Open scientist in the making
I plan to become an Open Scientist because I believe OS has so many benefits, as we learned along the course, including: increasing collaboration and sharing, more access to knowledge for all, creating greater opportunities for innovation and participation in the co-creation of knowledge, fostering transparency and inclusiveness, allowing reuse of works and data but also crediting the authors, helping us solve the many challenges we face us humans in a timely manner, obtaining economic benefits and better return on public investments, reducing duplication of costs by allowing reuse of data and materials, creating social impact of research and widening circulation of findings, allowing citizens to participate in research and creating the basis for a more democratic access to knowledge (UNESCO, 2020).
In Session 2 about Open Access we explored different topics I was very familiar with, for example I already publish my research in open access journals and oa repositories. But I realized that I had a vague understanding of “open peer review” and, reading the suggested papers, I learned that there are many “flavors” including 22 configurations of 7 traits of open peer review: open identities, open reports, open participation, open interaction, open pre-review manuscripts, open final-version commenting, open platforms or “decoupled review”. The first two traits are the most common, but all together they help to solve problems associated with traditional peer review such as unreliability and inconsistency, delay , high expense, lack of accountability, biases, lack of incentives among others (Ross-Hellauer, T. 2017). I plan to explore the options for publishing in an open access journal which also has open peer review, for example “Publications”, but the latter is not a “diamond” OA publication.
Exploration of tools proposed on Session 3 led me to try the Lens.org database, among others. I was delighted to learn that I was able to create a dashboard and analyze an institution’s publications including OA indicators. For example, I can see that there was an increase in OA publications since Argentina has a National Mandate starting at the end of 2013. My PhD research includes bibliometric studies and I plan to use Lens.org as one of my data sources. We also learned the importance of persistent identifiers (PIDs), specially managing our researcher presence in different platforms by using ORCID ID, which I added to BASE and my ResearchGate and Academica.edu and Google Scholar profiles. I also was able to add my ORCID ID to some of my outputs in OpenAIRE, but not all, depending on the content provider, that was puzzling. Besides, I explored OSF and found many interesting projects related to my research such as “FAIR4Health”, “The Open Scholarship Survey (OSS)”, “On open science practices in information systems (IS) research”, a project about OA in Argentina, “Faculty Use and Opinions of Open Access Publishing at University of Denver”. I plan to create a project at OSF for my thesis.
The CESSDA Data Management Expert Guide was very detailed with many examples and templates. It made me consider what other researchers and potential re-users will need in order to understand my data. We need to document and share about the doctoral project itself and data-level documentation, including types of data (survey, etc.) file type and formats (preferably open formats), size, data processing scripts and quantities. Regarding metadata, I plan to share the data from my thesis (social science) and use the Data Documentation Initiative (DDI) metadata standard specific for Social Sciences.
In order to learn from data sharing good practices in my field of study, I searched for datasets related to my theses in DataCite. I found some examples “Innovations in scholarly communication – data of the global 2015-2016 survey”, “Data from Open access levels: a quantitative exploration using Web of Science and oaDOI data”, “OA Diamond Journals Study. Dataset” and “Medición del Acceso Abierto Consorcio Madroño (2015-2019)“. In each case I observed not only the metadata attached, but also data-level documentation, data formats (open formats, original files, etc.), readme files where they explained the dataset, terms of use including open licenses.
I was pleased to learn the many services UC3M offers in terms of Open Science (Session 6). I found a few of my previous publications in e-Archivo and plan to deposit more there. I want to share my data in e-cienciaDatos Repositorio de Datos UC3M, which uses Dataverse software with DDI metadata for citation, project and data documentation, as well as licensing options, and other aspects of a trusted repository. I would like this repository to offer CoreTrustSeal or similar certification, considering that COAR (Confederation of Open Access Repositories) , CoreTrustSeal, the European University Association, Science Europe and the World Data System have published a joint position statement on “Data Repository Selection – Criteria That Matter”.
Learning about Citizen Science was eye opening because I now understand there are principles for meaningful citizen participation. I started to search for opportunities to apply this type of collaboration. We are exploring options for citizen science in a research project related to sustainable food production in Rosario and remote sensing horticulture data.
One apparent downside of OS practices is that it is time consuming, but it is worth it. Planning and documenting your research and DMP takes a lot of effort, making your data FAIR requires upfront commitment for preparing, assigning metadata and archiving your data in a trusted repository (Allen, 2019). On the other hand, as users, we have experienced the great benefit of having a well documented dataset made public for reuse, and we published a paper (Bongiovani et al., 2012) using well documented data of a larger dataset (Dallmeier-Tiessen, Suenje, et al., 2011).
The main drawback for practicing OS is the way evaluation of research works right now, with evaluation and promotion criteria that doesn’t favor open science. In general, evaluators favor traditional metrics such as high impact factors (Pagliaro, 2021). As PhD students we are expected to publish our research in prestigious publications and many of them are not open access or require to pay high APF (article processing fees). In order to be OS researchers we have to consider very carefully where to publish.
We learned that the landscape is slowly changing and there are several initiatives raising awareness and advocating to eliminate the use of journal-based metrics as the main parameter to evaluate the scientific output of individuals, groups and institutions.
Signing the Declaration on Research Assessment (DORA) is a way to promote new practices. There are universities committed to that change, such as Utrecht University where faculty will be evaluated by their contribution to open science. As explained by Leiden Manifesto we have to abandon obsolete reward systems. Prof. Eva Mendez eloquently demonstrated in her graphic that the reward systems are at the root of OS, incentives must be coherent with OS policies. That is the key for OS practices to be mainstream.
References
Allen, C., & Mehler, D. M. (2019). Open science challenges, benefits and tips in early career and beyond. PLoS biology, 17(5), e3000246. https://doi.org/10.1371/journal.pbio.3000587
Bosman, J., & Kramer, B. (2016). Innovations in scholarly communication – data of the global 2015-2016 survey [Data set]. Zenodo. http://doi.org/10.5281/zenodo.49583
CESSDA Training Team (2017 – 2020). CESSDA Data Management Expert Guide.
Bergen, Norway: CESSDA ERIC. Retrieved from https://www.cessda.eu/DMGuide
Kramer, Bianca, & Bosman, Jeroen. (2018). Data from: Open access levels: a quantitative exploration using Web of Science and oaDOI data [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1143707
Pagliaro, M. (2021). Purposeful evaluation of scholarship in the open science era. Challenges, 12(1), 6. https://doi.org/10.3390/challe12010006
Ross-Hellauer, T. (2017). What is open peer review? A systematic review. F1000Research, 6. https://doi.org/10.12688/f1000research.11369.2