Improving transparency and scientific rigor in academic publishing
Open accessAbstract
Progress in basic and clinical research is slowed when researchers fail to provide a complete and accurate report of how a study was designed, executed, and the results analyzed. Publishing rigorous scientific research involves a full description of the methods, materials, procedures, and outcomes. Investigators may fail to provide a complete description of how their study was designed and executed because they may not know how to accurately report the information or the mechanisms are not in place to facilitate transparent reporting. Here, we provide an overview of how authors can write manuscripts in a transparent and thorough manner. We introduce a set of reporting criteria that can be used for publishing, including recommendations on reporting the experimental design and statistical approaches. We also discuss how to accurately visualize the results and provide recommendations for peer reviewers to enhance rigor and transparency. Incorporating transparency practices into research manuscripts will significantly improve the reproducibility of the results by independent laboratories.
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Journals usually share a common objective of improving the methodological rigour and reproducibility of the research they publish, but different journals emphasise different pieces of information.
“Journals usually share a common objective of improving the methodological rigour and reproducibility of the research they publish, but different journals emphasise different pieces of information [56–58].”
From The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research (2020) · cites this paper as [56]
The Introduction says publishers and editors seek greater transparency and reproducibility, and gives Cell Press’s STAR Methods and JNR’s Transparent Science Questionnaire as examples of journal reporting tools. The paper does not establish that different journals emphasise different pieces of information; it shows that they use different tools. (LLM, conf=0.80)
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- Authors
- Prager, E. M., Chambers, K. E., Plotkin, J. L., McArthur, D. L., Bandrowski, A. E., Bansal, N., Martone, M. E., Bergstrom, H. C., Bespalov, A., & Graf, C.
- Affiliations
- John Wiley & Sons (United States) (US)
- Stony Brook University (US)
- University of California, Los Angeles (US)
- University of California San Diego (US)
- Vassar College (US)
- First Pavlov State Medical University of St. Petersburg (RU)
- John Wiley & Sons (United Kingdom) (GB)
- Corresponding author
- Prager, Eric M.
- ORCID
- Venue
- Journal of Neuroscience Research
- Publisher
- Wiley
- Published
- 2018-12-02
- Volume
- 97
- Issue
- 4
- Pages
- 377-390
- ISSN
- 0360-4012 (ISSN-L)
- 1097-4547
- PMC
- PMC12990824
- OpenAlex
- W3043305063
- Cited by
- 83
- References
- 99
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 15 · 2023: 6 · 2022: 9 · 2021: 8 · 2020: 12 · 2019: 5
- FWCI
- 30.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- European College of Neuropsychopharmacology
- National Institutes of Health (5u24da039832-02)
- National Institute on Drug Abuse (U24-DA039832)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- research transparency, scientific rigor, research reporting, reporting guidelines, experimental design, statistical reporting, data visualization, peer review, reproducibility, manuscript preparation
- MeSH terms
- Data Accuracy, Humans, Publishing, Research Design, Reproducibility of Results, Biomedical Research, Quality Improvement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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