Data Management The ways a researcher collects, organizes, stores, and accesses data they collect for research. Creating a data management plan allows a researcher to know what data they will be collecting and how they will store and organize it during the research project. Planning
Data Sharing as an Ethical Imperative
In keeping with the beneficence principle of maximizing research benefits, there is a growing consensus that publicly sharing research is not only a more efficient and effective use of research funding, but also an ethical imperative. Data sharing requirements are becoming more common with funding agencies and, as outlined by the 2022 Office of Science and Technology Policy (OSTP) Memo’s (the “Nelson Memo”) requirement that all funding agencies have public access policies for research data in place by the end of 2025, may become mandatory for all federally funded research in the US. While the implementation details of the memo’s mandate are not presently clear, at a minimum it represents a significant expansion in data sharing compliance requirements.
Researchers will have to wait and see how individual agencies’ policies address the “potential restrictions or limitations on data access, use, and disclosure, including those defined in terms and conditions of funding agreement or award or that convey from a data use agreement or stipulations of an Institutional Review Board A group that is charged with overseeing and approving research projects. The group ensures that research projects are ethical, meet regulations and standards, and protect any human subjects involved in the research.” (OSTP 2022, p5), but assuming human subjects data is not exempted, a universal requirement for data-sharing presents potential conflicts with other ethical requirements related to consent and confidentiality.
Data management planning therefore must consider competing, and possibly conflicting, ethical obligations to funders, research participants, and other constituents.
Elements of a Data Management Plan
In general, all federal funding agencies and many other funding organizations require a formal data management plan (DMP) as part of the application process. Even if a DMP is not required, most projects can benefit from data management planning prior to commencing data collection in order to assure good data management practices. Data management planning not only can save time during the research process but also can improve the quality of analysis, limit errors and mistakes, and ensure that data remains reusable by the researchers or others. Many research lifecycle problems can be addressed before they become problems by thinking through data management issues. While much of data management planning should be completed prior to data collection, DMPs should also be living documents that can be adjusted and modified during a project.
Thislesson provides a handout with a general outline of data management planning. Researchers may also want to consult more comprehensive discussions such as Briney, et. al. (2020) or Michener (2015).
Activity
Complete the following activity in your LPOL Workbook. This activity will help you work toward a final curriculum deliverable, and it will help you develop your overall research plan.
If you are applying for funding from an agency or foundation, consult their documentation for required elements in your data management plan. The website DMPTool.org created by the California Digital Library (free account required) maintains a database and fillable forms that comply with the requirements of specific funders and can be very helpful in drafting an appropriate DMP. Depending on how extensive a funder’s requirements are, or even if your project does not have a DMP mandate, you may wish to create an independent or more detailed internal DMP for your project.
Use the 3.3.4: Data Management Plan Template in your LPOL Workbook to guide you through this process. You can also utilize a DMP checklist tool such as the Checklist for Data Management Plan developed by the Digital Curation Centre (DCC 2013).
Topic 4 References
Briney, Kristin A. “Data Management Practices in Academic Library Learning Analytics: A Critical Review.” Journal of Librarianship and Scholarly Communication 7, 1 (2019). doi.org/10.7710/2162-3309.2268.
Briney, Kristin A. “Project Close-Out Checklist for Research Data.” Teaching Resource. May 2020. https://resolver.caltech.edu/CaltechAUTHORS:20200519-142758925.
Briney, Kristin, Coates, Heather, and Abigail Goben. “Foundational Practices of Research Data Management.” Research Ideas and Outcomes 6 (2020): e56508. doi.org/10.3897/rio.6.e56508.
DCC. (Checklist for a Data Management Plan. v.4.0. Edinburgh: Digital Curation Centre. 2013. http://www.dcc.ac.uk/resources/data-management-plans
Michener, William K. “Ten Simple Rules for Creating a Good Data Management Plan.” PLOS Computational Biology 11, 10 (2015): e1004525. doi.org/10.1371/journal.pcbi.1004525.
Sweeney, Latanya. “Simple Demographics Often Identify People Uniquely.” Health 671 (2000): 1–34. https://dataprivacylab.org/projects/identifiability/index.html
Sweeney, Latanya, von Loewenfeldt, Michael, and Melissa Perry. “Saying It’s Anonymous Doesn’t Make It So: Re-Identifications of ‘Anonymized’ Law School Data.” Technology Science. 2018. https://www.hks.harvard.edu/publications/saying-its-anonymous-doesnt-make-it-so-re-identifications-anonymized-law-school-data
Data ManagementData management The ways a researcher collects, organizes, stores, and accesses data they collect for research. Creating a data management plan allows a researcher to know what data they will be collecting and how they will store and organize it during the research project. Planning
Data Sharing as an Ethical Imperative
In keeping with the beneficence principle of maximizing research benefits, there is a growing consensus that publicly sharing research is not only a more efficient and effective use of research funding, but also an ethical imperative. Data sharing requirements are becoming more common with funding agencies and, as outlined by the 2022 Office of Science and Technology Policy (OSTP) Memo’s (the “Nelson Memo”) requirement that all funding agencies have public access policies for research data in place by the end of 2025, may become mandatory for all federally funded research in the US. While the implementation details of the memo’s mandate are not presently clear, at a minimum it represents a significant expansion in data sharing compliance requirements.
Researchers will have to wait and see how individual agencies’ policies address the “potential restrictions or limitations on data access, use, and disclosure, including those defined in terms and conditions of funding agreement or award or that convey from a data use agreement or stipulations of an Institutional Review BoardInstitutional Review Board A group that is charged with overseeing and approving research projects. The group ensures that research projects are ethical, meet regulations and standards, and protect any human subjects involved in the research.” (OSTP 2022, p5), but assuming human subjects data is not exempted, a universal requirement for data-sharing presents potential conflicts with other ethical requirements related to consent and confidentiality.
Data management planning therefore must consider competing, and possibly conflicting, ethical obligations to funders, research participants, and other constituents.
Elements of a Data Management Plan
In general, all federal funding agencies and many other funding organizations require a formal data management plan (DMP) as part of the application process. Even if a DMP is not required, most projects can benefit from data management planning prior to commencing data collection in order to assure good data management practices. Data management planning not only can save time during the research process but also can improve the quality of analysis, limit errors and mistakes, and ensure that data remains reusable by the researchers or others. Many research lifecycle problems can be addressed before they become problems by thinking through data management issues. While much of data management planning should be completed prior to data collection, DMPs should also be living documents that can be adjusted and modified during a project.
Thislesson provides a handout with a general outline of data management planning. Researchers may also want to consult more comprehensive discussions such as Briney, et. al. (2020) or Michener (2015).
Activity
Complete the following activity in your LPOL Workbook. This activity will help you work toward a final curriculum deliverable, and it will help you develop your overall research plan.
If you are applying for funding from an agency or foundation, consult their documentation for required elements in your data management plan. The website DMPTool.org created by the California Digital Library (free account required) maintains a database and fillable forms that comply with the requirements of specific funders and can be very helpful in drafting an appropriate DMP. Depending on how extensive a funder’s requirements are, or even if your project does not have a DMP mandate, you may wish to create an independent or more detailed internal DMP for your project.
Use the 3.3.4: Data Management Plan Template in your LPOL Workbook to guide you through this process. You can also utilize a DMP checklist tool such as the Checklist for Data Management Plan developed by the Digital Curation Centre (DCC 2013).
Topic 4 References
Briney, Kristin A. “Data Management Practices in Academic Library Learning Analytics: A Critical Review.” Journal of Librarianship and Scholarly Communication 7, 1 (2019). doi.org/10.7710/2162-3309.2268.
Briney, Kristin A. “Project Close-Out Checklist for Research Data.” Teaching Resource. May 2020. https://resolver.caltech.edu/CaltechAUTHORS:20200519-142758925.
Briney, Kristin, Coates, Heather, and Abigail Goben. “Foundational Practices of Research Data Management.” Research Ideas and Outcomes 6 (2020): e56508. doi.org/10.3897/rio.6.e56508.
DCC. (Checklist for a Data Management Plan. v.4.0. Edinburgh: Digital Curation Centre. 2013. http://www.dcc.ac.uk/resources/data-management-plans
Michener, William K. “Ten Simple Rules for Creating a Good Data Management Plan.” PLOS Computational Biology 11, 10 (2015): e1004525. doi.org/10.1371/journal.pcbi.1004525.
Sweeney, Latanya. “Simple Demographics Often Identify People Uniquely.” Health 671 (2000): 1–34. https://dataprivacylab.org/projects/identifiability/index.html
Sweeney, Latanya, von Loewenfeldt, Michael, and Melissa Perry. “Saying It’s Anonymous Doesn’t Make It So: Re-Identifications of ‘Anonymized’ Law School Data.” Technology Science. 2018. https://www.hks.harvard.edu/publications/saying-its-anonymous-doesnt-make-it-so-re-identifications-anonymized-law-school-data