Skip to Main Content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

Business & Economics Datasets: Data Management Resources

Data Management 101 for Business and Economics

Research data management (RDM) is the process of creating organized, documented, accessible, and reusable research data. It helps with research organization and comprehensibility, making onboarding to projects easier and allowing for ease in communicating research results to colleagues and the public. RDM helps improve research workflows to make them more resilient, efficient, maintainable, and reproducible.

RDM is research self care! The actions taken today to organize your research data will help future you feel more in control of your research project, and make it easier to share your results with your colleagues, companies, and relevant stakeholders in your field. 

Taking the extra time now to manage your data will save you an immense amount of time later on in your research! 

  • Meet grant requirements of funding bodies, who often require data management plans
  • Ensure research integrity 
  • Ensure your research data and records are accurate, complete, authentic, and reliable
  • Increase your own research efficiency - the less time you have to spend cleaning up data messes and deciphering your data, the more time you have to boost your own research agenda!
  • Save time and resources in the long run
  • Enhance data security and minimize the risk of data loss
  • Ensure proper handling and analysis of proprietary data
  • Prevent duplication of effort by enabling others to use your data
  • Comply with practices conducted in industry and commerce

Want to learn more? Check out our Data 101 LibGuide (https://guides.library.cmu.edu/data101for more information and recommended practices in research data management! 

Research Data Management at CMU Libraries

Did you know that at CMU Libraries, we have several folks who have been professionally trained in RDM education and support across each academic discipline? From writing data management plans, to helping set up an RDM protocol for a lab, we can help you develop strategies for managing your data, no matter what your data looks like.

Submit a form here to get started, or reach out directly to our Research Data Management Consultant Hannah Gunderman at hgunderm@andrew.cmu.edu

Data Documentation

Whether you are using proprietary data or collecting your own, documenting your research data means ensuring all data used or generated is easy to understand, analyze, and reuse (if applicable). A recommended practice is to consider whether your documentation would address the following situations:

1) If someone from another discipline outside of my own were to look at the data, would the documentation help provide important context to understanding the data?

2) If someone were to look at this data in 20 years, would they be able to understand why and how it analyzed a certain way?

3) If someone wanted to reuse my data, would they know which software to use to replicate my findings?

Types of research data documentation may include, but are not limited to:

  • lab notebooks (such as LabArchives); these can be discipline-agnostic and useful for research in any discipline
  • methodology reports
  • codebooks or data dictionaries with full variable and value labels
  • documenting decisions about software
  • tracking changes to different versions of the dataset through version control (such as in GitHub)
  • recording assumptions made during analysis

What should you think about when finding data?

When looking for data, ask yourself the following questions: 

  1. Is the source that is providing the data considering reputable?
  2. Is there appropriate metadata for the data that will help me know how it was collected?
  3. Was the data ethically collected?
  4. Does the data have any restrictions on reuse?

 

Navigating whether you can reuse data in your research can be a tricky process. Luckily, CMU Libraries can help you! Feel free to reach out to our Research Data Management Consultant Hannah Gunderman at hgunderm@andrew.cmu.edu for help!

Filenaming Schemes

It can be incredibly useful to use a consistent filenaming convention when naming your personal research files. Not only does this keep you more organized, but it also makes it easier for others to understand the contents of a file when sharing data. Some recommended practices for filenaming include: 

1. Avoid using spaces, periods, and special characters in your filename. It's always a safe bet to stick with using underscores when separating elements of your filename! 

Example: xxxx_xxx_xxx.pdf 

2. Your filename should give enough context on the contents of the file, and may include elements such as study title, your initials, the date, version number, and any other helpful information. Try to strike a balance between including enough helpful contextual information and keeping your filename short, ideally under 30 characters.

3. Dates should be formatted in ISO 8601 format (YYYYMMDD), the internationally-accepted way to represent date and time. 

Data, Gaming, and Popular Culture Librarian

Profile Photo
Hannah Gunderman
Contact:
410A Hunt Library
4909 Frew Street
Carnegie Mellon University
Pittsburgh, PA 15213
Office Phone: 412-268-7258
Contact: LinkedIn Page