Incentivizing Open Science: Examining the Open Data Citation Advantage in Visual Search Research
Abstract
In the wake of psychology’s credibility revolution, open science practices such as data sharing have become more prevalent, yet researchers often cite a lack of professional incentives as a barrier to adoption. This report examines the association between data sharing and scholarly impact in visual search and eye-movement literature, building on the systematic audit by Godwin et al. (2025). I extended their dataset with bibliometric metadata from the OpenAlex registry and compared field-normalized, accumulated, and time-resolved citation impact between articles that did and did not share data. Although articles that shared data showed a small citation advantage in unadjusted analyses, this difference was no longer statistically significant after adjusting for publication characteristics such as publication age and journal citedness. A time-resolved analysis revealed a small association between sharing status and the trajectory of citation accrual, but did not establish a robust early citation advantage. Citation impact also did not differ by data granularity: sharing fine-grained fixation-level data did not confer greater benefit than sharing coarser participant-level summaries. These observational findings provide no robust evidence of an overall citation advantage associated with data sharing in this research domain, nor of an additional advantage for finer-grained data. They do not establish that data sharing has no citation benefit, but highlight the importance of examining how its association with scholarly impact varies across research contexts and over time.
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BibTeX
@misc{nir2026incentivizing,
title = {{Incentivizing Open Science: Examining the Open Data Citation Advantage in Visual Search Research}},
author = {Jonathan Nir},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/q8jrd_v3},
url = {https://osf.io/preprints/psyarxiv/q8jrd_v3/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Incentivizing Open Science: Examining the Open Data Citation Advantage in Visual Search Research
AU - Jonathan Nir
PY - 2026
DA - 2026/10/22
PB - PsyArXiv
DO - 10.31234/osf.io/q8jrd_v3
UR - https://osf.io/preprints/psyarxiv/q8jrd_v3/
AB - In the wake of psychology’s credibility revolution, open science practices such as data sharing have become more prevalent, yet researchers often cite a lack of professional incentives as a barrier to adoption. This report examines the association between data sharing and scholarly impact in visual search and eye-movement literature, building on the systematic audit by Godwin et al. (2025). I extended their dataset with bibliometric metadata from the OpenAlex registry and compared field-normalized, accumulated, and time-resolved citation impact between articles that did and did not share data. Although articles that shared data showed a small citation advantage in unadjusted analyses, this difference was no longer statistically significant after adjusting for publication characteristics such as publication age and journal citedness. A time-resolved analysis revealed a small association between sharing status and the trajectory of citation accrual, but did not establish a robust early citation advantage. Citation impact also did not differ by data granularity: sharing fine-grained fixation-level data did not confer greater benefit than sharing coarser participant-level summaries. These observational findings provide no robust evidence of an overall citation advantage associated with data sharing in this research domain, nor of an additional advantage for finer-grained data. They do not establish that data sharing has no citation benefit, but highlight the importance of examining how its association with scholarly impact varies across research contexts and over time.
ER -