Why don't we share data and code? Perceived barriers and benefits to public archiving practices

Abstract

The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.

Data Mobilization Through the International Year of the Salmon Ocean Observing System

“…We found that some scientists were weary of sharing data, both internally and publicly, over fears of being scooped, data misinterpretation, or inadequate data management to facilitate sharing (meaning the data likely had issues such as discrepancies or caveats that were implicit). Though many of these concerns can be alleviated (Gomes et al, 2022), the notion of publishing stand-alone data (rather than publishing manuscripts with supplemental data files) was foreign to some scientists who were unaware of the advantages of the practice. Benefits can include increased collaboration opportunities by way of the 'data creators advantage' (Pasquetto et al, 2019) which puts data producers in an advantageous position because data re-users often request the help of data producers and in turn offer citation or co-authorship.…”

An updated end-to-end ecosystem model of the Northern California Current reflecting ecosystem changes due to recent marine heat waves

“…It is important to note, however, that this view is based on an incomplete snapshot in time and space based on limited available data. Ecosystem modeling efforts would benefit from additional surveys and, importantly, more readily available data [79] as is being done more effectively in the southern extent of the California Current [https://calcofi.org/data/]. As we move beyond single-species models towards holistic ecosystem-based fisheries management, we must openly and collaboratively integrate our disparate datasets and collective knowledge to solve the intricate problems we currently face in a changing world.…”

“…Further model details can be found in previous EcoTran articles [27,[32][33][34]76]. To be more open, reliable, transparent, and reproducible [79], we have provided data and code to reproduce and use this ecosystem model at https://doi.org/10.5281/zenodo.7079777.…”

Open science should be a pleonasm

“…Labour costs are also relevant, as open access to data and repositories can abrogate the time-salary costs Disadvantages of open science include increased workload for authors, data misuse and concerns regarding data ownership. Providing repositories with de-identified datasets and including data sharing policy on consent forms, project protocols and institutional board reviews may pose administrative burdens with a lack of perceived benefit for authors or patients [17,18]. Opponents to open science state that lay readers might misunderstand complex raw data, and non-scientists might unintentionally misinterpret it.…”