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Dimensions Research Integrity Dataset
Ensure published research is transparent and trustworthy
Discover the world’s largest research integrity dataset
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Why use Dimensions Research Integrity Dataset?

Covering over 33 million chapters, books, and articles, Dimensions Research Integrity offers the world’s largest dataset on research quality. Powered by Ripeta’s trust markers, you can now look beyond standard attention metrics to assess the quality of scientific communication and data sharing.

Using the Dimensions Research Integrity Dataset on Google BigQuery allows access to the trust marker data enrichments via Dimensions data. 

What Dimensions Research Integrity Dataset offers

An unrivalled research integrity dataset of over 33 million chapters, books, and articles 

Historical context from 2010 to present

Standard publishing requirements and policies of research quality indicators: Data availability statements, data repositories, and code availability; Ethical statements, conflicts of interest, and author contributions; Funding statements and grant IDs and more 

Papers analyzed
33,000,000
Quality Indicators Tracked
11
Years Covered
10
Trust Markers
10+

Who is Dimensions Research Integrity Dataset for?

Publishers

Analyse key quality indicators over time by journal.

Track the quality of how research has been communicated, not just the attention gained.
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Government & Funders

Track funding efficacy over time to assess trends in compliance. Identify key trends in data sharing and reproducibility across journals and institutions.
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Academic Institutions

Monitor research output for quality and compliance. Track your shared data to repositories.

Identify issues with preprints before they are published.
 
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Case Study

Wellcome Trust

  • Retrospective analysis of the change in public access and data sharing between 2016 and 2019
  • Provided an opportunity to gain an insight into whether data and code-sharing had improved over time
  • Ripeta checked for the inclusion of a statement in all funded publications which describes how underlying data and code can be accessed.

Read full case study | Read the report