Each school of thought offers its own theory about developments in science, leading to activities that support a number of distinct objectives. These combined activities lead to changed practices in the conduct and administration of research and form what is called the open science movement. The following section categorizes these different areas of activity.

Open Science Areas of Activity

The Foster Open Science portal is an online learning platform covering all topics related to open science. It is intended for people who want to integrate open science practices into their work processes. It is the result of the European project, Fostering the Practical Implementation of Open Science in Horizon 2020 and Beyond, which was funded by Horizon 2020 between 2017 and 2019. In its section, “What is Open Science? Introduction,” there is a representation of the open science facets designed by Gema Bueno de la Fuente (n.d.). Open science extends its principles of openness, transparency, sharing, and collaboration into the areas of activity covering the entire research process, from its conception to its dissemination. The table below summarizes significant open science developments of these facets; we’ve added the “Open Research Protocols” facet early in the design phase to better reflect recent developments. For each facet, we’ve proposed the school of thought that seems to guide that area of activity. We’ve also provided some RDM actions to illustrate that RDM is present in all of these aspects of open science.

As we now understand from previous chapters, RDM practices are useful throughout all phases of a research project. It is interesting to note that RDM practices are also found throughout the different open science areas of activity. We also note that none of the emerging open science practices are directly associated with the measurement school of thought, whereas most of them are strongly influenced by the pragmatic school (four out of nine categories). Remember that the pragmatic school aims to make science more efficient, particularly by promoting collaboration.

The Benefits of RDM in Context

Open science practices are believed to have many benefits, including those illustrated in Canada’s Roadmap for Open Science. The table below includes questions about some of the open science benefits put forward by promoters of open science. Take this opportunity to reflect upon each of these questions.

We must avoid seeing open science practices as a panacea for issues that have always existed. Even if these practices and related RDM activities help to redefine ways of doing research and pave the way for new solutions, these issues cannot truly be curtailed without taking into account the structural realities that underlie them — which open science does not do. Here are some reflections prompted by the questions in Table 3.

1. Improving accountability: According to Canada’s Roadmap for Open Science, “Open Access to scientific research outputs provides greater accountability to taxpayers and research funders” (Office of the Chief Science Advisor of Canada, 2020). However, accountability requires effective RDM policy implementation from all levels of government, which seems unlikely given how little follow-up there has been by the three federal research funding agencies concerning their open access policy (Paquet et al., 2022).
2. Increasing public trust in science: Trust can’t be established by simply making more data (and more articles) available. We must also work to improve the public’s data (and information) literacy. Implementing RDM practices in a vacuum, without aligning them with open science objectives and literacy issues, is unlikely to fulfill the potential to improve public trust.
3. Accelerating innovation: Open science promotes the practice of sharing and reusing data that can support innovation. This is a laudable goal, especially if it includes social innovation, which we believe is both most needed by society and would most benefit from evidence-based data to inform decision-makers. However, there are methodological and epistemological challenges in producing evidence-based data in the humanities and social sciences and in developing infrastructure to enable their use by decision-makers. Canada, along with a dozen other countries, is working to establish mechanisms and information flows that would make evidence-based data accessible to decision-makers (Global Commission on Evidence to Address Societal Challenges, 2023).

Beyond the Optimistic Discourse on Opening Science

Mirowski (2018) believes that open science has its roots in a neoliberal ideology that underpins present-day science. He posits that the open science movement’s conceptualization of scientific institutions and of the nature of knowledge is driven by market imperatives rather than actual new problems in the conduct of research. For those who are less familiar with this political current, we suggest reading the article by McKeown (2022), which lists some characteristics of the neoliberal university.

In this section, we invite you to take a more critical look at these new developments by considering two issues. The first discussion seeks to draw lessons from the historical evolution of open access publishing; the second addresses the challenges related to data sharing in the context of qualitative research.

What Open Access Publishing Teaches Us

Commercial publishers have played a significant role in the evolution of scholarly communication practices in recent decades. The context of the recent COVID-19 pandemic has made it possible to demonstrate the role they could play in open access to knowledge. In 2020, there was an impressive increase in accessibility to scientific publications on coronaviruses compared to the previous two decades, and it was thanks to the cooperation of commercial publishers (Belli et al., 2020). However, it remains to be seen whether this openness will be maintained, since the strong growth was made possible through a bronze open access model, meaning many of the published articles do not have licenses guaranteeing their continued free access. Their availability is dependent on the goodwill of commercial publishers.

Several funding bodies have come together to exert pressure on commercial journal publishers to transition their business models to open access. At the 14th Berlin Open Access Conference in 2018 (Max Planck Digital Library), organizations from 37 nations across five continents issued a joint statement of support for Plan S.^[2] This is a strategy supported by a consortium of funding bodies, together named cOAlition S, which aims to make open access to publications a reality. The Fonds de recherche du Québec (FRQ) was one of the first North American funding organizations to join cOAlition S in 2021. This extensive and evolving movement in the scholarly information economy ecosystem means that libraries will eventually manage very few subscriptions. In all likelihood, subscriptions will be replaced by financial agreements with commercial publishers which will include the payment of researchers’ article processing charges by their respective institutions.

Suffice it to say that commercial publishers of scholarly journals will continue to thrive; according to a 2017 study reported by Zhang and her colleagues (2022), article processing charges are increasing at a higher rate than the consumer price index. This is a familiar echo of how increasing journal subscription costs previously put a stranglehold on academic libraries around the world, with public funds being used to pay for unsustainable price increases. Funds provided for these new financial agreements will mostly end up in the pockets of commercial publishers who control APC price increases — to the detriment of the development of diamond-type open access models. These types of open access models allow scientists to publish open access and at no cost and are more consistent with the principles of open science, because the journals are financed by public funds, university funds, or by foundations (Institut Pasteur, 2021). Diamond-type models are also in perfect harmony with the original motives of the first open access initiatives, like the Bethesda Statement and the Berlin Declaration in 2003: to give the power of the dissemination of knowledge products back to research communities.

Many people and groups participate in the scholarly information economy; some have corporate interests that are more focused on profit than on supporting actual research. Considering that data sharing as a formal part of scholarly communication (i.e., with its own publishing standards and practices) is still in its infancy, one wonders if similar economic forces aren’t seeking to shape these standards and practices and to control the underlying data infrastructures. Can proponents of new data sharing practices learn from the experience of open access?

Data Sharing and Qualitative Research

Researchers working in the field of qualitative research wonder about the impact that open science will have on publishing requirements in their discipline. This questioning stems from both the definition often attributed to research data and the trend towards open data observed in several countries. For example, the OECD Principles and Guidelines for Access to Research Data from Public Funding state that

Sharing and open access to publicly funded research data not only helps to maximize the research potential of new digital technologies and networks, but provides greater returns from the public investment in research (OECD 2007, p. 10).

Funding agencies that encourage data sharing often have a cursory definition of research data. The three federal research funding agencies (the agencies) define research data as “facts, measurements, records, or observations collected by researchers and others, with a minimum of contextual interpretation” (Government of Canada, 2023).

We will demonstrate below how data sharing and the definition of research data raise concerns for qualitative researchers.

Question of Context and Reproducibility

As shown in Table 3, one of the benefits attributed to open science is that it increases reproducibility. However, members of the qualitative research community maintain that research context is vital and should be considered before a project’s research results can be reproduced. From the positivist perspective of the natural sciences or biomedical sector, data is generally considered to be context-free, as the definition from the three federal research funding agencies (above) states. On the other hand, in qualitative research, which often uses a constructivist perspective, the context is inseparable from the research question (Hesse, 2018, p. 566). As such, the issue of the reproducibility of results cannot be addressed in the same way as it is in the pure sciences. With these considerations in mind, how can data from qualitative research projects be shared and reused? In practical terms, will it be possible, with shared data, to take the context of data production into account?

Myth of Raw Data and Neutral Data

In the context of qualitative research, it is important to be aware that shared data will have previously been the subject of interpretation. Regardless of the discipline, a dataset is a construction that cannot be abstracted from the people who created it. Before being deposited in a repository, the dataset was the subject of deliberations, negotiations, and decisions of inclusion and exclusion that are well anchored in predominant discourses as well as historical and socioeconomic realities. Therefore, it is impossible to claim that shared data is neutral (Neff et al., 2017). The importance of dataset documentation is therefore clear, but documentation nonetheless does not capture tacit knowledge that is invaluable for understanding a dataset. From this perspective, data sharing appears to be an eminently complex exercise.

Promotion of Particular Types of Research and Prioritization of Methodologies

According to the OECD, for a dataset to be shareable, it must meet certain criteria, including, ideally, being available digitally (OECD, 2007). The digital nature of data can lead to promoting the use of big data, since these large datasets, produced quickly and in a variety of formats, are increasingly available and easy to access. The emphasis being put on research involving massive datasets raises the risk that qualitative methodologies will be subordinated to quantitative ones. In addition, a shift could occur so that techniques typically used to analyze qualitative data will only serve to confirm the results provided through quantitative methods (Hesse et al., 2018). Finally, Hesse et al. also report the fear that research using small datasets will receive less recognition than research involving large data collections (2018).

Conclusion: Being Open About Open Science

We have seen how RDM activities permeate open science practices, and we’ve discussed how the predominant, enthusiastic, and resolutely optimistic discourse on the adoption of these open science proposals overlooks the complexity of the real world issues they purport to solve. The limited space for this chapter and the overall educational purpose of this textbook prevent an in-depth treatment of the ideological foundations of this call to open science. However, it is interesting to note that the concerns raised by these new practices have produced a new field of study: critical data studies. This recent area of research offers possible solutions for RDM practices that take different disciplinary norms into account. More specifically, since qualitative research is being pushed to change before important disciplinary consensuses have emerged, we believe that using critical data studies to analyze professional practices relating to RDM will help prevent qualitative research communities from being subsumed by more dominant research cultures.

A critical or socio-political approach to interpreting open science developments would make it easier to step back and shed new light on the enthusiastic discourse surrounding the open science movement and its practices. We are delighted to conclude this chapter by inviting RDM practitioners to take accountability in their professional practices by digging into the discourse of this vast open science movement to try to develop potential answers to the following questions: Which economic and political systems are producing social structures, values, norms, ideologies, goods, and financial products? For whom? With what technologies and why those technologies? Where in all of this are the open science infrastructures situated? And who benefits from the opening of science?