Openness and trust in data-intensive science: the case of biocuration

Abstract

Data-intensive science comes with increased risks concerning quality and reliability of data, and while trust in science has traditionally been framed as a matter of scientists being expected to adhere to certain technical and moral norms for behaviour, emerging discourses of open science present openness and transparency as substitutes for established trust mechanisms. By ensuring access to all available information, quality becomes a matter of informed judgement by the users, and trust no longer seems necessary. This strategy does not, however, take into consideration the networks of professionals already enabling data-intensive science by providing high-quality data. In the life sciences, biological data-and knowledge bases managed by expert biocurators have become crucial for data-intensive research. In this paper, I will use the case of biocurators to argue that openness and transparency will not diminish the need for trust in data-intensive science. On the contrary, data-intensive science requires a reconfiguration of existing trust mechanisms in order to include those who take care of and manage scientific data after its production.

CeGAL: revisiting a widespread fungal-specific TF family using an in silico error-aware approach to identify missing zinc cluster domains

“…Our results clearly indicate that all these fungal features, which should improve the gene prediction quality, do not limit the error rates at least in the studied TF family. This notion of errors in public protein databases is a recurrent problem (34)(35)(36) and substantial efforts have been invested to identify and correct gene annotation errors (37)(38)(39). Some important causes of erroneous sequences have been identified, including the genome sequence quality and gene structure complexity (40), as well as redundant or conflicting information in different resources or in the literature (34,41).…”

A landscape of open science policies research

“…Kwon and Motohashi (2020) argue that policies on research data should institutionalize the legal protection of research data ownership and mandate research data disclosure. Gabrielsen (2020) argue that data policies should include digital curator communities which would give given more focus and jurisdiction, as well as frameworks to build and preserve trust. Curators should be included in the infrastructures for data-intensive research that are being developed.…”

“…Historically, the practice of secrecy in science is encouraged prior to patent and is maintained after patenting, making research communication difficult. Moreover, Gabrielsen (2020) argue that in the current science policy, the importance of trust has decreased, and there is a tendency towards openness and transparency. The lack of consensus on the establishment of grace periods (as an exception, allowing inventions to be disclosed before patent applications) has created legal uncertainty (Wong, Ramos-Toledano, Rojas-Mora, 2018).…”

The practical ethics of repurposing health data: how to acknowledge invisible data work and the need for prioritization

“…The metaphor of “data flow,” often used in policy reports, suggests that integration and reinterpretation of data are about ensuring that nothing “stops” the flow, as if data were water moving in pipes. However, a growing literature in philosophy of science and social science demonstrates that data integration and repurposing are far from straightforward but require meticulous data work and expertise to succeed (e.g., Hogle 2016 ; Leonelli 2014 ; 2016 ; Bossen et al 2019 ; Gabrielsen 2020 ; Pine et al 2020 ; Hoeyer 2023 ). While ethical debates about data reuse have raised important points about privacy, autonomy, discrimination, and inequality (see below), the reframing of health data as “assets” for administration, research, and innovation can also include costs and trade-offs in need of ethical attention (see also Hunt et al 2017 ; Vezyridis et al 2017 ; Birch et al 2021 ; Pinel 2021 ).…”