Manage and Share Your Data with HSLS Data Services

One of the best ways to ensure that research is robust, reliable, and replicable is to maintain organized, documented, and accessible datasets. The HSLS Data Services team can help you find, manage, publish, and share your data for any type of research project. We offer consultations, classes, and customized trainings in the following areas:

Research data management and sharing

Organizing files, writing documentation, and sharing datasets for reuse are crucial practices for improving the reproducibility of research. We offer personal consultations on data management topics at any time and teach workshops throughout the semester. In particular, we offer:

  • One-hour Introduction to Research Data Management classes that are suitable for everyone, but may be especially helpful for new graduate students and project staff
  • In-depth workshops on file-naming best practices, writing a data management plan for grant applications, and responsibly reusing data (or making your data available for reuse)
  • One-on-one meetings on writing and implementing a data management and sharing plan (DMSP) for the NIH’s data management and sharing policy, which went into effect in January 2023
  • Review of data management and sharing plans prior to submission with grant proposals. If you write your plan using one of the templates at DMPTool (, use the “request feedback” tab to get comments and suggestions. Please allow us at least five business days for an initial review of your plan.

Open science

Researchers, clinicians, and patients all benefit when scientists can examine each other’s work, build on existing data, and attempt to replicate experiments. The practice of making data, protocols, and scripts as publicly available as ethically and legally possible is called “open science.” But making your science open can be challenging, especially when you’re setting up new workflows and procedures for the first time. HSLS can help you with open science topics including:

  • Following the FAIR principles of findability, accessibility, interoperability, and reusability when putting your data and materials online
  • Locating, reusing, and citing data, and making your own data easily findable and citable
  • Disseminating articles, preprints, datasets, protocols, and software through formal publication or open distribution (please see our Where Should I Publish? guide for more info)
  • Managing complex projects on platforms like OSF (Open Science Framework)

Computational methods and tools

If you write or use code as part of your research, we can help you access better development tools and methods. Topics we can help with include:

  • Version control (tracking changes to files) with Git and GitHub
  • Choosing an appropriate open-source license for your code
  • Annotating research code, including both analysis scripts and standalone programs
  • Finding training for the fundamentals of research programming, such as using Python (Jupyter Notebook), R, and the command line

Note: We don’t offer help with statistical analysis or packages like SPSS, but we maintain a guide to statistical services on campus.

If you have a question about data, please contact us. We are available for one-on-one consultations as well as customized workshops for curricula, labs, and research groups. Our previously taught classes are available from the list of Data Services classes and customized trainings, but we’ll gladly put together something new just for you.

~Helenmary Sheridan