Some technical hurdles towards open science
This document discusses the current dysfunctional state of scholarly literature and proposes a new open and decentralized system to address these issues. It notes that currently scholarly literature provides limited access and discoverability, lacks global search and functional links. Data and software are also not properly archived. It proposes a single semantic database to archive all literature, data, and software according to global standards. All research outputs like publications, data, and code would be archived. Tools would allow interactive analysis and visualization of data as well as publishing updates in real-time. This new system would make the scientific process more transparent, collaborative, and improve discovery of information.
Some technical hurdles towards open science
- 1. Björn Brembs Universität Regensburg http://brembs.net
- 2. Institutions produce publications, data and software
- 4. • Limited access • No global search • No functional hyperlinks • No flexible data visualization • No submission standards • (Almost) no statistics • No text/data-mining • No effective way to sort, filter and discover …it’s like the • No scientific impact web in 1995! analysis • No networking feature • etc.
- 5. Scientific data in peril
- 11. Technically feasible today (almost) • No more corporate publishers – libraries archive everything and make it publicly accessible according to a world-wide standard • Single semantic, decentralized database of literature, data and software
- 12. • Institutional email • No archiving of • Institutional publications webspace • No archiving of • Institutional blog software • Library access card • No archiving of • Open access data repository
- 14. Software to control the experiment and save the data
- 15. Software to analyze and visualize the data
- 21. However, a version is already available
- 22. Get your API keys. Insert them in your R code
- 24. For instance, produce an image than can be previewed (instead of a multiple page pdf)
- 26. Same type of experiments → same script Default: → same categories → same tags → same authors → same links → same description → One complete article, in one click. Update the figure: Higher sample size directly published while analysed, your boss may see the results before you do! (or you may see the results of your student before they do) Possibility to make it public and citable in one click or directly in the R code.
- 30. One person is not an institutional infrastructure!