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==Introduction==
==Introduction==
Research data management (RDM), a term that encompasses activities related to the storage, organization, documentation, and dissemination of data{{efn|For the purposes of this report we are using the term “data” broadly to refer to the inputs or outputs required to evaluate, reproduce, or built upon the analyses or conclusions of a given research project. This includes, but is not limited to, raw data, processed data, research-related code, and documentation pertaining to study parameters and procedures.}}, is central to efforts aimed at maximizing the value of scientific investment (e.g., the Holdren memorandum<ref name="HoldrenIncreasing13">{{cite web |url=https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf |title=Increasing Access to the Results of Federally Funded Scientific Research |author=Holdren, J.P. |publisher=Office of Science and Technology Policy |date=22 February 2013}}</ref>) and addressing concerns related to the integrity of the research process (e.g., Collins and Tabak's discussion on reproducibility<ref name="CollinsPolicy14">{{cite journal |title=Policy: NIH plans to enhance reproducibility |journal=Nature |author=Collins, F.S.; Tabak, L.A. |volume=505 |issue=7485 |pages=612–13 |year=2014 |doi=10.1038/505612a}}</ref>). Unfortunately, when surveyed directly, researchers often acknowledge that they lack the skills and experience needed to manage and share their data effectively.<ref name="BaroneUnmet17">{{cite journal |title=Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators |journal=PLOS Computational Biology |author=Barone, L.; Williams, J.; Micklos, D. |volume=13 |issue=11 |pages=e1005858 |year=2017 |doi=10.1371/journal.pcbi.1005755 |pmid=29049281 |pmc=PMC5654259}}</ref><ref name="FedererBiomedical15">{{cite journal |title=Biomedical Data Sharing and Reuse: Attitudes and Practices of Clinical and Scientific Research Staff |journal=PLoS One |author=Federer, L.M.; Lu, Y.L.; Joubert, D.J. et al. |volume=10 |issue=6 |pages=e0129506 |year=2015 |doi=10.1371/journal.pone.0129506 |pmid=26107811  |pmc=PMC4481309}}</ref><ref name="TenopirResearch14">{{cite journal |title=Research data management services in academic research libraries and perceptions of librarians |journal=Library & Information Science Research |author=Tenopir, C.; Sandusky, R.J.; Allard, S.; Birch, B. |volume=36 |issue=2 |pages=84–90 |year=2014 |doi=10.1016/j.lisr.2013.11.003}}</ref> This disconnect demonstrates the need for tools that bridge the communication gap that exists between the research community, data service providers, and other local, national, and international data stakeholder groups. The development of one such tool, which we are tentatively referring to as “Support Your Data,” is the subject of this project report.
Research data management (RDM), a term that encompasses activities related to the storage, organization, documentation, and dissemination of data{{efn|For the purposes of this report we are using the term “data” broadly to refer to the inputs or outputs required to evaluate, reproduce, or built upon the analyses or conclusions of a given research project. This includes, but is not limited to, raw data, processed data, research-related code, and documentation pertaining to study parameters and procedures.}}, is central to efforts aimed at maximizing the value of scientific investment (e.g., the Holdren memorandum<ref name="HoldrenIncreasing13">{{cite web |url=https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf |title=Increasing Access to the Results of Federally Funded Scientific Research |author=Holdren, J.P. |publisher=Office of Science and Technology Policy |date=22 February 2013}}</ref>) and addressing concerns related to the integrity of the research process (e.g., Collins and Tabak's discussion on reproducibility<ref name="CollinsPolicy14">{{cite journal |title=Policy: NIH plans to enhance reproducibility |journal=Nature |author=Collins, F.S.; Tabak, L.A. |volume=505 |issue=7485 |pages=612–13 |year=2014 |doi=10.1038/505612a}}</ref>). Unfortunately, when surveyed directly, researchers often acknowledge that they lack the skills and experience needed to manage and share their data effectively.<ref name="BaroneUnmet17">{{cite journal |title=Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators |journal=PLOS Computational Biology |author=Barone, L.; Williams, J.; Micklos, D. |volume=13 |issue=11 |pages=e1005858 |year=2017 |doi=10.1371/journal.pcbi.1005755 |pmid=29049281 |pmc=PMC5654259}}</ref><ref name="FedererBiomedical15">{{cite journal |title=Biomedical Data Sharing and Reuse: Attitudes and Practices of Clinical and Scientific Research Staff |journal=PLoS One |author=Federer, L.M.; Lu, Y.L.; Joubert, D.J. et al. |volume=10 |issue=6 |pages=e0129506 |year=2015 |doi=10.1371/journal.pone.0129506 |pmid=26107811  |pmc=PMC4481309}}</ref><ref name="TenopirResearch14">{{cite journal |title=Research data management services in academic research libraries and perceptions of librarians |journal=Library & Information Science Research |author=Tenopir, C.; Sandusky, R.J.; Allard, S.; Birch, B. |volume=36 |issue=2 |pages=84–90 |year=2014 |doi=10.1016/j.lisr.2013.11.003}}</ref> This disconnect demonstrates the need for tools that bridge the communication gap that exists between the research community, data service providers, and other local, national, and international data stakeholder groups. The development of one such tool, which we are tentatively referring to as “Support Your Data,” is the subject of this project report.
As demonstrated by visualizations such as the research data lifecycle<ref name="CarlsonResearch14">{{cite book |chapter=The use of lifecycle models in developing and supporting data services |title=Research Data Management: Practical Strategies for Information Professionals |author=Carlson, J. |editor=Ray, J.M. |publisher=Purdue University Press |year=2014 |isbn=9781557536648}}</ref><ref name="CoxACritical18">{{cite journal |title=A critical analysis of lifecycle models of the research process and research data management |journal=Aslib Journal of Information Management |author=Cox, A.M.; Tam, W.W.T. |volume=70 |issue=2 |pages=142-57 |doi=10.1108/AJIM-11-2017-0251}}</ref>, RDM is continuous, iterative, and embedded throughout the course of a research project. Well thought out RDM practices make the research process more efficient, facilitate collaboration, and help prevent the loss of data (see Lowndes ''et al.'' 2017<ref name="LowndesOurPath17">{{cite journal |title=Our path to better science in less time using open data science tools |journal=Nature Ecology and Evolution |author=Lowndes, J.S.S.; Best, B.D.; Scarborough, C. et al. |volume=1 |page=0160 |year=2017 |doi=10.1038/s41559-017-0160}}</ref>). Effective RDM is also crucial to establishing the accessibility of data after a project’s conclusion, which is increasingly required by data stakeholders such as research funding agencies and scholarly publishers. Steps must be taken early in the research process to ensure that data can be shared later. For example, the sharing of data from human participants must be approved by an institutional review board (IRB) and described in informed consent documents before any data is collected.<ref name="MeyerPractical18">{{cite journal |title=Practical Tips for Ethical Data Sharing |journal=
Advances in Methods and Practices in Psychological Science |author=Meyer, M.N. |volume=1 |issue=1 |page=131-144 |year=2018 |doi=10.1177/2515245917747656}}</ref> More generally, data that are made available are only useful if formatted, documented, and organized in a manner that enables examination and reuse by others. Related guidance (e.g., from Goodman ''et al.''<ref name="GoodmanTen14">{{cite journal |title=Ten Simple Rules for the Care and Feeding of Scientific Data |journal=PLoS Computational Biology |author=Goodman, A.; Pepe, A.; Blocker, A.W. et al. |volume=10 |issue=4 |page=e1003542 |doi=10.1371/journal.pcbi.1003542}}</ref>) and standards (e.g., FAIR Guiding Principles<ref name="WilkinsonTheFAIR16">{{cite journal |title=The FAIR Guiding Principles for scientific data management and stewardship |journal=Scientific Data |author=Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J. et al. |volume=3 |pages=160018 |year=2016 |doi=10.1038/sdata.2016.18 |pmid=26978244 |pmc=PMC4792175}}</ref>) highlight that proper data management is a key factor in enabling effective data sharing, which is itself a key factor in establishing research transparency and reproducibility.


==Footnotes==
==Footnotes==

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Full article title Support your data: A research data management guide for researchers
Journal Research Ideas and Outcomes
Author(s) Borghi, John A.; Abrams, Stephen; Lowenberg, Daniella; Simms, Stephanie; Chodacki, John
Author affiliation(s) University of California Curation Center
Primary contact Email: john dot borghi at ucop dot edu
Year published 2018
Volume and issue 4
Page(s) e26439
DOI 10.3897/rio.4.e26439
ISSN 2367-7163
Distribution license Creative Commons Attribution 4.0 International
Website https://riojournal.com/articles.php?id=26439
Download https://riojournal.com/article/26439/download/pdf/ (PDF)
This article should not be considered complete until this message box has been removed. This is a work in progress.

Abstract

Researchers are faced with rapidly evolving expectations about how they should manage and share their data, code, and other research materials. To help them meet these expectations and generally manage and share their data more effectively, we are developing a suite of tools which we are currently referring to as "Support Your Data." These tools— which include a rubric designed to enable researchers to self-assess their current data management practices and a series of short guides which provide actionable information about how to advance practices as necessary or desired—are intended to be easily customizable to meet the needs of researchers working in a variety of institutional and disciplinary contexts.

Keywords: research data management, RDM, data sharing, open data, open science

Introduction

Research data management (RDM), a term that encompasses activities related to the storage, organization, documentation, and dissemination of data[a], is central to efforts aimed at maximizing the value of scientific investment (e.g., the Holdren memorandum[1]) and addressing concerns related to the integrity of the research process (e.g., Collins and Tabak's discussion on reproducibility[2]). Unfortunately, when surveyed directly, researchers often acknowledge that they lack the skills and experience needed to manage and share their data effectively.[3][4][5] This disconnect demonstrates the need for tools that bridge the communication gap that exists between the research community, data service providers, and other local, national, and international data stakeholder groups. The development of one such tool, which we are tentatively referring to as “Support Your Data,” is the subject of this project report.

As demonstrated by visualizations such as the research data lifecycle[6][7], RDM is continuous, iterative, and embedded throughout the course of a research project. Well thought out RDM practices make the research process more efficient, facilitate collaboration, and help prevent the loss of data (see Lowndes et al. 2017[8]). Effective RDM is also crucial to establishing the accessibility of data after a project’s conclusion, which is increasingly required by data stakeholders such as research funding agencies and scholarly publishers. Steps must be taken early in the research process to ensure that data can be shared later. For example, the sharing of data from human participants must be approved by an institutional review board (IRB) and described in informed consent documents before any data is collected.[9] More generally, data that are made available are only useful if formatted, documented, and organized in a manner that enables examination and reuse by others. Related guidance (e.g., from Goodman et al.[10]) and standards (e.g., FAIR Guiding Principles[11]) highlight that proper data management is a key factor in enabling effective data sharing, which is itself a key factor in establishing research transparency and reproducibility.

Footnotes

  1. ↑ For the purposes of this report we are using the term “data” broadly to refer to the inputs or outputs required to evaluate, reproduce, or built upon the analyses or conclusions of a given research project. This includes, but is not limited to, raw data, processed data, research-related code, and documentation pertaining to study parameters and procedures.

References

  1. ↑ Holdren, J.P. (22 February 2013). "Increasing Access to the Results of Federally Funded Scientific Research". Office of Science and Technology Policy. https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf. 
  2. ↑ Collins, F.S.; Tabak, L.A. (2014). "Policy: NIH plans to enhance reproducibility". Nature 505 (7485): 612–13. doi:10.1038/505612a. 
  3. ↑ Barone, L.; Williams, J.; Micklos, D. (2017). "Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators". PLOS Computational Biology 13 (11): e1005858. doi:10.1371/journal.pcbi.1005755. PMC PMC5654259. PMID 29049281. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5654259. 
  4. ↑ Federer, L.M.; Lu, Y.L.; Joubert, D.J. et al. (2015). "Biomedical Data Sharing and Reuse: Attitudes and Practices of Clinical and Scientific Research Staff". PLoS One 10 (6): e0129506. doi:10.1371/journal.pone.0129506. PMC PMC4481309. PMID 26107811. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4481309. 
  5. ↑ Tenopir, C.; Sandusky, R.J.; Allard, S.; Birch, B. (2014). "Research data management services in academic research libraries and perceptions of librarians". Library & Information Science Research 36 (2): 84–90. doi:10.1016/j.lisr.2013.11.003. 
  6. ↑ Carlson, J. (2014). "The use of lifecycle models in developing and supporting data services". In Ray, J.M.. Research Data Management: Practical Strategies for Information Professionals. Purdue University Press. ISBN 9781557536648. 
  7. ↑ Cox, A.M.; Tam, W.W.T.. "A critical analysis of lifecycle models of the research process and research data management". Aslib Journal of Information Management 70 (2): 142-57. doi:10.1108/AJIM-11-2017-0251. 
  8. ↑ Lowndes, J.S.S.; Best, B.D.; Scarborough, C. et al. (2017). "Our path to better science in less time using open data science tools". Nature Ecology and Evolution 1: 0160. doi:10.1038/s41559-017-0160. 
  9. ↑ Meyer, M.N. (2018). "Practical Tips for Ethical Data Sharing". Advances in Methods and Practices in Psychological Science 1 (1): 131-144. doi:10.1177/2515245917747656. 
  10. ↑ Goodman, A.; Pepe, A.; Blocker, A.W. et al.. "Ten Simple Rules for the Care and Feeding of Scientific Data". PLoS Computational Biology 10 (4): e1003542. doi:10.1371/journal.pcbi.1003542. 
  11. ↑ Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J. et al. (2016). "The FAIR Guiding Principles for scientific data management and stewardship". Scientific Data 3: 160018. doi:10.1038/sdata.2016.18. PMC PMC4792175. PMID 26978244. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. Footnotes were originally numbered but have been converted to lowercase alpha for this version. The original article lists references alphabetically, but this version—by design—lists them in order of appearance.

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