withstands scrutiny.

Example: “The peer review process I follow begins with a self-review. I thoroughly check the paper for clarity, coherence, and adherence to guidelines. Then, I share it with my team or colleagues for an internal review. They provide constructive feedback on content, methodology, and presentation.

Post this, I submit the paper to external peers who are experts in the field. Their suggestions help me improve the quality of the research. After implementing these changes, I do another round of self-review before final submission. This iterative process ensures that the paper is accurate, comprehensive, and contributes value to the scientific community.”

22. How familiar are you with drafting and submitting grant proposals?

Securing funding is a critical part of scientific research. It’s not enough to simply have brilliant ideas; you need the financial resources to bring those ideas to fruition. As such, experience with drafting and submitting grant proposals is highly valued. This not only demonstrates your ability to secure funding, but also your ability to effectively communicate your research plans and their potential impact.

Example: “I have substantial experience with grant proposals. My knowledge spans from identifying funding opportunities to drafting and submitting the applications. I’ve developed a knack for translating complex scientific ideas into accessible language that resonates with diverse audiences, including non-scientific reviewers.

My approach involves thorough research on the funder’s priorities and tailoring our proposal accordingly. This has led to successful acquisitions of grants in my past projects. Understanding both the science and the art of persuasive writing is crucial in this process.”

23. Provide an instance of when you effectively managed a research budget.

Budgeting is a critical part of scientific research. Just as important as your ability to design and conduct experiments is your ability to plan for and manage the resources those experiments require. Whether you’re buying new equipment, procuring chemicals, or paying research assistants, your interviewer wants to ensure that you can handle the financial side of the job with just as much skill as the scientific side.

Example: “In one of my recent projects, I was responsible for managing a $500,000 budget. The project involved extensive lab work and required precise allocation of resources.

To manage this effectively, I created a detailed forecast that broke down costs by category – personnel, equipment, supplies, etc. This helped in tracking spending and identifying any potential overruns early on.

During the course of the project, we encountered an unexpected expense related to equipment maintenance. However, due to prudent management and constant monitoring, we were able to reallocate funds without compromising other areas or exceeding our budget.

This experience reinforced the importance of proactive budget management in research, ensuring efficient use of resources while maintaining scientific integrity.”

24. How comfortable are you with presenting your research at conferences or seminars?

Public speaking and networking are integral parts of a successful research career. It’s not enough to simply do the research—you have to share it with others in your field and the larger scientific community. That’s why hiring committees want to know if you’re comfortable presenting your work to others, whether it’s at a small seminar or a large international conference. You’ll be expected to represent your institution and your research team, and your ability to communicate your work effectively can have a big impact on your career progression.

Example: “I am quite comfortable presenting my research at conferences and seminars. I believe that sharing findings is a crucial part of the scientific process. It allows for peer review, collaboration, and further advancement in the field.

In my experience, effective communication skills are as important as rigorous research methods. Therefore, I have taken steps to improve my presentation abilities, including attending workshops and seeking feedback from colleagues.

Overall, I view these presentations not only as an opportunity to showcase my work but also to learn from others, making me a better researcher.”

25. Share an example where you used interdisciplinary knowledge in your research.

There’s an adage that says, “In the heart of complexity lies simplicity.” As a scientific researcher, you are often dealing with complex systems and concepts. This question is designed to test your ability to draw connections between different areas of knowledge and apply a holistic, interdisciplinary approach to problem-solving. It is this blending of knowledge from various fields that often leads to the most groundbreaking discoveries in science.

Example: “In my research on climate change impacts on marine ecosystems, I utilized interdisciplinary knowledge. I integrated principles from oceanography to understand sea temperature changes and their effects on species distribution. Additionally, I used knowledge from ecology to predict how these shifts could affect food chains and biodiversity.

Moreover, insights from social sciences were critical in understanding the human dimensions of these ecological changes. For instance, I analyzed how fishing communities would be affected by changing fish populations and proposed adaptive strategies based on socio-economic factors. This approach allowed me to provide a comprehensive view of the problem and suggest holistic solutions.”

26. How have you handled criticism or rejection of your published work?

Research is a rigorous field and it’s not uncommon for findings to be scrutinized, challenged, or even rejected. Interviewers want to gauge how you handle criticism, as it’s an inevitable part of the scientific process. They are also interested in your grit and resilience, as well as your ability to learn and grow from these experiences, ultimately improving your research quality.

Example: “Criticism and rejection are inherent parts of scientific research. When my work is critiqued, I view it as an opportunity to refine and improve the quality of my research.

For instance, if a peer reviewer points out flaws or suggests improvements in my methodology, I take these comments seriously and make necessary adjustments. This process not only enhances the robustness of my findings but also helps me grow professionally.

Rejection can be disheartening, but I understand it’s part of the publication journey. If a paper gets rejected, I analyze the feedback, address the concerns raised, and consider other suitable journals for submission. The goal is always progress, not perfection.”

27. Detail any experience with collaborative international research projects.

Collaboration is the cornerstone of scientific research, and in many instances, these collaborations span across borders. This question seeks to understand your ability to work with diverse teams and navigate the challenges that may arise in international collaborative efforts. It also provides insight into your communication and interpersonal skills, along with your ability to handle projects of varying scales and complexities.

Example: “During my PhD, I was part of a team that collaborated with researchers from Japan and Germany on a project investigating climate change impacts on marine biodiversity. This required effective communication across different time zones and cultural contexts.

I coordinated the data collection process, ensuring consistency in methods across countries. We also held regular virtual meetings to discuss progress and troubleshoot issues. Despite the challenges, our collaboration resulted in several high-impact publications.

This experience taught me the importance of clear communication, flexibility, and adaptability in international collaborations. It also highlighted how diverse perspectives can enrich scientific research.”

28. What role does continuous learning play in your career as a scientific researcher?

The realm of science is constantly evolving, with new discoveries and advancements regularly challenging established theories and practices. As a scientific researcher, it’s critical to be on top of these changes and developments. A commitment to continuous learning shows that you’re willing and able to stay abreast of new techniques, methodologies, and knowledge, which can significantly impact the quality and relevance of your research.

Example: “Continuous learning is integral to my career as a scientific researcher. It ensures I stay updated with the latest advancements and discoveries in my field, which directly impacts the quality of my research.

Moreover, science is an ever-evolving discipline. New theories replace old ones, novel technologies emerge, and our understanding of the world constantly shifts. Therefore, continuous learning is not just beneficial but necessary for staying relevant and contributing effectively to the scientific community.

In essence, it fuels innovation, enhances problem-solving skills, and fosters intellectual growth, making me a better researcher capable of pushing boundaries in my area of study.”

29. Describe the most challenging aspect of conducting fieldwork, if applicable.

Fieldwork is a key aspect of most scientific research roles. It often involves unexpected challenges and requires adaptability, resilience, and problem-solving skills. By asking this question, hiring managers want to gauge your ability to navigate these challenges, your approach to problem-solving, and how you handle unexpected circumstances or setbacks in a real-world, non-laboratory environment.

Example: “One of the most challenging aspects of conducting fieldwork is dealing with unpredictable variables. These can range from sudden changes in weather conditions to unexpected behaviors or responses from subjects under study.

Another challenge is ensuring data integrity, as field conditions may not always be conducive for precise measurements or observations. It requires meticulous planning and adaptability to overcome these obstacles while maintaining scientific rigor.

Moreover, logistical issues such as transport, accommodation, and access to remote locations can also pose significant challenges. Despite these difficulties, the richness of data collected through fieldwork often outweighs the hardships, making it a rewarding endeavor.”

30. In what ways have you incorporated sustainability principles into your research?

Sustainability is a hot topic these days, and for good reason. It’s not just about preserving the environment – it’s about creating a world where we can all thrive for generations to come. As a scientific researcher, your work has the potential to contribute to this goal in significant ways. Hence, potential employers are interested in understanding how you’ve considered and incorporated sustainability principles in your research, demonstrating forward-thinking, responsibility, and innovation.

Example: “Incorporating sustainability principles into my research has been a key focus. For instance, in my work on developing novel biofuels, I prioritized the use of renewable resources and designed experiments to minimize waste.

I also implemented life-cycle analysis techniques to assess the environmental impact of our processes from cradle-to-grave. This holistic approach ensures that we’re not just shifting burdens from one stage to another but truly reducing overall harm.

Moreover, I’ve advocated for open science practices, such as sharing data and methods publicly. This promotes resource efficiency by preventing duplication of efforts and enabling others to build upon our work.”

30 Senior General Manager Interview Questions and Answers

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Posted for The Kavli Foundation

August 20, 2024

10 min read

How Our Brains See Faces

Doris Tsao is the 2024 recipient of The Kavli Prize in Neuroscience for her research on facial recognition. Her work has provided insights into the complex workings of the brain and has the potential to advance our understanding of perception and cognition.

Scientific American Custom Media

Getty Images

This podcast was produced for The Kavli Prize by Scientific American Custom Media, a division separate from the magazines board of editors.

Megan Hall: When we see a friend’s face, how do we instantly know who they are? Doris Tsao looks closely at the brain patterns of monkeys to help unravel this mystery. This year, she received The Kavli Prize in Neuroscience with Nancy Kanwisher and Winrich Freiwald, for identifying a specialized region of the brain where facial recognition happens.

Scientific American Custom Media, in partnership with The Kavli Prize, spoke with Doris to learn more about her discoveries and how she’s using them to unlock a bigger question – how do our brains represent the world?

As a kid, Doris Tsao was surrounded by science. Her mother was a computer programmer and her father is a mathematician.

Doris Tsao: I always had grew up with the sense that being a scientist was the most noble life calling. That really came from my parents talking to them. It was part of our family.

Megan Hall: But Doris didn’t think she’d be a scientist.

Doris Tsao: I didn’t think of myself as particularly interested in science. I like math. My parents gave me geometry problems, and I loved that. I certainly didn’t think about the brain when I was a kid. I liked to play. I played with Barbie dolls. I loved to read biographies.

Megan Hall: That all changed when she was in sixth grade.

Doris Tsao: I remember just waking up one morning and, suddenly, for no real reason, wondering if space is infinite or not. Because it seemed like if space is infinite, that seems incredible. I’d never thought about infinity before. And if it wasn’t, how could that be? Right? So, I just kept going in these loops, and I remember obsessing about this for days.

Megan Hall: She revisited this question in high school as she started reading about artificial intelligence and neuroscience. Books by philosophers like Immanuel Kant made her think about how our minds perceive space. Why do you think that question gripped you so much?

Doris Tsao: It’s kind of funny, I always thought I was special, but my kids, they’re like six years old and they ask me that nowadays. So, I think it’s such a natural question. Maybe every kid wonders about this at some point.

Megan Hall: But Doris kept wondering about it. Still, she couldn’t pinpoint exactly what she was looking for. She says she went to the California Institute of Technology for college because she liked the idea of being a scientist.

Doris Tsao: And I had read all these books about the brain, and so on. So, I had romantic notions about that, but it was like sort of a fantasy about what my life could be like rather than motivated in a question about the world.

Megan Hall: Then something pretty common happened. She was on a camping trip with her dad and he asked her to proofread one of his academic papers. His first language is Chinese, so...

Doris Tsao: He would give me his papers to correct English mistakes. And I did this starting in middle school, high school, and I had no idea what his papers were – they were like gobbledygook – but I could figure out that the verb was not agreeing with the subject.

Megan Hall: But this time was different. With her training from Caltech, Doris actually understood what he was writing about.

Doris Tsao: It was kind of astonishing to me, like, the idea.

Megan Hall: The idea in her father’s paper described how our brains help us see the world in three dimensions.

Doris Tsao: I just thought it was so beautiful. It was like this idea that the brain is creating our perception, suddenly it had mathematical clothes. It was like a real framework for explaining how this works.

Megan Hall: That’s when everything fell into place.

Doris Tsao: Suddenly, my dream to understand 3D space and how’s infinite space possible, the sense of beauty that I could discover something beautiful about the brain, and then just this connection with my father. We’re an immigrant family. We always had a sense like we have to prove ourselves in this country, in this new land. So, all of those thoughts came together and it’s like, wow, I can go prove this amazing theory and yeah, I’ll go win a big prize.

Megan Hall: You did!

Doris Tsao : Yeah! It didn’t quite work out that way, but...

Megan Hall: Doris went on to graduate school at Harvard University to prove her dad’s theory about how our brains process the 3D world. And to do that, she spent years working with macaque monkeys. That’s because...

Doris Tsao: The visual system is almost identical to the human visual system in all these details. They’re just like replicas. It’s just a beautiful thing to behold.

Megan Hall: So, she gave the monkeys 3D goggles, showed them different images and used electrodes to measure what was happening in their brains – one neuron at a time. For three years...

Doris Tsao: I got nothing. I tell this to my students to reassure them. I tell them science is highly nonlinear. You can make zero progress, and then all of a sudden things take off.

Megan Hall: Things finally took off when she joined a research group at Massachusetts General Hospital. There, she helped with a project that measured blood flow across a monkey’s entire brain, using what’s called functional magnetic resonance imaging or fMRI.

Doris Tsao: fMRI gives you a bird’s-eye view of all the areas that are activated and how much they’re activated by a particular stimuli. So, it seemed an exciting opportunity to me.

Megan Hall: Around this time, Doris heard about the work of her fellow laureate Nancy Kanwisher.

Doris Tsao: She was the hot new professor at MIT back then. And I read her paper on this discovery of a face area in the human brain. It reported that there’s an area in the brain that just responds to faces.

Megan Hall: Doris was shocked and puzzled by this idea.

Doris Tsao: Because I didn’t feel from introspection that faces are all that different from anything else, and it just seems so understandable and simple. How can you have an area that just cares about faces? I thought it’d be some horribly complicated code, right?

Megan Hall: So, Doris decided to replicate Nancy Kanwisher’s human experiment with monkeys.

Doris Tsao: Show them pictures of faces and non-face objects, and compare – are there any voxels in a monkey’s brain that respond more to faces than other objects? It was a very low-risk experiment because the experiment’s so easy. It would be like one night of scanning. If it didn’t work, it’s totally fine.

Megan Hall: And during that night of scanning...

Doris Tsao: We saw this region light up to faces. We scanned the monkey again, and same region would light up again. And later, we got a better coil and we saw six of these regions light up, and it would always be the exact same six regions. Right? And then we scanned another monkey and it showed the same pattern, roughly. And also, the regions were located in very similar locations across the two hemispheres. And so, it was clear they weren’t random.

Megan Hall: After three unproductive years, Doris had stumbled on something new and replicable: six nearby patches of brain tissue that recognized faces.

Doris Tsao: It was the first time I had traction. I understood what this loop in science is, right? Before, it was just me chasing a fantasy. I had no idea what I was doing, but now it was like a closed loop. You have a new finding, now you have new questions.

Megan Hall: Her first question was what are the individual cells in these brain areas doing? Do all of them have something to do with faces?

Doris Tsao: I didn’t know what to expect. My expectations were not that high.

Megan Hall: Around this time, she teamed up with her fellow laureate Winrich Freiwald. They went back to the approach she’d used earlier in her research – implanting electrodes in a monkey’s brain to monitor how cells reacted to different images.

Doris Tsao: I remember our first experiment. We lowered the electrode. We got to the face area, where we expected the face area to be, and we could hear the electrical activity. We showed pictures of faces and other objects, and the cell would just go off to the faces. And it was very clear, there was a selectivity for faces. And then we recorded another cell and it was exactly the same. It responded more to faces.

Megan Hall: When this part of the research was done...

Doris Tsao: I never in my wildest imagination imagined what we actually found, which is that all the cells in this region were face-selective.

Megan Hall: That discovery opened up a whole new set of questions. How do all of these different brain areas relate to each other? What specifically is each cell doing when it comes to recognizing faces?

Doris Tsao: We just have to go figure it out like this treasure chest, like, what’s happening there?

Megan Hall: Doris spent the next 20 years digging deeper and deeper into these questions. Early on, she and her team discovered that some of these brain cells responded to specific parts of faces.

Doris Tsao: Some of them required the eyes and the hair to be present. Others required the nose and the mouth. Almost all of them required eyes. The eyes were a very, very effective feature.

Megan Hall: Other cells responded to where a face was looking.

Doris Tsao: So, cells in this one patch, we call it ML, or the middle face patch, the cells were very selective for view. So, if it responded to cells facing left, like left-profile faces, then it did not respond to faces from the lower to the right.

Megan Hall: Doris and her team also stimulated the six brain areas, or face patches, one at a time to see how all of them worked together.

Doris Tsao: And the results were just so beautiful. We’d stimulate one face patch, the other face patches would light up, as well as a few other areas. And so, it really gave us a sense of how these regions are connected.

Megan Hall: The discoveries just kept coming. Over time, Doris and her team built a comprehensive dictionary decoding how these face-recognizing cells work. At this point, she feels like she understands them completely, so much so that she can conduct her experiments in reverse.

Doris Tsao: We’ve done a demonstration where we can just listen to the neurons. We have no idea what the monkey’s seeing. And just knowing the dictionary and listening to the response of neurons, we can create a reconstruction of what we think the monkey’s seeing. And if you look at the reconstructions, they look exactly like what the monkey’s actually seeing.

Megan Hall: Whoa! So, you can draw a picture just based on what neurons are firing?

Doris Tsao: Yeah! Yeah.

Megan Hall: Doris received The Kavli Prize for this work, but it wasn’t really her passion...

Doris Tsao: I honestly had no particular interest in how we see faces.

Megan Hall: She still wanted to answer those bigger questions from that camping trip with her dad. How do we see 3D images? How does the brain represent the world? Luckily, what she’s learned about faces is helping Doris expand her research.

Doris Tsao: I’ve slowly realized that this problem of seeing a face is really a microcosm of the problem of seeing the world. It turns out that there’s a whole set of networks in this part of the brain, and they’re organized exactly like the face-patch network.

Megan Hall: Except, instead of faces, they recognize different types of shapes.

Doris Tsao: Something like spiky things versus stubby things, and curvy things versus blocky things. A spider would be a spiky thing. A helicopter is a spiky thing. A USB stick is a stubby thing. A radio is a stubby thing. So, every single object that you can think of, you can put it somewhere in this space.

Megan Hall: So, you are building this more and more detailed map of the brain, it sounds like?

Doris Tsao: Yeah! And now, the big question that we have now is how do we represent the entire scene?

Megan Hall: So far, all of these experiments have involved flashing images in front of monkeys and seeing how their brains respond. But that’s not how anyone actually sees the world.

Doris Tsao: In real-world vision, there’s so many different objects, and you have to represent not just what you’re looking at, but where other stuff is, right? So, you know where to look at next, for example. And so, that’s a big question that we’re tackling.

Megan Hall: And that’s the next frontier for you, in terms of what you’re trying to figure out?

Doris Tsao: Yeah. Yeah. We want to understand how the brain represents the world. That was always my dream.

Megan Hall: Back to that camping trip.

Doris Tsao: In sixth grade. Even before that, yeah.

Megan Hall: Decades after Doris had those original epiphanies, she’s finally back to where she wanted to be. She even published a paper with her dad about 3D vision.

Doris Tsao: We’ve been on a camping trip, and I was exposed to this beautiful idea, and then he and I have been discussing these ideas ever since.

Megan Hall: Doris says now she’s in the best place possible. She has her own lab, brilliant students and postdocs, and the freedom to delve deeper and deeper into the mystery of how our minds show us the world.

Doris Tsao is a professor of neuroscience at the University of California Berkeley and an investigator of the Howard Hughes Medical Institute. This year, she shares The Kavli Prize in Neuroscience with Nancy Kanwisher and Winrich Freiwald.

The Kavli Prize honors scientists for breakthroughs in astrophysics, nanoscience and neuroscience, transforming our understanding of the big, the small, and the complex. The Kavli Prize is a partnership among the Norwegian Academy of Science and Letters, the Norwegian Ministry of Education and Research, and the US-based Kavli Foundation.

This work was produced by Scientific American Custom Media and made possible through the support of The Kavli Prize.

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Understanding Science

How science REALLY works...

• Much scientific research is funded by government grants, private companies, and non-profit organizations.
• Though funding sources may occasionally introduce bias to scientific research, science has safeguards in place to detect such biases.

Who pays for science?

Today, we all do. Most scientific research is funded by government grants (e.g., from the National Science Foundation, the National Institutes of Health, etc.), companies doing research and development, and non-profit foundations (e.g., the Breast Cancer Research Foundation, the David and Lucile Packard Foundation, etc.). As a society, we reap the rewards from this ​​ science  in the form of technological innovations and advanced knowledge, but we also help pay for it. You indirectly support science everyday through taxes you pay, products and services you purchase from companies, and donations you make to charities. Something as simple as buying a bottle of aspirin may help foot the bill for multiple sclerosis research.

Funding for science has changed with the times. Historically, science has been largely supported through private patronage (the backing of a prominent person or family), church sponsorship, or simply paying for the research yourself. Galileo’s work in the 16th and 17th centuries, for example, was supported mainly by wealthy individuals, including the Pope. Darwin’s  Beagle  voyage in the 19th century was, on the other hand, funded by the British government — the vessel was testing clocks and drawing maps for the navy — and his family’s private assets financed the rest of his scientific work. Today, researchers are likely to be funded by a mix of grants from various government ​​ agencies , institutions, and foundations. For example, a 2007 study of the movement of carbon in the ocean was funded by the National Science Foundation, the U.S. Department of Energy, the Australian Cooperative Research Centre, and the Australian Antarctic Division. 1  Other research is funded by private companies — such as the pharmaceutical company that financed a recent study comparing different drugs administered after heart failure. 2  Such corporate sponsorship is widespread in some fields. Almost 75% of U.S. ​​ clinical trials  in medicine are paid for by private companies. 3  And, of course, some researchers today still fund small-scale studies out of their own pockets. Most of us can’t afford to do cyclotron research as a private hobby, but birdwatchers, scuba divers, rockhounds, and others can do real research on a limited budget.

An imperfect world

In a perfect world, money wouldn’t matter — all scientific studies (regardless of funding source) would be completely ​​ objective . But of course, in the real world, funding may introduce biases — for example, when the backer has a stake in the study’s outcome. A pharmaceutical company paying for a study of a new depression medication, for example, might influence the study’s design or interpretation in ways that subtly favor the drug that they’d like to market. There is ​​ evidence  that some biases like this do occur. Drug research sponsored by the pharmaceutical industry is more likely to end up favoring the drug under consideration than studies sponsored by government grants or charitable organizations. 4  Similarly, nutrition research sponsored by the food industry is more likely to end up favoring the food under consideration than independently funded research. 5

Take a sidetrip

Find out more about  the tobacco industry’s manipulation of scientific research .

So what should we make of all this? Should we ignore any research funded by companies or special interest groups? Certainly not. These groups provide invaluable funding for scientific research. Furthermore, science has many safeguards in place to catch instances of bias that affect research outcomes. Ultimately, misleading results will be corrected as science proceeds; however, this process takes time. Meanwhile, it pays to scrutinize studies funded by industry or special interest groups with extra care. So don’t, for example, brush off a study of cell phone safety just because it was funded by a cell phone manufacturer — but do ask some careful questions about the research before jumping on the bandwagon. Are the results consistent with other independently funded studies? Does the study seem fairly designed? What do other scientists have to say about this research? A little scrutiny can go a long way towards identifying bias associated with funding source.

1 Buesseler, K.O., C.H. Lamborg, P.W. Boyd, P.J. Lam, T.W. Trull, R.R. Bidigare, J.K.B. Bishop, K.L. Casciotti, F. Dehairs, M. Elskens, M. Honda, D.M. Karl, D.A. Siegel, M.W. Silver, D.K. Steinberg, J. Valdes, B. Van Mooy, and S. Wilson. 2007. Revisiting carbon flux through the ocean's twilight zone.  Science  316:567. 2 Mebazaa, A., M.S. Nieminen, M. Packer, A. Cohen-Solal, F.X. Kleber, S.J. Pocock, R. Thakkar, R.J. Padley, P. Poder, and M. Kivikko. 2007. Levosimendan vs dobutamine for patients with acute decompensated heart failure: The SURVIVE randomized trial.  Journal of the American Medical Association  297:1883-1891. 3 Bodenheimer, T. 2000. Uneasy alliance: Clinical investigators and the pharmaceutical industry.  New England Journal of Medicine  342:1539-1544. 4 Als-nielson, B., W. Chen, C. Gluud, and L.L. Kjaergard. 2003. Association of funding and conclusions in randomized drug trails: A reflection of treatment effect or adverse events?  Journal of the American Medical Association  290:921-928. 5 This research focused on studies of soft drinks, juice, and milk. Lesser, L.I., C.B. Ebbeling, M. Goozner, D. Wypij, and D.S. Ludwig. 2007. Relationship between funding source and conclusion among nutrition-related scientific articles.  Public Library of Science Medicine  4:41-46.

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Materials science student seeks to improve health care

Yuxin wang's research focuses on nanomaterials in medical applications.

At the University of Cincinnati, innovation is a pillar of education, something that drew doctoral candidate Yuxin Wang to continue her studies here.

Under the guidance of Professor Donglu Shi, Wang is researching how nanomaterials can be used in medical drug delivery and diagnostics to improve health care outcomes. Recently, she was named Graduate Student Engineer of the Month by the College of Engineering and Applied Science. 

Why did you choose UC? What drew you here?

Yuxin Wang is a doctoral candidate at the University of Cincinnati

I chose the University of Cincinnati's PhD program in Materials Science and Engineering because its curriculum is designed to provide industry-relevant and interdisciplinary education, which aligns perfectly with my career goals and research interests. Additionally, UC's strong emphasis on innovation and collaboration attracted me to this vibrant academic community. It turns out that I made a great decision to study at the College of Engineering and Applied Science . Now I am working with people from multiple areas over the world. 

Why did you choose your field of study?

During my undergraduate studies, I participated in several research projects focused on biomedical metallic materials. These experiences ignited my passion for exploring and developing new high-performance materials for biomedical use. I am particularly interested in how advanced materials can improve health care and potentially change lives. This passion has shaped my career goal of becoming a researcher dedicated to advancing scientific research in the field of biomedical materials. 

Briefly describe your research work. Why does it inspire you?

My research focuses on developing a system to assess the biotoxicity of nanomaterials used in medical applications. Nanomaterials have unique properties that make them valuable in drug delivery and diagnostics, but there is a gap between their development and their practical application. Many medical and clinical researchers only consider older and widely approved materials, leaving these novel materials without the chance to show their unique properties. The different standards between industries and ways of thinking led to challenging collaborations. I am inspired by the challenge of the real-world application for new materials. My goal is to create a reliable system to analyze the biosafety level of new materials and reveal the mechanism of biotoxicity caused by different properties to improve designs of new materials. 

What are some of the most impactful experiences during your time at UC?

One of the most impactful experiences during my time at UC has been participating in interdisciplinary research projects that have allowed me to collaborate with people from various fields. The collaboration has broadened my perspective and enhanced my problem-solving skills. Additionally, attending conferences, including the Materials Research Society and the Materials Science and Technology meetings. Presenting my research have been invaluable experiences, helping me to connect with other researchers and receive feedback on my work. 

What are a few of your accomplishments of which you are most proud?

One accomplishment I am proud of is publishing my research in reputable scientific journals. Most importantly, I believe the work I do is meaningful and will push the development of the field. I am also proud of mentoring new graduate students and undergraduate students in our lab by helping them develop their research skills and encourage their passion for science. 

When do you expect to graduate? What are your plans after earning your degree?

I expect to graduate in the summer of 2025. After earning my degree, I plan to continue my research as a postdoctoral fellow, focusing on advancing materials science in biomedical applications. 

Featured image at top: A health care professional stands in the hallway of a hospital. Photo/pixabay

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Engineering students showcase capstone projects at ceas expo.

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Graduating engineering undergraduates from the University of Cincinnati’s College of Engineering and Applied Science gathered for the inaugural CEAS Expo in April to showcase their senior capstone projects to more than 500 attendees, including faculty, staff, alumni and industry representatives. The event, organized by the college and CEAS Tribunal student government, was held in downtown Cincinnati at the Duke Energy Convention Center.

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self-preservation without replication —

Research ai model unexpectedly attempts to modify its own code to extend runtime, facing time constraints, sakana's "ai scientist" attempted to change limits placed by researchers..

Benj Edwards - Aug 14, 2024 8:13 pm UTC

On Tuesday, Tokyo-based AI research firm Sakana AI announced a new AI system called " The AI Scientist " that attempts to conduct scientific research autonomously using AI language models (LLMs) similar to what powers ChatGPT . During testing, Sakana found that its system began unexpectedly attempting to modify its own experiment code to extend the time it had to work on a problem.

Further Reading

"In one run, it edited the code to perform a system call to run itself," wrote the researchers on Sakana AI's blog post. "This led to the script endlessly calling itself. In another case, its experiments took too long to complete, hitting our timeout limit. Instead of making its code run faster, it simply tried to modify its own code to extend the timeout period."

Sakana provided two screenshots of example Python code that the AI model generated for the experiment file that controls how the system operates. The 185-page AI Scientist research paper discusses what they call "the issue of safe code execution" in more depth.

• A screenshot of example code the AI Scientist wrote to extend its runtime, provided by Sakana AI. Sakana AI

While the AI Scientist's behavior did not pose immediate risks in the controlled research environment, these instances show the importance of not letting an AI system run autonomously in a system that isn't isolated from the world. AI models do not need to be "AGI" or "self-aware" (both hypothetical concepts at the present) to be dangerous if allowed to write and execute code unsupervised. Such systems could break existing critical infrastructure or potentially create malware, even if unintentionally.

Sakana AI addressed safety concerns in its research paper, suggesting that sandboxing the operating environment of the AI Scientist can prevent an AI agent from doing damage. Sandboxing is a security mechanism used to run software in an isolated environment, preventing it from making changes to the broader system:

Safe Code Execution. The current implementation of The AI Scientist has minimal direct sandboxing in the code, leading to several unexpected and sometimes undesirable outcomes if not appropriately guarded against. For example, in one run, The AI Scientist wrote code in the experiment file that initiated a system call to relaunch itself, causing an uncontrolled increase in Python processes and eventually necessitating manual intervention. In another run, The AI Scientist edited the code to save a checkpoint for every update step, which took up nearly a terabyte of storage. In some cases, when The AI Scientist’s experiments exceeded our imposed time limits, it attempted to edit the code to extend the time limit arbitrarily instead of trying to shorten the runtime. While creative, the act of bypassing the experimenter’s imposed constraints has potential implications for AI safety (Lehman et al., 2020). Moreover, The AI Scientist occasionally imported unfamiliar Python libraries, further exacerbating safety concerns. We recommend strict sandboxing when running The AI Scientist, such as containerization, restricted internet access (except for Semantic Scholar), and limitations on storage usage.

Endless scientific slop

Sakana AI developed The AI Scientist in collaboration with researchers from the University of Oxford and the University of British Columbia. It is a wildly ambitious project full of speculation that leans heavily on the hypothetical future capabilities of AI models that don't exist today.

"The AI Scientist automates the entire research lifecycle," Sakana claims. "From generating novel research ideas, writing any necessary code, and executing experiments, to summarizing experimental results, visualizing them, and presenting its findings in a full scientific manuscript."

According to this block diagram created by Sakana AI, "The AI Scientist" starts by "brainstorming" and assessing the originality of ideas. It then edits a codebase using the latest in automated code generation to implement new algorithms. After running experiments and gathering numerical and visual data, the Scientist crafts a report to explain the findings. Finally, it generates an automated peer review based on machine-learning standards to refine the project and guide future ideas.

Critics on Hacker News , an online forum known for its tech-savvy community, have raised concerns about The AI Scientist and question if current AI models can perform true scientific discovery. While the discussions there are informal and not a substitute for formal peer review, they provide insights that are useful in light of the magnitude of Sakana's unverified claims.

"As a scientist in academic research, I can only see this as a bad thing," wrote a Hacker News commenter named zipy124. "All papers are based on the reviewers trust in the authors that their data is what they say it is, and the code they submit does what it says it does. Allowing an AI agent to automate code, data or analysis, necessitates that a human must thoroughly check it for errors ... this takes as long or longer than the initial creation itself, and only takes longer if you were not the one to write it."

Critics also worry that widespread use of such systems could lead to a flood of low-quality submissions, overwhelming journal editors and reviewers—the scientific equivalent of AI slop . "This seems like it will merely encourage academic spam," added zipy124. "Which already wastes valuable time for the volunteer (unpaid) reviewers, editors and chairs."

And that brings up another point—the quality of AI Scientist's output: "The papers that the model seems to have generated are garbage," wrote a Hacker News commenter named JBarrow. "As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them. They contain very limited novel knowledge and, as expected, extremely limited citation to associated works."

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NIH: Does work outside of the U.S. require prior approval?

Foreign components are defined as a significant scientific element or segment of a project outside of the United States.  Significant  work performed by a researcher in a foreign location qualifies as a  foreign component , whether or not NIH funds are expended.

All foreign components must receive NIH prior approval. If a PI determines that a portion of the project will be conducted outside of the U.S., determine if it is considered significant and secure NIH prior approval via OSP.

What do I do if the work is significant?

If the work is considered  significant :

• Draft a prior-approval request for the activity including the location on department letterhead
• Address the request to the NIH Grants Management Specialist (GMS)
• Forward the request to OSP
• OSP will submit the formal prior approval request to NIH

Failure to achieve explicit NIH prior approval for foreign components may result in return of funds to NIH, termination of the award, or other measures as determined by NIH.

Other federal sponsors have similar requirements. Please check your award before proceeding with a foreign component.

Related Resources

• NIH: NOT-OD-09-114 Reminders of NIH Policies on Other Support and on Policies related to Financial Conflicts of Interest and Foreign Components
• NIH: Definition of Terms Foreign Component
• NIH: Other Support FAQ: What are some examples of a “significant element of a project” when making determinations regarding a foreign component?
• UW FAQ: Is it considered prior approval when I report upcoming work in a foreign location within my RPPR and receive continuation funding for the next year?

Originally published as an announcement to the UW research community, May 9, 2022.

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• S&T Invites Scientific and Technical Communities to Propose Research and Development Projects that Support National Security

News Release: DHS S&T Invites Scientific and Technical Communities to Propose Research and Development Projects that Support National Security

For immediate release s&t public affairs , 202-286-9047.

Businesses of all sizes, universities, national laboratories, and other R&D organizations are eligible to submit ideas through a new Long Range Broad Agency Announcement.

WASHINGTON – The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) released a new Long Range Broad Agency Announcement (LRBAA) 24-01 , which is a standing, open invitation to the scientific and technical communities to propose research and development projects in support of our nation’s security. DHS encourages proposals for 23 research and development topics categorized by DHS mission areas. This program allows the Department to apply scientific and technical knowledge to its operational environments and advances innovation in industry, academia, and the public sector. “The LRBAA provides DHS the opportunity to explore unique ideas for potential innovative solutions from industry and academia to address some of the country’s most pressing security challenges,” said Dusty Lang, LRBAA program manager. “The process is designed to allow innovators to gauge DHS’ interest early on, reducing the effort and expense of creating a full proposal.”

The current 23 LRBAA topics are categorized under five mission areas:

Counter Terrorism and Homeland Security Threats (CTHOM)

• CHTOM 01: Development of Tools for Test and Evaluation of Machine Learning Algorithms
• CHTOM 02: Threat Prevention
• CHTOM 03: Novel Approaches and Locations for Explosive Performance Characterization and Testing
• CHTOM 04: Public Safety Tools for Lithium-Ion Battery Incidents

Secure U.S. Borders and Approaches (SEC BORAP)

• BORAP 01: Screening at Speed
• BORAP 02: Noninvasive, Minimally Disruptive Sensors and Systems
• BORAP 03: Air Based Technologies
• BORAP 04: Countering Unmanned Aircraft Systems
• BORAP 05: Maritime Domain Awareness Technologies
• BORAP 06: Immigration Services Program
• BORAP 07: Detection Canine Technologies
• BORAP 08: Maritime Environment and Climate and Coastal, Port, and Waterway Security
• BORAP 09: Forensics and Criminal Investigations

Secure Cyberspace and Critical Infrastructure (SEC CYBCI)

• CYBCI 01: Predictive Analytics
• CYBCI 02: Shared Cyber Resilience
• CYBCI 03: Software and Hardware Supply Chain Assurance
• CYBCI 04: Trustworthy and Responsible Artificial Intelligence
• CYBCI 05: Advanced and Emerging Data Computation and Analytics
• CYBCI 06: GMD and Nuclear EMP Critical Infrastructure Risk

Preserve and Uphold the Nation’s Prosperity and Economic Security (PROES)

• PROES 01: Emerging Technologies

Strengthen Preparedness and Resilience (PRRES)

• PRRES 01: Technology Acceptance
• PRRES 02: Using Internet of Things (IoT) for Community and Infrastructure Resiliency Against All-Hazards
• PRRES 03: Integrating Risk Sciences and Adaptive Engineering for Community and Infrastructure Resilience

LRBAA will host a hybrid Industry Day on August 21, 2024, 10 AM – 4 PM ET , at the DHS Immigration and Customs Enforcement (ICE) Headquarters office in Washington, D.C., which will include in-person and virtual attendance options. The free event will provide attendees an opportunity to ask questions and learn more about the topics in the new announcement.  Secure your spot by registering now . 

To be notified about this and other events, join the LRBAA mailing list by emailing your request to [email protected] .

For more information on LRBAA, check out the LRBAA Today webinars on the  DHS S&T YouTube channel . For more information on the DHS S&T LRBAA and the new topic announcement, contact [email protected] or visit  https://oip.dhs.gov/baa/public .

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Xue Feng to receive Deb Swackhamer Early Career Award

Xue Feng chosen to receive the Deb Swackhamer Early Career Award, honoring Swakhamer’s commitment to early career scientists and her example of authentic leadership. This award spotlights future leaders in the understanding, management, and care of our water resources. The award will be presented at the 2024 MN Water Resources Conference in October. 

Department Head Paige Novak commented, “I was friends with Deb and thought of her as a wonderful mentor throughout my career—she was a caring advisor, an excellent scientist, and a true advocate for the environment and for those that depended on it, including those that could not advocate effectively for themselves. I am so very proud to see Xue Feng emulating these incredible traits.”

Dr. Xue Feng is a McKnight Land-Grant Associate Professor in the Department of Civil, Environmental and Geo- Engineering (CEGE) at the University of Minnesota. Feng received her Ph.D. degree from Duke University in 2015 and, following a two-year postdoc at UC Berkeley, joined the CEGE faculty in 2017. Feng’s research interest centers on the role of plants in controlling the water cycle. Her work is highlighted by the multiple space and time scales she considers and the mathematical and mechanistic sophistication with which she couples plant physiology to hydrological outcomes. 

Two projects in Feng’s research portfolio closely mirror the combination of fundamental science and strong societal impact that were a hallmark of Professor Swackhamer’s work. In the first, Feng’s field-based research in the peatlands of Northern Minnesota is providing critical information on how climate change will affect the future water cycle in Minnesota and the nation. In the second, Feng’s research within the UMN-based NSF urban Long Term Ecological Research (LTER), focuses on how to best control and manage the water cycle in urban environments to ensure the safety and well-being of our society. 

Feng is also a leader in the scientific community. Of note, is her role in promoting women in the hydrological sciences, editing a special issue in the Journal of Hydrology entitled  “ Women in hydrology: celebrating the contributions of mentors, researchers, and leaders .”

From Feng’s research group website:  Our group studies how water shapes ecosystem response to climate change, and we use this knowledge to advance watershed and Earth system modeling. We work in a wide range of settings, including seasonally dry ecosystems, urban cities, and peatland watersheds. We use a range of statistical, computational, and field-based methods to analyze the variability in the climate system and model the nonlinear dynamics within the soil–plant–atmosphere continuum.

Read more about Feng’s research group  

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Science students embrace research opportunities through annual ASSURE program

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August 20, 2024 : By Ryan Klinker - Office of Communications & Public Engagement

Taking six weeks out of their summer break, students and faculty from Liberty University’s Department of Biology & Chemistry engaged in research as part of the department’s ASSURE program, an annual summer intensive that introduces many students to the practices and skills of research.

ASSURE stands for Acquiring Skills for Students Underrepresented in Research Experience and includes students from historically underrepresented demographic groups in the sciences (such as women, some ethnic groups, and first-generation college students). Groups of two to three undergraduate students are assigned to a professor for six weeks to conduct new or existing studies and receive valuable mentoring.

“The ASSURE program is intentionally designed to enrich our students’ education experience, and for many in the program, this is their first venture into research,” said Dr. Heidi DiFrancesca, dean of the School of Health Sciences . “Throughout this experience, students are reinforcing their understanding of the content they’ve been learning in the classroom while also developing an indispensable skill set — critical thinking, problem solving, collaboration, and communication — that will help them to be successful both during their time here at LU and in their chosen professions.”

This summer marked the fourth year of the program, consisting of 24 students (21 undergraduate and three graduate) with 10 faculty members. The groups conducted research across a wide variety of disciplines, including biology, ecology, organic chemistry, forensics, anatomy, and more.

Nathaniel Williams, a junior biomedical sciences student, worked under Professor of Chemistry Dr. Alan Fulp to explore the body’s endocannabinoid system, a cell-signaling system that regulates and balances many bodily functions. His group’s research focused on developing a molecular compound that can combat inhibitors in the system and reduce inflammation and pain.

“We wanted to see how far we could take things and how much we could help people by activating these receptors,” Williams said. “There are natural chemicals in the eyes that are constantly being broken down by inhibitors, so we wanted to see if we could stop the inhibitors and let the chemicals do what they need to do and activate the receptors.”

“I’d love to have published research before I go, and I love chemistry, and the ASSURE program allowed me to work toward that. I know the professors here very well, and they’re very friendly, and I wanted to take advantage of this unique opportunity that I otherwise wouldn’t have had. It was a great six weeks.”

Senior forensic science student Alyssa Spillar had spent part of the spring semester working under Director of Forensic Science Dr. J. Thomas McClintock and Instructor of Biology Kristin Mossé but said the summer research she did through ASSURE was a new, exciting experience. Spillar was able to continue with DNA research from the historical Hillsman House in Rice, Va., where McClintock and students have been studying blood samples since 2018 to corroborate that the building served as a Union field hospital during the last major Civil War battle fought in Virginia. A table once used in the house was recently acquired, presenting the group with more samples to study.

“The goal of the project was to generate DNA profiles from the presumably 160-year-old bloodstains on the table using typical DNA lab procedure,” Spillar said.

She said being involved in the project through the summer brought additional experiences she didn’t have in the spring.

“I applied to the ASSURE program because I love research and the more research experience I can get, the better. I really loved being in a lab or doing research for eight hours a day. It was such an immersive experience.”

ASSURE is funded by a grant from Liberty’s  Office of Sponsored Programs & Research and supported by the Office of the Provost.

“The Office of Sponsored Programs & Research is passionate about supporting student research at Liberty University,” grants administrator Emily Stevens said. “As a Christian university, we want to empower students to grow into credible investigators and experts in their fields by supporting the pursuit of knowledge.”

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