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Data Science Journal

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About this journal

The CODATA  Data Science Journal  is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data.

All data is in scope, whether born digital or converted from other sources.

Announcements

Special collection call for papers: building an open data collaborative network in the asia-oceania area, guest editors, deadline of expression of interest: 29 february 2024, deadline of article submission: 31 july 2024, final publishing online: 31 march 2025 (provisional), special collection call for papers: data and ai policy, systems, and tools for times of crisis.

The Data Science Journal invites researchers, practitioners, policymakers, and stakeholders to contribute to a special collection of articles on ‘Data and AI policy, systems, and tools for times of crisis’. This special collection explores the challenges, opportunities, and innovative approaches related to data policy development and implementation to address crises, such as natural disasters, public health emergencies, humanitarian crises, or other disruptive events.

The collection seeks high-quality articles that address various aspects of data and AI policy as well as data and AI systems and tools for crisis situations, encompassing theoretical, empirical, and practical perspectives. We welcome submissions that examine the intersection of data science, policy, and crisis management, shedding light on the ethical, legal, social, and technical dimensions of data governance and utilization.

The primary objective of this special collection is to explore the transformative potential of data and AI policy in relation to data and AI systems and tools for crisis management and crisis governance while contributing to building a more resilient and data-driven world. In this context, the special collection will pursue the following specific objectives:

• examining the scientific, political, and societal frameworks involved in data and AI policy addressing crisis situations;
• exploring the underlying ethical, human rights, and humanitarian frameworks needed to support data and AI policy during crisis situations; and
• supporting the development of systems, tools, and services that promote the responsible practice and use of data and AI when generating scientific evidence in crisis situations and guiding decision making in preparedness and response.

Overall this special collection will contribute to advancing knowledge and fostering effective data and AI policy frameworks as well as the data and AI science system and tools that can support decision-making, improve response efforts, and enhance the resilience of first responders and communities in times of crisis.

This special collection is driven and supported by a workstream within the ISC CODATA International Data Policy Committee (IDPC) engaged in analysis, consultation, and the development of position papers on data policy in times of crisis. The IDPC’s work contributes to international efforts in this area focused on the collection, processing, and use of data in situations of natural disaster, health crises, geo-political conflicts, and other disruptive circumstances. It examines the data and AI policy frameworks necessary to ensure that scientific projects, particularly regarding data collection and processing, are viable and relevant to crisis situations while also contributing to scientific results in preparing for, responding to, and recovering from crises.

Another working group is being established on ‘Data Systems, Tools, and Services for Crisis Situations’ whose mission it is to elucidate scientific as well as the ethical, legal, and social impact (ELSI) features of data systems, tools, and services in relationship to the needs of scientists, policy/decision-makers, emergency responders, media, and affected communities by providing overview of those characteristics and how they are expressed in the architecture, design, interoperability standards, and application of these instruments to crisis situations worldwide.

The Centre for Science Futures of the International Science Council provides a focal point for discussions on the role of data and AI policy in science in connection with crises.

This DSJ special collection contributes to the work of these interrelated groups while broadening the scope throughout the communities of stakeholders.

Topics of interest for this special collection include the following:

• Approaches to data and AI quality, data reliability, and data integrity during times of crisis
• Policy frameworks for data management and sharing during crises
• Data and AI governance models and institutional arrangements in the context of crises
• Ethical considerations and guidelines for responsible data collection, analysis, and (re)use in crisis situations
• Data privacy, security, and protection in crisis preparation, response, and recovery efforts
• Consent for the use of data and AI in times of crisis
• Open data initiatives and practices for enhanced crisis preparedness and response
• Data and AI policy topics related to open science, including the UNESCO Declaration on Open Science , African Open Science Platform, Global Open Science Cloud (GOSC), China Science and Technology Cloud (CSTCloud), Australian Research Data Commons (ARDC), Open Science Framework, European Open Science Cloud (EOSC)
• How data and AI policy contribute to the alignment of human rights and fundamental freedoms while supporting humanitarian principles, such as humanity, impartiality, neutrality, and independence.
• Policy as it relates to data, AI, system, and tool interoperability, integration, and standardization in crisis management and crisis governance systems
• Community engagement, participation, and empowerment in data policy development for crises
• Legal and regulatory challenges and solutions for data utilization during crises
• Technological advancements and tools supporting data and AI policy in crisis management
• Impact evaluation, lessons learned, and best practices in data policy implementation during crises

Authors are encouraged to present case studies, theoretical frameworks, policy analyses, empirical studies, and practical experiences that contribute to the understanding and advancement of data policy in crisis situations.

About the Data Science Journal

The CODATA Data Science Journal (DSJ) is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data and databases across all research domains, including science, technology, the humanities and the arts. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for open data.

As with all DSJ articles, submissions to this special collection will undergo a rigorous peer review process to ensure scholarly quality and relevance.

Collection editors (in alphabetical order)

Burçak Başbuğ Erkan, Gnana Bharathy, Paul Box, Francis P. Crawley, Mathieu Denis, Perihan Elif Ekmekci, Simon Hodson, Stefanie Kethers, Virginia Murray, Hans Pfeiffenberger, Lili Zhang

Submission and dates

Please review carefully the DSJ Editorial Policies and Submission Guidelines when preparing your manuscript for review. Submissions must be of high scientific quality and prepared with attention to correct English grammar and usage requirements.

• Submission deadline : accepted contributions will be published on a rolling basis spread across issues of the DSJ . Submissions close on Friday 28 June 2024
• Expected publication : expect a four-week period for peer-review upon submission. Accepted papers will be published based on the DSJ issue space availability and publication schedule.

For more information on the special issue, you may contact the journal editors through this link .

Call for Papers: Data Science and Machine Learning for Cybersecurity

Manuscript Submission Deadline: April 30, 2023

Recent changes in data science are transforming cybersecurity in a computing context. Applied science is the process of applying scientific methods, machine learning techniques, processes, and systems to data. While Cybersecurity Data Science (CSDS) enables more actionable and intelligent computing in the domain of cybersecurity as compared to traditional methods. It encompasses the rapidly growing practice of applying data science to prevent, detect, and remediate cybersecurity threats.

Cybersecurity data science is a fast-developing field that uses data science techniques to address cybersecurity issues. Data-driven, statistical, and analytical methodologies are increasingly used to close security holes. It examines the healthcare, transportation, surveillance, social media, and law enforcement sectors, in order to evaluate the specific issues they pose and how they can be addressed.

Cybersecurity data science is the focus of this special issue, with analytics supporting the most recent trends to optimize security solutions. The data is acquired from reliable cybersecurity sources. Using machine learning, the problem also aims to develop a multi-layered cybersecurity modeling framework. Data-driven intelligent decision-making can help defend systems against cyberattacks as we address cybersecurity data science and pertinent methodologies.

• Potential topics include, but are not limited to:
• Cloud-based cybersecurity analytics
• Real-time IoT/endpoint-based detection
• Deep learning and reinforcement learning
• Human-in-the-loop cyclical machine learning
• Adversarial attacks on machine learning systems
• AI-driven fake news and disinformation campaigns
• Cybercrime analysis, intelligence, and security
• Big crime data science algorithms and open-source situational awareness
• Misinformation and hate speech detection and mitigation
• Data-driven cyber knowledge base development
• Data Science to demonstrate cyber weakness
• Robustness and interpretability in ML for security tasks

Special Collection Editors:

Zhenfeng Liu, Shanghai Maritime University

Xiaogang Ma, University of Idaho

Anwar Vahed, Data Intensive Research Initiative of South Africa