ArticleMedicine, health care, and philosophy2020
Openness and trust in data-intensive science: the case of biocuration.
Article in Medicine, health care, and philosophy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Working in biocuration: contemporary experiences and perspectives.Database : the journal of biological databases and curation · 2025Article
- CeGAL: Redefining a Widespread Fungal-Specific Transcription Factor Family Using an In Silico Error-Tracking Approach.Journal of fungi (Basel, Switzerland) · 2023Article
- The practical ethics of repurposing health data: how to acknowledge invisible data work and the need for prioritization.Medicine, health care, and philosophy · 2023Article
- A survey of computable biomedical knowledge repositories.Learning health systems · 2023Article
- Validation Through Collaboration: Encouraging Team Efforts to Ensure Internal and External Validity of Computational Models of Biochemical Pathways.Neuroinformatics · 2022Review
- Knowledge repositories. In digital knowledge we trust.Medicine, health care, and philosophy · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.