Measuring the integration of dataset contributors into the publication team: Metrics for assessing team integration in genomics and biomedicine and implications for citation impact and scientific capacity

Despite the significant role of datasets in advancing genomics and biomedicine, few metrics exist for assessing team integration of dataset contributors into the publication. Drawing from set theory, this study develops several measures of data-intensive team integration. We develop and describe 'data labor integration ratio' and variants and apply them two case studies in the biomedical sciences and genomics by analyzing the metadata of scientific datasets and their associated publications deposited to GenBank over 29 years (1992–2021). Findings indicate that using these measures, we can newly estimate whether teams with highly integrated dataset contributors are strongly correlated with higher citation impact. We also find that tightly integrated teams tend to publish more quickly. Results suggest that a higher data-and-publication team integration promotes conditions for the exchange of expertise and access to resources, thereby bolstering research capacity. The study offers effective metrics for quantifying team integration in biomedicine and genomics. We argue that these measures of dataset contributor integration are superior to approaches that rely solely on publication information advancing the assessment of data-intensive team integration on citation impact and scientific capacity-building in international collaborations.