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GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.

Swedish School of Library and Information Science, University of Borås, Sweden

Department of Arts and Cultural Sciences, Lund University, Sweden

Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden

Research Questions

• Where are questionable publications produced with generative pre-trained transformers (GPTs) that can be found via Google Scholar published or deposited?
• What are the main characteristics of these publications in relation to predominant subject categories?
• How are these publications spread in the research infrastructure for scholarly communication?
• How is the role of the scholarly communication infrastructure challenged in maintaining public trust in science and evidence through inappropriate use of generative AI?

research note Summary

• A sample of scientific papers with signs of GPT-use found on Google Scholar was retrieved, downloaded, and analyzed using a combination of qualitative coding and descriptive statistics. All papers contained at least one of two common phrases returned by conversational agents that use large language models (LLM) like OpenAI’s ChatGPT. Google Search was then used to determine the extent to which copies of questionable, GPT-fabricated papers were available in various repositories, archives, citation databases, and social media platforms.
• Roughly two-thirds of the retrieved papers were found to have been produced, at least in part, through undisclosed, potentially deceptive use of GPT. The majority (57%) of these questionable papers dealt with policy-relevant subjects (i.e., environment, health, computing), susceptible to influence operations. Most were available in several copies on different domains (e.g., social media, archives, and repositories).
• Two main risks arise from the increasingly common use of GPT to (mass-)produce fake, scientific publications. First, the abundance of fabricated “studies” seeping into all areas of the research infrastructure threatens to overwhelm the scholarly communication system and jeopardize the integrity of the scientific record. A second risk lies in the increased possibility that convincingly scientific-looking content was in fact deceitfully created with AI tools and is also optimized to be retrieved by publicly available academic search engines, particularly Google Scholar. However small, this possibility and awareness of it risks undermining the basis for trust in scientific knowledge and poses serious societal risks.

Implications

The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated  (Simon et al., 2023).

Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.

To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.

The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few.  While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.

Evidence hacking and backfiring effects

Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.

The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.

However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.

Recommendations

Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of  science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.

Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.

Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.

Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.

Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.

The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs.  Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.

As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.

A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster.  Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”

The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).

Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.

Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.

We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .

We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.

The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.

To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.

We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”

• Artificial Intelligence
• / Search engines

Cite this Essay

Haider, J., Söderström, K. R., Ekström, B., & Rödl, M. (2024). GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-156

• / Appendix B

Bibliography

Antkare, I. (2020). Ike Antkare, his publications, and those of his disciples. In M. Biagioli & A. Lippman (Eds.), Gaming the metrics (pp. 177–200). The MIT Press. https://doi.org/10.7551/mitpress/11087.003.0018

Barbour, R. S. (2001). Checklists for improving rigour in qualitative research: A case of the tail wagging the dog? BMJ , 322 (7294), 1115–1117. https://doi.org/10.1136/bmj.322.7294.1115

Bom, H.-S. H. (2023). Exploring the opportunities and challenges of ChatGPT in academic writing: A roundtable discussion. Nuclear Medicine and Molecular Imaging , 57 (4), 165–167. https://doi.org/10.1007/s13139-023-00809-2

Cabanac, G., & Labbé, C. (2021). Prevalence of nonsensical algorithmically generated papers in the scientific literature. Journal of the Association for Information Science and Technology , 72 (12), 1461–1476. https://doi.org/10.1002/asi.24495

Cabanac, G., Labbé, C., & Magazinov, A. (2021). Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals . arXiv. https://doi.org/10.48550/arXiv.2107.06751

Carrion, M. L. (2018). “You need to do your research”: Vaccines, contestable science, and maternal epistemology. Public Understanding of Science , 27 (3), 310–324. https://doi.org/10.1177/0963662517728024

Centre for Digital Humanities Uppsala (2023). CDHUppsala/word-rain [Computer software]. https://github.com/CDHUppsala/word-rain

Chinn, S., & Hasell, A. (2023). Support for “doing your own research” is associated with COVID-19 misperceptions and scientific mistrust. Harvard Kennedy School (HSK) Misinformation Review, 4 (3). https://doi.org/10.37016/mr-2020-117

Cholewiak, S. A., Ipeirotis, P., Silva, V., & Kannawadi, A. (2023). SCHOLARLY: Simple access to Google Scholar authors and citation using Python (1.5.0) [Computer software]. https://doi.org/10.5281/zenodo.5764801

Dadkhah, M., Lagzian, M., & Borchardt, G. (2017). Questionable papers in citation databases as an issue for literature review. Journal of Cell Communication and Signaling , 11 (2), 181–185. https://doi.org/10.1007/s12079-016-0370-6

Dadkhah, M., Oermann, M. H., Hegedüs, M., Raman, R., & Dávid, L. D. (2023). Detection of fake papers in the era of artificial intelligence. Diagnosis , 10 (4), 390–397. https://doi.org/10.1515/dx-2023-0090

DeGeurin, M. (2024, March 19). AI-generated nonsense is leaking into scientific journals. Popular Science. https://www.popsci.com/technology/ai-generated-text-scientific-journals/

Dunlap, R. E., & Brulle, R. J. (2020). Sources and amplifiers of climate change denial. In D.C. Holmes & L. M. Richardson (Eds.), Research handbook on communicating climate change (pp. 49–61). Edward Elgar Publishing. https://doi.org/10.4337/9781789900408.00013

Fares, M., Kutuzov, A., Oepen, S., & Velldal, E. (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources. In J. Tiedemann & N. Tahmasebi (Eds.), Proceedings of the 21st Nordic Conference on Computational Linguistics (pp. 271–276). Association for Computational Linguistics. https://aclanthology.org/W17-0237

Google Scholar Help. (n.d.). Inclusion guidelines for webmasters . https://scholar.google.com/intl/en/scholar/inclusion.html

Gu, J., Wang, X., Li, C., Zhao, J., Fu, W., Liang, G., & Qiu, J. (2022). AI-enabled image fraud in scientific publications. Patterns , 3 (7), 100511. https://doi.org/10.1016/j.patter.2022.100511

Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods , 11 (2), 181–217.   https://doi.org/10.1002/jrsm.1378

Haider, J., & Åström, F. (2017). Dimensions of trust in scholarly communication: Problematizing peer review in the aftermath of John Bohannon’s “Sting” in science. Journal of the Association for Information Science and Technology , 68 (2), 450–467. https://doi.org/10.1002/asi.23669

Huang, J., & Tan, M. (2023). The role of ChatGPT in scientific communication: Writing better scientific review articles. American Journal of Cancer Research , 13 (4), 1148–1154. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164801/

Jones, N. (2024). How journals are fighting back against a wave of questionable images. Nature , 626 (8000), 697–698. https://doi.org/10.1038/d41586-024-00372-6

Kitamura, F. C. (2023). ChatGPT is shaping the future of medical writing but still requires human judgment. Radiology , 307 (2), e230171. https://doi.org/10.1148/radiol.230171

Littell, J. H., Abel, K. M., Biggs, M. A., Blum, R. W., Foster, D. G., Haddad, L. B., Major, B., Munk-Olsen, T., Polis, C. B., Robinson, G. E., Rocca, C. H., Russo, N. F., Steinberg, J. R., Stewart, D. E., Stotland, N. L., Upadhyay, U. D., & Ditzhuijzen, J. van. (2024). Correcting the scientific record on abortion and mental health outcomes. BMJ , 384 , e076518. https://doi.org/10.1136/bmj-2023-076518

Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74 (5), 570–581. https://doi.org/10.1002/asi.24750

Martín-Martín, A., Orduna-Malea, E., Ayllón, J. M., & Delgado López-Cózar, E. (2016). Back to the past: On the shoulders of an academic search engine giant. Scientometrics , 107 , 1477–1487. https://doi.org/10.1007/s11192-016-1917-2

Martín-Martín, A., Thelwall, M., Orduna-Malea, E., & Delgado López-Cózar, E. (2021). Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: A multidisciplinary comparison of coverage via citations. Scientometrics , 126 (1), 871–906. https://doi.org/10.1007/s11192-020-03690-4

Simon, F. M., Altay, S., & Mercier, H. (2023). Misinformation reloaded? Fears about the impact of generative AI on misinformation are overblown. Harvard Kennedy School (HKS) Misinformation Review, 4 (5). https://doi.org/10.37016/mr-2020-127

Skeppstedt, M., Ahltorp, M., Kucher, K., & Lindström, M. (2024). From word clouds to Word Rain: Revisiting the classic word cloud to visualize climate change texts. Information Visualization , 23 (3), 217–238. https://doi.org/10.1177/14738716241236188

Swedish Research Council. (2017). Good research practice. Vetenskapsrådet.

Stokel-Walker, C. (2024, May 1.). AI Chatbots Have Thoroughly Infiltrated Scientific Publishing . Scientific American. https://www.scientificamerican.com/article/chatbots-have-thoroughly-infiltrated-scientific-publishing/

Subbaraman, N. (2024, May 14). Flood of fake science forces multiple journal closures: Wiley to shutter 19 more journals, some tainted by fraud. The Wall Street Journal . https://www.wsj.com/science/academic-studies-research-paper-mills-journals-publishing-f5a3d4bc

The pandas development team. (2024). pandas-dev/pandas: Pandas (v2.2.2) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.10957263

Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science , 379 (6630), 313–313. https://doi.org/10.1126/science.adg7879

Tripodi, F. B., Garcia, L. C., & Marwick, A. E. (2023). ‘Do your own research’: Affordance activation and disinformation spread. Information, Communication & Society , 27 (6), 1212–1228. https://doi.org/10.1080/1369118X.2023.2245869

Vikramaditya, N. (2020). Nv7-GitHub/googlesearch [Computer software]. https://github.com/Nv7-GitHub/googlesearch

This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).

Competing Interests

The authors declare no competing interests.

The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

Data Availability

All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X

Acknowledgements

The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.

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What a Thesis Paper is and How to Write One

From choosing a topic and conducting research to crafting a strong argument, writing a thesis paper can be a rewarding experience.

It can also be a challenging experience. If you've never written a thesis paper before, you may not know where to start. You may not even be sure exactly what a thesis paper is. But don't worry; the right support and resources can help you navigate this writing process.

What is a Thesis Paper?

A thesis paper is a type of academic essay that you might write as a graduation requirement for certain bachelor's, master's or honors programs. Thesis papers present your own original research or analysis on a specific topic related to your field.

“In some ways, a thesis paper can look a lot like a novella,” said Shana Chartier , director of information literacy at Southern New Hampshire University (SNHU). “It’s too short to be a full-length novel, but with the standard size of 40-60 pages (for a bachelor’s) and 60-100 pages (for a master’s), it is a robust exploration of a topic, explaining one’s understanding of a topic based on personal research.”

Chartier has worked in academia for over 13 years and at SNHU for nearly eight. In her role as an instructor and director, Chartier has helped to guide students through the writing process, like editing and providing resources.

Chartier has written and published academic papers such as "Augmented Reality Gamifies the Library: A Ride Through the Technological Frontier" and "Going Beyond the One-Shot: Spiraling Information Literacy Across Four Years." Both of these academic papers required Chartier to have hands-on experience with the subject matter. Like a thesis paper, they also involved hypothesizing and doing original research to come to a conclusion.

“When writing a thesis paper, the importance of staying organized cannot be overstated,” said Chartier. “Mapping out each step of the way, making firm and soft deadlines... and having other pairs of eyes on your work to ensure academic accuracy and clean editing are crucial to writing a successful paper.”

How Do I Choose a Topic For My Thesis Paper?

What your thesis paper is for will determine some of the specific requirements and steps you might take, but the first step is usually the same: Choosing a topic.

“Choosing a topic can be daunting," said Rochelle Attari , a peer tutor at SNHU. "But if (you) stick with a subject (you're) interested in... choosing a topic is much more manageable.”

Similar to a thesis, Attari recently finished the capstone  for her bachelor’s in psychology . Her bachelor’s concentration is in forensics, and her capstone focused on the topic of using a combined therapy model for inmates who experience substance abuse issues to reduce recidivism.

“The hardest part was deciding what I wanted to focus on,” Attari said. “But once I nailed down my topic, each milestone was more straightforward.”

In her own writing experience, Attari said brainstorming was an important step when choosing her topic. She recommends writing down different ideas on a piece of paper and doing some preliminary research on what’s already been written on your topic.

By doing this exercise, you can narrow or broaden your ideas until you’ve found a topic you’re excited about. " Brainstorming is essential when writing a paper and is not a last-minute activity,” Attari said.

How Do I Structure My Thesis Paper?

Thesis papers tend to have a standard format with common sections as the building blocks.

While the structure Attari describes below will work for many theses, it’s important to double-check with your program to see if there are any specific requirements. Writing a thesis for a Master of Fine Arts, for example, might actually look more like a fiction novel.

According to Attari, a thesis paper is often structured with the following major sections:

Introduction

• Literature review
• Methods, results

Now, let’s take a closer look at what each different section should include.

Your introduction is your opportunity to present the topic of your thesis paper. In this section, you can explain why that topic is important. The introduction is also the place to include your thesis statement, which shows your stance in the paper.

Attari said that writing an introduction can be tricky, especially when you're trying to capture your reader’s attention and state your argument.

“I have found that starting with a statement of truth about a topic that pertains to an issue I am writing about typically does the trick,” Attari said. She demonstrated this advice in an example introduction she wrote for a paper on the effects of daylight in Alaska:

In the continental United States, we can always count on the sun rising and setting around the same time each day, but in Alaska, during certain times of the year, the sun rises and does not set for weeks. Research has shown that the sun provides vitamin D and is an essential part of our health, but little is known about how daylight twenty-four hours a day affects the circadian rhythm and sleep.

In the example Attari wrote, she introduces the topic and informs the reader what the paper will cover. Somewhere in her intro, she said she would also include her thesis statement, which might be:

Twenty-four hours of daylight over an extended period does not affect sleep patterns in humans and is not the cause of daytime fatigue in northern Alaska .

Literature Review

In the literature review, you'll look at what information is already out there about your topic. “This is where scholarly articles  about your topic are essential,” said Attari. “These articles will help you find the gap in research that you have identified and will also support your thesis statement."

Telling your reader what research has already been done will help them see how your research fits into the larger conversation. Most university libraries offer databases of scholarly/peer-reviewed articles that can be helpful in your search.

In the methods section of your thesis paper, you get to explain how you learned what you learned. This might include what experiment you conducted as a part of your independent research.

“For instance,” Attari said, “if you are a psychology major and have identified a gap in research on which therapies are effective for anxiety, your methods section would consist of the number of participants, the type of experiment and any other particulars you would use for that experiment.”

In this section, you'll explain the results of your study. For example, building on the psychology example Attari outlined, you might share self-reported anxiety levels for participants trying different kinds of therapies. To help you communicate your results clearly, you might include data, charts, tables or other visualizations.

The discussion section of your thesis paper is where you will analyze and interpret the results you presented in the previous section. This is where you can discuss what your findings really mean or compare them to the research you found in your literature review.

The discussion section is your chance to show why the data you collected matters and how it fits into bigger conversations in your field.

The conclusion of your thesis paper is your opportunity to sum up your argument and leave your reader thinking about why your research matters.

Attari breaks the conclusion down into simple parts. “You restate the original issue and thesis statement, explain the experiment's results and discuss possible next steps for further research,” she said.

Find Your Program

Resources to help write your thesis paper.

While your thesis paper may be based on your independent research, writing it doesn’t have to be a solitary process. Asking for help and using the resources that are available to you can make the process easier.

If you're writing a thesis paper, some resources Chartier encourages you to use are:

• Citation Handbooks: An online citation guide or handbook can help you ensure your citations are correct. APA , MLA and Chicago styles have all published their own guides.
• Citation Generators: There are many citation generator tools that help you to create citations. Some — like RefWorks — even let you directly import citations from library databases as you research.
• Your Library's Website: Many academic and public libraries allow patrons to access resources like databases or FAQs. Some FAQs at the SNHU library that might be helpful in your thesis writing process include “ How do I read a scholarly article? ” or “ What is a research question and how do I develop one? ”

It can also be helpful to check out what coaching or tutoring options are available through your school. At SNHU, for example, the Academic Support Center offers writing and grammar workshops , and students can access 24/7 tutoring and 1:1 sessions with peer tutors, like Attari.

"Students can even submit their papers and receive written feedback... like revisions and editing suggestions," she said.

If you are writing a thesis paper, there are many resources available to you. It's a long paper, but with the right mindset and support, you can successfully navigate the process.

“Pace yourself,” said Chartier. “This is a marathon, not a sprint. Setting smaller goals to get to the big finish line can make the process seem less daunting, and remember to be proud of yourself and celebrate your accomplishment once you’re done. Writing a thesis is no small task, and it’s important work for the scholarly community.”

A degree can change your life. Choose your program  from 200+ SNHU degrees that can take you where you want to go.

Meg Palmer ’18 is a writer and scholar by trade who loves reading, riding her bike and singing in a barbershop quartet. She earned her bachelor’s degree in English, language and literature at Southern New Hampshire University (SNHU) and her master’s degree in writing, rhetoric and discourse at DePaul University (’20). While attending SNHU, she served as the editor-in-chief of the campus student newspaper, The Penmen Press, where she deepened her passion for writing. Meg is an adjunct professor at Johnson and Wales University, where she teaches first year writing, honors composition, and public speaking. Connect with her on LinkedIn .

Explore more content like this article

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About southern new hampshire university.

SNHU is a nonprofit, accredited university with a mission to make high-quality education more accessible and affordable for everyone.

Founded in 1932, and online since 1995, we’ve helped countless students reach their goals with flexible, career-focused programs . Our 300-acre campus in Manchester, NH is home to over 3,000 students, and we serve over 135,000 students online. Visit our about SNHU  page to learn more about our mission, accreditations, leadership team, national recognitions and awards.

Lydon and O'Leary libraries will be closing at 2:00pm on Wednesday, July 3rd , and will be closed on Thursday, July 4th . If you have any questions, please contact [email protected] .

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Google Scholar Search Strategies

• About Google Scholar
• Manage Settings
• Enable My Library
• Google Scholar Library
• Cite from Google Scholar
• Tracking Citations
• Add Articles Manually
• Refine your Profile Settings

Using Google Scholar for Research

Google Scholar is a powerful tool for researchers and students alike to access peer-reviewed papers. With Scholar, you are able to not only search for an article, author or journal of interest, you can also save and organize these articles, create email alerts, export citations and more. Below you will find some basic search tips that will prove useful.

This page also includes information on Google Scholar Library - a resource that allows you to save, organize and manage citations - as well as information on citing a paper on Google Scholar.

Search Tips

• Locate Full Text
• Sort by Date
• Related Articles
• Court Opinions
• Email Alerts
• Advanced Search

Abstracts are freely available for most of the articles and UMass Lowell holds many subscriptions to journals and online resources. The first step is make sure you are affiliated with the UML Library on and off campus by Managing your Settings, under Library Links. 

When searching in Google Scholar here are a few things to try to get full text:

• click a library link, e.g., "Full-text @ UML Library", to the right of the search result;
• click a link labeled [PDF] to the right of the search result;
• click "All versions" under the search result and check out the alternative sources;
• click "More" under the search result to see if there's an option for full-text;
• click "Related articles" or "Cited by" under the search result to explore similar articles.

Your search results are normally sorted by relevance, not by date. To find newer articles, try the following options in the left sidebar:

• click "Sort by date" to show just the new additions, sorted by date;  If you use this feature a lot, you may also find it useful to setup email alerts to have new results automatically sent to you.
• click the envelope icon to have new results periodically delivered by email.

Note: On smaller screens that don't show the sidebar, these options are available in the dropdown menu labeled "Any time" right below the search button .

The Related Articles option under the search result can be a useful tool when performing research on a specific topic. 

After clicking you will see articles from the same authors and with the same keywords.

You can select the jurisdiction from either the search results page or the home page as well; simply click "select courts". You can also refine your search by state courts or federal courts. 

To quickly search a frequently used selection of courts, bookmark a search results page with the desired selection. 

 How do I sign up for email alerts?

Do a search for the topic of interest, e.g., "M Theory"; click the envelope icon in the sidebar of the search  results page; enter your email address, and click " Create alert ". Google will periodically email you newly published papers that match your search criteria. You can use any email address for this; it does not need to be a Google Account. 

If you want to get alerts from new articles published in a specific journal; type in the name of this journal in the search bar and create an alert like you would a keyword. 

How do I get notified of new papers published by my colleagues, advisors or professors?

First, do a search for your their name, and see if they have a Citations profile. If they do, click on it, and click the "Follow new articles" link in the right sidebar under the search box.

If they don't have a profile, do a search by author, e.g., [author:s-hawking], and click on the mighty envelope in the left sidebar of the search results page. If you find that several different people share the same name, you may need to add co-author names or topical keywords to limit results to the author you wish to follow.

How do I change my alerts?

If you created alerts using a Google account, you can manage them all on the "Alerts" page . 

From here you can create, edit or delete alerts. Select cancel under the actions column to unsubscribe from an alert. 

This will pop-open the advanced search menu

Here you can search specific words/phrases as well as for author, title and journal. You can also limit your search results by date.

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18 Google Scholar tips all students should know

Dec 13, 2022

[[read-time]] min read

Think of this guide as your personal research assistant.

“It’s hard to pick your favorite kid,” Anurag Acharya says when I ask him to talk about a favorite Google Scholar feature he’s worked on. “I work on product, engineering, operations, partnerships,” he says. He’s been doing it for 18 years, which as of this month, happens to be how long Google Scholar has been around.

Google Scholar is also one of Google’s longest-running services. The comprehensive database of research papers, legal cases and other scholarly publications was the fourth Search service Google launched, Anurag says. In honor of this very important tool’s 18th anniversary, I asked Anurag to share 18 things you can do in Google Scholar that you might have missed.

1. Copy article citations in the style of your choice.

With a simple click of the cite button (which sits below an article entry), Google Scholar will give you a ready-to-use citation for the article in five styles, including APA, MLA and Chicago. You can select and copy the one you prefer.

2. Dig deeper with related searches.

Google Scholar’s related searches can help you pinpoint your research; you’ll see them show up on a page in between article results. Anurag describes it like this: You start with a big topic — like “cancer” — and follow up with a related search like “lung cancer” or “colon cancer” to explore specific kinds of cancer.

Related searches can help you find what you’re looking for.

3. And don’t miss the related articles.

This is another great way to find more papers similar to one you found helpful — you can find this link right below an entry.

4. Read the papers you find.

Scholarly articles have long been available only by subscription. To keep you from having to log in every time you see a paper you’re interested in, Scholar works with libraries and publishers worldwide to integrate their subscriptions directly into its search results. Look for a link marked [PDF] or [HTML]. This also includes preprints and other free-to-read versions of papers.

5. Access Google Scholar tools from anywhere on the web with the Scholar Button browser extension.

The Scholar Button browser extension is sort of like a mini version of Scholar that can move around the web with you. If you’re searching for something, hitting the extension icon will show you studies about that topic, and if you’re reading a study, you can hit that same button to find a version you read, create a citation or to save it to your Scholar library.

Install the Scholar Button Chrome browser extension to access Google Scholar from anywhere on the web.

6. Learn more about authors through Scholar profiles.

There are many times when you’ll want to know more about the researchers behind the ideas you’re looking into. You can do this by clicking on an author’s name when it’s hyperlinked in a search result. You’ll find all of their work as well as co-authors, articles they’re cited in and so on. You can also follow authors from their Scholar profile to get email updates about their work, or about when and where their work is cited.

7. Easily find topic experts.

One last thing about author profiles: If there are topics listed below an author’s name on their profile, you can click on these areas of expertise and you’ll see a page of more authors who are researching and publishing on these topics, too.

8. Search for court opinions with the “Case law” button.

Scholar is the largest free database of U.S. court opinions. When you search for something using Google Scholar, you can select the “Case law” button below the search box to see legal cases your keywords are referenced in. You can read the opinions and a summary of what they established.

9. See how those court opinions have been cited.

If you want to better understand the impact of a particular piece of case law, you can select “How Cited,” which is below an entry, to see how and where the document has been cited. For example, here is the How Cited page for Marbury v. Madison , a landmark U.S. Supreme Court ruling that established that courts can strike down unconstitutional laws or statutes.

10. Understand how a legal opinion depends on another.

When you’re looking at how case laws are cited within Google Scholar, click on “Cited by” and check out the horizontal bars next to the different results. They indicate how relevant the cited opinion is in the court decision it’s cited within. You will see zero, one, two or three bars before each result. Those bars indicate the extent to which the new opinion depends on and refers to the cited case.

In the Cited by page for New York Times Company v. Sullivan, court cases with three bars next to their name heavily reference the original case. One bar indicates less reliance.

11. Sign up for Google Scholar alerts.

Want to stay up to date on a specific topic? Create an alert for a Google Scholar search for your topics and you’ll get email updates similar to Google Search alerts. Another way to keep up with research in your area is to follow new articles by leading researchers. Go to their profiles and click “Follow.” If you’re a junior grad student, you may consider following articles related to your advisor’s research topics, for instance.

12. Save interesting articles to your library.

It’s easy to go down fascinating rabbit hole after rabbit hole in Google Scholar. Don’t lose track of your research and use the save option that pops up under search results so articles will be in your library for later reading.

13. Keep your library organized with labels.

Labels aren’t only for Gmail! You can create labels within your Google Scholar library so you can keep your research organized. Click on “My library,” and then the “Manage labels…” option to create a new label.

14. If you’re a researcher, share your research with all your colleagues.

Many research funding agencies around the world now mandate that funded articles should become publicly free to read within a year of publication — or sooner. Scholar profiles list such articles to help researchers keep track of them and open up access to ones that are still locked down. That means you can immediately see what is currently available from researchers you’re interested in and how many of their papers will soon be publicly free to read.

15. Look through Scholar’s annual top publications and papers.

Every year, Google Scholar releases the top publications based on the most-cited papers. That list (available in 11 languages) will also take you to each publication’s top papers — this takes into account the “h index,” which measures how much impact an article has had. It’s an excellent place to start a research journey as well as get an idea about the ideas and discoveries researchers are currently focused on.

16. Get even more specific with Advanced Search.

Click on the hamburger icon on the upper left-hand corner and select Advanced Search to fine-tune your queries. For example, articles with exact words or a particular phrase in the title or articles from a particular journal and so on.

17. Find extra help on Google Scholar’s help page.

It might sound obvious, but there’s a wealth of useful information to be found here — like how often the database is updated, tips on formatting searches and how you can use your library subscriptions when you’re off-campus (looking at you, college students!). Oh, and you’ll even learn the origin of that quote on Google Scholar’s home page.

18. Keep up with Google Scholar news.

Don’t forget to check out the Google Scholar blog for updates on new features and tips for using this tool even better.

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Google Scholar

Using Google Scholar with your HarvardKey allows you to make the most of provided links, granting access to full text available through Harvard Library subscriptions.

Google Scholar can quickly surface highly cited peer-reviewed articles, abstracts, books, patents, scholarly web pages, and more. 

Explore Google Scholar

Connect Google Scholar To Your Library Access

Connecting Google Scholar to your Harvard Library access is a good way to make sure you get access to articles that Harvard Library subscribes to.

Here's how: 

• Go to Google Scholar and sign in to your Google account
• Look for the menu options
• Go into the settings and select "Library links"
• Type in Harvard and select: Harvard University - Try Harvard Library
• Deselect the box for WorldCat if shown
• Save your preferences
• Search your topic and look for the "Try Harvard Library" links to the right of the articles. This link should take you to Harvard's access to that item

Google Scholar Tips

• Like Google, Google Scholar allows searching of metadata terms, but unlike Google, it also indexes full text. 
• Choose the default search or select “Advanced search” to search by title, author, journal, and date.
• For more advanced researchers, it is possible to specify phrases in quotation marks, enter Boolean queries, or search within fields.
• You may also create an account to set up your author profile or sign up for alerts.
• In settings, you may elect to limit your search by language and show citation import links.
• Results are returned in relevance-ranked order, generally favoring entries when search terms appear in document titles and prioritizing documents with larger citation counts.

Reference management. Clean and simple.

The top list of academic search engines

1. Google Scholar

4. science.gov, 5. semantic scholar, 6. baidu scholar, get the most out of academic search engines, frequently asked questions about academic search engines, related articles.

Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind paywalls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of free academic search engines.

Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only lets you find research papers for all academic disciplines for free but also often provides links to full-text PDF files.

• Coverage: approx. 200 million articles
• Abstracts: only a snippet of the abstract is available
• Related articles: ✔
• References: ✔
• Cited by: ✔
• Links to full text: ✔
• Export formats: APA, MLA, Chicago, Harvard, Vancouver, RIS, BibTeX

BASE is hosted at Bielefeld University in Germany. That is also where its name stems from (Bielefeld Academic Search Engine).

• Coverage: approx. 136 million articles (contains duplicates)
• Abstracts: ✔
• Related articles: ✘
• References: ✘
• Cited by: ✘
• Export formats: RIS, BibTeX

CORE is an academic search engine dedicated to open-access research papers. For each search result, a link to the full-text PDF or full-text web page is provided.

• Coverage: approx. 136 million articles
• Links to full text: ✔ (all articles in CORE are open access)
• Export formats: BibTeX

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need anymore to query all those resources separately!

• Coverage: approx. 200 million articles and reports
• Links to full text: ✔ (available for some databases)
• Export formats: APA, MLA, RIS, BibTeX (available for some databases)

Semantic Scholar is the new kid on the block. Its mission is to provide more relevant and impactful search results using AI-powered algorithms that find hidden connections and links between research topics.

• Coverage: approx. 40 million articles
• Export formats: APA, MLA, Chicago, BibTeX

Although Baidu Scholar's interface is in Chinese, its index contains research papers in English as well as Chinese.

• Coverage: no detailed statistics available, approx. 100 million articles
• Abstracts: only snippets of the abstract are available
• Export formats: APA, MLA, RIS, BibTeX

RefSeek searches more than one billion documents from academic and organizational websites. Its clean interface makes it especially easy to use for students and new researchers.

• Coverage: no detailed statistics available, approx. 1 billion documents
• Abstracts: only snippets of the article are available
• Export formats: not available

Consider using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with Google Scholar and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

Google Scholar is an academic search engine, and it is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.

Semantic Scholar is a free, AI-powered research tool for scientific literature developed at the Allen Institute for AI. Sematic Scholar was publicly released in 2015 and uses advances in natural language processing to provide summaries for scholarly papers.

BASE , as its name suggest is an academic search engine. It is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).

CORE is an academic search engine dedicated to open access research papers. For each search result a link to the full text PDF or full text web page is provided.

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!

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Google Brain Team

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Research Freedom

Google Brain team members set their own research agenda, with the team as a whole maintaining a portfolio of projects across different time horizons and levels of risk.

Google Scale

As part of Google and Alphabet, the team has resources and access to projects impossible to find elsewhere. Our broad and fundamental research goals allow us to actively collaborate with, and contribute uniquely to, many other teams across Alphabet who deploy our cutting edge technology into products.

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We believe that openly disseminating research is critical to a healthy exchange of ideas, leading to rapid progress in the field. As such, we publish our research regularly at top academic conferences and release our tools, such as TensorFlow, as open source projects.

Working on the Brain Team

• Looking Back on 2017
• Meet a few of our machine learning makers
• Colin Raffel's Year at Brain
• Google Brain Residency Program — 7 months in and looking ahead
• Google Brain Residency Program — One Year Later
• Google Brain Team's Approach to Research
• Looking Back on 2016
• Reddit AMA 2016
• Reddit AMA 2017
• The Great A.I. Awakening

Papers Accepted to NIPS, 2017

• A Meta-Learning Perspective on Cold-Start Recommendations for Items
• AdaGAN: Boosting Generative Models
• Affine-Invariant Online Optimization
• Approximation and Convergence Properties of Generative Adversarial Learning
• Attention is All You Need
• Avoiding discrimination through causal reasoning
• Bridging the Gap Between Value and Policy Based RL
• Dynamic Routing between Capsules
• Filtering Variational Objectives
• Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
• Investigating the learning dynamics of deep neural networks using random matrix theory
• Learning Hierarchical Information Flow with Recurrent Neural Modules
• Modulating early visual processing by language
• Multi-Armed Bandits with Metric Movement Costs
• Nonlinear random matrix theory for deep learning
• On Blackbox Backpropagation and Jacobian Sensing
• PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
• REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
• Reducing Reparameterization Gradient Variance
• SGD learns the conjugate class of the network
• SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement
• The Unreasonable Effectiveness of Random Orthogonal Embeddings
• Value Prediction Network

Papers Accepted to CoRL, 2017

• End-to-End Learning of Semantic Grasping
• Learning Robotic Manipulation of Granular Media
• Manifold Regularization for Kernelized LSTD
• Trust-PCL: An Off-Policy Trust Region Method for Continuous Control

Some of Our Research Areas

Some of our team, join the team.

Most of the Brain team is based in Mountain View, but we have smaller groups of team members in Cambridge (Massachusetts), London, Montreal, New York, San Francisco, Toronto and Zurich.

Tackling the most challenging problems in computer science

Our teams aspire to make discoveries that positively impact society. Core to our approach is sharing our research and tools to fuel progress in the field, to help more people more quickly. We regularly publish in academic journals, release projects as open source, and apply research to Google products to benefit users at scale.

Featured research developments

Mitigating aviation’s climate impact with Project Contrails

Consensus and subjectivity of skin tone annotation for ML fairness

A toolkit for transparency in AI dataset documentation

Building better pangenomes to improve the equity of genomics

A set of methods, best practices, and examples for designing with AI

Learn more from our research

Researchers across Google are innovating across many domains. We challenge conventions and reimagine technology so that everyone can benefit.

Publications

Google publishes over 1,000 papers annually. Publishing our work enables us to collaborate and share ideas with, as well as learn from, the broader scientific community.

Research areas

From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Tools and datasets

We make tools and datasets available to the broader research community with the goal of building a more collaborative ecosystem.

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Our teams collaborate with the research and academic communities across the world

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Google Scholar reveals its most influential papers for 2021

Early clinical observations of COVID-19 and its mortality risk factors among the most cited output, while a five-year-old AI paper continues to command attention.

Examples of using SSD, an object-detection algorithm described in a highly cited artificial intelligence paper. Credit: Wei Liu et al. European Conference on Computer Vision (2016)

24 August 2021

Wei Liu et al. European Conference on Computer Vision (2016)

Examples of using SSD, an object-detection algorithm described in a highly cited artificial intelligence paper.

COVID-19-related papers have eclipsed artificial intelligence research in the annual listing of the most highly-cited publications in the Google Scholar database. The most highly cited COVID-19 paper, published in The Lancet in early 2020, has garnered more than 30,000 citations to date (see below for paper summary).

But, in the database of almost 400 million academic papers and other scholarly literature, even it fell a long way short of the most highly cited paper of the last five years, ‘Deep Residual Learning for Image Recognition’, published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition by a team from Microsoft in 2016.

The five-year-old paper’s astonishing ascendancy continues, from 25,256 citations in 2019 to 49,301 citations in 2020 to 82,588 citations in 2021. We wrote about it last year here .

The 2021 Google Scholar Metrics ranking tracks papers published between 2016 and 2020, and includes citations from all articles that were indexed in Google Scholar as of July 2020. Google Scholar is the largest database in the world of its kind.

Below we describe selections from Google Scholar’s most highly-cited articles for 2021. COVID-19 research dominated new arrivals in the list, but we’re also featuring a popular AI paper from 2016, and research that provides an economical shortcut to seeing patterns of human genetic variation, also from 2016.

See our coverage of the 2019 and 2020 lists.

‘Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China’

30,529 citations

Published in February 2020, this is one of the earliest papers to describe the clinical characteristics of COVID-19. It was authored by researchers in China and doctors working in hospitals in Wuhan, the city where COVID-19 was first detected in late 2019.

The team, from institutions such as the Jin Yin-tan Hospital in Wuhan and China-Japan Friendship Hospital in Beijing, reviewed the clinical and nursing reports, chest X-rays and lab results of the first 41 COVID-19 patients. They noted that the novel virus acts similarly to SARS and MERS, in that it causes pneumonia, but is different in that it seldom manifests as a runny nose or intestinal symptoms.

The final sentences of the paper call for robust and rapid testing, because of the likelihood of the disease spreading out of control:

“Reliable quick pathogen tests and feasible differential diagnosis based on clinical description are crucial for clinicians in their first contact with suspected patients. Because of the pandemic potential of 2019-nCoV, careful surveillance is essential to monitor its future host adaption, viral evolution, infectivity, transmissibility, and pathogenicity.”

The paper has been referenced or cited in almost 100 policy documents to date , including several released by the World Health Organization on topics such as mask-wearing and clinical care of patients with severe symptoms .

‘Clinical Characteristics of Coronavirus Disease 2019 in China’

New England Journal of Medicine

19,656 citations

Published online in February 2020, this study was a retrospective review of medical records for 1,099 COVID-19 cases reported to the National Health Commission of the People's Republic of China between 11 December 2019 and 29 January 2020.

The team, which included almost 40 researchers from China from institutions such as the Guangzhou Medical University in Guangzhou and Wuhan Jinyintan Hospital in Wuhan, accessed electronic medical records from 552 hospitals in mainland China to summarise exposure risk, signs and symptoms, laboratory and radiologic findings related to COVID-19 infection.

The study garnered a lot of media attention based on the evidence it put forward that men might be more severely impacted by disease – 58% of the patient cohort were male.

However, as Sharon Begley reported for STAT , “It’s possible the apparent sex imbalance reflects patterns of travel and contacts that make men more likely to be exposed to carriers of the virus, not any inherent biological differences. It’s also possible the apparent worse disease severity in men could skew the data.”

A paper published in JAMA around the same time by researchers in the United States reported that, among hospitalized patients, there is “a slight predominance of men”.

A Nature Communications meta-analysis , published in December 2020, looked at 92 studies covering more than three million patients and concluded that, while males and females appeared to be susceptible to infection, men were 2.84 times more likely to be end up in intensive care and 1.39 times more likely to die from the disease.

‘Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study’

17,047 citations

Published in March 2020, The Lancet described this study as the first time researchers have examined risk factors associated with severe symptoms and death in hospitalised or deceased patients. Of the 191 patients studied, 137 were discharged from hospital and 54 died.

The study, by researchers from hospitals in China, also presented new data on viral shedding – information that informed early understanding of how the virus spreads and can be detected over the cause of infection.

“The extended viral shedding noted in our study has important implications for guiding decisions around isolation precautions and antiviral treatment in patients with confirmed COVID-19 infection,” said co-lead author, Bin Cao, from the China-Japan Friendship Hospital and Capital Medical University in Beijing.

“However, we need to be clear that viral shedding time should not be confused with other self-isolation guidance for people who may have been exposed to COVID-19 but do not have symptoms, as this guidance is based on the incubation time of the virus.”

‘A Novel Coronavirus from Patients with Pneumonia in China, 2019’

The New England journal of medicine

16,194 citations

On 31 December 2019, the Chinese Center for Disease Control and Prevention (China CDC) dispatched a rapid response team to accompany health authorities in Hubei province and Wuhan city in conducting COVID-19 investigations.

This study, published in January 2020, reported the results of that investigation, including the clinical features of the pneumonia of two patients.

Described by Jose Manuel Jimenez-Guardeño, a researcher in the Department of Infectious Diseases at King's College London , UK and colleagues in an article for The Conversation as “the article that released this virus to the world”, the paper details how the virus was isolated from patients with pneumonia in Wuhan in cell cultures.

“In fact, actual photographs of SARS-CoV-2 were shown to the world for the first time here,” say Jimenez-Guardeño and his co-authors .

The study authors urged that more epidemiologic investigations were needed in order to characterize transmission modes, reproduction intervals and other characteristics of the virus to inform strategies to control and stop its spread.

‘SSD: Single Shot MultiBox Detector’

European Conference on Computer Vision

15,368 citations

A change of pace from recent COVID-19 studies, this paper, led by Wei Liu from the University of North Carolina at Chapel Hill and published in 2016, remains one of the most highly cited in the field of artificial intelligence (AI). It describes a new method for detecting objects in images or video footage using a single deep neural network – a set of AI algorithms inspired by the neurological processes that fire in the human cerebral cortex.

The approach, called the Single Shot MultiBox Detector, or SSD, has been described as faster than Faster R-CNN – another object detection technology that was described in a very highly cited paper published in 2015 ( see our coverage here ).

SSD works by dividing the image into a grid, with each grid cell responsible for detecting objects within that part of the image. As the name indicates, the network is able to identify all objects within an image in a single pass, allowing for real-time analysis.

SSD is now one of a handful of object detection technologies that are now available. YOLO (You Only Look Once) is a similar single-shot object detection algorithm, whereas R-CNN and Faster R-CNN use a two-step approach , which involves first identifying the regions where objects might be, and then detecting them.

‘Analysis of protein-coding genetic variation in 60,706 humans’

7,696 citations

Led by Monkol Lek from the University of Sydney in Australia and Daniel MacArthur from the Broad Institute of MIT and Harvard University , this 2016 paper presents an open-access catalogue of more than 60,000 human exome sequences (exomes are the coding portions of genes) from people of European, African, South Asian, East Asian, and Latinx ancestry.

The collection was compiled as part of the Exome Aggregation Consortium project, run by an international group of researchers with a focus on exome sequencing. As exomes only make up about 2% of the human genome , the approach has been praised for being able to highlight patterns of genetic variation, including known disease-related variants, in a more cost-effective way than whole-genome sequencing.

Presented at a 2015 genomics conference, the catalogue encompasses 7.4 million genetic variants, which can be used to identify those connected to rare diseases. “Large-scale reference datasets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes,” Lek said when the paper was published.

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Public Access

Google Scholar Profiles

Google Scholar Profiles provide a simple way for authors to showcase their academic publications. You can check who is citing your articles, graph citations over time, and compute several citation metrics. You can also make your profile public, so that it may appear in Google Scholar results when people search for your name, e.g., richard feynman .

Best of all, it's quick to set up and simple to maintain - even if you have written hundreds of articles, and even if your name is shared by several different scholars. You can add groups of related articles, not just one article at a time; and your citation metrics are computed and updated automatically as Google Scholar finds new citations to your work on the web. You can choose to have your list of articles updated automatically or review the updates yourself, or to manually update your articles at any time.

Set up your Google Scholar Profile

Setting up your profile

Start here. It's quick and free.

• First, sign in to your Google account, or create one if you don't yet have one. We recommend that you use a personal account, not an account at your institution, so that you can keep your profile for as long as you wish.
• Once you've signed in to your Google account, open the Scholar profile sign up form , confirm the spelling of your name, enter your affiliation, interests, etc. We recommend that you also enter your university email address; this would make your profile eligible for inclusion in Google Scholar search results.
• On the next page, you'll see groups of articles written by people with names similar to yours. Add all articles that you have written; keep in mind your articles may be in several different groups, and some groups may occasionally include articles by several different authors. If you publish under several different names, you may need to do several searches to add all your articles.
• Once you're done with adding articles, it will ask you what to do when the article data changes in Google Scholar. You can either have the updates applied to your profile automatically, or you can choose to review them beforehand. In either case, you can always go to your profile and make changes by hand.
• Finally, you will see your profile. This is a good time to add a few finishing touches - upload your professional looking photo, visit your university email inbox and click on the verification link, double check the list of articles, and, once you're completely satisfied, make your profile public. Voila - it's now eligible to appear in Google Scholar when someone searches for your name!

Select the "Add articles" option from the menu. Search for your articles using titles, keywords, or your name. Your citation metrics will update immediately to account for the articles you added.

If your search doesn't find the right article, click "Add article manually". Then, type in the title, the authors, etc., and click "Save". Keep in mind that citations to manually added articles may not appear in your profile for a few days.

To add a group of related articles, click "Add article groups". If you have written articles under different names, with multiple groups of colleagues, or in different journals, you may need to select multiple groups. Your citation metrics will update immediately to account for the groups you added.

If the menu doesn't appear, sign in to the Google account that you used to create your profile.

Alas, we have no way of knowing which articles are really yours . Author names are often abbreviated and different people sometimes share similar names. We use a statistical model to try to tell different authors apart but such automatic processes are not always accurate. The best way to fix this is to look through the articles in your profile and remove the ones that were written by others.

Select the articles you would like to remove and then click the "Delete" button.

If the article checkboxes don't appear, sign in to the Google account that you used to create your profile.

Deleted articles are moved to the Trash. To view articles in the Trash, select the "View trash" option from the menu. To restore an article from the Trash, select the article and click the "Restore" button.

Click the title of the article and then click the "Edit" button. When you finish your changes, click the "Save" button.

If the "Edit" button doesn't appear, sign in to the Google account that you used to create your profile.

If you've made substantial changes to the article, please keep the following in mind.

• The list of "Scholar articles" at the bottom of the page may no longer match the article you've edited. We recommend that you review this list and "unmerge" the Scholar articles that no longer correspond to your article. Scholar articles affect the computation of your "Cited by" counts and citation metrics.
• As with manual additions of articles, it may take several days for all citations to the edited article to be collected in your profile. You can speed up the process by adding the appropriate article from Google Scholar and then merging it with your version; then, your citation metrics will update right away.
• It's possible that the article you've edited was already in your profile as a separate record. We recommend that you merge duplicate records - click the "Title" column header to sort your articles by title, select the checkboxes next to the duplicate entries, which should now be adjacent, and then click the "Merge" button.

Select both versions of the article and click the "Merge" button. You will then see both citations for the article listed. Select the best citation to the article (you can edit it later if you wish) and click "Merge". This will merge the two versions. Your citation metrics will automatically update to count the versions you've merged as a single article, not two different articles.

Nope, the "Cited by" count after the merge is the number of papers that cite the merged article. One of these probably cites both versions that you've merged; the 27+4=31 formula counts this citation twice. But if the count has dropped below 27... ugh, please do let us know.

The ∗ indicates that the "Cited by" count includes citations that might not match this article. It is an estimate made automatically by a computer program. You can check these citations by clicking on the article's title and looking for "Scholar articles" with a ∗ next to their title.

Making your profile public

Your profile is private and visible only to you until and unless you make your profile public.

Click the "Edit" button next to your name, check the "Make my profile public" box, and click "Save".

You can share the URL displayed by the browser. It looks like this: https://scholar.google.com/citations?user=ID&hl=en …where ID identifies your Google Scholar profile. You can link to it from your homepage, email it to colleagues, or share it on social media. The link only works if the profile is public, and only lets other people see the profile but not make changes to it.

Click the "Edit" button next to your name, uncheck the "Make my profile public" box, and click "Save".

You also need to add a verified email address at your university or institution.

To be eligible for inclusion in Google Scholar search results, your profile needs to be public and needs to have a verified email address at your university (non-institutional email addresses, such as gmail.com, hotmail.com, aol.com, yahoo.com, qq.com, etc., are not suitable for this purpose). To add a verified email to your profile, click the "Edit" button next to your name, add your email address at your institution and click "Save". We will send you an email message with a verification link. Once you click on this link, the email address will be marked verified. Your profile will now be eligible for inclusion in Google Scholar search results.

Rest assured, we will not display your email address on your public profile. Nor will we sell it, trade it, or use it to send you email unrelated to Google Scholar.

Exploring citations to your articles

Click the "Cited by" number for the article.

Click the title of the article.

Click the "Follow" button next to your name, check the "New citations to my articles" box, and click "Done". We'll then email you when newly published articles cite any of the works in your profile.

Click the "Cited by" number for your article and then click the envelope icon in the left sidebar. Then we'll email you when newly published articles cite yours.

Google Scholar considers this article the same as another article in your profile. We display the "Cited by" count next to both of the duplicates, but we only count them once in your citation metrics.

We recommend that you merge the duplicates - select both articles and click the "Merge" button.

Probably not. We compute two versions, All and Recent, of three metrics - h-index, i10-index and the total number of citations. While there's no shortage of other reasonable metrics, the incremental usefulness of adding each number generally goes down, while the user confusion generally goes up.

Your "Cited by" counts come from the Google Scholar index. You can change the articles in your profile, but citations to them are computed and updated automatically as we update Google Scholar.

To change the "Cited by" counts in your profile, you would need to have them updated in Google Scholar. Google Scholar generally reflects the state of the web as it is currently visible to our search robots and to the majority of users. If some of the citations to your article are not included, chances are that the citing articles are not accessible to our search robots or are formatted in ways that make it difficult for our indexing algorithms to identify their bibliographic data or references.

To fix this, you'll need to identify the specific citing articles with indexing problems and work with the publisher of these articles to make the necessary changes (see our inclusion guidelines for details). For most publishers, it usually takes 6-9 months for the changes to be reflected in Google Scholar; for very large publishers, it can take much longer.

Many research funding agencies promote broad access to funded research by mandating that articles describing the research should be publicly available. The Public Access section of a Google Scholar profile contains the articles that are expected to be publicly available based on funding agency mandates. For each article, you can view the applicable mandates and see if it is publicly available. If the article has a publicly available version, a link marked [PDF] or [HTML] appears on the right hand side.

An article can be publicly available from several sources including its publisher, an institutional repository, a research area specific repository and others. The Google Scholar indexing system tries to include all publicly accessible versions that follow our inclusion guidelines . For your own profile, you can update the list of articles and make corrections. You can also make an article publicly available by uploading a PDF of the article to your own Google Drive.

The Public Access Mandates table presents summary statistics about public access mandates for different funding agencies. For each funding agency, you can view the level of public availability of mandated articles overall and over several recent years.

The Google Scholar indexing system automatically extracts funding information from the acknowledgement sections of articles. You can see the funding acknowledgement for an article by clicking on its title on the public access page.

Public access mandates usually specify that funded articles should be publicly available within a given period of time (referred to as the "embargo") after publication. You can see the embargo period for an article by clicking on its title on the public access page.

Your profile should only include articles that you wrote. To remove an article that you didn't write, click "REVIEW", then "MAKE A CORRECTION", select "I'm not an author of this article" from the list of options and click "DONE". The article will be removed from your public access page. It will also be deleted from your profile. If you don't see "MAKE A CORRECTION", sign in to the Google account that you used to create your profile.

Click "REVIEW", then "MAKE A CORRECTION", select "This article wasn't funded by any of these agencies" from the list of options and click "DONE". The article will be removed from your public access page. If you don't see "MAKE A CORRECTION", sign in to the Google account that you used to create your profile.

Click "REVIEW", then "MAKE A CORRECTION", select "This article's publication date is incorrect" from the list of options, enter the correct date and click "DONE". If the new publication date is outside the scope of the mandate, the article will be removed from your public access page. This will also update the publication date in your profile. If you don't see "MAKE A CORRECTION", sign in to the Google account that you used to create your profile.

The simplest thing would be to ask your coauthor to make the article publicly available. Once the publicly available version is included in the Google Scholar index, your public access page will be automatically updated.

You can also indicate this on your public access page. To do this, click "REVIEW", then "MAKE A CORRECTION", select "I'm not the responsible author" from the list of options, select the authors that are responsible for making the article publicly available and then click "DONE". The article will then be listed in the "Recused" section on your public access page. If you don't see "MAKE A CORRECTION", sign in to the Google account that you used to create your profile. Once the article is publicly available from another source, it will automatically move to the "Available" section.

The star indicates that one or more of your mandated articles is not yet publicly available and that you have recused yourself from making them available.

The Public Access section is only available for public profiles. If your profile is currently private, click the "Edit" button next to your name, check the "Make my profile public" box, and click "SAVE". If the "Edit" button doesn't appear, sign in to the Google account that you used to create your profile.

If your profile is already public and you still don't see the Public Access section, the Google Scholar indexing system has not yet identified any articles with public access mandates in your profile.

The Google Scholar indexing system tries to include all publicly accessible versions that follow our inclusion guidelines . Please contact your publisher and ask them to make sure that the publicly available version is accessible to our search robots.

You can also make the article publicly available by uploading a PDF of the article to your own Google Drive. To do that, click the "UPLOAD PDF" button next to the article on your public access page. If you don't see the "UPLOAD PDF" button, sign in to the Google account that you used to create your profile. Please upload only articles that you've written and have the rights to share.

The Google Scholar indexing system tries to include all publicly accessible versions that follow our inclusion guidelines . Please contact the repository administrators and ask them to make sure that the article is accessible to our search robots.

You can also make the article publicly available by uploading a PDF of the article to your own Google Drive . To do that, click the "UPLOAD PDF" button next to the article on your public access page. If you don't see the "UPLOAD PDF" button, sign in to the Google account that you used to create your profile. Please upload only articles that you've written and have the rights to share.

Yes, the uploaded article will be linked from your profile. It will also be eligible for inclusion in the Google Scholar index.

Click on the title of the article, and then click "UPDATE PDF". Follow the prompts to upload a PDF file from your computer. The link in your profile will be updated immediately. If the original version was indexed in Google Scholar, it will be replaced in a few days.

Note, the original uploaded article will still be in the "Public research articles" folder in your Google Drive . If you wish, you can delete it from there as well. If you don't see the original article or the 'Public research articles' folder in your Google Drive, sign in to the Google account that you used to create your profile.

Go to the "Public research articles" folder in your Google Drive , and delete the article from there. The link to the article will disappear from your profile and the Google Scholar index in a few days.

If you don't see the "Public research articles" folder or the article in question in your Google Drive, sign in to the Google account that you used to create your profile.

This error message means that your domain's administrators have disabled public sharing through Google Drive possibly as a part of organizational policy. Please contact your domain's administrators for assistance and clarification.

Public Access Reports for Agencies

In addition to reviewing public access for all your articles, you can also view and export public access reports for individual funding agencies. You can view an agency-specific report by clicking the agency’s name on the left-hand side of the Public Access page on desktops, or by selecting the agency in the dropdown list on the top-left side of the page on mobile phones.

Funding agencies can require articles to be available at a particular repository (e.g., PubMed Central), at a group of repositories (any subject or institutional repository), or anywhere on the web. Agency-specific reports take these requirements into account. When an article is available at a suitable location, it will be linked on the right. If you don’t see the link for an available article, you can provide the link to us. For agencies that specify a particular repository, there is also a link to submit your article to that repository. You can also fix errors - remove articles, correct publication dates, or update funding information.

Updates to your profile

You don't need to do anything! Your citation metrics and citation graph will be automatically updated whenever Google Scholar is updated.

Select "Configure article updates" from the menu. Choose the automatic updates setting and click "Update settings". Your profile will be automatically updated when Google Scholar is updated.

This setting only controls the updates to your list of articles. It does not control the updates to your "Cited by" counts and citation metrics - those are always updated to reflect the current state of the web.

To add a missing article to your profile, select "Add articles" from the menu and search for it. If you can't find your article in Google Scholar, select "Add article manually" to enter its bibliographic record by hand.

Select "Configure article updates" from the menu. Choose the confirmation email setting and click "Update settings". When we identify suitable updates for your profile, we'll send you an email message so that you can review and apply the updates.

You don't need to do anything. Automated updates will not make changes to an article that you have edited.

Reviewing updates to your profile

This happens when the Google Scholar search index changes, and it now considers this entry a duplicate of some other article in your profile. This could happen, e.g., if the publisher re-formats their papers or fixes a typo. We recommend that you accept this suggestion. You can, of course, choose to keep duplicate entries in your profile, but only one of them will be counted towards your citation metrics.

This happens when the Google Scholar search index has changed, and we have been unable to match an article in your profile with the new index. Most of the time, this is because it was considered to be a duplicate of some other article in your profile, but we weren't able to determine which one. Occasionally, the article may have been removed from Google Scholar entirely, e.g., because it's no longer available on the web, or because articles that reference it have become unavailable to our search robots.

To check if the article is a duplicate, go to your profile, click the "Title" column header to sort by title, and look for the article in question. If the same article is indeed listed multiple times, you can safely accept the suggestion to delete the unmatched entry. However, if it isn't a duplicate entry, you can choose to keep it in your profile. Though, since it is not matched in Google Scholar, its "Cited by" count will be zero.

Note that your decision to keep an unmatched entry in your profile will not reinstate the entry in Google Scholar. See the inclusion guidelines for help on including your articles in Google Scholar.

General questions

It's under " My profile " on top of the page or in the side drawer. If this link shows a profile creation form, sign in to the Google account that you used to create your profile and try again.

Select the articles you'd like to export - or check the box next to the "Title" column header to select all articles in your profile - and click the "Export" button. Follow the prompts to download a BibTeX, EndNote, RefMan, or CSV file.

Click the column header labeled "Year".

Click the "Edit" button next to your name, paste the URL into the "Homepage" field, and click "Save".

If the profile is yours, sign in to the Google account that you used to create it, and follow the instructions in the Setup section to make corrections. You can add, delete, edit, and merge articles in your own profile.

If the profile is someone else's, it's best to contact its author and ask them to make a correction.

Note that profile owners can't change their "Cited-by" counts, and that updating an article in a profile does not change it in the Google Scholar search results. To make those kinds of corrections, you usually need to talk to the article's publisher; please refer to the inclusion guidelines .

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