Supporting data for "A streamlined workflow for conversion, peer review and publication of genomics metadata as Omics Data Papers"

Dataset type: Software, Workflow, Bioinformatics
Data released on April 13, 2021

Dimitrova M; Meyer R; Buttigieg PL; Georgiev T; Zhelezov G; Demirov S; Smith V; Penev L (2021): Supporting data for "A streamlined workflow for conversion, peer review and publication of genomics metadata as Omics Data Papers" GigaScience Database. http://dx.doi.org/10.5524/100889

DOI10.5524/100889

Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peerreview and increases the impact of datasets by enhancing their visibility, accessibility, and re-usability.
We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivise researchers to publish well-described data sets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript.
An omics data paper template was designed by defining key article sections which encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress and BioSamples databases.
The template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing.
The omics data paper structure and workflow for import of genomics metadata help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata re-use for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.





File NameSample IDData TypeFile FormatSizeRelease Date 
GitHub archivezip826.09 KB2021-04-08
readme.txtTEXT2.89 KB2021-04-13
Displaying 1-2 of 2 File(s).
Funding body Awardee Award ID Comments
H2020 Marie Skłodowska-Curie Actions M Dimitrova 764840
Pensoft Publishers G Zhelezov & S Demirov
Date Action
April 13, 2021 Dataset publish
April 19, 2021 Manuscript Link added : 10.1093/gigascience/giab034
November 29, 2021 Manuscript Link updated : 10.1093/gigascience/giab034