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A metadata schema for data objects in clinical research

Overview of attention for article published in Trials, November 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
twitter
20 tweeters
googleplus
1 Google+ user
video
1 video uploader

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
26 Mendeley
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Title
A metadata schema for data objects in clinical research
Published in
Trials, November 2016
DOI 10.1186/s13063-016-1686-5
Pubmed ID
Authors

Steve Canham, Christian Ohmann

Abstract

A large number of stakeholders have accepted the need for greater transparency in clinical research and, in the context of various initiatives and systems, have developed a diverse and expanding number of repositories for storing the data and documents created by clinical studies (collectively known as data objects). To make the best use of such resources, we assert that it is also necessary for stakeholders to agree and deploy a simple, consistent metadata scheme. The relevant data objects and their likely storage are described, and the requirements for metadata to support data sharing in clinical research are identified. Issues concerning persistent identifiers, for both studies and data objects, are explored. A scheme is proposed that is based on the DataCite standard, with extensions to cover the needs of clinical researchers, specifically to provide (a) study identification data, including links to clinical trial registries; (b) data object characteristics and identifiers; and (c) data covering location, ownership and access to the data object. The components of the metadata scheme are described. The metadata schema is proposed as a natural extension of a widely agreed standard to fill a gap not tackled by other standards related to clinical research (e.g., Clinical Data Interchange Standards Consortium, Biomedical Research Integrated Domain Group). The proposal could be integrated with, but is not dependent on, other moves to better structure data in clinical research.

Twitter Demographics

The data shown below were collected from the profiles of 20 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 23%
Student > Ph. D. Student 4 15%
Librarian 3 12%
Student > Bachelor 2 8%
Student > Master 2 8%
Other 4 15%
Unknown 5 19%
Readers by discipline Count As %
Computer Science 4 15%
Medicine and Dentistry 4 15%
Social Sciences 2 8%
Arts and Humanities 2 8%
Agricultural and Biological Sciences 2 8%
Other 4 15%
Unknown 8 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 19. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 July 2017.
All research outputs
#949,009
of 14,535,614 outputs
Outputs from Trials
#293
of 3,811 outputs
Outputs of similar age
#34,991
of 381,834 outputs
Outputs of similar age from Trials
#36
of 432 outputs
Altmetric has tracked 14,535,614 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,811 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 381,834 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 432 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.