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LIBRARY GUIDE TO Research Data Management

What is data? Research data management?
Data is generally defined as recorded factual material commonly accepted in the academic community as necessary to document and support research findings (Modified from the NIH's Data Sharing Policy and Implementation Guidance)

A simpler definition: Data is anything you perform analysis on.  (Source: Data Management for Researchers by Kristin Briney)

Across academic disciplines, what consitutes data can vary greatly. Data can include:
  • research notes or lab notebooks
  • survey responses
  • software and code
  • measurements from laboratory or field equipment
  • images (such as photographs, films, scans, or autoradiograms)
  • audio recordings
  • physical samples 
(Source: Research Data Management, University of Pittsburgh Libraries)

Research data management (RDM) is broadly defined as the compilation of small practices that make data easier to find, easier to understand, less likely to be lost, and more likely to be usable during a project or ten years later. RDM can include data management planning, documenting data, organizing data, improving analysis procedures, securing sensitive data properly, having adequate storage and backups during a project, taking care of data after a project, sharing data effectively, and finding data for reuse in a new project. (Source: Data Management for Researchers by Kristin Briney)

Research Data Management Best Practices pdf

Confused about research data management?
At the Kresge Library, we're here to help! We provide:
  • individualized consultations on developing and implementing research data management practices. 
  • other methods of instruction as requested (workshops, seminars, etc.)
Please contact the liaison librarian for your subject area for more information about research data management in your discipline. 
Data Discovery & Re-Use
Due to the proliferation of digital data and the increase in data sharing mandates, vast amounts of research data is now available online. In most cases, you can re-use this data for your own research. 

Common data sources
  • figshare - open repository of data from all disciplines
  • Dataverse - open repository of data from all disciplines
  • Data.gov - "Home of the U.S. government's open data"
  • ICPSR - Political and social science research
  • iPoll - US public opinion poll data
  • US Census Bureau - Demographic data about the US, Puerto Rico, and island territories from censuses and other surveys
  • UK Data Archive - "UK's largest collection of digital research data in the social sciences and humanities" 
  • GitHub - Computer code 

Finding data used in a specific article
  • Look on the publisher's website for a supplmentary data file
  • Contact the corresponding author to request that they share the article's data with you
  • Contact Jim Van Loon, Research Data Librarian at OU Libraries. Librarians are experts at finding things! 
How to be a Good Data Consumer - if you are re-using data collected by another researcher, follow these two rules to be a courteous consumer of that data
  1. Look at the license on the dataset - The license will tell you what you can (and can't) do with the data. Common licenses include a Creative Commons license or an Open Data Commons License. But not all datasets have a license. 
  2. Give credit where credit is due - In a publication or presentation, give the complete citation for the dataset (like you would for a journal article). At a minimum, the citation should include the authors, data of publication, title, publisher or distributor and electronic location or identifier (such as a permanent URL or DOI). See this online guide for citing datasets using MLA, APA and Chicago styles. 
Data Storage & Security
Your research data is very valuable so it should be stored in a safe and secure location. Doing so will reduce the likelihood of data loss. When thinking about storing research data, follow the LOCKSS principle: lots of copies, keep stuff safe. 

OU Policy 860 states that data (including research data) should only be stored on University Provided Data Systems. This policy excludes using services such as Dropbox and Box to store data. 

Best Practices: 
  • Choose stable storage hardware such as an external hard drive or server. More portable and failable storage hardware (such as USB drives) should be used for data transfer, not storage. 
  • Practice the 3-2-1 rule. You should have three copies of your data on two different storage media with one copy in an offsite location. 
  • Test your backups regularly, to ensure they are working properly and that you can access your data in case you lose the primary copy of your data. 
Do you have data in a physical form (e.g. research notes, physical samples, etc.)? Think critically about storage location and size as well as who will manage this storage. If possible, create digital copies of any physical data. For example, you could scan your lab notebooks or research notes in order to create digital backups. 

Best Practices for Data Storage & Security pdf
Data Organization & Documentation
A major challenge with research is keeping track of your research data. Following consistent data organization and documentation procedures will help you (and others) to be able to find and understand your research data.  

Data documentation provides context to understand and use your data. Documentation can take many forms including methods, ReadMe.txt files, research notes and code books. Data organization provides a structure to your research data. Examples of data organization methods include file naming conventions and file versioning. 

Best Practices: 
  • Create a procedure for creating documentation for your data. The type of documentation needed and how to capture it is depedent on the research project. Make sure that your documentation is securely stored along with your research data!  
  • Use a file naming convention (FNC). A FNC gives a unique name to each file that describes both its contents and its relation to other files. Purdue University Libraries' File Naming Conventions guide provides clear guidelines on developing your own FNC. Don't forget to document the meaning of your FNC! 
  • Use file versioning to track your progress. The easiest way to track file versions is to add 'v01', 'v02', etc. at the end of your FNC. Update the version number after each change to the file. Don't use version names like 'last', 'initial', 'final', etc. Or use software that tracks file versioning for you such as Google Docs. 
  • Most importantly: be consistent with your data documentation and organization practices. Consistency is key to ensuring that your data is discoverable and usable!  
Best Practices for Data Documentation & organization pdf

Data Management Plans (DMPs)
A data management plan (DMP) is a document stating your data management practices both during and after a specific research project. They are usually written in conjunction with a grant proposal. In general, a DMP should include the type(s) of data being collected, data format (including metadata), plans for data sharing and plans for data preservation.  (This University of Oregon Library guide provides details more information to include in your DMP). 

Resources for writing a DMP: Because DMP requirements vary greatly by funding agency, always check their DMP requirements before starting to write. You can find the DMP requirements of various funding agency on the DMPTool website. Also, note that some funding agencies have strict page limits on DMPs. 

Best Practices for Data Management Plans pdf
Library Contact
Picture: Jim Van Loon

Jim Van Loon
Research Data Librarian

Available: Virtually by Appointment
Book appointment
Why is research data management important?
 You know that your research data is important. But why is research data management important? 

Managing your research data will help you: 
  • Save time and resources
  • Preserve your data
  • Maintain data integrity
  • Meet grant requirements
  • Promote new discoveries
  • Support open access
(Source: Data Management, MIT Libraries)

Data Preservation
Your research data is valuable to you (and others) beyond the life of the research project. Data preservation strategies can help you to ensure access to your data. In fact, many funding agencies require that research data is preserved for a certain number of years after the research project concludes. 

Data preservation is the series of managed activities necessary to ensure continued access to digital materials for as long as necessary. (Source: International Federation of Data Organizations 

Using a data repository is an ideal way to ensure that your data is preserved. The main advantage of using some data repositories is that they commit to the long-term preservation of your research data. This includes migrating file formats, quality assurance checks, providing high quality storage and many other actions to ensure that your research data is discoverable and usable. But not all data repositories commit to long-term preservation. Always check to repository's policies and procedures before depositing your data. 

Best Practices: 
  • Always retain data that support publication(s) or can't be reproduced. For example, some ethnographic research data can't be reproduced because it is linked to a certain time or location. 
  • You can't preserve every piece of data from your research project. It is best to err on the side of preserving more data than less but don't keep everything. 
  • In general, keep research data for 5-10 years. After 10 years, review if the data is still necessary; if not, remove it.
  • Use non-proprietary file formats. Examples of non-proprietary file formats include: ZIP, CSV, MOV, MP3, ASCII, TIFF, PDF and many more. Using this type of format increases the chance that you (and others) will be able to open the file at a later date. Stanford University Libraries has an excellent guide on best practices for file formats, including suggested non-proprietary file formats. 
Best Practices for Data Preservation pdf

Data Sharing
Why share data? Data sharing is increasingly becoming mandated by institutions, funding agencies and publishers. There are several benefits to sharing your research data:
  • Increased research impact and citation rates
  • Promotes collaboration
  • Reduces redundant research
  • Increases reproducibility of research 
Your funding agency may have specific data sharing requirements. For example, they may require that the data must be made publicly available for a certain period of time. They also may require using specific data respositories or that you provide them with a citation for the dataset. Check the funder's website for more information. 

How to share data?
Data can also be shared on a personal or research group website or other web-based tool. This approach is data sharing is not recommended because it doesn't facilitate discoverability as well as not ensuring the long-term preservation of your data. 

What data to share? You should share any data that supports a publication such as data used to create figures or tables. However, data that contains sensitive information or supports intellectual property claims should not be publicly shared. 

When to share data?
Usually you should share your data around the time of publication. But some funders and publishers require that data is available prior to publication. 

Best Practices: 
  • Before sharing, check that you aren't violating intellectual property rights by sharing the research data.  For example, data itself isn't subject to copyright but an expression of the data (such as a table or database) can be subject to copyright. 
  • If possible, apply an Open Data Commons license or a Creative Commons license to your data. These licenses allow other researchers to know how they can reuse your data.
Best Practices for Data Sharing pdf

Original Author
This guide was originally created by Joanna Thielen, currently the Biomedical Engineering Librarian at the Art, Architecture & Engineering Library, University of Michigan. From 2016 to 2019, Joanna served at the Research Data Librarian at Kresge library and this content is the result of that work.


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