Evidence map›Paper›PMID 36463884›Full record

ArticleYearbook of medical informatics2022

Towards an Interoperable Ecosystem of Research Cohort and Real-world Data Catalogues Enabling Multi-center Studies.

Morris Swertz, Esther van Enckevort, José Luis Oliveira, Isabel Fortier, Julie Bergeron, Nicolas H Thurin, Eleanor Hyde, Alexander Kellmann, Romin Pahoueshnja, Miriam Sturkenboom and 5 more

Abstract read
In one paragraph

Article in Yearbook of medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Canadian Metadata Catalogue for Health Science Research.International journal of population data science · 2026
    Article
  5. Article
  6. Article
  7. Review
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

15 authors.

Morris SwertzDepartment of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Esther van EnckevortDepartment of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
José Luis OliveiraDETI/IEETA, University of Aveiro, Portugal.
Isabel FortierResearch Institute of the McGill University Health Center, Montreal, Canada.
Julie BergeronResearch Institute of the McGill University Health Center, Montreal, Canada.
Nicolas H ThurinUniv. Bordeaux, INSERM CIC-P 1401, Bordeaux PharmacoEpi, Bordeaux, France.
Eleanor HydeDepartment of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Alexander KellmannDepartment of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Romin PahoueshnjaUniversity of Utrecht, The Netherlands.
Miriam SturkenboomDepartment of Datascience & Biostatistics, Julius Center, University Medical Center Utrecht, Utrecht, The Netherlands.
Marianne CunningtonGlaxoSmithkline, Stevenage, Herts, SG1 2NY, UK.
Anne-Marie Nybo AndersenUniversity of Copenhagen, Copenhagen, Denmark.
Yannick MarconEpigeny, France.
Gonçalo GonçalvesHuman-Centered Computing and Information Science, INESC TEC, Portugal.
Rosa GiniARS Toscana, Florence, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesExisting individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness.

methodsIn this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records.

resultsWe observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact.

conclusionsWe propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata 'common data elements' using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR.

Indexed as

Common Data ElementsEcosystemCohort StudiesHumansInformation Dissemination

Identifiers

PMID36463884
PMCPMC9719789

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.