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    Harmonizing GCW Cryosphere Vocabularies with ENVO and SWEET. Towards a General Model for Semantic Harmonization
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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.


  • Special Collection Call for Papers: Building an Open Data Collaborative Network in the Asia-Oceania Area


    This special collection derives from the International Symposium on Data Science (DSWS-2023; that was held in Tokyo, Japan (11-15 December 2023). The symposium was organized by the Joint Support-Center for Data Science Research, Research Organization of Information and Systems (ROIS-DS) in collaboration with the Committee of International Collaborations on Data Science and the Science Council of Japan (SCJ). The event was also strongly supported and facilitated by the global data community, led by the World Data System (WDS) and the Committee on Data (CODATA) of the International Science Council (ISC). It aimed to facilitate information exchange regarding the archiving, publication, and utilization of diverse data relating to societal and global challenges such as COVID-19, information proliferation, global warming, extreme weather events, regional conflicts, etc., and their impact on the Asia-Oceania region.

    The symposium was organized in several interdisciplinary scientific sessions involving international data activities in the Asia-Oceania region and beyond. They included various aspects of accreditation schemes and their benefits, individual international initiatives, data centres and networks, data management planning, data policies, legacy data, historical data, data sharing, citation and publication across disciplines.

    Over 80 presentations were made, triggering fruitful discussions that focused on forming international collaborative networks related to open data in the region and establishing concrete cooperation frameworks within the global framework. The goal of the symposium was to build consensus on various aspects of research data management by stakeholders in alignment with open research policies and FAIR principles. The conducted scientific sessions could potentially lead to new ways of promoting interdisciplinary and collaborative research, data management platforms, and efficient data reuse under different scientific disciplines, based on evidence and feedback from the Asia and Oceania communities.

    This special collection targets articles that outline best practices for attaining the foregoing goal. In particular, it seeks to publish research articles that relate to developing data systems and data analysis procedures from a multidisciplinary viewpoint. Contributions are not restricted to presentations made at the symposium, and so the editors would welcome submissions from any authors, globally, whose research and practical interests align with the symposium themes.
    Further inquiries regarding the Special Issue can be directed to the Guest Editors.

    Guest Editors

    • Tomoya Baba (Research Organization of Information and Systems)
    • David Castle (University of Victoria)
    • Tyng-Ruey Chuang (Academia Sinica)
    • Masaki Kanao (Research Organization of Information and Systems)
    • Johnathan Kool (Australian Antarctic Division)
    • Kassim S. Mwitondi (Sheffield Hallam University)
    • Yubao Qiu (GEO Cold Regions Initiative)
    • Juanle Wang (China Academy of Science; Editorial Board of the Data Science Journal)

    Deadline of Expression of Interest: 29 February 2024

    Please input your Expression of Interest for the "Special Collection" to the following Google Form:

    You are required to input the information on “Author(s), Affiliation(s), Contact Address and Tentative Article Title(s)”. This EoI Form will be closed by 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:

    1. examining the scientific, political, and societal frameworks involved in data and AI policy addressing crisis situations;
    2. exploring the underlying ethical, human rights, and humanitarian frameworks needed to support data and AI policy during crisis situations; and
    3. 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:

    1. Approaches to data and AI quality, data reliability, and data integrity during times of crisis
    2. Policy frameworks for data management and sharing during crises
    3. Data and AI governance models and institutional arrangements in the context of crises
    4. Ethical considerations and guidelines for responsible data collection, analysis, and (re)use in crisis situations
    5. Data privacy, security, and protection in crisis preparation, response, and recovery efforts
    6. Consent for the use of data and AI in times of crisis
    7. Open data initiatives and practices for enhanced crisis preparedness and response
    8. 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)
    9. 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.
    10. Policy as it relates to data, AI, system, and tool interoperability, integration, and standardization in crisis management and crisis governance systems
    11. Community engagement, participation, and empowerment in data policy development for crises
    12. Legal and regulatory challenges and solutions for data utilization during crises
    13. Technological advancements and tools supporting data and AI policy in crisis management
    14. 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

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