Group 1: Microbial and Neural Science
- Data sharing: many data are lost because they are not published. One way to counter that is to deposit data on data repositories. Online servers allow to publish data or protocols that are not published in journals. This increases the visibility and the number of citations of the research. However, this is time-consuming and some of these repositories are not free.
- Reproducibility : bad statistics, p-hacking, … --> barrier for science in general. Solution : Pre-registration forces you to think about the analysis of your data before collection. Data sharing for more transparency.
Group 2: Computational Biology
- Scooping: sharing data or other key information before submission could result in it being published by others. This is not an issue with preprints, having a previous proof with a timestamp most likely avoids this. Furthermore, in an open research group, members doing this practice could be more easily identified and weeded out. What should be done when scooped, how can open science tools help?
- solutions: a standardized way to "register" the research outcome if not the journal paper
- solutions: Post publication peer review: An online and public place where you can discuss and criticize research. (Pubpeer, Twitter, ...)
- solutions: Licensing different usages, restrict access and usage of data
Problem: Sharing analysis code is not enough for reproducibility, data should be shared too, but it has several barriers including the fear of being scooped or the confidentiality of personal data.
Stakeholders:
- Researcher does not want to be scooped.
- Subject of experiments want their data to stay protected and anonymous
Phases which are concerned
- Data collection - subjects and their information
- Data analysis - tools (code) which are created
- Publication - (post-publication) availability of tools and data, re-use for other projects/purposes
Solutions:
- Share dummy simulated data which allows to test the analysis code (example: Building Data Genome Project)
- Control access and rights on the data (ex. licensing, access on request)
- Online platform which allows to run the code on the data without having access to it (within certain limits). Could also grant access on a case-by-case basis for running arbitrary code, although may be an issue with popular software and lots of requests
- Advances in cryptography can allow to run code on encrypted data
Group 3:
The quality of the open access journals is not recognized by the conventional credit system. (e.g. EPFL does not finance to publish in hybrid open-access journals)
- pros for publishing in open access journals: more citations, a further outreach of knowledge dissemination
- cons for publishing in open access journals: money, and not recognized in the conventional crediting systems
Solutions:
- Use alternative systems to evaluate the impact of a single article, but not the journal. examples: Altmetric, PLOS (showing numbers on sharing/viewing/citating)
- Give credit on efforts that have been put into the process of data collection/data analysis/publication.
- Have a standardized way to reference the data that is published. (e.g. create a doi from a github project)
- Make data open, but control the access to datasets (Fig.1)
- Famous and well-known Professors should be encouraged to publish more in Open access journals (penguin effect).