Evidence map›Paper›PMID 41112063›Full record

ArticleOpen research Europe2025

Sustainable open data ecosystems through data quality, governance, and infrastructure: Unlocking social, political and economic value.

Ramya Chandrasekhar, Melanie Dulong de Rosnay

Abstract readLetter
In one paragraph

Article in Open research Europe, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ramya ChandrasekharCentre Internet et Société, CNRS, Paris, 75017, France.ORCID https://orcid.org/0009-0007-1313-1551
Melanie Dulong de RosnayCentre Internet et Société, CNRS, Paris, 75017, France.ORCID https://orcid.org/0000-0002-0297-7603

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Open data are crucial for scientific knowledge production, transparency and accountability, as well as innovation. The European Union has implemented various policies and regulatory frameworks for open government data and open scientific data, as well as for data sharing and re-use of non-government data. However, the mere availability of open data does not ensure its reuse and distributional benefit to society, and its production can meet sustainability challenges. Working with open data requires data skills, access to data infrastructures, and regulatory guidance to address privacy, confidentiality and intellectual property requirements. Further, critical scholarship has cautioned against the de facto valorisation of open data, and urges focus on the socio-technical and political aspects of production, dissemination and use of open data beyond mere economic value. This open letter is building upon findings of an interdisciplinary Marie Curie Action Innovative Training Network focussed on 'Open Data ECOsystems' (ODECO). It claims that in a data-driven economy and a datafied society, more attention needs to be paid to the conditions within which open data is produced, disseminated and used, and by whom. Accordingly, this open letter provides a set of actionable recommendations for both practitioners and policymakers, to support sustainability as well as economic and social value in open data initiatives, through proposals in areas including data quality, governance, participation and infrastructure.

Indexed as

economic valueOpen dataopen data governanceopen data infrastructureparticipationprinciplesrecommendationssocial value

Identifiers

PMID41112063
PMCPMC12531620

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.