Research Data, Data Management & Planning: Metadata and ReadMes

A ReadMe provides information about a data file and is intended to help ensure that the data can be correctly interpreted, by yourself at a later date or by others when sharing or publishing data. ReadMes can help:

• To document changes to files or file names within a folder
• To explain file naming conventions, practices, etc. "in general" for future reference
• To specifically accompany files/data being deposited in a repository
• To help ensure that the data can be correctly interpreted, by yourself at a later date or by others when sharing or publishing data

For more information on what content should go into a Readme, check out the resources below. 

A codebook, or data dictionary, defines specific details of your data  -- the variables, column headers for spreadsheets, participant aliases, or qualitative tags are some examples of facets of a dataset that should be described in a codebook. This differs from a ReadMe in that it focuses on specific details of the data, not information about the data file as a whole.

Metadata is documentation that describes data. Properly describing and documenting data allows users (yourself included) to understand and track important details of the work. Having metadata about the data also facilitates search and retrieval of the data when deposited in a data repository. 

Standards by discipline

To find an appropriate metadata standard for your discipline, consider the Disciplinary Metadata guide (via the Digital Curation Center).

Additionally, a community-driven project manges an open directory of metadata standards (via Research Data Alliance).

A lab notebook is a complete record of procedures (the actions you take), the reagents you use, the observations you make (these are the data), and the relevant thought processes that would enable another scientist to reproduce your observations. Another way to view your notebook is that it is your scientific legacy for that lab. Long after you have moved on from the lab, your notebook will remain and be referenced. Others will be building on the research that you are doing now and it is imperative that they can replicate what you have done. A proper notebook will allow those who come after you to do that. A poorly kept notebook will not. Lab notebooks can be physical objects or they can be digital objects. The following resources will provide excellent overviews of how to manage, organize, and use a lab notebook.

Several helpful resources that may help inform your decision on what ELN suites you best:

• Article from Nature describing how to pick an electronic lab notebook.
• Comparison grid from Harvard to picking the best-suited ELN for  you.
• Helpful advice and guide from labfolder.
• Additional comparison tables from University of Cambridge

In general, they will all differ in: 1.) User-interfaces; 2.) interoperability and flexibility; and 3.) the pricing model.

In no particular order, here are some of the more popular, higher rated ELNs:

• Hivebench
• Benchling
• RSpace - Video overview here
• SciNote - Video overview here
• LabArchives - Video overview here
• Docollab
• Labfolder
• Mbook
• Labguru

*Information adapted from Cold Spring Harbor Laboratory