ArticleHarvard data science review2022
Sharing Begins at Home: How Continuous and Ubiquitous FAIRness Can Enhance Research Productivity and Data Reuse.
Article in Harvard data science review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- A guide to developing harmonized research workflows in a team science context.Experimental neurology · 2025Review
- Datagraphy: toward a systematic approach to dataset discovery.GigaScience · 2025Article
- CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine.JAMIA open · 2024Article
- Exchanging words: Engaging the challenges of sharing qualitative research data.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- VoDEx: a Python library for time annotation and management of volumetric functional imaging data.Bioinformatics (Oxford, England) · 2023Article
- FAIR for AI: An interdisciplinary and international community building perspective.Scientific data · 2023Article
- VoDEx: a Python library for time annotation and management of volumetric functional imaging data.ArXiv · 2023Article
- Continuous Validation Across Macromolecular Structure Determination Process.Nihon Kessho Gakkai shi · 2023Article
- The past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR.Frontiers in neuroinformatics · 2023Review
- FaceBase: A Community-Driven Hub for Data-Intensive Research.Journal of dental research · 2022Review
- Promoting validation and cross-phylogenetic integration in model organism research.Disease models & mechanisms · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The broad sharing of research data is widely viewed as critical for the speed, quality, accessibility, and integrity of science. Despite increasing efforts to encourage data sharing, both the quality of shared data and the frequency of data reuse remain stubbornly low. We argue here that a significant reason for this unfortunate state of affairs is that the organization of research results in the findable, accessible, interoperable, and reusable (FAIR) form required for reuse is too often deferred to the end of a research project when preparing publications-by which time essential details are no longer accessible. Thus, we propose an approach to research informatics in which FAIR principles are applied
Indexed as
Identifiers
What Socratic 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 Socratic graph.