Abstract: The Horizon Europe project ISIDORe is dedicated to pandemic preparedness and responsiveness research. It brings together 17 research infrastructures (RIs) and networks to provide a broad range of services to infectious disease researchers. An efficient and structured treatment of data is central to ISIDORe’s aim to furnish seamless access to its multidisciplinary catalogue of services, and to ensure that users’ results are treated FAIRly. ISIDORe therefore requires a data management plan (DMP) covering both access management and research outputs, applicable over a broad range of disciplines, and compatible with the constraints and existing practices of its diverse partners. Here, we describe how, to achieve that aim, we undertook an iterative, step-by-step, process to build a community-approved living document, identifying good practices and processes, on the basis of use cases, presented as proof of concepts. International fora such as the RDA and EOSC, and primarily the BY-COVID project, furnished registries, tools and online data platforms, as well as standards, and the support of data scientists. Together, these elements provide a path for building an umbrella, FAIR-compliant DMP, aligned as fully as possible with FAIR principles, which could also be applied as a framework for data management harmonisation in other large-scale, challenge-driven projects. Finally, we discuss how data management and reuse can be further improved through the use of knowledge models when writing DMPs and, how, in the future, an inter-RI network of data stewards could contribute to the establishment of a community of practice, to be integrated subsequently into planned trans-RI competence centres.
“Most funders expect to see a data management plan when you apply for a grant. Major funders specify templates for the plan but, where there isn’t a template or you are not applying for external funding, there are general principles to follow….”
“To make data sharing easier and to establish a clear baseline for what well-considered data-sharing policies should encompass, we recommend that funders:
1. Clearly specify which data grantees are required to share. Do you want grantees to share only data underlying published studies or all data generated during the funded project? Do you want raw or pre-processed data? If qualitative (not just quantitative) data are also covered by your policy, do you provide guidance for grantees on good practices for sharing qualitative data?
2. Consider incorporating code- and software-sharing requirements as a necessary extension of their data-sharing policies. To be able to reproduce results accurately and build upon shared data, researchers must not only have access to the files but also the code and software used to open and analyze data. Only then are data truly findable, accessible, interoperable, and reusable. The ORFG and the Higher Education Leadership Initiative for Open Scholarship (HELIOS) have prepared a more detailed brief.
3. Clearly specify the required timing of data sharing. The timing will vary based on what data are to be shared and what constitutes the event that triggers the sharing requirement. If data underlie a published study, complying or aligning with new federal policies will require data to be shared immediately at the time of publication. If, however, the policy requires sharing of all data, then the timing may be tied to the award period (as the NIH requires).
4. Require grantees to deposit data in trusted public repositories that assign a persistent identifier (e.g., DOI), provide the necessary infrastructure to host and export quality metadata, implement strategies for long-term preservation, and otherwise meet the National Science and Technology Council’s Desirable Characteristics of Data Repositories. To make compliance easier for grantees, funders should provide a list of approved data repositories that meet these characteristics and are appropriate for the disciplines they fund.
5. Require grantees to share data under licenses that facilitate reuse. The recommended free culture license for data is the Creative Commons Public Domain Dedication (CC0). The reasoning behind this is two-fold: first, data do not always incur copyright and, therefore, reserving certain rights under other licenses may be inappropriate, and second, we should avoid attribution or license stacking that may occur as datasets are remixed and reused. Other options include the Creative Commons Attribution (CC BY) or ShareAlike (CC BY-SA) licenses.
6. Strongly encourage grantees to share data according to established best practices. These include, but are not limited to: a) the FAIR Principles, which outline how to share data so they are Findable, Accessible, Interoperable, and Reusable; b) the CARE Principles for Indigenous Data Governance, which emphasize the importance of Collective Benefit, Authority to Control, Responsibility, and Ethics in the context of Indigenous data, but could also inform the responsible management and sharing of data for other populations; and c) privacy rules, such as those provided under HIPAA. Funders should communicate that it is the responsibility of grantees to get the appropriate consent and ethical approval (e.g., from their institutional review board) that will allow them to collect and subsequently openly share de-identified data.
7. Allow grantees to include data sharing costs in their grant budgets. This could include costs associated with data management, curation, hosting, and long-term preservation. For many projects, data hosting costs will likely be minimal—several public repositories allow researchers to store significant amounts of data for free. For projects that will generate larger amounts of data, additional hosting costs can be budgeted. The most important cost may be the personnel time and expertise required to properly prepare data for sharing and reuse. Funders should consider increasing the allowable personnel costs to secure extra curation time for team
“Everything you need to know to make your research data open and FAIR.”
“Beginning with the first funding deadlines in January, all NIH grant proposals will be required to include a formal, two-page Data Management and Sharing Plan (DMSP), which must include the following elements….
Crucially, in addition to adding a required DMSP, the data management strategies stated in the plan will be audited and monitored externally, and compliance with stated plans may affect the funding status of grants.
Fortunately, here at Harvard affiliates have access to a variety of computing infrastructure and systems to effectively manage and steward a wide range of research outputs associated with modern, data-driven, computational research.
Harvard’s libraries, Harvard University Information Technology (HUIT), Research Computing, and Sponsored Programs offices have all been adding services and building capacity to support researchers complying with this new policy next year.
In the resources section below, we’ve included links to an executive summary of the policy and a collection of FAQs that we created specifically for Harvard users. We’ve also included resources from the NIH designed to support researchers writing and implementing a DMSP for the 2023 funding cycles.
Along with the requirement to make research data publicly available, in its new policy the NIH strongly encourages the use of established data repositories. When selecting an appropriate repository, researchers should plan to utilize subject- or domain-specific repositories for their data types if possible. When a disciplinary repository does not exist, researchers should use generalist repositories that accept all data types. We’ve included information on Harvard Dataverse and other generalist repositories in the resources section below….”
Abstract: Data management plans (DMPs) are an essential part of planning data-driven research projects and ensuring long-term access and use of research data and digital objects; however, as text-based documents, DMPs must be analyzed manually for conformance to funder requirements. This study presents a comparison of DMPs evaluations for 21 funded projects using 1) an automated means of analysis to identify elements that align with best practices in support of open research initiatives and 2) a manually-applied scorecard measuring these same elements. The automated analysis revealed that terms related to availability (90% of DMPs), metadata (86% of DMPs), and sharing (81% of DMPs) were reliably supplied. Manual analysis revealed 86% (n = 18) of funded DMPs were adequate, with strong discussions of data management personnel (average score: 2 out of 2), data sharing (average score 1.83 out of 2), and limitations to data sharing (average score: 1.65 out of 2). This study reveals that the automated approach to DMP assessment yields less granular yet similar results to manual assessments of the DMPs that are more efficiently produced. Additional observations and recommendations are also presented to make data management planning exercises and automated analysis even more useful going forward.
“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 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….”
“In Norway, the proportion of research being published openly has increased considerably in the past ten years. While less than 40% of Norwegian research articles were published openly in 2013, in 2021 that proportion had increased to around 75%, according to the OA barometer from the service provider, Sikt.
Sharing data is not quite as common….
Wenaas and Gulbrandsen also believe that data sharing is a question of culture. It is new to many, for others it may have been the practice for a long time….”
Research data management (RDM) has been called a “ground-breaking” area for research libraries and it is among the top future trends for academic libraries. Hence, this study aims to systematically review RDM practices and services primarily focusing on the challenges, services and skills along with motivational factors associated with it.
A systematic literature review method was used focusing on literature produced between 2016–2020 to understand the latest trends. An extensive research strategy was framed and 15,206 results appeared. Finally, 19 studies have fulfilled the criteria to be included in the study following preferred reporting items for systematic reviews and meta-analysis.
RDM is gradually gaining importance among researchers and academic libraries; however, it is still poorly practiced by researchers and academic libraries. Albeit, it is better observed in developed countries over developing countries, however, there are lots of challenges associated with RDM practices by researchers and services by libraries. These challenges demand certain sets of skills to be developed for better practices and services. An active collaboration is required among stakeholders and university services departments to figure out the challenges and issues.
The implications of policy and practical point-of-view present how research data can be better managed in the future by researchers and library professionals. The expected/desired role of key stockholders in this regard is also highlighted.
RDM is an important and emerging area. Researchers and Library and Information Science professionals are not comprehensively managing research data as it involves complex cooperation among various stakeholders. A combination of measures is required to better manage research data that would ultimately move forward for open access publishing.
“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 Association of Research Libraries (ARL), Association of American Medical Colleges (AAMC), and Association of Academic Health Sciences Libraries (AAHSL) have released a new report, Institutional Strategies for the NIH Data Management and Sharing Policy. The report shares infrastructure, services, and policies that institutions have developed to meet the requirements of the forthcoming US National Institutes of Health (NIH) policy.
In addition to the report, the site aamc.org/nihdatasharing will be a continually updated resource that contains links to ongoing institutional efforts and other relevant initiatives.”
Abstract: The National Institutes of Health (NIH) Policy for Data Management and Sharing (DMS Policy) recognizes the NIH’s role as a key steward of United States biomedical research and information and seeks to enhance that stewardship through systematic recommendations for the preservation and sharing of research data generated by funded projects. The policy is effective as of January 2023. The recommendations include a requirement for the submission of a Data Management and Sharing Plan (DMSP) with funding applications, and while no strict template was provided, the NIH has released supplemental draft guidance on elements to consider when developing a plan. This article provides 10 key recommendations for creating a DMSP that is both maximally compliant and effective.
“The draft of the Presidential Policy on University of California Research Data is now open for a second round of systemwide review. The purposes of the policy are to 1) clarify ownership of and responsibility for research data generated during the course of University Research, 2) encourage active data management practices, and 3) provide guidance with respect to procedures when a researcher leaves the University.
Ownership of research data by the UC Regents is a long-standing precept originally articulated in Regulation 4 (Academic Personnel Manual 020), where it states “Notebooks and other original records of the research are the property of the University.” Not since Regulation 4’s issuance in 1958, however, has any other systemwide UC policy provided further information on this stance. To provide more guidance to the UC community, the Research Policy and Analysis (RPAC) unit within Academic Affairs at the Office of the President began work in 2017 on a draft research data policy document, originally consulting with a small advisory group of representatives from UC San Diego, UCLA, UC Berkeley, the Office of General Counsel, and California Digital Library. …”
“In January 2023, the US National Institutes of Health (NIH) will begin requiring most of the 300,000 researchers and 2,500 institutions it funds annually to include a data-management plan in their grant applications — and to eventually make their data publicly available.
Researchers who spoke to Nature largely applaud the open-science principles underlying the policy — and the global example it sets. But some have concerns about the logistical challenges that researchers and their institutions will face in complying with it. Namely, they worry that the policy might exacerbate existing inequities in the science-funding landscape and could be a burden for early-career scientists, who do the lion’s share of data collection and are already stretched thin….
Such a seismic shift in practice has left some researchers worried about the amount of work that the mandate will require when it becomes effective….
Others worry that data-management activities will further sap funds from under-resourced labs. Although the policy outlines certain fees that researchers can add to their proposed budgets to offset the costs of compliance with the mandate, it doesn’t specify what criteria the NIH will use to grant these requests….
Despite its potential pitfalls, Ross thinks that the policy will have a ripple effect that will persuade smaller funding agencies and industry to adopt similar changes. “This policy establishes what people expect from clinical research,” he says. “It’s essentially saying the culture of research needs to change.” ”