Six key recommendations for managing data/digital materials to ensure their longevity and usefulness are outlined by the University of Wisconsin-Madison Research Data Services:
The six recommendations can be consolidated into four broad categories, which are reviewed in this section of the guide:
"Data organization refers to structuring project directories to aid the storage and finding of files, naming files to enable logical grouping and/or chronological sorting within directories, and structuring the contents of files to facilitate analysis" (Oregon Health & Sciences University, 2023, Research Data and Reproducibility [LibGuide], "Organizing Data" section).
Data organization includes considering the relationships between materials within a project. Some things to address may include:
Data organization can also include considering the roles and responsibilities within the project team and the designation of responsible parties, or data stewards. According to the University of Wisconsin-Madison Research Data Services Center Data Organization page, roles and responsibilities are of increasing interest to funders.
Additional Resources
Documentation about data is often referred to as "metadata" and is important for understanding the context of your research project. Documentation includes project level information as well as file-level information and should be generated continually throughout the research process (The Ohio State University Libraries Research Commons Research Data Management - Best Practices).
Metadata can include:
Types of documentation may vary between disciplines but can include:
When sharing data or depositing datasets into a repository, there may be metadata standards you need to follow, which may be discipline specific. Standards allow for enhanced interoperability and exchange of data. For example, ODU Digital Commons uses Dublin Core metadata, which contains 15 elements (UC Santa Cruz University Library Metadata Creation).
Remember: Metadata is not only useful to researchers who may want to reuse your data, but can also improve tasks such as on-boarding and off-boarding procedures in research groups by streamlining knowledge transfer processes. The Harvard Longwood Medical Area (LMA) Research Data Management Working Group: Project Work via the Open Science Framework (OSF) contains additional information, such as checklists and templates generally licensed under a CC-By Attribution 4.0 International license.
Additional Resources
Storage and access are important parts of research data management. Some data needs to be stored in specific locations due to confidentiality and/or sensitivity concerns and accessed by a limited number of people. In addition, it is important to understand whether or not your data is being backed up automatically or if this is something you will need to plan for.
ODU offers several file and research data storage options for researchers and students. For additional information about any of these options or questions regarding the best option for your project, please contact Information Technology Services.
Students should consult with their faculty mentor for storage options when working with research data.
Important: University employees must be careful to protect confidential or restricted data. Before storing or sharing University information with any of these services, review the Regulated Data Storage Matrix and the Regulated Research Data Storage Matrix.
In addition to ODU-provided storage options, there are additional collaboration and storage platforms, such as the Open Science Framework (OSF).
In contrast to network drives, computing clusters, local, and cloud file storage, which are generally used during active projects, repositories are used for long-term preservation, storage, and access to completed research outputs.
According to Harvard: Data Repositories, "data repositories are a centralized place to hold data, share data publicly, and organize data in a logical manner." Repositories help you cite your data by providing a persistent identifier, provide long-term preservation for your data, and facilitate access, discovery, and reuse of your data.
Some repositories will help you curate your data (ICPSR: Data Management & Curation, DRYAD: Dataset curation). The definition of data curation by the ICPSR provides a good overview of what data curation entails:
"Data curation is akin to work performed by an art or museum curator. Through the curation process, data are organized, described, cleaned, enhanced, and preserved for public use, much like the work done on paintings or rare books to make the works accessible to the public now and in the future. With the modern Web, it's increasingly easy to post and share data. Without curation, however, data can be difficult to find, use, and interpret. Through curation, ICPSR provides meaningful and enduring access to data."
It is generally recommended to find an appropriate disciplinary repository for your data if one exists; if there is no discipline-specific repository, a generalist repository may be the best place for your data. Always be sure to check the data sharing requirements of your publisher or funder because they may have specific repository requirements.
Please refer to the Data Repositories to Store, Share, and Search for Data section for more information.
In addition, be sure to follow proper data citation practices, which is an important part of sharing and reusing research data. Refer to the Persistent Identifiers section of this guide for additional information.
Important: There may be ethical considerations around sharing data when personally identifiable information (PII) might be a concern. It's always best to contact the ODU Office of Research for assistance.