Research Data Management
Conducting research involves working with data and involves processes from start to finish, including naming files, preparing and cleaning your data, performing analyses, documenting your work, and more. Below are selected resources help improve your workflows through better data management practices.
Following a few basic recommendations when working with research data in spreadsheets can save you time when it comes to analyzing your data. Consider these practices adapted from Data Organization in Spreadsheets, Karl W. Broman & Kara H. Woo.
Basic Spreadsheet Practices
Data Science for the Biomedical Sciences - Spreadsheets
Data Carpentry Spreadsheet Lesson
DataONE Data Entry and Manipulation (creating files, missing values, data validation)
Data Organization in Spreadsheets, Karl W. Broman & Kara H. Woo
Describing your Project
CESSDA has a useful guide for creating project-level documentation:
Describing your Dataset(s)
Nice overview on Readme, Data Dictionaries, Codebooks with examples (Iowa)
A readme is typically a plain-text file that provides information about a datafile to help facilitate use and re-use of the data. Typical elements to a readme include the following (adapted from Guide to Writing "readme" Style Metadata). Using one of the templates below can help ensure you create a useful readme file.
Readme Content: General Information
Readme Content: Data and Files
Readme Content: Methods
Additional Readme Resources
Metadata and Standards
Disciplinary Metadata (Digital Curation Centre) - links to information about metadata standards by discipline/field
Selecting a Data Repository
An effective way to make your data accessible is to store it in a repository. In this case, a data repository refers to a storage service that offers a mechanism for managing and storing digital content, where users can upload final datasets to make them accessible and discoverable.
Benefits of digital repositories include:
NIH Data Management and Sharing Requirements
Get assistance with writing your plan for the new NIH Data Management and Sharing Policy from our Guide.
Journal Sharing Requirements
To make your data/supplements available, first make sure that they are appropriate for sharing (e.g. de-identified if needed), and properly organized and labeled. Typically uploading datasets or supplements are straightforward.
More general considerations when deciding where to deposit your data
First, check funder or journal requirements for recommended or preferred repositor/ies
Repository Directories and Lists
Sample Discipline-Specific Repositories
General and Cross-Disciplinary Repositories
UVA Data Repository
In general, NIH does not endorse any particular repository. Overall, NIH encourages researchers to select the repository that is most appropriate for their data type and discipline. This list of NIH-supported repositories provides examples of suitable repositories.
General Data Repositories
The Generalist Repository Ecosystem Initiative (GREI) includes seven established generalist repositories that will work together to establish consistent metadata, develop use cases for data sharing, train and educate researchers on FAIR data and the importance of data sharing, and more:
UVA-Contracted Cloud Storage:
Additional UVA Storage Resources:
Backing Up Your Data:
Archiving and Sharing:
UVA Resources for Computing and Analysis:
The Open Science Framework (OSF) is a free, open web-based platform to support your research and enable collaboration. Think of it as a site where you can manage your whole research project. UVA is an institutional member and you can log in with your NetBadge (select "Sign in via institution.")
Project TIER seeks to enhance transparency and reproducibility of empirical research in the social sciences. Resources include:
Need more information on managing your research data? We are here to help: