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IDGenerator: unique identifier generator for epidemiologic or clinical studies

Overview of attention for article published in BMC Medical Research Methodology, September 2016
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38 Mendeley
Title
IDGenerator: unique identifier generator for epidemiologic or clinical studies
Published in
BMC Medical Research Methodology, September 2016
DOI 10.1186/s12874-016-0222-3
Pubmed ID
Authors

Matthias Olden, Rolf Holle, Iris M. Heid, Klaus Stark

Abstract

Creating study identifiers and assigning them to study participants is an important feature in epidemiologic studies, ensuring the consistency and privacy of the study data. The numbering system for identifiers needs to be random within certain number constraints, to carry extensions coding for organizational information, or to contain multiple layers of numbers per participant to diversify data access. Available software can generate globally-unique identifiers, but identifier-creating tools meeting the special needs of epidemiological studies are lacking. We have thus set out to develop a software program to generate IDs for epidemiological or clinical studies. Our software IDGenerator creates unique identifiers that not only carry a random identifier for a study participant, but also support the creation of structured IDs, where organizational information is coded into the ID directly. This may include study center (for multicenter-studies), study track (for studies with diversified study programs), or study visit (baseline, follow-up, regularly repeated visits). Our software can be used to add a check digit to the ID to minimize data entry errors. It facilitates the generation of IDs in batches and the creation of layered IDs (personal data ID, study data ID, temporary ID, external data ID) to ensure a high standard of data privacy. The software is supported by a user-friendly graphic interface that enables the generation of IDs in both standard text and barcode 128B format. Our software IDGenerator can create identifiers meeting the specific needs for epidemiologic or clinical studies to facilitate study organization and data privacy. IDGenerator is freeware under the GNU General Public License version 3; a Windows port and the source code can be downloaded at the Open Science Framework website: https://osf.io/urs2g/ .

X Demographics

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 37 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 26%
Student > Ph. D. Student 5 13%
Student > Bachelor 5 13%
Professor 3 8%
Student > Doctoral Student 2 5%
Other 7 18%
Unknown 6 16%
Readers by discipline Count As %
Medicine and Dentistry 6 16%
Computer Science 5 13%
Engineering 4 11%
Psychology 4 11%
Biochemistry, Genetics and Molecular Biology 3 8%
Other 10 26%
Unknown 6 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 April 2017.
All research outputs
#14,917,504
of 22,950,943 outputs
Outputs from BMC Medical Research Methodology
#1,454
of 2,026 outputs
Outputs of similar age
#193,601
of 321,533 outputs
Outputs of similar age from BMC Medical Research Methodology
#29
of 46 outputs
Altmetric has tracked 22,950,943 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,026 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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 321,533 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.