{
  "abstract": "Background:Rapid adoption of digital technologies across research disciplines underlines the need for accessible and reusable computational data and code.Methods:An anonymous, multidisciplinary survey examined researchers’ perceptions, needs, barriers, and self-reported practices concerning open science, data and code publishing and reuse.Results: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.Conclusions: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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Yuri Andrei Gelsleichter"
    },
    {
      "name": "Rita Banzi"
    },
    {
      "name": "Florian Naudet"
    },
    {
      "name": "Constant Vinatier"
    },
    {
      "name": "István Kertész"
    },
    {
      "name": "Monika Varga"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fekneernpmxqp.bib",
  "cited_by": 0,
  "doi": "10.12688/f1000research.172013.3",
  "fulltext_url": null,
  "id": "fekneernpmxqp",
  "issns": [
    "2046-1402"
  ],
  "landing_url": "https://hal.science/hal-05742638v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://hal.science/hal-05742638/document",
  "pmcid": null,
  "pmid": "42728931",
  "published": "2026-08-11",
  "publisher": "Faculty of 1000",
  "ris": "https://research.draconitic.com/paper/fekneernpmxqp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:58Z",
      "native_id": "hal-05742638",
      "source": "hal",
      "url": "https://hal.science/hal-05742638v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    }
  ],
  "title": "Exploratory Insights into Barriers and Open Practices for Computational Reproducibility in Scientific Research",
  "title_text": "Exploratory Insights into Barriers and Open Practices for Computational Reproducibility in Scientific Research",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:58Z",
  "url": "https://research.draconitic.com/paper/fekneernpmxqp",
  "venue": "F1000Research",
  "versions": []
}