Event box

Countway Library

Community Data Preservation: A Climate & Health Datathon

Wednesday, February 11, 2026, 2:00pm - 5:00pm
Countway Library Classrooms 102 & 103
Online, Harvard ID required, Love Data Week, Workshop,
Registration has closed.

Join Countway librarians, members of the Public Environmental Data Partners, and fellow data enthusiasts to capture and preserve our public health care data in the CAFE Harvard Dataverse Collection. Celebrate Love Data Week by ensuring access to federal environmental data.

Health and environmental data is crucial to our work and our everyday lives. It is important that this invaluable public data is maintained and kept available in its true unaltered form for us now and in the future.

This is an open, three-hour event where we will chat about the importance of data preservation and good data management, then pivot to capturing crucial public health information, reports, and datasets for preservation in Harvard Dataverse. No data science skills are needed!

Agenda:

  1. Presentation: Data preservation practices (hybrid)
  2. Hands-on Activity: Health and environmental data capture (in-person)

The data capture portion of the event is in-person only. If you would like to learn more about data preservation practices, but are unable to participate in the hands on portion, please feel free to join us online instead!

This event is open to all Harvard students, postdocs, researchers, faculty, and staff, as well as the larger concerned public health community.

Add to: Google Calendar Other calendar (.ics)

Looking for more events about finding and using data? Check out the Harvard Library Love Data Week event page for details on our weeklong celebration of research data. If you have questions about Love Data Week, please email emily_kilcer@harvard.edu.

Event Organizer

Profile photo of Julie Goldman
Julie Goldman

 

Need help with data management? Feel free to book a 30 minute meeting slot with me.

Happy to help with data organization, cleaning, and sharing for fostering reproducible workflows and open science!