Ten simple rules for maximizing the recommendations of the NIH data management and sharing plan

Gonzales, Sara; Carson, Matthew B.; Holmes, Kristi L.

doi:10.1371/journal.pcbi.1010397

Cited by 13 publications

(13 citation statements)

References 9 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…9 Since the system is designed to work with any research output type and can also be integrated with third‐party tools such as GitHub 10 and Jupyter Notebooks, preservation of NMEDW research reports is an ideal use case for this system. This work is part of our orchestrated efforts to enrich the sociotechnical environment at Northwestern through projects to improve communication and training for the clinical and translational workforce, 11 , 12 properly attribute contributors of research outputs, enhance data practice and security workflows, and incorporate standards and persistent identifiers throughout. Some cRDM workshops (notably “Making Your Data FAIR” and “Sharing and Preserving Biomedical Data”) incorporate InvenioRDM to introduce best practices in data sharing.…”

Section: Resultsmentioning

confidence: 99%

Bridging the gap: A library‐based collaboration to enhance data skills for clinical researchers

Carson

Gonzales

Shaw

et al. 2022

Learning Health Systems

Self Cite

View full text Add to dashboard Cite

Introduction Enterprise data warehouses (EDWs) serve as foundational infrastructure in a modern learning health system, housing clinical and other system‐wide data and making it available for research, strategic, and quality improvement purposes. Building on a longstanding partnership between Northwestern University's Galter Health Sciences Library and the Northwestern Medicine Enterprise Data Warehouse (NMEDW), an end‐to‐end clinical research data management (cRDM) program was created to enhance clinical data workforce capacity and further expand related library‐based services for the campus. Methods The training program covers topics such as clinical database architecture, clinical coding standards, and translation of research questions into queries for proper data extraction. Here we describe this program, including partners and motivations, technical and social components, integration of FAIR principles into clinical data research workflows, and the long‐term implications for this work to serve as a blueprint of best practice workflows for clinical research to support library and EDW partnerships at other institutions. Results This training program has enhanced the partnership between our institution's health sciences library and clinical data warehouse to provide support services for researchers, resulting in more efficient training workflows. Through instruction on best practices for preserving and sharing outputs, researchers are given the tools to improve the reproducibility and reusability of their work, which has positive effects for the researchers as well as for the university. All training resources have been made publicly available so that those who support this critical need at other institutions can build on our efforts. Conclusions Library‐based partnerships to support training and consultation offer an important vehicle for clinical data science capacity building in learning health systems. The cRDM program launched by Galter Library and the NMEDW is an example of this type of partnership and builds on a strong foundation of past collaboration, expanding the scope of clinical data support services and training on campus.

show abstract

Section: Resultsmentioning

confidence: 99%

Bridging the gap: A library‐based collaboration to enhance data skills for clinical researchers

Carson

Gonzales

Shaw

et al. 2022

Learning Health Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…access to data are nuanced rather than binary [2], it is important for Korean government research institutes to provide ongoing training and support for affiliated researchers so that they can make informed decisions and adequate justifications if restricted data sharing is needed. In addition, developing an appropriate infrastructure that enables researchers to share and preserve their data is necessary, and funding agencies and journals strongly recommend depositing data into robust repositories [4,5]. Therefore, research institutes should make commitments to implement data repositories and encourage the submission of DMPs to build good DMS practices.…”

Section: Discussionmentioning

confidence: 99%

“…The US National Institute of Health (NIH) released a new Data Management and Sharing (DMS) policy in October 2020, effective as of January 25, 2023, which requires all applicants to submit DMS plans if the proposed research generates scientific data. Similar to the principle of ORD, the new NIH policy intends to "encourage data sharing to the extent that it is possible" [4]. In addition, publishers and individual journals influence researchers' data sharing behavior by establishing data sharing policies that encourage or require making data available along with the publication of research articles [5,6].…”

Section: Background/rationalementioning

confidence: 99%

Data sharing attitudes and practices of researchers in Korean government research institutes: a survey-based descriptive study

Kim¹,

Hwang²,

Jung³

et al. 2023

Sci Ed

View full text Add to dashboard Cite

Purpose: This study explored to what extent and how researchers in five Korean government research institutes that implement research data management practices share their research data and investigated the challenges they perceive regarding data sharing.Methods: The study collected survey data from 224 respondents by posting a link to a SurveyMonkey questionnaire on the homepage of each of the five research institutes from June 15 to 29, 2022. Descriptive statistical analyses were conducted.Results: Among 148 respondents with data sharing experience, the majority had shared some or most of their data. Restricted data sharing within a project was more common than sharing data with outside researchers on request or making data publicly available. Sharing data directly with researchers who asked was the most common method of data sharing, while sharing data via institutional repositories was the second most common method. The most frequently cited factors impeding data sharing included the time and effort required to organize data, concerns about copyright or ownership of data, lack of recognition and reward, and concerns about data containing sensitive information.Conclusion: Researchers need ongoing training and support on making decisions about access to data, which are nuanced rather than binary. Research institutes’ commitment to developing and maintaining institutional data repositories is also important to facilitate data sharing. To address barriers to data sharing, it is necessary to implement research data management services that help reduce effort and mitigate concerns about legal issues. Possible incentives for researchers who share data should also continue to be explored.

show abstract

“…Advocacy and support for data sharing are often discussed with an emphasis on understanding and supporting the practices of individual investigators or scientific communities [ 2 , 3 ]. However, a researcher’s ability to successfully engage in and benefit from sound data sharing depends on their organizational setting and, specifically, the organization’s data sharing capacity.…”

Section: Introductionmentioning

confidence: 99%

Ten simple rules for organizations to support research data sharing

et al. 2023

Self Cite

View full text Add to dashboard Cite

Ten simple rules for maximizing the recommendations of the NIH data management and sharing plan

Cited by 13 publications

References 9 publications

Bridging the gap: A library‐based collaboration to enhance data skills for clinical researchers

Bridging the gap: A library‐based collaboration to enhance data skills for clinical researchers

Data sharing attitudes and practices of researchers in Korean government research institutes: a survey-based descriptive study

Ten simple rules for organizations to support research data sharing

Contact Info

Product

Resources

About