On the design of linked datasets mapping networks of collaboration in the genomic sequencing of Saccharomyces cerevisiae, Homo sapiens, and Sus scrofa

Wong, M. and Leng, R. (2019) On the design of linked datasets mapping networks of collaboration in the genomic sequencing of Saccharomyces cerevisiae, Homo sapiens, and Sus scrofa. F1000Research, (doi:10.12688/f1000research.18656.1) (Early Online Publication)

[img]
Preview
Text
190820.pdf - Published Version
Available under License Creative Commons Attribution.

423kB

Abstract

This paper describes a unique two-step methodology used to construct six linked bibliometric datasets covering the sequencing of Saccharomyces cerevisiae, Homo sapiens, and Sus scrofa genomes. First, we retrieved all sequence submission data from the European Nucleotide Archive (ENA), including accession numbers associated with each species. Second, we used these accession numbers to construct queries to retrieve peer-reviewed scientific publications that first linked to these sequence lengths in the scientific literature. For each species, this resulted in two associated datasets: 1) A .csv file documenting the PMID of each article describing new sequences, all paper authors, all institutional affiliations of each author, countries of institution, year of first submission to the ENA, and the year of article publication, and 2) A .csv file documenting all institutions submitting to the ENA, number of nucleotides sequenced, number of submissions per institution in a given year, and years of submission to the database. In several upcoming publications, we utilise these datasets to understand how institutional collaboration shaped sequencing efforts, and to systematically identify important institutions and changes in network structures over time. This paper, therefore, should aid researchers who would like to use these data for future analyses by making the methodology that underpins it transparent. Further, by detailing our methodology, researchers may be able to utilise our approach to construct similar datasets in the future.

Item Type:Articles
Additional Information:Version 1; peer review: awaiting peer review. The research reported in this project was funded by the European Research Council Starting Grants scheme, award number 678757.
Status:Early Online Publication
Refereed:No
Glasgow Author(s) Enlighten ID:Wong, Dr Mark Tsun On
Authors: Wong, M., and Leng, R.
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:F1000Research
Publisher:F1000Research
ISSN:2046-1402
ISSN (Online):2046-1402
Published Online:26 July 2019
Copyright Holders:Copyright © 2019 Wong M and Leng R
First Published:First published in F1000Research 2019
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record