Towards a more open scientific community: go beyond open data and FOSS
The word ‘open data’ is never new to researchers in the data science/ statistics field. They can rapidly find and reuse various types of high-quality datasets provided by global companies, institutions and universities via open data repositories, thanks to the FAIR (Findable, Accessible, Interoperable, Reuseable) principle. GitHub, which allows users to share their code as Free and Open Source Software (FOSS), facilitates them to publish code openly for peers review. Researchers also prefer to publish the original version of articles on the arXiv so the community identifies them as the author of articles. Is it reasonable to call such workflow using open access repositories throughout a good practice of open science (OS)?
The answer could be yes if OS is merely defined as FAIR data and shared knowledge. Plenty of software, platforms and communities together consist of an ecosystem in which other researchers can contribute and collaborate in every stage of the research cycle, with data, result, protocol, etc. For instance, the RIO helps the researchers sharing the proposal and being open at the very first stage of research; a shared literature repository like Zotero is becoming the common choice to research references for academia. Besides the mentioned GitHub, there are also AsPredicted for pre-registering, Zenodo for sharing data, Protocals.io to share workflow in the analysis step. This ecosystem also ensures the writing process and publishment open the community. The list goes on.
However, the OS movement cares more than open data and share knowledge. The OS is a paradigm that goes beyond the conceptions of FAIR and FOSS. As the UNESCO (United Nations Educational, Scientific and Cultural Organization) states in the Draft recommendations to OS (2021), OS is an inclusive construct aiming to make scientific knowledge openly available, accessible and reusable for everyone, not only the scientific community but the whole society. The purpose is to benefit academia and society by increasing collaboration and share, and to open the process of scientific knowledge creation, evaluation and communication to the societal actors. It builds on the following key pillars: open access to scientific knowledge, open science infrastructures, open science communication, open engagement of societal actors and open dialogue with other knowledge systems. This blog will discuss particular pillars from a personal perspective.
Open access to scientific knowledge It is no doubt that every researcher, even student is a beneficiary. The researchers obtain inspiration from all open objects, rather than solely the journals and conferences in a traditional community. The students are closer to the state-of-art academic results more than ever. An open scientific community connects every member more closer such that everyone is not studying silently. But the challenges exist. The wildly used open publishment repository Sci-Hub was suspended by Twitter due to “counterfeit content”. Before this incident, it has been banned in several countries after Elsevier filed a series of lawsuits against it. Therefore, some archivists launched a rescue action on Reddit to save this open repository. Will the OS movement finally become to conflicts and compromises among academia, commercial companies and governments? In the initial stage of the open science movement, these mentioned platforms act as the pioneers to support the scientific community in converting more open. If someday the OS paradigm becomes the requirement of all research activities, will these free-using platforms turn to be profitable? Will the enthusiasm to open accessible science eventually creating more monopoly companies/organisations?
Open science infrastructures You can never overemphasize the importance of infrastructure to research in the STEM field. But building and maintaining advanced science infrastructures is a heavy financial burden to a university or even a country. The shared infrastructures are therefore built, maintained and accessed by members inside of alliances. Personally, it is a fantastic pillar. With more science infrastructures open to the scientific community, researchers performing big-scale computing can save tremendous time and funds wasted on rent/built supercomputers. But when the infrastructure is no more supercomputers, but devices involving national security, such as space station if it is infrastructure, is the rule “as open as possible, as close as necessary” of open data applicable here? Certainly, open means no threat to safety. Providers reserve the right to close the access to infrastructures, but abuse of this right to set a boundary to open science, to maintain the technical advantages, will aggravate the Mathew effect between developed countries and developing countries.
Other than the discussed pillars, transforming scientific culture and aligning incentives is the action that interests me. The universal assessment metric to research is the publishment in the traditional scientific community. Therefore, researchers manually modify results or data supporting the conclusion in some academic fraud cases, aiming to publish their discoveries in top journals. Except for calling for academic integrity, exploring the new incentives is a crucial issue for the scientific community. When every stage of the research process is open to the community and society, the contributions on data collection, analysis can be tracked and evaluated.
In my case, I have already uploaded the data to Zenodo and the code to Github. I also applied an ORCID and combined it with my Zenodo and GitHub repository. This is my first step in practising research that meets with the OS paradigm. But as the title indicated, OS is more than open data and FOSS. I will open more research outcomes at every stage of my research process such that I could contribute to other researcher’s work and the scientific community evolving to a more open one.
