Will Data Science Approaches Impact Our Science?
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This document summarizes a presentation about how data science approaches may impact scientific research. It notes that deep learning and other data science techniques are already being used in areas like protein structure prediction. However, academic research may be "behind the curve" compared to private industry in adopting these methods. The presentation aims to discuss how open data sources and analytical techniques from data science could be applied to research, and how to maintain researchers and attract data scientists in the field. It provides examples of relevant areas of data science and possible discussion topics.
![From Tim Clark]](https://image.slidesharecdn.com/3dsig2017-170724053141/85/Will-Data-Science-Approaches-Impact-Our-Science-7-320.jpg)
![Through distortions in its social use that include bias, amplification,
and invention, citation can be used to generate information
cascades resulting in unfounded authority of claims.
From Tim Clark]](https://image.slidesharecdn.com/3dsig2017-170724053141/85/Will-Data-Science-Approaches-Impact-Our-Science-8-320.jpg)
Will Data Science Approaches Impact Our Science?
- 1. Will Data Science Approaches Impact Our Science? Philip E. Bourne PhD, FACMI Stephenson Chair of Data Science Director, Data Science Institute Professor of Biomedical Engineering peb6a@virginia.edu https://www.slideshare.net/pebourne 6/15/17 Dataverse 2017 1
- 2. We Have Already Been Impacted • Deep learning is a part of data science • Definition • Feature extraction from large sometimes sparse datasets • Examples from this meeting • Residue identification for protein ligand docking - trained on PMC applied to PubMed. • Improving ranking in fold recognition • Folding membrane proteins • Prediction of RNA-RNA interactions
- 3. Why have this session….
- 4. Data analytics has become a major economic driver in the private and government sectors… It could be argued academic research is now behind the curve not ahead of it ... This has implications for our science, both in the research itself and in recruitment of students and faculty
- 5. Areas of Data Science Yes much of this we have been doing, but much is becoming mainstream which we can learn from • Effective use of cloud computing. • Model-based inference. • Predictive modeling. • Recognizing unusual and anomalous behavior in data. • Geometry and topology of data. • Indexing, labelling and retrieval, query engines • Large-scale optimization. • Machine learning. • Network and graph theory. • Privacy and security. • Monitoring and analytics. • Economics, governance and ethics. • Controlled vocabularies. • Visualization. • Compression. • Data mining.
- 6. What motivates me to want to have this discussion… I am developing a Data Science Institute where the students are discovering open data and analytical techniques that I think, Hmm I could apply this to my own research.. It seems worthwhile to have a discussion to see if a) you agree b) what might you share with our community c) we cant ignore it...
- 8. Through distortions in its social use that include bias, amplification, and invention, citation can be used to generate information cascades resulting in unfounded authority of claims. From Tim Clark]
- 9. Possible Items for Discussion • Data • New open data sources relevant to 3Dsig attendees • Analytics • Little known analytical techniques that could be more broadly applicable to our community eg for clustering, machine learning, statistical rigor … • Societal • Maintaing researchers in the field • Attracting data scientists to work on our problems