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Recent Publications Using IRIS Data

Women are Credited Less in Science than are Men
Authors: Ross M, Glennon B, Murciona-Goroff R, Berkes E, Weinberg B, Lane J
Nature, 22 June 2022
There is a well-documented gap in the observed number of scientific works produced by women and men in science, with clear consequences for the retention and promotion of women in science. The gap might be a result of productivity differences, or it might be due to women’s contributions not being acknowledged. This paper finds that at least part of this gap is due to the latter: women in research teams are significantly less likely to be credited with
authorship than are men. The findings are consistent across three very different sources of data. Analysis of the first source – large scale administrative data on research teams, team scientific output, and attribution of credit – show that women are significantly less likely to be named on any given article or patent produced by their team relative to their peers. The gender gap in attribution is found across almost all scientific fields and career stages. The second source – an extensive survey of authors – similarly shows that women’s scientific contributions are systematically less likely to be recognized. The third source – qualitative responses – suggests that the reason is that their work is often not known, not appreciated, or ignored. At least some of the observed gender gap in scientific output may not be due to differences in scientific contribution, but to differences in attribution.

Replication Data for: Ross, M.B., Glennon, B.M., Murciano-Goroff, R. et al. “Women are credited less in science than men.” Nature.
This is a full replication package, including the underlying data and a set of code (python and stata files); this replicates results in the article of: Ross, M.B., Glennon, B.M., Murciano-Goroff, R. et al. Women are credited less in science than men. Nature 608, 135–145 (2022).

Aggregate Reports on Universities’ Research-Related Employment and Spending
These reports summarize data aggregated from IRIS member universities on their employment and spending on research-related goods and services from sponsored research projects. The data are from 2013 through 2018.

Possible Paths from the Pandemic
Author: Jason Owen-Smith
Springer Nature, 21 October 2020
This white paper uses recent public data to identify what we can know systematically about how the COVID-19 pandemic is currently affecting the large, research-intensive universities that represent the core of the US-ARE. It uses those, admittedly preliminary and partial, findings to extrapolate about possible long-term effects of decisions that academic leaders, state and federal policy makers are taking right now. The descriptive story presented here isn’t determinative, but it suggests that the pandemic poses unique dangers for the national and global research systems.

Staff Title and Role (STAR) Classification
Authors: Clara del Junco, Stefan de Jong
This technical paper documents the Staff Title and Roles (STAR) classification method, which is a semi-automated method to classify job titles in the IRIS UMETRICS data that are labeled with occupational class “Staff” – i.e., those who are not on the academic track (undergraduate and graduate students, postdocs, and faculty) – into a more detailed set of occupational roles. The method was developed and implemented by authors Clara del Junco and Stefan de Jong. The method was developed on the NIH-funded positions in the UMETRICS data, and this documentation shows the ability of the code to classify other samples of titles in the UMETRICS data.

IRIS UMETRICS Object Code Classification 
Authors: Zhuoqi Zhang, Natsuko Nicholls, Matthew VanEseltine, Christopher A Brown, Jason Owen-Smith
This documentation provides researchers with details about the recently completed object code classification project and its product which supplements the IRIS UMETRICS 2022 release dataset. IRIS receives administrative data on research spending and the purposes of spending associated with funded research projects. As part of data submission to IRIS, universities share object codes (these are often interchangeably used with “spend categories” or “expense categories”) and corresponding descriptions. IRIS has cleaned, processed, and standardized these object code descriptions through a two-tier classification system, with the first tier consisting of nine general object categorizations; the second tier is a dis-aggregate of those general categories. This documentation is publicly available.