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Data Management Onboarding: Procedures for Research Consistency In-Person

This class is part of the Research Data Management Seminar Series

Research Data Management is essential for responsible research and should be introduced when starting a new project or joining a new lab. 

This training session will present a checklist designed to guide employee/trainee onboarding as they join a lab or begin new projects. We will provide an overview of the various elements that should be addressed at both the lab and project level. 

Included in the document are two checklists:

  1. Checklist for Joining a New Lab
  2. Checklist for Starting or Joining a New Project

Through hands-on activities, participants will engage with the checklists to review and understand the core tools available and foundational data management processes that should be established. The key take-away from this session will be how to incorporate the checklists within your individual project or lab.

Audience: Faculty, Staff, Lab Managers.

Location: Modell 100A Fred S. Rosen Lecture Hall

Instructors: Julie Goldman, Research Data Services, Countway Library
Sarah Hauserman, Research Data Analyst, Research Information Technology Solutions
Meghan Kerr, Archivist and Records Manager, Center for the History of Medicine


Monday, September 23, 2019
2:00pm - 4:00pm
Time Zone:
Eastern Time - US & Canada (change)
Campus Location:
Harvard Longwood Campus
  Classes     Data Management  
Registration has closed.

Complete the Research Lifecycle with Data Management Offboarding: Essential Steps for a Smooth Transition

Date  Time Location  Registration
 Monday, September 16, 2019  2:00pm - 4:00pm  Modell 100A Fred S. Rosen Lecture Hall  Register
 Monday September 30, 209  10:00am - 12:00pm  Countway Library, Minot Room  Register


See more: Harvard Biomedical Data Management News & Events

Event Organizer

Meghan Kerr
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!

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