Research data management is a critical aspect of empirical research that enhances its ability to advance scientific inquiry. Managing data involves performing a series of tasks – from identifying and locating potentially useful data sources; to collecting, organizing, reducing, and processing them; to generating informative documentation for them; to storing and preserving them. Effectively managing research data begins with developing a data management plan (DMP) during the research design phase of a project. A DMP outlines a strategy for handling research materials systematically throughout the research lifecycle.
QDR offers a series of web pages addressing key topics in managing research data:
We are continuously updating our site and will be adding additional screens on documenting data, copyright and fair use, and guidelines for research involving human participants shortly.
Effective data management has multiple benefits. First, it increases the quality and the value of the data you collect, which can strengthen the research that the data support. Effectively documenting data, for instance, ensures that you will be able to interpret and employ your data into the future. Managing data effectively also protects them from damage or loss. In addition, effective data management facilitates compliance with ethical and legal obligations, and with journal and funder requirements. Increasingly, funding agencies are requiring DMPs as part of funding applications. For example, the National Science Foundation’s DMP Requirements describe that organization’s expectations with regard to data management.
Further, effective management of social science data is a pre-requisite for you to share data in a meaningful way – i.e., ensuring that the data can be understood by researchers who were not involved in their generation or the research project with which they are originally associated. Sharing data and accompanying documentation that makes them comprehensible is important whether you share the data so other scholars can learn from them and/or (re)analyze them; in order to facilitate the assessment of empirically based inquiry (i.e., to achieve research transparency); or for pedagogical purposes. Often, funders’ requirements with regard to data management involve data sharing. For instance, the NSF’s Data Archiving Policy highlights that grantees’ DMPs must outline plans for data sharing.
Due to the heterogeneity of qualitative data, the core principles for managing data outlined on these pages are pitched at a general level. You will need to tailor those ideas to the peculiarities of your own research projects. QDR staff is available for consultations on data management issues related to your work. The guidance offered here draws on insights and logics generated by the data management community, and takes into consideration how emerging technologies inform and reshape how data can and should be managed.
Some additional resources for data management can be found here:
- The UK Data Archive
- Online course “Research Data Management and Sharing,” created by UNC Chapel Hill and the University of Edinburgh, on Coursera
- Data Management questions on DataQ, a platform for research data professionals.
We have also assembled some materials that can be used to teach data management.