Exploratory Insights into Barriers and Open Practices for Computational Reproducibility in Scientific Research

Rapid adoption of digital technologies across research disciplines underlines the need for accessible and reusable computational data and code.

An anonymous, multidisciplinary survey examined researchers' perceptions, needs, barriers, and self-reported practices concerning open science, data and code publishing and reuse.

Of 254 respondents who initiated the survey, 133 completed it, mostly from Europe. Registered reports, replication studies and pre-registration were among the least frequently reported practices (52%, 38% and 42%, reported as Never applied), while open software and OA publishing demonstrated widespread adoption (83% and 69%) of the respondents, respectively. The main perceived barriers to data sharing were lack of time (60%) and insufficient funding (44%). For code sharing, they were lack of time to prepare documentation (65%), publication pressure (51%), and insufficient funding (42%). Journal requirements (score: 482) and institutional incentives and rewards (score: 439) were the highest-ranked supporting measures. 28% of respondents indicated that they never tried to reproduce a study, and when replication was attempted, researchers often found that open data (70%), open code (71%), and metadata (86%) were never, rarely, or only sometimes available in the publications they read.. Open-ended responses emphasized training, career-stage guidelines, and basic programming skills.

As survey was disseminated through open-science channels using volunteer sampling, no response rate could be calculated, and the sample was self-selected toward researchers already engaged with reproducibility. Findings should therefore not be generalized to the wider research community. Within this group, the central finding is a gap between awareness and implementation. High endorsement of open and reproducible practices coexists with lower self-reported adoption. Responses also emphasized the need for structural incentives and institutional support, reflecting perceived limitations in time, resources, expertise, and professional recognition.