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  • 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)

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  • 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.

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  • 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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  • Call for Papers: Deadline Extended to December 15th 2022

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  • Call for Papers: Data Management Planning across Disciplines and Infrastructures

    The Data Science Journal ( is seeking papers for a special collection devoted to “Data Management Planning across Disciplines and Infrastructures”. Papers should focus on work related to Data Management Plans and their implementation, not data management per se.

    Deadline for submissions: November 15th 2022 (UPDATE: deadline is now December 15th 2022

    Data Management Plans (DMPs) are evolving. Several communities address challenges in developing DMP templates, e.g., to provide proper guidance across disciplines, used collaboratively along the data life cycle, or in the development of machine-actionable tools to facilitate efficient data management and sharing as well as fostering the reuse of (pieces) of already existing DMPs in new research context. Thereby, DMP templates do not only have to provide tailored guidance, but they should also ensure interoperability across research disciplines to foster multidisciplinary research. Although DMPs support the management of research data, DMP tools are often not integrated into researchers’ project workflows yet, but serve as an additional component next to project management software, electronic lab notebooks, metadata tools, etc..

    The international Data Science Journal seeks papers describing practical experiences, concepts, and future directions on the design and deployment of effective data management. Journal’s topics cover hypothesis and investigation planning, data management planning, organization and operation, reproducibility planning, provenance recording, and data maintenance and publication are considered in their entirety.

    For the collection, we welcome papers from researchers, data professionals, data managers and curators, data providers, founders, IT specialists, software developers, and others, who are using, developing, or experimenting with the effective use of data management planning. Submissions from lab researchers and practitioners are particularly encouraged, as contributions from open-source movements. Papers might, e.g., focus core activities of experimentation, data processing and analysis, long-term preservation or data sharing, or consider the integration of data management planning into existing or future infrastructures. This could reflect innovative research into new directions for DMPs. Moreover, papers detailing practical examples on supporting researchers are welcomed.

    Submissions can be made in one or several of the following categories:

    1. implementation of DMPs in project management strategies, e.g. combining DMP update cycles with (agile) project management, development of practicable implementing strategies in projects, e.g., by data stewards or data managers, or automatic triggers between linked tools / systems, e.g., CRIS system updating DMP once funding has been awarded
    2. data management across disciplines, examining interoperability and cross-disciplinary (re-)use of Data Management Plans, as, e.g., intended by the FAIR Data Principles, e.g., in the context of templates and guidance
    3. discipline-specific data management planning, providing examples and/or use cases on how discipline-specific support can foster data management and processing of data following the FAIR Data Principles, e.g., in the context of Science Europe’s domain data protocols,
    4. data management planning integrating infrastructures, presenting DMP tools, machine-actionable DMPs, concepts of mapping and harvesting, connecting DMP tools to interfaces of other RDM tools such as Current Research Information Systems (CRIS) and Electronic Lab Notebooks (ELN), FAIR Digital Objects in the context of DMPs, etc.
    5. future directions in the development of Data Management Plans, e.g., in terms of innovative approaches or technical solutions to support to data management planning

    For further information and submission details see guidance of the Data Science Journal:

    Published articles will be charged with a fee of £650, covering all publication costs (editorial processes; web hosting; indexing; marketing; archiving; DOI registration; etc.) and ensuring full (golden) open access, under a Creative Commons Attribution License.

    Collection editors (in alphabetical order):

    Ivonne Anders, Kevin Ashley, Daniela Hausen, Christin Henzen, Sarah Jones, Tomasz Miksa, Sebastian Netscher, Maria Praetzellis, Chris Wiley

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  • Indigenous Knowledge in the Data Science Journal

    The Data Science Journal values the perspectives of all data professionals and practitioners and explicitly wants to recognize the insight that comes from Indigenous knowledge and how it is understood and managed. Indigenous knowledge has long been undervalued, misrepresented, and exploited in science, and the Indigenous contributors often remain invisible in science. 

    The DSJ is honored to have published The CARE Principles for Indigenous Data Governance and seeks to live up to those principles of Collective benefit, Authority to control, Responsibility, and Ethics. We are also inspired by the recent position statement from a consortium of rural health journals who will publish “nothing about Indigenous peoples, without Indigenous peoples”.

    DSJ will adopt the same principles. How this will be precisely applied will vary depending on the people and knowledge in question, but in essence, we will reject submitted papers that concern Indigenous communities but do not provide evidence of the care taken towards engagement with Indigenous communities including appropriate attribution, appropriate access, and ideally Indigenous authorship. We encourage authors to include details of their perspective and background in the author description.

    DSJ will continue to explore how we can foster inclusive principles of data science in our publications and practice.

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  • Celebrating 20 years of publication!

    The Data Science Journal is excited to be celebrating 20 years of publication!

    When the first issue of the Data Science Journal was published by CODATA in 2002, it was perhaps the first publication dedicated to the new concept of “data science.” Since the beginning, Data Science Journal papers have advanced understanding of many aspects of the science of data, including the capture of data, their analysis, metadata, retrieval, archiving, exchange, mining to find unexpected knowledge and data relationships, and visualization, along with intellectual property rights and other legal and ethical issues related to data.

    Data science has evolved significantly over the past two decades, becoming a topic of significant interest in academic research, public and private sector workplaces, and in government policies and practices. The boom of data science has been stimulated by the large volumes and varieties of data being made public on the internet, via the explosive growth in digital technologies such as personal computers, cell phones, social media, smart devices, and sensor networks. Data science has also grown with a recognition that research integrity is enhanced with the increased availability of the data that underpins research. All this has resulted in a need for new infrastructure, skills and support in the research process and the ability to work with data.

    Data science has emerged as a panoply of techniques, tools, and skills that can be applied to derive value (economical or intellectual) out of the growing piles of data. We recognize that this includes advanced analytics and must also include concerns of ethics, infrastructure development, information theory, pragmatics, and more. Data science must consider the science of data and issues of data in science.


    What have we learned?

    Where are we now?

    What’s next?

    The journal plans several activities to celebrate the anniversary and explore these questions. We welcome your contributions and ideas.

    Special collection: The evolution and future directions of data science.

    Throughout 2022, DSJ encourages submissions related to the evolution and future directions of data science (broadly defined), including recent advances, retrospective analyses, and community inspirations and provocations. We encourage a broad range of contributions. Please contribute.

    Events at International Data Week

    We plan several events at IDW including a research session, community discussion, and convivial reception. We’ll provide more information as the program develops.

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  • DSJ announces New Joint Editor-in-Chief

    CODATA is pleased to welcome Matthew Mayernik as Joint Editor-in-Chief of the Data Science Journal, joining Mark Parsons who has been Editor- in-Chief since 2019. Beginning 1 September, Matt and Mark will work as co-equals to collaborate around the duties of the Editor-in-Chief.

    Mark writes: 
    I am thrilled that Matt will be joining me as Joint Editor-in-Chief. I have known Matt for a decade or so, and have always admired his holistic yet precise and insightful views of the “science of data”. Matt has been a long-time, dedicated member of the DSJ editorial board. He is responsible and productive, but more importantly, Matt is thoughtful and considered in his work. It will be fun to work with him.

    Matt writes:
    I am very excited to join Mark Parsons as co-Editor in Chief of the Data Science Journal. I have long felt that the DSJ has a unique role in the data science publication landscape, with its focus on the "data" part of data science. The journal has been instrumental in advancing the science of data broadly, including data system design and implementation, analytics and visualization, metadata and standards, and data policies. This broad view of data science is critical as data continues to grow in importance in all sectors of our societies. I look forward to working with Mark, the DSJ editorial board and staff, and CODATA to move the journal forward.

    Matthew Mayernik is a Project Scientist and Research Data Services Specialist at the National Center for Atmospheric Research (NCAR), based in Boulder, CO, USA. His work is focused on research and service development related to research data curation. His professional interests include metadata practices and standards, data curation education, data citation and identity, and social and institutional aspects of research data. He is also a member of the Committee on Open Environmental Information Services (COEIS) within the American Meteorological Society. He received his Master's in Library and Information Science and Ph.D. in Information Studies from the University of California, Los Angeles (UCLA).
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  • Change in Publication Fees

    CODATA and the DSJ editor have decided that we need to increase our publication fees for the Data Science Journal to £650. This is to cover increasing costs, more rigorous copyediting, support for the editorial board and to contribute to our waiver fund. Waivers are available for authors who are unable to meet the cost of the APC, especially early-career researchers and practitioners and those from low to middle income countries.  The agreement with Ubiquity allows us to grant waivers for up to 10% of published articles.  CODATA also contributes to the waiver fund to provide additional waivers (above the 10%).
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  • Special Collection Call for Papers: Multidisciplinary Data Activities Bridging the Research Community and Society

    The global data community, led by the World Data System of the International Science Council (WDS), organized the International Symposium on ‘Global Collaborations on Data beyond Disciplines’ on 23–25 September 2020 ( This online conference was conducted whilst the world was in the grips of the COVID-19 pandemic. The significance of multidisciplinary approaches to data management and of Open Data were comprehensively discussed by participants. Many presentations highlighted the close connection between data-oriented activities and society. Examples included mitigating against detrimental environmental changes, disasters, and health crises worldwide, including the large efforts to combat COVID-19. An overriding conclusion of the symposium is the vital role of data scientists in developing multidisciplinary data systems that can contribute to solving the pressing problems faced by society; in particular, the achievement of the United Nations Sustainable Development Goals that necessitates the integration of multiple sources of data. This proposed ‘Special collection’ will include article submissions that outline best practices for developing data systems and data analysis procedures from a multidisciplinary viewpoint. Contributions are not restricted to presenters at the symposium or to any specific region, rather the editors welcome submissions from anyone globally for whom the symposium themes resonate.

    Guest Editors

    • Tomoya Baba (Research Organization of Information and Systems)

    • Rorie Edmunds (International Programme Office of the World Data System)

    • Elaine Faustman (University of Washington)

    • Masaki Kanao (Research Organization of Information and Systems)

    • Juanle Wang (China Academy of Science; Editorial Board of the Data Science Journal)

    Deadline of Article Submission: 31 March 2021

    Final Publishing Online for All Articles: 31 December 2021 (provisional)

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  • Open Science for a Global Transformation: Call for Papers for a Special Collection in Data Science Journal

    2021 is likely to be a very significant year for the transformation of science and the adoption of Open Science and FAIR practices.  UNESCO, the educational, scientific and cultural organization of the United Nations, is preparing a Recommendation on Open Science to be adopted (it is hoped) by the UNESCO General Assembly in November 2021.  Against the background of the COVID-19 pandemic—which has accentuated the need for international research cooperation, scientific transparency and data sharing for robust evidence and informed decisionmaking—UNESCO has conducted a global consultation and drafting process for the Recommendation on Open Science.

    In June 2020, CODATA coordinated and published ‘Open Science for a Global Transformation’, a response to the UNESCO consultation from a number of partner international data organisations. The first draft of the UNESCO Recommendation on Open Science was released for feedback from member states and the scientific community in early October 2020.  

    To encourage further discussion around the issues addressed in ‘Open Science for a Global Transformation’ and the draft Recommendation on Open Science, we invite the global research data community to share their views, critiques and positions in an open discussion prompted by the draft recommendation and the CODATA-coordinated document.  Our intention is to create a forum for debate and ultimately a body of reasoned argumentation which can be referenced throughout the UNESCO process.  In the Data Science Journal, this will also form a significant body of scholarly material exploring and defining issues around Open Science. 

    Three venues are envisaged for this discussion:

    1. A Special Collection in the CODATA Data Science Journal 

    We invite scholarly essays, review articles, practice papers and research articles that discuss issues around Open Science and relate their argumentation to topics addressed in ‘Open Science for a Global Transformation’ and in the draft UNESCO Recommendation on Open Science.  Please consult the scope of the Data Science Journal and the descriptions of the categories of article.  All submissions should be scholarly and will be peer reviewed.  While ensuring quality and rigour, the editorial team will do its best to expedite publication.  The collection will serve as a scholarly contribution to the global debate on the content of the UNESCO Recommendation and on the contours and characteristics of Open Science in general.  We will aim to ensure that any articles submitted by 15 December, will be published in time to be referenced during the timescales of the UNESCO review process (see below).  Accepted articles submitted after that date will be included in the collection on Open Science and will still be relevant to the ongoing discussion and debate around the Recommendation.  Submit contributions to the Special Collection at 

    1. A Curated Collection of Posts and Opinion Pieces in the CODATA Blog 

    If you would like to contribute to this discussion through something more like a blog post, and opinion piece, or if you would like to test your ideas before submitting an more scholarly contribution to the Data Science Journal, then you can do this through a curated collection on the CODATA blog.  To do so, please send your piece to  The proposed blog posted will be checked by the CODATA secretariat and a member of the author group and then published.

    1. Threads on the CODATA International List

    Finally, we also encourage the community to share ideas and discussion of the draft Recommendation through the CODATA International news and discussion list.  Simply subscribe to the list and send your ideas and views to  Be sure to start the title of your message with ‘UNESCO Open Science Recommendation’.

    We welcome any and all contributions to these forums!

    The UNESCO Consultation and Recommendation on Open Science

    The practices of Open Science and calls for transformations of the way science is practiced, communicated and assessed have accelerated in the past two decades.  Leading transnational organisations including the International Council for ScienceOECD and European Commission, have recognised Open Science as the key mode for research in the 21st century.  Recognising the significance of the movement, but also aware that in a ‘fragmented scientific and policy environment, a global understanding of the meaning, opportunities and challenges of Open Science is still missing’, UNESCO launched a global consultation in March 2020.  This has as its objective ‘to build a coherent vision of Open Science and a shared set of overarching principles and shared values’ through the development of ‘an international standard-setting instrument on Open Science in the form of a UNESCO Recommendation on Open Science’ to be agreed at the UNESCO General Assembly in November 2021.

    This is a precious opportunity for the worldwide research community to express priorities, report relevant experiences, and share visions for the future, thus helping to shape a new global order for research and its governance. A UNESCO Recommendation is a timely, important and urgent way to promote Open Science and provide concrete suggestions to national governments and research organisations, including scholarly societies, universities, and research groups.

    Consultation on the Draft UNESCO Recommendation

    The first draft of the UNESCO Recommendation was produced, on the basis of the consultation and supported by the UNESCO Open Science team, by an international Open Science Advisory Committee, and was published for consultation in early October 2020.  Feedback on the draft Recommendation is invited from UNESCO Member States and from the global research community until the end of January 2021.  After that point, the Advisory Committee will resume its work to produce a second draft.  The revised draft, approved by the UNESCO Director General will be sent to Member States in April 2021.  This will be followed by a process of negotiation culminating, it is hoped, in the adoption of the text at the General Conference in November 2021.

    The draft Recommendation offers a definition of Open Science and it presents a set of core values and principles.  Importantly, it lays out seven key areas of action, directed at Member States and other named stakeholders:

    1. Promoting a common understanding of Open Science and diverse paths to Open Science

    2. Developing an enabling policy environment for Open Science 

    3. Investing in Open Science infrastructures

    4. Investing in capacity building for Open Science

    5. Transforming scientific culture and aligning incentives for Open Science

    6. Promoting innovative approaches for Open Science at different stages of the scientific process  

    7. Promoting international cooperation on Open Science

    Like any such document, the draft Recommendation tries to synthesise and reconcile a range of views and positions (not necessarily opposed or divergent, but with different emphases, concerns and priorities).  Therefore, discussion and critique of the ‘Open Science for a Global Transformation’ document and the draft Recommendation are to be expected and encouraged.  It is precisely through such scrutiny that we can ensure that this global statement on Open Science is as robust as possible.

    We invite the global research data community, such as the readership of the Data Science Journal and those engaged with the Data Together organisations and other data and information organisations, to seize this opportunity and to use these venues described above to share scholarly discussion, opinion pieces, critiques and proposals in relation to the UNESCO process and Recommendation.  This will both provide a resource which can be fed into the direct process of consultation and feedback, and offer a longer-lasting collection of public and reasoned views and debate on the age-defining issue of Open Science.

    We are particularly interested in articles documenting regional dimensions, exploring neglected issues, critiques and arguments to improve the Recommendation, and discussions of issues to address in order to ensure positive and equitable outcomes from Open Science implementation. There will also be opportunities for further discussion at the International (Virtual) FAIR Convergence Symposium in December 2020 and other events such as the Virtual RDA Plenary meeting in November 2020.  

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  • An Invitation To Take Part In The CODATA Connect – Data Science Journal Early Career Essay Competition

    The first of its kind, CODATA Connect Early Career and Alumni Network in collaboration with the CODATA Data Science Journal (DSJ) is organizing an Essay Competition for Early Career Researchers (ECR), defined as university undergraduate, graduate, post-graduate students or early career researchers within five years of completing their highest qualification. CODATA Connect exists to ensure a structural and sustained collaboration and support for the activities of the alumni of CODATA-RDA Summer Schools, of the CODATA China and Trieste Training Workshops and other early career researchers and data professionals involved in the CODATA community and those of partner organisations.

    Topic for the Essay: “Open Data Challenges to Address Global and Societal Issues” 

    • Participants interested in disseminating information on research data in the sciences, humanities and the arts using essays as the medium are welcome to apply.
    • Topics might include but not limited to human health, climate change, resilience, etc. 
    • Essays which discuss the necessary limits in openness are also in scope and are welcome (e.g. personal health information, indigenous sovereignty etc.)
    • Essays that discuss the challenges in making data as open as possible and how such data can be used to address global and societal issues (Crime, Disease, Governance, etc.)
    • Essays might discuss challenges at any point in the data lifecycle and in relation to any of a number of global and societal challenges.  
    • We discourage essays that simply present well known and generic arguments in favour of open data.   
    • Essays are encouraged to use local or specific examples for the benefits of open data and to discuss how challenges can be overcome in both specific circumstances and more globally.  


    • The dates for the essay writing are: 1st April to 31st July 2020. 
    • The Essay Writing Competition is open to all CODATA-RDA Summer Schools Alumni and any Early Career researchers. The essay writing time is 3 months and the length of the essay should be 2000-3000 words. References do not count as part of words limit. Each essay must contain the full name(s), affiliations and contacts details of the authors. Essay is accepted only in English Language.


    The panel of judges consists of representatives from the CODATA Community and Editors of the CODATA Data Science Journal. The judges decision is final and may not be appealed. The review process will be single blind. Judges will not know the identity of the authors.


    Essays should be interesting, original and provocative but also clearly situated in a body of knowledge. It is important that authors show an appropriate knowledge of the chosen research area for discussions. Other criteria include but are not limited to:

    • Contemporary relevance of the topic to science of data
    • Demonstrated awareness of the state of discussion around the topic. Essays do not need to include a comprehensive literature review, but must illustrate where their argument fits into broader discussion in the field.
    • Strength and persuasiveness of the argument, underpinned by reference to literature, evidence and data.
    • Clarity of writing and appeal of the style.


    Essays cover topics and controversies of interest to the community and aim to stimulate discussion and debate. Essays may be provocative and less focused on reporting original research work but should still consist of original thoughts and ideas. Essays should be no longer than 3,000 words in length. The content of the essay should be at the author’s preference.  

    More information on the criteria for Essay submission at the DSJ can be found here:


    • The winning essay will be published in DSJ for free and the winner will receive a trip to the CODATA Symposium in October 2020 or future CODATA event as appropriate along with a formal certification of their achievement.
    • Shortlisted essays will be invited for submission to the CODATA Data Science Journal (the usual peer review, article processing charges and waiver terms will apply).


    • The deadline for submitting the essays is 23:59 UTC on Tuesday 30 June 2020. Essays should be submitted at Click Here or sent by e-mail as pdf attachments to
    • Please make sure that you write “Essay Writing Competition April 2020: Your Name [Surname, Firstname]” in the subject of your email. 
    • Please ensure that the PDF of the essay does not include any information about the author.
    • Please provide your Name, Degree, Address and Institutional Affiliation in the covering email or in an accompanying document BUT NOT ON THE PDF OF THE SUBMITTED ESSAY. 
    • If you are in the ECR category please provide the date of completion of the last degree with the pdf of the essay in one email.


    The selected essay winner will be announced on the CODATA website  by Saturday 15 August 2020. The winner will also be contacted through the CODATA Connect email.

    CODATA reserves the right to publish the winners’ name and affiliation, as well as the winning essay on the CODATA Data Science Journal: 


    For further enquiries and information, contact:

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  • Revised Focus and Scope

    The DSJ editorial board has revised the focus and scope of the journal. It is not a big change, but rather one of clarification in a changing world. We primarily want to specify our definition of 'data science' as the classic sense of the science of data practices that advance human understanding and knowledge — the evidence-based study of the socio-technical developments and transformations that affect science policy; the conduct and methods of research; and the data systems, standards, and infrastructure that are integral to research. 

    We recognize the contemporary emphasis on data science, which is more concerned with data analytics, statistics, and inference. We embrace this new definition but seek papers that focus specifically on the data concerns of an application in analytics, machine learning, cybersecurity or what have you. We continue to seek papers addressing data stewardship, representation, re-use, policy, education etc.

    Most importantly, we seek broad lessons on the science of data. Contributors should generalize the significance of their contribution and demonstrate how their work has wide significance or application beyond their core study. 

    We look forward to hearing from you. 

    Mark A. Parsons on behalf of the Editorial Board


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  • CODATA is pleased to announce Mark Parsons as the new Editor-in-Chief of the Data Science Journal

    In his blog post, Mark writes: 'I am especially interested in helping DSJ build its niche as an influential journal of the ‘science of data’ in the sense that CODATA described it decades ago. We need more fora that encourage dialog across research and practice to understand all the issues around the socio-technical work necessary for data to be findable, accessible, interoperable, reusable, ethical, secure, etc.’ ...

    'I have been a member of the DSJ editorial board since the journal moved to Ubiquity Press, and I have been impressed at how Sarah Callaghan and other editors have worked to increase the journal’s quality. I want to continue this momentum. I want to further bolster the review quality and also raise the possibility of open reviews. The nature of DSJ is that it often attracts submissions and requires reviews from practitioners who have much less of a mandate to publish than researchers. I believe practitioners should be encouraged to contribute (with research as well as practice papers), so we should do what we can to recognize and model excellent contributions in this area. ...

    'Thanks to Sarah’s great work, DSJ has a bright future as submissions continue to increase in number and quality. DSJ was ahead of its time when it was founded in the 1990s. I am eager to explore how it can continue to push important conversations forward. I welcome all your ideas. Please tell me what you think. Better yet, tell the community through a submission to DSJ!


    Mark replaces Sarah Callaghan, who has served since 2015, when the Data Science Journal was moved to its current platform with Ubiquity Press.

    Sarah writes:

    'In my four year tenure, I am very proud of the fact that 135 papers have been published, along with 6 Special Collections with another 5 Special Collections in the pipeline. The journal has grown more popular and is steadily publishing research that is more impactful as time goes on, and this is a testament to the hard work of all involved – including our reviewers and authors.

    'It is time for me to hand over the role of EiC to another, and it is with no small amount of sadness that I do so. Being EiC has been incredibly rewarding (and occasionally infuriating) and I have learned a great deal from it. I am very pleased to know that Mark Parsons is taking over the role, and know that the journal will be in safe, knowledgeable hands.

    'It only remains for me to say my farewells and thank yous. Thank you to the authors, without whom there would be no articles to publish. A thousand thank yous to all my editors, reviewers, colleagues and friends – your efforts on behalf of the journal are deeply, deeply appreciated, as is your wisdom and expertise. I wish you all the very best for the future, and look forward to reading more excellent papers published in the DSJ!’

    Read more at 

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  • Call for Nominations and Applications: Editor-in-Chief, Data Science Journal, Deadline 14 April

    The Data Science Journal is currently accepting nominations and applications to become the Editor-in-Chief of the journal.

    Applications can be made through the Google form at

    The deadline for applications is 12 midnight GMT on Sun 14 April.

    The Data Science Journal is concerned with all aspects of the science of data.  It 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.  For the journal scope please see

    The main role of the Editor-in-Chief is:

    1. to set the strategic direction of the Journal and develop policies to support the strategy (in collaboration with the editorial board)

    2. to advocate for and promote the journal in general conversation and day-to-day work and by soliciting papers of interest to the community

    3. to manage the editorial process (assisted by editors selected from the editorial board)

    More specifically, the responsibilities expected are:

    • in coordination with CODATA and with the Ubiquity Press managing editor, the EiC, selects and manages the Editorial Board
    • respond to initial queries about the journal (please see already drafted boilerplate text regarding time to publication, APCs, requests for waivers etc.)
    • perform regular management tasks such as reminding editors to look at the status of their papers and make decisions/invite new reviewers/etc. as appropriate
    • assign papers to other editors according to their interests and workload
    • approve other editors' decisions regarding papers
    • send papers to be copy-edited/typeset as appropriate
    • to adhere to COPE Core Practices
    • to shepherd submissions through the publication system, assisting in locating suitable peer reviewers, and making judgements on the quality of the submitted articles according to their area of expertise
    •  to distribute Calls for Papers as widely as possible and to promote the journal whenever possible to gain submissions
    • to carry out occasional peer review for the journal
    • to let the rest of the editorial board know of conferences/events that could be used as journal promotion opportunities (especially relevant if they will be attending the event)
    • to provide editorial advice/opinion to fellow editors on submissions, if required
    • to be available to contribute to a yearly editorial board review, should it be required
    • to help promote specific publications and press releases,
    • to consider submitting to the journal their own publications

    The previous Editor-in-Chief, Sarah Callaghan, allocated c.10% or 3-4 hours per week to this role. As is often the case for journal editors, her employer allowed this time (0.1 FTE) as part of her activities.

    The Editor-in-Chief of the Data Science Journal is an ex officio member of the CODATA Executive Committee and CODATA provides a small fund to support travel and promotion of the journal.

    The position is not a paid role but an honorarium can be negotiated with excellent candidates for outstanding performance.

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  • Reproducibility Update

    As a part of our commitment to encourage open and reproducible research, we have updated our editorial policies and author guidelines to move towards more open data, methods, and code when appropriate.

    In brief, our policies now include sections on:

    • Open Data: If data has been produced as part of the research, it should be deposited in an open repository.
    • Structured Methods: If an in-paper Methods section is not adequate to realistically and accurately replicate research, authors are encouraged to provide a detailed 'structured methods' on, which will allow for more detail, discussion, versioning, and reuse.
    • Open Code: If research includes code, statistical analyses, or algorithms, we recommend that authors upload a working instance of their code and data to Code Ocean, which will allow users to replicate the results without any need to install software.

    We believe these changes will help to increase the value, impact, and transparency of the research, as well as aid the transition to a more open research environment as a whole.

    Read our reproducibility guide for more information on best practice and maximising the impact of your open data.

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  • FAIR and Responsible Research Data Management (FAIR-RRDM)

    Responsible data management embodies the FAIR principles of making data findable, accessible, interoperable, and reusable. FAIR has helped focus minds and provided readily adopted terminology and guidelines, which in turn will help realise the benefits of accelerated analysis, with machines, at scale. In consequence, research communities and research institutions are faced with the task of rising to the challenge of FAIR and responsible data management.

    Advancing the adoption of FAIR requires sharing protocols, practices, policies, methodologies, and approaches for responsible data management. The open science and open data movements have made significant progress in certain research communities and domains, but less so in others. While good practices have been developed within some research communities, it is in research institutions and universities that data management and some long term stewardship must take place. Sometimes reluctantly, research institutions have been obliged to take greater responsibility for research data management by the needs of researchers and their communities on the one hand and by the requirements of national funders on the other.  

    There are opportunities for knowledge sharing and coordination across a number of these axes: between research disciplines and communities; between research communities and institutions; and internationally among institutions.  The biomedical and genomics fields, for example, have made considerable progress with data sharing and with issues of nomenclature and semantics. Much research activity of the last two decades could not have happened without community agreements on data sharing and mechanisms for managing concepts, semantic specifications and ontologies.  Likewise, many biomedical research domains are addressing the challenges of controlled sharing of sensitive and restricted data, following the FAIR principles but with respect to ethical and legal criteria where these prevent certain data from being fully Open. 

    The aim of this workshop is to bring together researchers, data management experts, policy leaders and to facilitate knowledge sharing between research communities and between institutions. Perspectives from all domains and from research institutions are in scope. At least one session will examine progress in the biomedical community and lessons to be learnt, particularly in relation to good practice and mechanisms for controlled sharing of sensitive and restricted data.


    Outputs and Impact

    The workshop will feature invited speakers and an expert panel discussion, selected research and practice papers from an open call, a poster and lightning talks session, as well as workshop sessions on mechanisms for knowledge sharing and on issues of responsibility and sensitive data.  

    The workshop will:

    1) highlight and scrutinise innovative approaches and key developments fulfilling FAIR principles;

    2) promote broad interest and participation in the pursuit of solutions across disciplines and institutions; and

    3) identify concrete mechanisms for knowledge sharing between research communities and between research institutions.

    The workshop builds on a successful Göttingen-CODATA Symposium on RDM in Institutions which was held as a pre-event to the RDA Plenary 11 in Berlin in March 2018 and like its predecessor will result in a special collection of the CODATA Data Science Journal.


    Call for Presentations and Posters/Lightning Talks

    Submissions are to be made via the CODATA Conference Platform at:

    The deadline will be 18 February and accepted speakers will be notified no later than 1 March.

    Recommended proposal lengths for the three categories of presentation are:

    Long, research presentation, addressing the workshop themes by reporting on an original research activity: 800-1200 words

    Short, practice presentation, addressing the workshop themes by reporting on a project or institutional activity: 400-800 words

    Poster and lightning talk addressing the workshop themes: 300-600 words 

    After the workshop, selected presenters will be invited to submit a full paper to the CODATA Data Science Journal where they will form a special collection.


    Workshop Themes / Sessions

    1: FAIR data: implications and responsibilities 1) for research communities and 2) for research institutions.

    2: FAIR data stewardship and knowledge sharing.  What progress has been made in RDM and FAIR data stewardship?  What can be learnt from biomedical research and from other domains?

    3: Limits of open data and how do deal responsibly with sensitive data.  What can be learnt from biomedical fields and other fields for the controlled sharing of sensitive data?

    4: RDM, FAIR stewardship services and research infrastructures 1) for research communities and 2) for research institutions.  How are research communities and/or research institutions implementing research infrastructures for RDM and FAIR stewardship?  How are they tackling related and supporting issues such as: a) developing skills and capacity; b) addressing policy, legal and ethical issues; c) aligning strategies and priorities with FAIR and RDM responsibilities?

    5: Alignment of domain and institutional RDM and FAIR stewardship: What experiences exist and mechanisms are there for aligning domain and institutional RDM and FAIR stewardship? Examples of collaboration between research communities, domain research infrastructures and institutions will be particularly welcome.    



    The workshop is free.  Places are limited. Please register at:



    The programme committee and organisers are committed to ensuring this is a no-fee event.  To help with this ambition, we will be very grateful for financial support and sponsorship.  Please contact Jane Greenberg <>, Simon Hodson <> for further information.


    Program Committee


    Jane Greenberg, Alice B. Kroeger Professor, Director of the Metadata Research Center, College of Computing and Informatics, Drexel University, USA

    Simon Hodson, Executive Director CODATA

    Devika Madalli, Professor, Documentation Research and Training Center (DRTC), Indian Statistical Institute (ISI), Bangalore, India


    Programme Committee Members:

    Jan Brase, Head of Research and Development Georg-August-Universität Göttingen, Göttingen State and University Library, Germany

    Sarah Callaghan, Editor-in-Chief of the Data Science Journal

    Kedma Duarte, Technical-Scientific Advisor, Goiás State Research Support Foundation (Fapeg), Goiania, Goiás, Brazil

    Megan Force, Editor, Data Citation Index, Clarivate Analytics

    Wolfram Horstman, Director, Göttingen State and University Library, Germany

    Eva Mendez, Deputy Vice-Rector for Scientific Policy. Open Science, Universidad Carlos III, Madrid, Spain, and Chair of the EU Open Science Policy Platform

    Jeffrey Pennington,Associate Vice President and Chief Research Informatics Officer, Children's Hospital of Philadelphia

    Rosina Weber, Associate Professor, College of Computing and Informatics, Drexel University.

    Michael Witt, Head of the Distributed Data Curation Center (D2C2), Purdue University

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  • Call For Papers: Special Collection on Research Data Alliance Results

    The Research Data Alliance (RDA) is an international member based organization focused on the development of infrastructure and community activities that reduce barriers to data sharing and exchange, and the acceleration of data driven innovation worldwide. With more than 6,900 members globally representing 137 countries, RDA includes researchers, scientists and data science professionals working in multiple disciplines, domains and thematic fields and from different types of organisations across the globe.

    This special collection aims to collect and give visibility to research results and outcomes stemming from RDA activities.

    In particular, it solicits high quality papers describing the latest results of RDA working groups (WGs) or interest groups (IGs) that have recently produced an output, including recommendation and associated use cases that could highlight the added value of RDA work in the data related fields.

    The scope of the special collection is to become a point of reference for experts from the international community that are committed to directly or indirectly enabling data sharing, exchange, or interoperability. The aim is to promote the work carried out in IGs and WGs and bring the results obtained to the global data expert community. Specific tools, code, best practices, standards, surveys, recommendations, reports, and WG case statements will be brought to a larger audience via the peer-reviewed, open access Data Science Journal.

     Important dates

    • 1 September 2018 – First Submission deadline
    • Accepted articles will be published on-line on a rolling basis to the special collection as they are completed.

    Publication fees

    Publication fees (i.e. Article Processing Charges) for the first 6 articles will be covered by the “RDA Europe 4.0” project (funded by the European Commission, grant nr. 777388). No other fees will be charged to the Authors of this special collection for publication.

    Guest editors

    • Leonardo Candela, Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, Italian National Research Council, Pisa, Italy
    • Donatella Castelli, Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, Italian National Research Council, Pisa, Italy
    • Emma Lazzeri, Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, Italian National Research Council, Pisa, Italy
    • Paolo Manghi, Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, Italian National Research Council, Pisa, Italy

    Paper Submission

    Papers submitted to this special collection for possible publication must be original and must not be under consideration for publication in any other journal or conference. Previously published or accepted conference papers must contain at least 30% new material to be considered for the special collection.

    All papers are to be submitted by following the instructions and guidelines availailabe at During the submission process authors should select the article type (Research Papers, Practice Papers, Reviews, Essays) and specify that the submission is to be considered for the special issue “Research Data Alliance Results” in the “Cover letter” to be compiled during the submission process.

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  • Latest Codata deadlines

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  • Data Science Journal - Call for Reviewers

    The Data Science Journal is currently looking to expand its network of specialised reviewers. The journal is seeking reviewers who can provide constructive analysis of papers, thereby enhancing the quality of the published papers and the overall reputation of the journal.  The Data Science Journal is indexed in Scopus, with indexing in Web of Science pending.

    Reviewer requirements:

    Reviewers should have good experience in their specific research field and this research field must conform to the scope of the journal (

    Reviewers are responsible for evaluating the quality, relevance, and merit of submitted papers, and should provide complete, constructive and detailed comments to support their decision, providing clear opinions about strengths, weaknesses, relevance, and importance to the field.

    Reviewers must:

    • Agree to follow the COPE Ethical Guidelines for Peer Reviewers (, including considering supplementary materials and references submitted as part of the publication;
    • Agree to return manuscripts in a timely manner;
    • Agree not to distribute the manuscript or to disclose information within the manuscript;
    • Agree to review the first revision of a manuscript for which he/she provided the initial review;

    Data Science Journal operates a single-blind peer review process, meaning that reviewers remain anonymous for the review process. The review period is expected to take no more than four weeks.

    Reviewer Benefits

    Every year, the best reviewer (as judged by the editorial team) will be awarded an APC-free publication in Data Science Journal as a thank you for their work. Academic credit for doing reviews can also be obtained via Publons (, and of course, reviewing provides learning and development opportunities in one of the fastest emerging research areas today.

    To apply:

    Please visit to register as a reviewer.

    If you are not registered with the journal then you will be prompted to register, and if you are already an author you will be given the opportunity to be further added to the reviewer pool. Please do update your profile with your research interests, as this allows the editors to assign appropriate papers to you.

    About the 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 physical and social sciences, technology, the humanities and the arts.

    The Data Science Journal is dedicated to the advancement of data science and its application in practices, policies and management of data to ensure that data are used in the most effective and efficient way in promoting knowledge and learning. It is a peer-reviewed, open access, electronic journal that is relevant to the whole range of computational, natural and social science and the humanities. 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, practices and policies for data.

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  • Special Collection: Data Models

    The Data Science Journal is editing a new special collection of papers focused on conceptual, logical, and physical data models that support research. Our intention is to publish research and practices papers that shed light on new methods of design and development, as well as reviews of recently released data models. Submissions to the collection are ongoing, and papers published in the collection can be found here 

    To submit a paper to this collection, please use the usual submission process and leave a note in the author comments box to inform the editors that it is a submission for the Data Models special collection.

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  • Call for Papers

    Data Science Journal is currently soliciting submissions for papers on a wide range of data science topics, across the whole range of computational, natural and social science, and the humanities. 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 data.

    All data is in scope, whether born digital or converted from other sources, and all research disciplines are covered. Data is a cross-domain, cross-discipline topic, with common issues, regardless of the domain it serves. The Data Science Journal publishes a variety of article types (research papers, practice papers, review articles and essays). The Data Science Journal also publishes data articles, describing datasets or data compilations, if the potential for reuse of the data is significant or if considerable efforts were required in compilation. Similarly, the Data Science Journal also publishes descriptions of online simulation, database, and other experiments, partnering with digital repositories on ‘meta articles’ or ‘overlay articles’, which link to and allow visualisation of the data, thereby adding an entirely new dimension to the communication and exchange of data research results and educational materials.

    For further information, and to submit a manuscript, please visit

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  • A new phase for Data Science Journal

    CODATA is delighted to announce the re-launch of Data Science Journal in partnership with Ubiquity Press

    Dr. Sarah Callaghan, of the British Atmospheric Data Centre and a co-chair of a number of CODATA Task Groups, including the TG on Data Citation, has been appointed as the first Editor-in-Chief of the next generation Data Science Journal.

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