Abcd iqs ssoftware-projects-mercecrosas
- 1. Research and Academic Software Projects at the Institute for Quantitative Social Science Mercè Crosas, Ph.D. Chief Data Science andTechnology Officer IQSS, Harvard University twitter: @mercecrosas web: mercecrosas.com
- 2. The Big Picture Identify a problem or need in research or academia
- 3. The Big Picture Identify a problem or need in research or academia Build a technology solution, easy- to-use, gives control to researcher
- 4. The Big Picture Identify a problem or need in research or academia Build a technology solution, easy- to-use, gives control to researcher Generalizable, Open-source
- 5. The Big Picture Identify a problem or need in research or academia Build a technology solution, easy- to-use, gives control to researcher Build a community that makes the technology better Generalizable, Open-source
- 7. Example: Dataverse ๏ How do we increase data sharing to improve research transparency and replication with incentives to researchers?
- 8. Example: Dataverse ๏ How do we increase data sharing to improve research transparency and replication with incentives to researchers? ๏ Provide a repository solution, where researchers have control of branding and access of their data, and get credit through data citation.
- 10. Example: OpenScholar ๏ How do we enable scholars to build their academic web sites in a cost effective way?
- 11. Example: OpenScholar ๏ How do we enable scholars to build their academic web sites in a cost effective way? ๏ Provide a web site builder with pre-set features for academics, where a single hosting serves thousands of sites.
- 12. Example: Zelig
- 13. Example: Zelig ๏ How do we simplify using thousands of R statistical methods built by different authors?
- 14. Example: Zelig ๏ How do we simplify using thousands of R statistical methods built by different authors? ๏ Provide a statistical package that uses the same three commands for all methods, with consistent documentation.
- 16. Example: Consilience ๏ How do we make sense of thousands (or millions!) of texts?
- 17. Example: Consilience ๏ How do we make sense of thousands (or millions!) of texts? ๏ Provide an application that helps researchers explore many possible ways of categorizing documents.
- 18. The Process Research, standards & best practices Development, testing & releases Input from users, community, stakeholders Dataverse Case Study
- 19. metadata standards, harvesting protocols, data transfer, data citation, provenance, connecting to journals, integrating with cloud computing, …. The Process Research, standards & best practices Development, testing & releases Input from users, community, stakeholders Dataverse Case Study
- 20. metadata standards, harvesting protocols, data transfer, data citation, provenance, connecting to journals, integrating with cloud computing, …. The Process Research, standards & best practices Development, testing & releases Input from users, community, stakeholders Dataverse Case Study usability testing, community calls, annual community meeting, pull requests
- 21. The Process DetailsDataverse Case Study
- 22. The Process DetailsDataverse Case Study An agile process, integrating Waffle + GitHub + Jenkins, including these steps:
- 23. The Process DetailsDataverse Case Study An agile process, integrating Waffle + GitHub + Jenkins, including these steps: Backlog > Ready > Dev > Code Review > QA > Usability Test > Polishing > Done
- 24. The Process DetailsDataverse Case Study An agile process, integrating Waffle + GitHub + Jenkins, including these steps: Backlog > Ready > Dev > Code Review > QA > Usability Test > Polishing > Done Pull Requests
- 25. Not only Best Practices in Process, but also in Coding
- 26. Not only Best Practices in Process, but also in Coding
- 27. Not only Best Practices in Process, but also in Coding 1. Write programs for people, not computers.
- 28. Not only Best Practices in Process, but also in Coding 1. Write programs for people, not computers. 2. Let the computer do the work.
- 29. Not only Best Practices in Process, but also in Coding 1. Write programs for people, not computers. 2. Let the computer do the work. 3. Make incremental changes.
- 30. Not only Best Practices in Process, but also in Coding 1. Write programs for people, not computers. 2. Let the computer do the work. 3. Make incremental changes. 4. Don't repeat yourself (or others).
- 31. Not only Best Practices in Process, but also in Coding 1. Write programs for people, not computers. 2. Let the computer do the work. 3. Make incremental changes. 4. Don't repeat yourself (or others). 5. Plan for mistakes.
- 32. Not only Best Practices in Process, but also in Coding 1. Write programs for people, not computers. 2. Let the computer do the work. 3. Make incremental changes. 4. Don't repeat yourself (or others). 5. Plan for mistakes. 6. Optimize software only after it works correctly.
- 33. Not only Best Practices in Process, but also in Coding 1. Write programs for people, not computers. 2. Let the computer do the work. 3. Make incremental changes. 4. Don't repeat yourself (or others). 5. Plan for mistakes. 6. Optimize software only after it works correctly. 7. Document design and purpose, not mechanics.
- 34. Not only Best Practices in Process, but also in Coding 1. Write programs for people, not computers. 2. Let the computer do the work. 3. Make incremental changes. 4. Don't repeat yourself (or others). 5. Plan for mistakes. 6. Optimize software only after it works correctly. 7. Document design and purpose, not mechanics. 8. Collaborate.
- 35. Impact at Harvard 6,833 OpenScholar sites created 13,904 Registered users 75,378 Publications posted 24 Academic departments
- 36. Impact at Harvard 243 Dataverses from Harvard affiliates 1,226 Datasets by Harvard affiliates as authors 1,427 Registered Harvard users
- 37. Broader Impact
- 41. 53 Stats Models, easy-to-use
- 42. Thank you! Presented by @mercecrosas mercecrosas.com dataverse.org dataverse.harvard.edu openscholar.org projects.iq.harvard.edu scholar.harvard.edu zeligproject.org Coming soon! iq.harvard.edu