Association of Research Libraries Welcomes Increased Investment in Research and Data Sharing in Reauthorization of National Science Foundation – Association of Research Libraries

“Data Management Plans

ARL is heartened to see Congress acknowledge the necessity of machine-readable data management plans (DMPs) and open repositories in supporting the academic research enterprise. At a National Science Foundation–funded conference on effective data practices in December 2019, ARL, along with the Association of American Universities, the Association of Public and Land-grant Universities, and the California Digital Library, convened stakeholders including university research officers, scientists, and librarians. Conference participants agreed that data management planning is important for sharing and use of research data and outputs. Participants suggested that the ability to update plans (“just in time”) across the project life cycle and as part of progress reporting would accelerate the value and adoption of DMPs among researchers, beyond what is required for compliance.

Open Repositories

ARL encourages the development of a collaborative set of data repository criteria. Coordination among federal agencies will be necessary, as will stakeholder input from researchers, repository managers, librarians, and others. ARL looks forward to continuing these conversations and building upon work already underway within groups such as the Confederation of Open Access Repositories, the Research Data Alliance, and the World Data System….”

Guide to Accelerate Public Access to Research Data

“Advancing public access to research data is important to improving transparency and reproducibility of scientific results, increasing scientific rigor and public trust in science, and — most importantly — accelerating the pace of discovery and innovation through the open sharing of research results. Additionally, it is vital that institutions develop and implement policies now to ensure consistency of data management plans across their campuses to guarantee full compliance with federal research agency data sharing requirements. Beyond the establishment of policies, universities must invest in the infrastructure and support necessary to achieve the desired aspirations and aims of the policies. The open sharing of the results of scientific research is a value our two associations have long fought to protect and preserve. It is also a value we must continue to uphold at all levels within our universities. This will mean overcoming the various institutional and cultural impediments which have, at times, hampered the open sharing of research data….”

A Brave New PID: DMP-IDs

“Despite the challenges over the last year, we are pleased to share some exciting news about launching the brave new PID, DMP IDs. Two years ago we set out a plan in collaboration with the University of California Curation Center and the DMPTool to bring DMP IDs to life. The work was part of the NSF Eager grant DMP Roadmap: Making Data Management Plans Actionable and allowed us to explore the potential of machine-actionable DMPs as a means to transform the DMP into a critical component of networked research data management.

The plan was to develop a persistent identifier (PID) for Data Management Plans (DMPs). We already have PIDs for many entities, such as articles, datasets etc. (DOIs), people (such as ORCID iDs) and places (such as ROR IDs). We knew that it would be important for DataCite to support the community in establishing a unique persistent identifier for DMPs. Until now, we had no PID for the document that “describes data that will be acquired or produced during research; how the data will be managed, described, and stored, what standards you will use, and how data will be handled and protected during and after the completion of the project”. There was no such thing as a DMP-ID; and today that changes….”

Research Data Management

“The Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council (NSERC) and the Social Sciences and Humanities Research Council (SSHRC) (the agencies) are pleased to announce the launch of the Tri-Agency Research Data Management Policy. The agencies would like to thank the stakeholders and partners who contributed to the policy’s development….

The policy includes requirements related to institutional research data management (RDM) strategies, data management plans (DMPs), and data deposit. It is aligned with the data deposit requirement in the Tri-Agency Open Access Policy on Publications (2015), CIHR’s Health Research and Health-Related Data Framework (2017), the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans—TCPS 2 (2018), and the agencies’ Setting new directions to support Indigenous research and research training in Canada 2019-2022 (2019)….”

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….”


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. …”