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UMass Amherst Libraries

Managing Your Data

Take care of the products of your research -- the tips here will help your work be available long into the future!

Prepare for sharing

Sharing your data takes planning, and should be conducted as early in your data lifecycle as possible. 

In the tabs below, learn more about: 

  • Giving context to your data, so you and others have the necessary details to interpret your data, and that these details are available long into the future.
  • Giving context to your methodology, so you and others understand the steps you took to generate, collect, analyze, or otherwise manipulate your data. 
  • Defining your data workflow, so you can reflect on how your data is collected in order to identify areas of improvement, and plan for your future storage options.  

Explore how to prepare for sharing

Construct detailed explanations of your data to enable future use.

Giving context to your data is of immense benefit to any future users of your data -- including yourself! Memories are fallible, and documenting important aspects of your data will help prevent you from second-guessing your work. 

Context can be as simple as a README file with a few relevant details, to as complex as a robust metadata schema, including granular details to greatly enhance reuse. Data dictionaries go hand-in-hand with giving context to your data.


Questions to think about: 

  • What documentation will be provided with your data in order for you and others to use it in the future?
    • Consider including a description of your research project. 
  • Does your field have any standards?
    • See the Metadata Directory to explore more on metadata standards. 
    • Some disciplines don’t have established standards - and that’s okay. Adopting a documentation structure that captures the right amount of information so someone else can quickly orient to your data will be the sweet spot. 
  • What documentation will be needed for future replication?
    • What steps did you take to analyze your data? 
    • Include information on steps taken to prepare data for analysis:
      • Did you use any tools to clean your data? 
      • Do you need to pre-process your data ahead of analysis? How was this completed?



Create a README file.  A readme file is a simple and powerful way to improve the use and future use of your data. These are traditionally saved as .txt files — one of our staple stable file formats — but it can be saved as another stable text document, too.

At a minimum, create a readme file for your project as a whole, and for your project files. 

Research project readme file. Fill out this worksheet to give you an overview of your project.

Project file readme file(s)

Both the research project readme and the project file readme are important to share along with their related data!

Further reading

Construct a detailed description of how you gather, prepare, clean, analyze, and interpret your data.

Methods sections of papers are well established recipes for how you gathered, cleaned, and analyzed your data. It’s important to package your methods with your data -- this will help reduce misunderstandings of how to use the data, and improve transparency and reproducibility of your work. 

For some papers, methods sections are limited by word count, making it difficult to craft the entire picture of your methods. This can sometimes make it more difficult to reproduce your work, especially as time goes on.

Documenting your methods as you go along is a powerful tool in improving reproducibility and transparency of your work. Documenting as you go will also help when you go to write the methods section of your article, as you will already have prepared a great deal of information.


Questions to think about: 

  • What details would you need to replicate this study? Because you are not limited by a word count, you can go into much greater detail. Please see the activity. below, for more ideas. This might include:
    • How data was collected
    • Steps taken to prepare data for analysis
    • How artifacts were removed
    • Protocols used 
    • Code used
    • Units of measurement
    • Instruments used
    • Transformations applied
  • What are the limitations of your methods?
  • Do you use any templates or tools to streamline and standardize data capture? 
  • How will you share your methods with your data? 


Create a detailed overview of your methods. Your methods section improves the reproducibility of your work. Explore being overly honest. And, because you are not limited by a word count, you can go into much greater detail. 

Check out the Open Science Framework's Registries for a rich overview, or use our worksheet, below.

Further reading:

Describe the necessary steps you take throughout your research process -- from data collection to preservation. 

Defining your workflow can help you determine places where there are inefficiencies, and can improve your efficiency and the reproducibility of your work. As the complexity of your work increases, so too will your need to outline your data workflow.


Questions to think about: 

  • How would you map out your data workflow? 
    • What tools do you need to accomplish mapping out a workflow? 
    • Can you do this alone, or will you need to spend a meeting (or two) with your collaborators to determine how your data moves through a project?
  • Where are there points of friction? 
    • How can you mitigate or improve these points in your workflow?
  • How do you manage competing interests?


Create a data workflow map. You can approach mapping in whatever way feels appropriate for your work, or use our suggested steps in the worksheet below.

Further reading: