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dbGaPCheckup: pre-submission checks of dbGaP-formatted subject phenotype files

Overview of attention for article published in BMC Bioinformatics, March 2023
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Title
dbGaPCheckup: pre-submission checks of dbGaP-formatted subject phenotype files
Published in
BMC Bioinformatics, March 2023
DOI 10.1186/s12859-023-05200-8
Pubmed ID
Authors

Lacey W. Heinsberg, Daniel E. Weeks

Abstract

Data archiving and distribution are essential to scientific rigor and reproducibility of research. The National Center for Biotechnology Information's Database of Genotypes and Phenotypes (dbGaP) is a public repository for scientific data sharing. To support curation of thousands of complex data sets, dbGaP has detailed submission instructions that investigators must follow when archiving their data. We developed dbGaPCheckup, an R package which implements a series of check, awareness, reporting, and utility functions to support data integrity and proper formatting of the subject phenotype data set and data dictionary prior to dbGaP submission. For example, as a tool, dbGaPCheckup ensures that the data dictionary contains all fields required by dbGaP, and additional fields required by dbGaPCheckup; the number and names of variables match between the data set and data dictionary; there are no duplicated variable names or descriptions; observed data values are not more extreme than the logical minimum and maximum values stated in the data dictionary; and more. The package also includes functions that implement a series of minor/scalable fixes when errors are detected (e.g., a function to reorder the variables in the data dictionary to match the order listed in the data set). Finally, we also include reporting functions that produce graphical and textual descriptives of the data to further reduce the likelihood of data integrity issues. The dbGaPCheckup R package is available on CRAN ( https://CRAN.R-project.org/package=dbGaPCheckup ) and developed on GitHub ( https://github.com/lwheinsberg/dbGaPCheckup ). dbGaPCheckup is an innovative assistive and timesaving tool that fills an important gap for researchers by making dbGaP submission of large and complex data sets less error prone.

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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 07 March 2023.
All research outputs
#14,974,414
of 23,947,581 outputs
Outputs from BMC Bioinformatics
#4,834
of 7,467 outputs
Outputs of similar age
#200,083
of 422,555 outputs
Outputs of similar age from BMC Bioinformatics
#76
of 135 outputs
Altmetric has tracked 23,947,581 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,467 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 31st percentile – i.e., 31% 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 422,555 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.