Evaluation of Data Sharing After Implementation of the International Committee of Medical Journal Editors Data Sharing Statement Requirement | Medical Journals and Publishing | JAMA Network Open | JAMA Network

“Question  What are the rates of declared and actual sharing of clinical trial data after the medical journals’ implementation of the International Committee of Medical Journal Editors data sharing statement requirement?

Findings  In this cross-sectional study of 487 clinical trials published in JAMA, Lancet, and New England Journal of Medicine, 334 articles (68.6%) declared data sharing. Only 2 (0.6%) individual-participant data sets were actually deidentified and publicly available on a journal website, and among the 89 articles declaring that individual-participant data would be stored in secure repositories, data from only 17 articles were found in the respective repositories as of April 10, 2020.

Meaning  These findings suggest that there is a wide gap between declared and actual sharing of clinical trial data.”

A Review of the History, Advocacy and Efficacy of Data Management Plans | International Journal of Digital Curation

Abstract:  Data management plans (DMPs) have increasingly been encouraged as a key component of institutional and funding body policy. Although DMPs necessarily place administrative burden on researchers, proponents claim that DMPs have myriad benefits, including enhanced research data quality, increased rates of data sharing, and institutional planning and compliance benefits.

In this article, we explore the international history of DMPs and describe institutional and funding body DMP policy. We find that economic and societal benefits from presumed increased rates of data sharing was the original driver of mandating DMPs by funding bodies. Today, 86% of UK Research Councils and 63% of US funding bodies require submission of a DMP with funding applications. Given that no major Australian funding bodies require DMP submission, it is of note that 37% of Australian universities have taken the initiative to internally mandate DMPs. Institutions both within Australia and internationally frequently promote the professional benefits of DMP use, and endorse DMPs as ‘best practice’. We analyse one such typical DMP implementation at a major Australian institution, finding that DMPs have low levels of apparent translational value. Indeed, an extensive literature review suggests there is very limited published systematic evidence that DMP use has any tangible benefit for researchers, institutions or funding bodies.

We are therefore led to question why DMPs have become the go-to tool for research data professionals and advocates of good data practice. By delineating multiple use-cases and highlighting the need for DMPs to be fit for intended purpose, we question the view that a good DMP is necessarily that which encompasses the entire data lifecycle of a project. Finally, we summarise recent developments in the DMP landscape, and note a positive shift towards evidence-based research management through more researcher-centric, educative, and integrated DMP services.

NOT-OD-21-013: Final NIH Policy for Data Management and Sharing

“The National Institutes of Health (NIH) is issuing this final NIH Policy for Data Management and Sharing (DMS Policy) to promote the management and sharing of scientific data generated from NIH-funded or conducted research. This Policy establishes the requirements of submission of Data Management and Sharing Plans (hereinafter Plans) and compliance with NIH Institute, Center, or Office (ICO)-approved Plans. It also emphasizes the importance of good data management practices and establishes the expectation for maximizing the appropriate sharing of scientific data generated from NIH-funded or conducted research, with justified limitations or exceptions. This Policy applies to research funded or conducted by NIH that results in the generation of scientific data….”

New Report Provides Recommendations for Effective Data Practices Based on National Science Foundation Research Enterprise Convening – Association of Research Libraries

“Today a group of research library and higher education leadership associations released Implementing Effective Data Practices: Stakeholder Recommendations for Collaborative Research Support. In this new report, experts from library, research, and scientific communities provide key recommendations for effective data practices to support a more open research ecosystem. In December 2019, an invitational conference was convened by the Association of Research Libraries (ARL), the California Digital Library (CDL), the Association of American Universities (AAU), and the Association of Public and Land-grant Universities (APLU). The conference was sponsored by the US National Science Foundation (NSF).

The conference focused on designing guidelines for (1) using persistent identifiers (PIDs) for data sets, and (2) creating machine-readable data management plans (DMPs), two data practices that were recommended by NSF. Professor Joel Cutcher-Gershenfeld, of Heller School for Social Policy and Management at Brandeis University, designed and facilitated the convening with the project team….”

OPTIMISING THE OPERATION AND USE OF NATIONAL RESEARCH INFRASTRUCTURES

Abstract:  Research Infrastructures (RIs) play a key role in enabling and developing research in all scientific domains and represent an increasingly large share of research investment. Most RIs are funded, managed and operated at a national or federal level, and provide services mostly to national research communities. This policy report presents a generic framework for improving the use and operation of national RIs. It includes two guiding models, one for portfolio management and one for user-base optimisation. These guiding models lay out the key principles of an  effective national RI portfolio management system and identify the factors that should be considered by RI managers with regards to optimising the user-base of national RIs. Both guiding models take into consideration the diversity of national systems and RI operation approaches.

This report also contains a series of more generic policy recommendations and suggested actions for RI portfolio managers and RI managers.

[From the body of the report:]

As described in Section 8.1.2, data-driven RIs often do not have complex access mechanisms in place, as they mostly provide open access. Such access often means reducing the number of steps needed by a user to gain access to data. This can have knock-on implications for the ability of RIs to accurately monitor user access: for instance, the removal of login portals that were previously used to provide data access statistics….

Requiring users to submit Data Management Plans (DMPs) prior to the provision of access to an RI may encourage users to consider compliance with FAIR (Findable, Accessible, Interoperable, Reusable) data principles whilst planning their project (Wilkinson et al., 2016[12]). The alignment of requirements for Data Management Plans (Science Europe, 2018[13]) used for RI access provision and those used more generally in academic research should be considered to facilitate their adoption by researchers….

The two opposing extremes, described above, of either FAIR / open access or very limited data access provision, highlight the diversity in approaches of national RIs towards data access, and the lack of clear policy guidance…..

It is important that RIs have an open and transparent data policies in line with the FAIR principles to broaden their user base. Collaborating with other RIs to federate repositories and harmonize meta-data may be an important step in standardising open and transparent data policies across the RI community. …

There are a wide variety of pricing policies, both between and also within individual RIs, and the need for some flexibility is recognised. RIs should ensure that their pricing policies for all access modes are clear and cost-transparent, and that merit-based academic usage is provided openly and ‘free-from-costs’, wherever possible. …

Integrating FAIR Data Science Competences in Higher Education Curricula: The Role of Academic and Research Libraries  | FAIRsFAIR

“Our point of departure for the workshop was to present the findings from FAIRsFAIR survey activites conducted by the European University Association (EUA) in collaboration with partners of the FAIRsFAIR project during 2019 to investigate the extent to which FAIR research data management principles are present in university curricula. These findings and the related recommendations are documented in the recently published report D7.1 FAIR in Higher Education.  For easy reference, a quick graphic overview of the report is provided at this webpage. 

The findings most pertinent to workshop participants include:

Awareness of the FAIR principles is considered high among professional and support staff (e.g. data stewards, librarians), moderate among the institutional leadership, but still rather low among researchers and especially students.
Higher education institutions are increasingly aware of the need to integrate digital skills into their curricula. Only 38% of respondents to this question stated that their organisation had a related strategy in place at institutional or departmental level – or both. However 31% stated that although there was no strategy yet in place, their institution was developing one.
The extent to which data science skills are currently being addressed in university teaching is reported to be rather low overall at the bachelor and master level and moderate at the doctoral level. Respondents expressed an urgent need to strengthen the teaching of data-related competences at all three levels. …”

Data Availability Statements Tips – STM Research Data

“6 Quick General Tips

Encourage the use of persistent identifiers or PIDs (for example, DOIs for datasets, ORCIDs for authors, RRIDs for reagents – more information can be found on the ORCID website here)
Engage with journal editors, learned societies and other domain leaders to work out what standards, identifiers and language are appropriate for the community. You could use the RDA policy framework as the outline for the conversation. 
It is preferable to upload data to a repository, and include a link within a research article, rather than hosting via a supplementary material facility.
Sometimes data do need to be kept closed, but this doesn’t need to be the default situation. Ask the researcher/author why should it be closed rather than why should it be open. 
Where possible, have some information (metadata) in front of any paywall to point to where underlying data can be found. See the following examples:…”

2020: A turning point for research data policy?

“An important tool, used by PLOS and others, for introducing a consistent data policy is a data availability statement in every published article. These statements indicate if, how and where the data supporting claims made in an article are available. Many journal and publisher research data policies still make data sharing and data availability statements optional rather than mandatory, but we welcome this steady progress on open research policies in the scholarly publishing community.

Since mandating data sharing and data availability statements in 2014, PLOS has published more than 127,000 articles with a data availability statement and more than one study has analysed them. 

Requiring a new section in every article published incurs costs, which at PLOS we see as a worthwhile investment in open research. It takes time, training and resources for editors, authors, peer reviewers and editorial office staff, so mandating these statements is understandably a consideration for other publishers of thousands of articles per year.

There is growing recognition from funders, academic societies, editorial groups such as the ICMJE, that data availability statements are a practical, achievable and meaningful improvement to support transparency in research….

The STM Association is recommending the use of a common policy framework for journal research data policy to promote consistent approaches to journal research data policies, at its wide variety of members.

The policy framework – published last week in a peer-reviewed journal after being available as a preprint – is an output of an initiative, begun in 2016, within the Research Data Alliance organisation. The framework includes 14 features, or common elements, of journal research data policies – including data citation, data repositories, and data peer review – and reusable policy text for journal editors and publishers to implement on their journals.

In 2019, we compared PLOS’ data availability policy to this framework and, as a first step, updated some of the language, such as to give explicit support for sharing Data Management Plans (DMPs) – a document increasingly required in funding agency data policies. In doing so, PLOS continues to lead the way, by being the first publisher, to our knowledge, to align its entire journal portfolio with this new framework. As well increasing data sharing, another anticipated benefit of harmonising policy is reducing the burden on researchers and support staff with different or conflicting requirements between journals, and funders. The framework also provides future opportunities to review data policy language to ensure requirements are easily understood….”

ARL Responds to US Office of Science and Technology Policy Request for Information on American Research Environment – Association of Research Libraries

“ARL endorses the recommendations in the 2018 National Academies of Science, Engineering, and Medicine (NASEM) consensus report Open Science by Design: Realizing a Vision for 21st Century Research. The report, grounded in FAIR principles, promotes essential actions for research ecosystem stakeholders to improve openness and transparency in research processes, and share and reuse research products, in order to accelerate scientific discovery and innovation.

In particular, research funders and research institutions are in the best position to develop policies and procedures to identify the data, code, specimens, and other research products that ensure long-term public availability, and they are best positioned to provide the resources necessary for the long-term preservation and stewardship of those research products.1 Successful implementation of policies to identify research outputs for reuse and long-term preservation will require integration and alignment between the scientific community (e.g., managers of domain repositories and scholarly societies) and the stewardship community. ARL is committed to partnering with and convening the relevant stakeholders to work towards this alignment….

ARL recommends that federal agencies provide maintenance funding and require maintenance plans for community-governed tools and services that enable rapid dissemination, interlinking research through registries of persistent identifiers, data sharing, and collaboration to advance scientific progress. New modes of research publication enable researchers to publish executable code and data alongside articles, share preprints with associated data and code, enable post-publication peer review through overlay journals, and facilitate collaboration and team science.

Scientific tools and infrastructure such as outlined above, including tools like Jupyter Notebooks, ReproZip, and Code Ocean, accelerate the progress of science and facilitate replicability. Openness enables both interoperability and preservation for future research and the scholarly record. A recent paper on the arXiv.org preprint server, “Publishing Computational Research—A Review of Infrastructures for Reproducible and Transparent Scholarly Communication,” provides an excellent review of the issues from major stakeholder perspectives….”

Sorbonne declaration on research data rights

Signed by nine major university consortia. 

(The file is an image scan that doesn’t support cutting and pasting. Otherwise, this description would be longer and more useful.)

The declaration is undated, but was officially released on January 27, 2020.