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Title: Double-anonymous Peer Review Mitigates Country Homophily and the Harms of Low Reviewer Diversity: Deidentified data and replication code Tombstone Deposited

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Methodology
  • This dataset was deprecated and tombstoned in favor of an updated dataset available at  https://doi.org/10.7302/vvj6-x579

  • This collection consists of 1 anonymized dataset and 2 analysis replication files:

  • 1. invited_revs_deid_df.csv: This file provides metadata on invited reviewers for submissions to 60 IOP Publishing journals from January 2018-December 2022. Only the first round of review was analyzed; desk-rejected submissions and revisions were excluded. The following steps were taken to ensure anonymity: - Deidentification: Journal, manuscript ID, reviewer name, year-month of invitation, and year-month of submitted review were deidentified using randomly generated, 10-character strings. Unique countries in the dataset were gathered from the union of corresponding and lead authors' and reviewers' countries. Countries were then deidentified using randomly generated, 10-character strings, so that they are consistent across the three variables (i.e., USA code is same for author and reviewer country columns). - Noise: For each numerical variable, a random 1% of values were shuffled around (including missing values). - Quintiles: Team size, the only non-binary numerical variable, was logged and demeaned in the main paper analysis. This variable was also converted to quintiles for deidentification.

  • 2. deid_replication_main_figs.ipynb: This file replicates figures and analyses from the paper including estimates of differential access to same-country reviewers and the back of the envelope calculation. The recommendation has been converted to a binary variable ("Positivity") where 1=Accept or R/R and 0=Reject. - Note: Top 3 submitting countries (USA, China, India) were reidentified for country aggregate analysis. Since these are the most common countries in the dataset, this information should not be sufficient for identifying individual reviewers or submissions

  • 3. deid_replication_regs.R: This file replicates analyses from the paper estimating the effects of same-country reviewer status on agreeing to review and reviewing positively. The recommendation has been converted to a binary variable ("Positivity") where 1=Accept or R/R and 0=Reject.
Description
  • The data sources and methods used to process the raw data are described in the paper forthcoming in Science and the associated Supplementary Information. A preprint for an earlier version of this paper is available here:  https://osf.io/preprints/socarxiv/754e3. These data are anonymized (see Methodology for details). Consequently, running the same code on these data vs. the data in the paper does not yield *identical* results but qualitatively similar ones.
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  • true
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  • Other Funding Agency
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  • Schmidt Futures

  • Science for Progress Initiative
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  • HUM00194927
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Citations to related material
  • J. M. Z. Dumlao, M. Teplitskiy, Science, forthcoming.
  • Zumel Dumlao, J. M. and M. Teplitskiy. 2023. “The Effect of Reviewer Geographical Diversity on Evaluations Is Reduced by Anonymizing Submissions”. Retrieved (osf.io/preprints/socarxiv/754e3).
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Last modified
  • 06/10/2025
Published
  • 10/11/2024
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  • https://doi.org/10.7302/9kxh-6e41
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