Data Management for Research : Manage Your Data

Managing your research data helps you:

• Streamline data collection & findability within your lab or research group 
• Achieve greater visibility for your research
• Simplify collaboration and sharing
• Fulfill grant funding agency requirements

We can help! We provide:

• Assistance with developing data management plans for grant proposals
• Advice and support in data documentation and organization to support reuse and preservation
• Access to Northeastern's Digital Repository Service for depositing, sharing, and reusing data

These checklists, tailored to stages of the research lifecycle, can help you streamline and organize work on your project.

General guidance for data management

This site offers helpful tips on a host of data management topics, ranging from non-proprietary file formats to naming conventions to persistent identifiers to data documentation.

Searching for advice on how to best organize your project or your research group's work? 

→  Check out The Practice of Reproducible Research 

This free Gitbook features over 30 case studies that help bridge the gap between theory and real-world application.  The case studies describe in detail how researchers in various disciplines have combined tools and workflows to make their lives easier, and maximize the reproducibility of their work.

README files describe your data, and help facilitate accurate understanding and reuse of your work.

Getting started

• Create your README as a plain text file to avoid potential issues with proprietary file formats.  PDF can be used if formatting is important.
• Choose whether to create a separate README file for each data file, or a README for the entire data package.
  • Example - separate README for each data file
  • Example template - README for entire data package

Recommended README file content, in brief

• Names and contact information for personnel involved with the project
• Short description of the data contained in each file
• File list, including a description of the relationships between the files
• For tabular data, full names and definitions of column headings
• Units of measurement
• Any specialized abbreviations, codes, or symbols used
• Copyright/licensing information
• Limitations of the data
• Funding sources

README templates for data (from Cornell - available in .txt and .md formats)

README templates for software (from Cornell - available in .txt and .md formats)

Additional resources

• Guide to writing readme style metadata from Cornell
• README guidance from Dryad

NLM-Scrubber is a freely available clinical text de-identification tool. Its goal is to produce HIPAA compliant deidentified health information for scientific use.

Learn more and download NLM-Scrubber