Data sharing: what do we know and where can we go?

“OASPA is pleased to announce our next webinar which will focus on the what about and the why of data sharing.

The recent OSTP “Nelson memo” served as a re-focus on data as a first class research output. But maybe that’s a misrepresentation for those of us who think ‘hold on, we’ve been focused on data this whole time!?’ Well here’s a chance to learn from and with a group of experts who are thinking carefully about data sharing: what that means from different perspectives, tangible steps to take and policies to make around data, and what we can do next in our communities of practice. Attendees are more than welcome to bring their own perspectives! The webinar will be chaired by Rachael Lammey. We welcome our panelists: Sarah Lippincott will give a repository perspective with insights into where data is going post Nelson Memo and NIH Policy. Aravind Venkatesan will share the thinking, data science and workflows employed at EuropePMC to support data linking. Shelley Stall will talk about how AGU are leading the line with their data policies, and Kathleen Gregory will conclude by considering researchers’ perspectives regarding sharing and reusing data.”

Reminder: NIH Policy for Data Management and Sharing effective on January 25, 2023.

“The purpose of this notice is to remind the community of the effective date of the NIH Policy for Data Management and Sharing (DMS Policy) and summarize available key resources.

As noted in the Final NIH Policy for Data Management and Sharing (NOT-OD-21-013), the effective date of the DMS Policy is January 25, 2023 for competing grant applications submitted to NIH for the January 25, 2023 and subsequent receipt dates; proposals for contracts  submitted to NIH on or after January 25, 2023; NIH Intramural Research Projects conducted on or after January 25, 2023; and other funding agreements (e.g., Other Transactions)  executed on or after January 25, 2023, unless otherwise stipulated by NIH.

The DMS Policy applies to all NIH research, funded or conducted in whole or in part by NIH, that results in the generation of scientific data. Note that the DMS Policy does not apply to research and other activities that do not generate scientific data, for example: research training, fellowships, infrastructure development, and non-research activities. See Research Covered Under the Data Management & Sharing Policy for more details.

The DMS Policy has two basic requirements:

Submission of a Data Management and Sharing (DMS) Plan outlining how scientific data and any accompanying metadata will be managed and shared, considering any potential restrictions or limitations. 
Compliance with the Plan approved by the funding NIH Institute, Center, or Office.

DMS Plans should describe how data will be managed and appropriately shared. See Writing a Data Management & Sharing Plan for details, sample Plans, and an optional format page which includes six elements recommended to be included in a Data Management and Sharing Plan. Guidance on planning and budgeting and selecting a data repository are available on the NIH Scientific Data Sharing website. Application Guide instructions have been updated to provide instructions for DMS policy implementation.

Ultimately, the new DMS Policy promotes transparency and accountability in research by setting a minimum set of expectations for data management and sharing. This means that other NIH policies or NIH Institutes, Centers, Offices, or programs may build upon these expectations, for instance, by specifying scientific data to share, relevant standards, repository timelines, and/or shorter data sharing timelines for meeting programmatic needs, the DMS Policy sets a consistent baseline across NIH.

In preparing for DMS Policy implementation, NIH has developed a number of helpful resources that we encourage investigators and institutions to review:

DMS Policy Overview
DMS Policy FAQs
Learning Resources including 2-part webinar series on DMS Policy
Statements and Guide Notices …”

Canadian policy: Data management requirement takes effect in March

“Canadian institutions are preparing for a research data management policy developed by three major federal granting agencies to go into effect this March. The policy of the Tri-Agency Council, comprising the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Social Sciences and Humanities Research Council of Canada (SSHRC), asserts that “research data collected through the use of public funds should be responsibly and securely managed and be, where ethical, legal and commercial obligations allow, available for reuse by others.” Dryad would be pleased to assist any Canadian institution seeking a solution to help support their affiliated researchers with this policy….”

NIH’s new data sharing policy is coming, and it’s a ‘big cultural shift’ | News | Chemistry World

“Biochemists and other researchers who apply for funding from the US National Institutes of Health (NIH) will have to include comprehensive data management and sharing plans in grants from 25 January. These will be formal strategies for managing, preserving and sharing scientific data, as well as the accompanying metadata.

The new rule, which is generating some concern within the research community, replaces the NIH’s existing data sharing policy that has been around since 2003, and applies to only those seeking at least $500,000 (£419,200) in direct costs from the agency in any given year. The original regulation required researchers to submit a plan that describes how they will share the underlying data, or if they cannot share it then why not.

By contrast, the latest policy affects all NIH grants, regardless of specific budget. It will apply to competing grant applications, proposals for contracts and other funding agreements submitted to the NIH on or after 25 January.

The agency will now mandate that researchers describe their strategy to share scientific data needed to ‘validate and replicate’ their research findings, whether or not the data is used to support scholarly publications….”

Realities of Academic Data Sharing (RADS) Initiative: Research Update #2—Activities for Making Research Data Publicly Accessible – Association of Research Libraries

“Public access to research data is critical to advancing science and solving real world problems. The Realities of Academic Data Sharing (RADS) Initiative project team has spent considerable time this year developing surveys designed for campus administrators and funded researchers, inquiring into public-access research data management and sharing (DMS) activities and their costs. Public-access DMS activities are often distributed across institutional departments and units and, as such, the expenditures to support these activities are rarely captured holistically within one institution and may not even be captured at the unit or department level. The goal of surveying campus administrators and funded researchers is twofold: to determine where, within the institution, these activities are occurring, and to understand the costs to the institution to support them. Defining public-access DMS activities further provides a common framework for gathering expenses for the staffing, services, and infrastructure of these activities, which then provides a more comprehensive view of the overall cost of making research data publicly accessible.

Although the RADS studies focus on data management practices over the last decade, the project team recognizes that many of these activities may be helpful for those in the higher education community currently defining institutional processes for supporting public access to research data. Identifying necessary services, infrastructure, and staffing, and ways in which to categorize expenses and budgeting for open access to research data, is timely due to the 2023 NIH Data Management and Sharing Policy and the revised federal agency policies that will result from the 2022 OSTP Nelson memo.

The RADS project team and the Association of Research Libraries (ARL) have released a report, Public Access Data Management and Sharing Activities for Academic Administration and Researchers, that defines the data management and sharing activities used in our research. We hope that the research community will provide feedback around these activities, as this report presents version 1 of the RADS public access DMS activities. Additional versions will be released in response to community feedback and best practices as more institutions and agencies implement DMS policies in the coming year….”

NIH Data Management and Sharing Policy | University Libraries | Virginia Tech

“After January 25, 2023, all NIH grant applications or renewals that generate scientific data must include a robust and detailed plan for how researchers will manage and share data during the entire funded period. This includes information on data storage during a project, access policies/procedures, data preservation after a project is completed, metadata standards, and sharing approaches. Researchers must provide this information to NIH in a data management and sharing plan (DMSP) at the time of proposal submission. The DMSP is similar to what other funders call a data management plan (DMP).

The DMSP is assessed by NIH program staff as part of your grant application. Reviewers also have access to this document. The NIH Institute, Center or Office (ICO)-approved plan is important because it becomes a term and condition of award if you are awarded your grant.

This policy will supersede the NIH Data Sharing Policy of 2003, but will not supersede other NIH research sharing policies. Plans for sharing genomic data as expected by the Genomic Data Sharing (GDS) Policy are to be described in the DMSP submitted at the time of proposal submission, and not in a separate GDS Plan or at Just-in-Time….”

On International Open Access Week, IBEC is launching its virtual Open Science space – Institute for Bioengineering of Catalonia

“Taking advantage of the renewal of its website, IBEC has created a new virtual space dedicated to Open Science. This space is a public demonstration of IBEC’s commitment to Open Science, in accordance with its own values ??and mission, which has been realized with various initiatives and positions that the new virtual space gathers and makes visible. 

Due to its own conviction and due to the growth of the practical requirements of Open Science in the European, Spanish and Catalan research environments, the IBEC is articulating in recent years its alignment with this movement by including its principles in its own strategic plans, the approval in September 2021 of the research data management policy, the creation of an Open Science pillar with the Strategic Initiatives and Communication departments, and the incorporation of a new Knowledge Manager profile as support staff for researchers. 

These measures have made possible to carry out an internal training plan in aspects of Open Science; the improvement in the support and promotion of the publication in open access; the revision of the own research evaluation processes following the principles established in the DORA declaration; initiate the internal improvement process of research data management and facilitate its open publication, adopting the CSUC Research Data Repository (CORA), as an institutional repository; organize the didactic materials generated in the collaboration programs with the educational world, so that they are Open Educational Resources; development of citizen science projects and days for patients, or the reformulation of the Commission for Research Integrity which explicitly adds among its attributions monitoring the deployment of the Open Science strategy at IBEC; etc. …”

Institutional Strategies for the NIH Data Management and Sharing Policy: Infrastructure, Policies, and Services

“In the fall of 2020, the National Institutes of Health (NIH) released its new policy for data management and sharing that will go into effect in January 2023. This policy applies to all NIH-funded research and requires investigators to submit data management and sharing (DMS) plans.

As research data sharing has started to become an enforced requirement from funders and publishers, many academic institutions, libraries, and individual researchers have developed services, technology, and workflows to meet this requirement. As institutions gear up to meet what will be a greater demand for support among researchers on their campuses given the upcoming NIH DMS policy, identifying and sharing existing tactics and expected strategic opportunities for academic institutions is critical to meeting this demand.

The Association of Academic Health Science Libraries (AAHSL), the AAMC (Association of American Medical Colleges), and the Association of Research Libraries (ARL) conducted a mixed methods research project to identify and share these existing or proposed innovations for other institutions to reuse, build upon, or otherwise leverage to meet this upcoming NIH requirement….”

The State of Open Data Report 2022: Researchers need more support to assist with open data mandates – Digital Science

“Primary findings from this year’s report indicated that:

There is a growing trend in researchers being in favour of data being made openly available as common practice (4 out of every five researchers were in agreement with this), supported somewhat by now over 70% of respondents being required to follow a policy on data sharing.
However, researchers still cite a key need in helping them to share their data as being more training or information on policies for access, sharing and reuse (55%) as well as long-term storage and data management strategies (52%).
Credit and recognition were once again a key theme for researchers in sharing their data. Of those who had previously shared data, 66% had received some form of recognition for their efforts – most commonly via full citation in another article (41%) followed by co-authorship on a paper that had used the data.
Researchers are more inclined to share their research data where it can have an impact on citations (67%) and the visibility of their research (61%), rather than being motivated by public benefit or journal/publisher mandate (both 56%)….”

FASEB DataWorks! Salon

“Monthly conversation space for researchers to discuss issues related to data sharing and reuse. Visit for more information.

September 8: Digitizing your lab with e-notebooks and digital tools
October 6: Selecting a repository
November 10: Preparing for 2023 NIH Data Management and Sharing Policy
December 15: Budgeting for data management…”

Taking the pain out of data sharing

“All journals in the study required the authors to state whether they would share their data. But because sharing wasn’t a condition of publication, it’s unclear why the authors who did not intend to share their data didn’t simply say so. “Maybe they were giving socially acceptable answers,” Puljak says. “Probably, people don’t really think about what will happen when somebody actually asks for data.”

Tom Jefferson, an epidemiologist at the University of Oxford, UK, says authors should face consequences for making false data-availability statements. “The editors should take action, whether it’s a correction or retraction,” he says, adding that the excuse of no longer having the data to hand is like saying “the cat ate my filing cabinet”. But David Mellor, director of policy at the Center for Open Science (COS) in Charlottesville, Virginia, is not a fan of retraction. “It’s kind of a blunt instrument,” he says. Referring to the study’s findings, he notes, “there’s a possibility that the e-mail was simply not seen.”…”

No evidence that mandatory open data policies increase error correction | Nature Ecology & Evolution

Berberi, I., Roche, D.G. No evidence that mandatory open data policies increase error correction. Nat Ecol Evol (2022).


Abstract: Using a database of open data policies for 199 journals in ecology and evolution, we found no detectable link between data sharing requirements and article retractions or corrections. Despite the potential for open data to facilitate error detection, poorly archived datasets, the absence of open code and the stigma associated with correcting or retracting articles probably stymie error correction. Requiring code alongside data and destigmatizing error correction among authors and journal editors could increase the effectiveness of open data policies at helping science self-correct.


Neither carrots nor sticks? Challenges surrounding data sharing from the perspective of research funding agencies—A qualitative expert interview study | PLOS ONE

Abstract:  Background

Data Sharing is widely recognised as crucial for accelerating scientific research and improving its quality. However, data sharing is still not a common practice. Funding agencies tend to facilitate the sharing of research data by both providing incentives and requiring data sharing as part of their policies and conditions for awarding grants. The goal of our article is to answer the following question: What challenges do international funding agencies see when it comes to their own efforts to foster and implement data sharing through their policies?


We conducted a series of sixteen guideline-based expert interviews with representatives of leading international funding agencies. As contact persons for open science at their respective agencies, they offered their perspectives and experiences concerning their organisations’ data sharing policies. We performed a qualitative content analysis of the interviews and categorised the challenges perceived by funding agencies.


We identify and illustrate six challenges surrounding data sharing policies as perceived by leading funding agencies: The design of clear policies, monitoring of compliance, sanctions for non-compliance, incentives, support, and limitations for funders’ own capabilities. However, our interviews also show how funders approach potential solutions to overcome these challenges, for example by coordinating with other agencies or adjusting grant evaluation metrics to incentivise data sharing.

Discussion and conclusion

Our interviews point to existing flaws in funders’ data sharing policies, such as a lack of clarity, a lack of monitoring of funded researchers’ data sharing behaviour, and a lack of incentives. A number of agencies could suggest potential solutions but often struggle with the overall complexity of data sharing and the implementation of these measures. Funders cannot solve each challenge by themselves, but they can play an active role and lead joint efforts towards a culture of data sharing.