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 email@example.com on behalf of the Editorial Board https://datascience.codata.org/about/editorialteam/
Posted on 11 Jun 2019
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!
Read more at http://codata.org/blog/2019/04/29/mark-parsons-joins-codata-as-editor-in-chief-data-science-journal/
Mark replaces Sarah Callaghan, who has served since 2015, when the Data Science Journal was moved to its current platform with Ubiquity Press.
'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 http://codata.org/blog/2019/04/29/so-long-and-thanks-for-all-the-fish-a-farewell-from-outgoing-data-science-journal-editor-in-chief-sarah-callaghan/
Posted on 01 May 2019
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 https://goo.gl/forms/ey60x1N2jO9YM1rY2
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 https://datascience.codata.org/about/
The main role of the Editor-in-Chief is:
to set the strategic direction of the Journal and develop policies to support the strategy (in collaboration with the editorial board)
to advocate for and promote the journal in general conversation and day-to-day work and by soliciting papers of interest to the community
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.
Posted on 25 Mar 2019
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 protocols.io, 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.
Posted on 19 Mar 2019
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: https://conference.codata.org/Drexel_CODATA_2019/
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: http://bit.ly/Drexel-CODATA-Workshop-Register
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 <firstname.lastname@example.org>, Simon Hodson <email@example.com> for further information.
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
Posted on 11 Feb 2019
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.
- 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 (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.
- 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
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 https://datascience.codata.org/about/submissions/. 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.
Posted on 19 Jun 2018
Latest Codata deadlines
Posted on 04 Jun 2018
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.
Reviewers should have good experience in their specific research field and this research field must conform to the scope of the journal (http://datascience.codata.org/about/).
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.
- Agree to follow the COPE Ethical Guidelines for Peer Reviewers (http://publicationethics.org/files/Peer%20review%20guidelines_0.pdf), 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.
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 (https://publons.com/home/), and of course, reviewing provides learning and development opportunities in one of the fastest emerging research areas today.
Please visit http://datascience.codata.org/author/register/reviewer/ 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.
Posted on 03 May 2017
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.
Posted on 30 Jan 2017
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 http://datascience.codata.org/about/submissions/
Posted on 18 Nov 2015
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.
Posted on 24 Mar 2015