Thinking about Open Science
The course presented to the participants a conceptual and practical journey to know, deepen, and rethink the new scientific paradigm called Open Science (OS). The classes and Open Coffee modules were designed as complementary instances where the encounter between the theoretical aspects and the concrete tools and initiatives was fostered. This methodology allowed bringing theoretical issues into daily practice and showing different examples of good OS practices.
The reasons why we need Open Science were clearly established: visibility and transparency. Scientific knowledge must flow freely, be available, findable, searchable, and usable. The research must be transparent in the processes, data, and results, but also in the evaluation to which it is subjected. In order to give meaning to these words, it is necessary to rethink the scientific system thoroughly, what scientists are expected to do, how they are evaluated, and what is rewarded. Transforming a competitive and closed system into a collaborative and open one implies a profound cultural change, which this course greatly contributes to.
The program presented a broad set of applications, infrastructures, and information systems among other initiatives that institutions have been promoting to trigger a change in scientific culture. These initiatives, oriented to different aspects of OS, both at European and international level, provide theoretical and conceptual support to open science and promote good research practices. For example, we learned about OpenAire, EOSC, Unesco declarations, FAIR principles, Horizont Europe policies, Sherpa Romeo and Juliet, among many others.
Ticket to OS gave me the opportunity to refresh and update my knowledge on topics that I have been working on, such as Open Access, while allowing me to delve into topics I was barely familiarized with, such as citizen science and Open Data.
Furthermore, the course allowed me to rethink my thesis in OS code, particularly on the topics related to open data and FAIR data. I wondered about the possibility of carrying out a Data Management Plan. The DMP should consider not only the data but also the procedures and instructions to be executed with the data. Ideally, in a OS level, having Open Data based on the FAIR principles would be the most suitable for any thesis. I would be delighted for my data to be findable, accessible, interoperable, and reusable; FAIR would provide transparency, reproducibility, and reliability to the research. However, in my thesis I am going to work with data that comes from a subscription source: Web of Science. I wonder if the data can be shared and understand that it may not be possible. I will need to investigate further to see if there is a way that this data can be shared. I think this is an issue that needs to be addressed because those of us who work with bibliometric studies tend to use data source from commercial providers that hinder their sharing. As for how I intend to become an open scientist, I will start by working with open source systems such as R, publish the result of my research openly, deposit my work in the repository of my universities (UC3M and University of the Republic), use the permanent identifiers, and continue to look for ways to use data that can be shared.
My opinion about OS is that it is a new scientific paradigm, because it undermines the very structure on which the scientific system is built: competition for access to information and data, blind peer review, large oligopolies editorials, the scientific evaluation system according to the number of peers and their impact, among other aspects. In contrast, OS proposes a collaborative, transparent science, available to everyone, including the non-academic community. A science for the citizen, which addresses complex problems that arise from societies with an interdisciplinary approach; a science where the results are within reach with no other obstacle than access to a device with internet where the data is also shared. Open Science proposes open review systems. It also proposes an evaluation in which scientific quality is not only measured from the impact factor but with alternative metrics, where open publication, open data, and the transparency of research are well valued for the promotion of individuals, groups, and institutions. Otherwise, it would be inconsistent to promote certain changes and continue to reward outdated practices.