In the tabs below, learn more about:
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.
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.
Both the research project readme and the project file readme are important to share along with their related 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.
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.
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.
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.