ArticleHarvard data science review2022
A Linked Data Mosaic for Policy-Relevant Research on Science and Innovation: Value, Transparency, Rigor, and Community.
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 1 paper.
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
1 citing paper in PubMed.
- Using gamification to engage citizens in micro-mobility data sharing.Data in brief · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
This article presents a new framework for realizing the value of linked data understood as a strategic asset and increasingly necessary form of infrastructure for policy-making and research in many domains. We outline a framework, the 'data mosaic' approach, which combines socio-organizational and technical aspects. After demonstrating the value of linked data, we highlight key concepts and dangers for community-developed data infrastructures. We concretize the framework in the context of work on science and innovation generally. Next we consider how a new partnership to link federal survey data, university data, and a range of public and proprietary data represents a concrete step toward building and sustaining a valuable data mosaic. We discuss technical issues surrounding linked data but emphasize that linking data involves addressing the varied concerns of wide-ranging data holders, including privacy, confidentiality, and security, as well as ensuring that all parties receive value from participating. The core of successful data mosaic projects, we contend, is as much institutional and organizational as it is technical. As such, sustained efforts to fully engage and develop diverse, innovative communities are essential.
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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.