The Harvard PhD Program in Neuroscience presents two workshops as part of the new Certificate in Comp Neuro (CiCN): Structuring Code & Data and Thinking about Neuroscience Models
Open to all Harvard affiliates but limited spots available so register soon if interested!
Organized by Ella Batty (CiCN coordinator/lecturer) with help from Jan Drugowitsch (CiCN co-director) and Sam Gershman (CiCN co-director)
Please direct questions to Eleanor_Batty@hms.harvard.edu
These workshops assume some knowledge of Python - if you'd like to learn Python, you may be interested in this J-term course: https://locator.tlt.harvard.edu/course/colgsas-217822/2020/winter/22723
Structuring Code & Data Workshop
January 14th, 20th 2020, 9 am - 3 pm. Held remotely
Have you come back to a project after time away and not known what is where or what you've done? Come learn strategies for organizing your code and data workflow to optimize reproducibility, both for future you and for others. Featuring sessions on best coding practices, open science, data management, DataJoint, git, and practical advice from fellow postdocs and grad students.
The assumed language is Python but most content will be helpful for the language of your choice. Most sessions are general to all disciplines, although some are focused on neuroscience research.
- Patrick Mineault (CTO of Neuromatch academy, former Google software engineer)
- Jessica Forde (Machine learning researcher, Brown University, formerly at Project Jupyter)
- Julie Goldman (Research Data Services Librarian, Harvard)
- Dimitris Yatsenko & DataJoint team (VP, Research and Development, DataJoint Neuro)
- Alex Cullen (HMS Research Computing)
Thinking about Neuroscience Models Workshop
January 12th, 2020, 9:15 am - 3 pm. Held remotely
Come join a smaller interactive workshop on modeling in neuroscience! We will discuss broad categories of models, what types of questions you can ask, and the diversity of goals in modeling. Featuring a deeper dive into model fitting by Sam Gershman.
Assumes some familiarity with Python