Biomedical Data Science Curriculum Initiative
February 2018 Workshop
The first workshop of the Biomedical Data Science Curriculum Initative will be taking place on February 7th and 8th at the Department of Biomedical Informatics in Countway Library at Harvard Medical School.
Schedule
February 7, 2018 |
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8:15 am | Breakfast | Ballard Room | |
8:45 am | Opening Remarks and Introductions | Minot Room | |
David E Golan, MD, PhD Dean for Basic Science and Graduate Education, Harvard Medical School Valerie Florance, PhD Director of Extramural Programs, National Library of Medicine |
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9:30 am | Session 1: Quantitative & Computational Methodology | Minot Room | |
Moderator - Lucila Ohno-Machado Discussants - Tianxi Cai, Harry Hochheiser, Peter Park |
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10:30 am | Break | ||
10:45 am | Session 2: Quantitative & Computational Foundations | Minot Room | |
Moderator - Lydia Kavraki Discussants - Shannon McWeeney, Noemie Elhadad, Peter Szolovits |
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11:45 am | Session 3: Data Skills | Minot Room | |
Moderator - Alexa T. McCray |
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12:45 pm | Lunch | Ballard Room | |
2:00 pm | Breakout Groups | Countway Library | |
Notes on Google Docs | |||
4:00 pm | Reporting Back | Minot Room | |
5:00 pm | Dinner | Waterhouse Room (Gordon Hall) | |
February 8, 2018 |
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8:15 am | Breakfast | Ballard Room | |
8:45 am | Opening Remarks | Minot Room | |
9:00 am | Session 4: Biomedical Skills | Minot Room | |
Moderator - Maha Farhat |
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10:00 am | Break | ||
10:15 am | Session 5: Professional Skills | Minot Room | |
Moderator - Cynthia Gadd |
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11:15 am | Breakgroup Groups and Working Lunch | Countway Library | |
Notes on Google Docs | |||
1:15 pm | Reporting Back | Countway Library | |
2:15 pm | Plenary Discussion | Minot Room | |
3:15 pm | Closing Remarks | Minot Room | |
Workshop ends at 3:30 pm |
Sessions
Each session will have a moderator and up to three discussants who will present some brief prepared remarks at the beginning of the session. There are no formal presentations, as we want these sessions to be a conversation among all members of the working group. The breakout groups in the afternoon will provide opportunities to discuss some of the issues in more depth.
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Session 1. Quantitative & Computational Methodology: What are the essential quantitative and computational methods that students need to master in order to become successful biomedical data scientists?
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Session 2. Quantitative & Computational Foundations: What previous knowledge do students need in the areas of mathematics, statistics, and computer science in order to succeed in a biomedical data science graduate program?
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Session 3. Data Skills: What do students need to learn about data management, including data description and curation? What skills in identifying and mediating limitations of data (e.g. data quality, biases, incomplete data) do we need to teach?
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Session 4. Biomedical Skills: What is critical knowledge about biology and medicine that biomedical data scientists need to be familiar with? What do we need to teach them to allow them to come up with meaningful questions to be answered with biomedical data?
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Session 5. Professional Skills: What do biomedical data scientists need to know about ethical issues, such as responsible use and generation of biomedical data? What do we need to teach them about reproducibility? What skills are needed to make them successful communicators and collaborator?
Breakout Groups
After the morning sessions, we will be forming breakout groups that will be working on topics selected as part of the discussions in the morning. Each breakout group will have 6 to 7 participants.
Open Notes
We have prepared Google Docs files to collaboratively create notes for each session and breakout group. The documents are linked from the agenda and can be accessed via https://bit.ly/bmdsci-2018. There is also a folder to upload documents that working group members would like to share.
Workshop Report and Resources
We have assembled a workshop report that is available for download as a PDF (19 pages). Additionally, the following resources were recommended by workshop participants:
- National Academy of Sciences Report – "Envisioning the Data Science Discipline: The Undergraduate Perspective", including the Data Science Oath
- Reproducible Research edX course
- Readings on Fairness in Machine Learning
- Weapons of Math Destruction
- Predictive model best practices (EHRs)
- Article related to useful data from public repositories
- NIGMS Training Modules to Enhance Data Reproducibility
- 10 Simple Rules for Responsible Big Data Research
- NIH Office of Research Integrity
- Committee on Publication Ethics
- Case Studies for Biomedical Data Science (see e.g. slides 18 - 21 in this presentation by Brad Malin, Vanderbilt)
Contact
If you have any questions about logistics for the workshop, please contact Nichole Parker (nichole_parker@hms.harvard.edu). If you have any questions about the program of the workshop, please contact Nils Gehlenborg (nils@hms.harvard.edu).