The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Reproducible Research in Computational Science
Open accessAbstract
Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The difficulty in auditing spreadsheets makes them generally incompatible with the principles of computational reproducibility [5].”
From Gene name errors: Lessons not learned (2021) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The rough consensus reduces to three ingredients.”
From Designing reproducible large-language-model-assisted scientific analyses (2026) · cites this paper as [2]
Experimental — accuracy not yet measuredIn the citing paper
“We develop a two-step attribution process which combines a transparent and reproducible 39,40 machine learning approach to identifying studies on observed climate impacts with model-based assessments of detectable anthropogenic contributions to historical temperature and precipitation trends.”
From Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies (2021) · cites this paper as [39]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Repeatability of published microarray gene expression analyses
Ioannidis, J. P. A., Allison, D. B., Ball, C. A., Coulibaly, I., Cui, X., Culhane, A. C., Falchi, M., Furlanello, C., Game, L., Jurman, G., et al. (2009) · Nature Genetics · doi:10.1038/ng.295 · Cited by 551
King, G. (1995) · PS Political Science & Politics · doi:10.1017/s1049096500057607 · Cited by 473
Publish your computer code: it is good enough
Barnes, N. (2010) · Nature · doi:10.1038/467753a · Cited by 317
Accessible Reproducible Research
Mesirov, J. P. (2010) · Science · doi:10.1126/science.1179653 · Cited by 311
Reproducible Epidemiologic Research
Peng, R. D., Dominici, F., & Zeger, S. L. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj093 · Cited by 292
Self-citationReproducible research and Biostatistics
Peng, R. D. (2009) · Biostatistics · doi:10.1093/biostatistics/kxp014 · Cited by 245
Self-citationMaking scientific computations reproducible
Schwab, M., Karrenbach, N., & Claerbout, J. F. (2000) · Computing in Science & Engineering · doi:10.1109/5992.881708 · Cited by 232
Reproducible Research: Moving toward Research the Public Can Really Trust
Laine, C., Goodman, S. N., Griswold, M. E., & Sox, H. C. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-146-6-200703200-00154 · Cited by 213
Making Data Maximally Available
Hanson, B., Sugden, A. M., & Alberts, B. (2011) · Science · doi:10.1126/science.1203354 · Cited by 172
King, G. (1995) · PS Political Science & Politics · doi:10.2307/420301 · Cited by 145
Flagged first, then by citations
Research works printed but not linked: 1
- Yale Law School Roundtable on Data and Code Sharing, Comput. Sci. Eng. 12, 8 (2010).
Cited by
Sorted by citations
Items 901–1,000 of 1,003
BISTRO: Berkeley Integrated System for Transportation OptimizationPreprint
Feygin, S., Lazarus, J. R., Forscher, E. H., Golfier-Vetterli, V., Lee, J., Gupta, A., Waraich, R. A., Sheppard, C., & Bayen, A. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03821 · Cited by 3
The Economics of Computational ReproducibilityPreprint
Colliard, J.-É., Hurlin, C., & Hurlin, C. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3418896 · Cited by 3
DolphinNext: A graphical user interface for creating, deploying and executing Nextflow pipelinesPreprint
Yukselen, O., Türkyılmaz, O., Ozturk, A. R., Garber, M. G., & Küçükural, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/689539 · Cited by 3
A Python-based optimization framework for high-performance genomicsPreprint
Shajii, A., Numanagić, I., Leighton, A. T., Greenyer, H. W., Amarasinghe, S. P., & Berger, B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.29.361402 · Cited by 3
Opening Doors to Open Science and Scholarship for School Psychology Research, Training, and PracticePreprint
Roberson, A. J., Farmer, R. L., Shaw, S. R., Upton, S. R., & Zaheer, I. (2020) · doi:10.31234/osf.io/su53p · Cited by 3
Reproducible Research and GIScience: an evaluation using GIScience conference papersPreprint
Ostermann, F., Nüst, D., Granell, C., Hofer, B., & Konkol, M. (2020) · doi:10.31223/x5zk5v · Cited by 3
Towards reproducible software studies with MAO and Renku
Spillner, J., Gkikopoulos, P., Delgado, P. O., & Choirat, C. (2021) · SoftwareX · doi:10.1016/j.softx.2021.100947 · Cited by 3
Experimental Standards for Deep Learning in Natural Language Processing ResearchPreprint
Ulmer, D., Bassignana, E., Müller-Eberstein, M., Varab, D., Zhang, M., van der Goot, R., Hardmeier, C., & Plank, B. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.06251 · Cited by 9
Bollmann, S., Renton, A., Dao, T. T., Johnstone, T., Civier, O., Sullivan, R., White, D., Lyons, P., Slade, B., Abbott, D. F., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2649734/v1 · Cited by 3
Detecting The Corruption Of Online Questionnaires By Artificial IntelligencePreprint
Lebrun, B., Temtsin, S., Vonasch, A. J., & Bartneck, C. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.07499 · Cited by 3
Code sharing increases citations but remains uncommonPreprint
Maitner, B., Andrade, P. S., Lei, L., Barbosa, G., Boyle, B., Castorena, M., Enquist, B. J., Feng, X., Kass, J. M., Owens, H., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3222221/v1 · Cited by 3
Data and Code Availability in Political Science Publications from 1995 to 2022Preprint
Rainey, C., Roe, H., Wang, Q., & Zhou, H. (2024) · doi:10.31235/osf.io/a5yxe · Cited by 3
The Role Of Software Engineering In Bioinformatics
Lawlor, B. (2021) · doi:10.34719/rjbz6161 · Cited by 3
Version 2 - BibexPy: harmonizing the bibliometric symphony of scopus and web of science
Kara, B. C., Şahin, A., & Dirsehan, T. (2026) · SoftwareX · doi:10.1016/j.softx.2026.102944 · Cited by 3
Ciencia reproducible: ¿qué, por qué, cómo?
Rodríguez‐Sánchez, F., Pérez‐Luque, A. J., Bartomeus, Í., & Varela, S. (2016) · Ecosistemas · doi:10.7818/ecos.2016.25-2.11 · Cited by 6
Systematic data analysis pipeline for quantitative morphological cell phenotyping
Ghanegolmohammadi, F., Eslami, M., & Ohya, Y. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.07.012 · Cited by 2
Wood, J., & van Schalkwyk, I. (2025) · Journal of Transportation Technologies · doi:10.4236/jtts.2025.151010 · Cited by 2
TAOD-Net: Automated detection and analysis of thyroid-associated orbitopathy in facial imagery
Oh, Y., Koh, G., Kwak, J., Shin, K., Kim, G.-S., Lee, M. J., Choung, H.-K., Kim, S., Moon, J. H., & Kim, S. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111024 · Cited by 2
Modeling complex systems in animal science: A pedagogical framework for new researchers
Tedeschi, L. O., Tulpan, D., Menendez, H. M., & Vieira, R. A. M. (2025) · Revista Brasileira de Zootecnia · doi:10.37496/rbz5420240170 · Cited by 2
LLM-Powered, Expert-Refined Causal Loop Diagramming via Pipeline Algebra
Reinholtz, K., Shahroudi, K. E., & Lawrence, S. (2025) · Systems · doi:10.3390/systems13090784 · Cited by 3
Reproducibility and Objectivity in Academic Research with R
Muthu, V., & Soundararajan, S. (2025) · Journal of Postgraduate Medicine Education and Research · doi:10.5005/jp-journals-10028-1741 · Cited by 2
Yalçınyiğit, S., & Akbaş, H. E. (2026) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.70428 · Cited by 2
Enquist, B. J., Boyle, B. L., Maitner, B., Merow, C., Carlson, B., Feng, X., MOULATLET, G. M., Newman, E. A., Park, D., Pillet, M., et al. (2026) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70274 · Cited by 5
Advancing reproducibility and replicability in simulation: Challenges and opportunities
Huang, Y., & Çetinkaya, D. (2026) · SIMULATION · doi:10.1177/00375497261444678 · Cited by 2
Mai, A., Luan, Y., Loy, S. L., Qin, C., Ji, H., Obeng, A., Oladuti, M., Ejima, K., Mok, K. T., Kaur, S., et al. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1805704 · Cited by 4
Discovery Over Application: A Case Study of Misaligned Incentives in Software Engineering
Rizzi, E. F. (2015) · Lincoln (University of Nebraska) · Cited by 2
Version Control Systems to Facilitate Research Collaboration in Economics
Rodrigues, B. (2015) · Computational Economics · doi:10.1007/s10614-015-9513-8 · Cited by 2
A Case Study in Using ADAGE for Compute-Intensive Financial Analysis Processes
Yao, L., Rabhi, F., & Peat, M. (2013) · Lecture notes in business information processing · doi:10.1007/978-3-642-36219-4_6 · Cited by 2
The Collage Authoring Environment: From Proof-of-Concept Prototype to Pilot Service
Ciepiela, E., Harężlak, D., Kasztelnik, M., Meizner, J., Dyk, G., Nowakowski, P., & Bubak, M. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.05.241 · Cited by 2
Sinha, R., & Sudhish, P. S. (2016) · IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS) · doi:10.1109/ethics.2016.7560050 · Cited by 2
Alencar, P., Cowan, D. D., & Mulholland, D. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622373 · Cited by 2
Publish and who should perish: you or science?
Kun, Á. (2019) · Science Editor and Publisher · doi:10.24069/2542-0267-2019-1-2-76-93 · Cited by 2
Ruddle, A. R. (2020) · IET Science Measurement & Technology · doi:10.1049/iet-smt.2019.0320 · Cited by 2
Towards supporting reproducibility of experimental studies in GUI visual design
Burny, N. (2020) · ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS) · doi:10.1145/3393672.3398644 · Cited by 2
Hall, L. M., & Hendricks, A. E. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03817-7 · Cited by 2
Hersh, W. (2020) · Health informatics · doi:10.1007/978-3-030-47686-1_2 · Cited by 2
Design of a Scientific Data Analysis Support Platform
Martindale, N., Hite, J. M., STEWART, S. L., & Adams, M. (2022) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-212e5952-01b · Cited by 2
A Study in Reproducibility: The Congruent Matching Cells Algorithm and cmcR Package
Zemmels, J., VanderPlas, S., & Hofmann, H. (2023) · The R Journal · doi:10.32614/rj-2023-014 · Cited by 2
Reproducible High Performance Computing without Redundancy with Nix
Goswami, R. R., Ruhila, S., Goswami, A., Goswami, S., & Goswami, D. (2022) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc56933.2022.10053342 · Cited by 2
Open data policy and journal impacts: a synthetic difference-in-differences approach
Huang, H.-C. R., Ma, Y., & Wang, Y. (2023) · Applied Economics Letters · doi:10.1080/13504851.2023.2187017 · Cited by 2
Fish Freshness Detection Using UV Light Based On Deep Neural Network
Pertiwi, N. K., Suyanto, S., Hatta, A. M., Setiadi, I. C., Stendafity, S., Damayanti, A. A., Hartati, A. D., Susanti, M. D., & Shoffiana, N. A. (2023) · International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA) · doi:10.1109/icamimia60881.2023.10427618 · Cited by 2
The role of theory in mitigating the ‘reproducibility crisis’
Marone, L. (2024) · Ecología Austral · doi:10.25260/ea.24.34.1.0.2291 · Cited by 3
Estimating Transcriptome Diversity and Specialization in Capsicum annuum L.
Ochoa‐Alejo, N., Reyes‐Valdés, M. H., & Martínez, O. (2024) · Plants · doi:10.3390/plants13070983 · Cited by 2
MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology
Bartnik, A., Serra, L. M., Smith, M., Duncan, W. D., Wishnie, L., Ruttenberg, A., Dwyer, M. G., & Diehl, A. D. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09664-8 · Cited by 2
Lee, Y., Lin, C., Hsiao, C.-C., Mo, P., Guo, J., & Jan, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3432691 · Cited by 2
Tooling for Reproducible Research: Considerations for the Past and Future of Data Analysis
Peng, R. D. (2025) · Austrian Journal of Statistics · doi:10.17713/ajs.v54i3.2052 · Cited by 3
Self-citationQuantifying the Alignment of a Data Analysis Between Analyst and Audience
McGowan, L. D., Peng, R. D., & Hicks, S. C. (2025) · Journal of Data Science · doi:10.6339/25-jds1189 · Cited by 2
Self-citationCompRep: A Dataset For Computational Reproducibility
Costa, L., Barbosa, S., & Cunha, J. (2025) · ACM Conference on Reproducibility and Replicability (ACM REP) · doi:10.1145/3736731.3746160 · Cited by 2
Xie, M., Liao, X., & YAGUCHI, T. (2025) · Land · doi:10.3390/land14112240 · Cited by 3
Eickhoff, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18714-3_14 · Cited by 2
Implementation of the BC Congenital Anomalies Surveillance System (BCCASS)
de Escalante, Y. N., Abayomi, A., Erickson, A. C., Ye, X., Armour, R., Arbour, L. T., Langlois, S. F., & Henry, B. (2022) · Canadian Journal of Public Health · doi:10.17269/s41997-021-00607-3 · Cited by 2
Hyperspectral Modeling of Material Appearance: General Framework, Challenges and Prospects
Baranoski, G. V. G. (2015) · Revista de Informática Teórica e Aplicada · doi:10.22456/2175-2745.56437 · Cited by 1
A Proposed Framework for Evaluating the Effectiveness of Financial News Sentiment Scoring Datasets
Al-Qudah, I., Rabhi, F., & Peat, M. (2015) · Lecture notes in business information processing · doi:10.1007/978-3-319-28151-3_3 · Cited by 2
Biostatistics and Bioinformatics in Clinical Trials
Berry, D. A., & Coombes, K. R. (2014) · Elsevier eBooks · doi:10.1016/b978-1-4557-2865-7.00019-9 · Cited by 2
Supporting Data-Intensive Analysis Processes
Yao, L., Rabhi, F., & Peat, M. (2014) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-4666-6178-3.ch019 · Cited by 2
Domínguez, E. R., Regot, J., Fonseca, D., Dalmau, F. V., & Mateu, L. G. (2016) · Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'21) · doi:10.1145/3012430.3012502 · Cited by 2
Are Research Datasets FAIR in the Long Run?
Wehrle, D., & Rechert, K. (2019) · International Journal of Digital Curation · doi:10.2218/ijdc.v13i1.659 · Cited by 2
Bivariate spatial point patterns in the retina: a reproducible review
Eglen, S. J. (2016) · French digital mathematics library (Numdam) · Cited by 2
AMEND: Open Source and Data-Driven Oversight of Water Quality in New England
Sanders, N. E. (2019) · Media and Communication · doi:10.17645/mac.v7i3.2136 · Cited by 2
Wang, P. D. C., Soares, V. S., de Souza, J. M., Esteves, M. G. P., Schots, N. C. L., & Duarte, F. (2019) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2019.8791853 · Cited by 2
Versypt, A. N. F. (2020) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--32072 · Cited by 2
Giuliani, G., Câmara, G., Killough, B., & Minchin, S. (2020) · MDPI eBooks · doi:10.3390/books978-3-03928-093-3 · Cited by 2
Research Data, Reproducibility, and Curation
Borgman, C. L. (2012) · eScholarship (California Digital Library) · Cited by 2
Decentralized Storage for Scientific Data
Patel, S., & Rhodes, P. J. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671480 · Cited by 2
Distributed Digital Artifacts on the Semantic Web
Kurian, S. P., & Sekhar, V. S. (2016) · International Journal of Computer Applications Technology and Research · doi:10.7753/ijcatr0502.1012 · Cited by 2
Gayle, V., & Connelly, R. (2022) · Methodological Innovations · doi:10.1177/20597991221111681 · Cited by 2
Reproducibility in 2023 – An End-to-End Template for Analysis and Manuscript Writing
Mang, J. M., Prokosch, H., & Kapsner, L. A. (2023) · Studies in health technology and informatics · doi:10.3233/shti230064 · Cited by 2
Pokutnaya, D. Y., van Panhuis, W. G., Childers, B. R., Hawkins, M., Arcury‐Quandt, A. E., Matlack, M., Carpio, K., & Hochheiser, H. (2023) · BMC Infectious Diseases · doi:10.1186/s12879-023-08729-4 · Cited by 2
Rahul, R., Stinchcombe, A. R., Joseph, J. W., & Ingalls, B. (2023) · IET Systems Biology · doi:10.1049/syb2.12077 · Cited by 1
What ranking journals has in common with astrology
Brembs, B. (2013) · Riviste UNIMI (Università degli studi di Milano) · doi:10.13130/2282-5398/3378 · Cited by 2
El balance entre producir y usar el conocimiento
Prieto, F. E. (2017) · Revista Investigación en Salud Universidad de Boyacá · doi:10.24267/23897325.222 · Cited by 2
Advancing Reproducibility Through Shared Data: Bridging Archival and Library Practice
Pakstis, J., Calkins, H., Dobrzynski, C., Lamm, S., & McNamara, L. (2019) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2019.00017 · Cited by 2
Personality and Individual Differences
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.006 · Cited by 2
Ellis, D. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108671408.003 · Cited by 2
Biostatistics and Bioinformatics in Clinical Trials
Hobbs, B. P., Berry, D. A., & Coombes, K. R. (2020) · Elsevier eBooks · doi:10.1016/b978-0-323-47674-4.00017-7 · Cited by 3
Revealing Earth science code and data-use practices using the Throughput Graph Database
Thomer*, A. K., Wofford, M. F., Lenard, M., Vidaña, S. D., & Goring, S. J. (2023) · Geological Society of America eBooks · doi:10.1130/2022.2558(10 · Cited by 3
Reproducible Research: a Dissenting OpinionPreprint
Drummond, C. (2012) · CogPrints (University of Southampton) · Cited by 2
Crisis in What Exactly?Preprint
Guest, O. (2016) · doi:10.15200/winn.146590.01538 · Cited by 2
Storing Reproducible Results from Computational Experiments using Scientific Python Packages
Oxvig, C. S., Arildsen, T., & Larsen, T. (2016) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-629e541a-006 · Cited by 2
Replication in agent based models using formal methods and multiple updating strategies
Kehoe, J. (2017) · Dublin City University Open Access Institutional Repository (Dublin City University) · Cited by 2
OpenHeart Project—An Open‐Source Research Community in the Field of Mechanical Circulatory Support
Pauls, J. P., Miotto, A., Stephens, A. F., Gregory, S. D., & Tansley, G. D. (2018) · Artificial Organs · doi:10.1111/aor.13361 · Cited by 3
EMULSION: transparent and flexible multiscale stochastic models in epidemiologyPreprint
Picault, S., Huang, Y.-L., Sicard, V., Arnoux, S., Beaunée, G., & Ezanno, P. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/563791 · Cited by 2
Kollaps: Decentralized and Dynamic Topology EmulationPreprint
Gouveia, P., Neves, J., Segarra, C., Liechti, L., Issa, S., Schiavoni, V., & Matos, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.02253 · Cited by 2
Zeevi, Y., Astashenko, S., Mudrik, L., & Benjamini, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.11585 · Cited by 4
Khan, K., Luo, H., & Xi, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.01285 · Cited by 2
Prabhu, A., & Fox, P. (2021) · Encyclopedia of earth sciences series/Encyclopedia of earth sciences · doi:10.1007/978-3-030-26050-7_277-1 · Cited by 3
Wilson, M. K., Strickland, L. J. G., Ballard, T., & Griffin, M. A. (2022) · doi:10.31234/osf.io/e8awh · Cited by 2
Tool-driven Revolutions in Archaeological SciencePreprint
Schmidt, S. C., & Marwick, B. (2019) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/4nkxv · Cited by 2
Auer, S., Haelterman, N. A. T., Weissgerber, T. L., Erlich, J. C., Susilaradeya, D., Julkowska, M., Gazda, M. A., Abitua, A., Niraulu, A., Shah, A. P., et al. (2020) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/dxw67 · Cited by 2
NIT: an open-source tool for information theoretic analysis of neural population dataPreprint
Maffulli, R., Casal, M. A., Celotto, M., Zucca, S., Safaai, H., Fellin, T., & Panzeri, S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.11.519966 · Cited by 4
Cerna-Turoff, I., Chillrud, L., Rudolph, K. E., & Casey, J. A. (2023) · International Journal of Epidemiology · doi:10.1093/ije/dyad066 · Cited by 2
Bainbridge, D., Nichols, D. M., Hinze, A., & Downie, J. S. (2019) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2019.00124 · Cited by 1
Teaching reproducible research for medical students and postgraduate pharmaceutical scientists
Meid, A. D. (2021) · BMC Research Notes · doi:10.1186/s13104-021-05862-8 · Cited by 1
On Developing Better Practices for Reproducible Data Analysis in Laboratory Medicine
Mathias, P. C. (2023) · The Journal of Applied Laboratory Medicine · doi:10.1093/jalm/jfac097 · Cited by 1
Should I disclose my dataset? Caveats between reproducibility and individual data rights
Benatti, R. M., Villarroel, C. M. L., Avila, S., Colombini, E. L., & Severi, F. C. (2022) · Natural Legal Language Processing Workshop (NLLP) · doi:10.18653/v1/2022.nllp-1.20 · Cited by 1
Open Science Practices in Psychiatric Genetics: A Primer
Kępińska, A. P., Johnson, J., & Huckins, L. M. (2023) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2023.08.007 · Cited by 2
One4All: An Open Source Portal to Validate and Share Microplastics Data and Beyond
Sherrod, H., Leong, N., Hapich, H., Gomez, F., Moore, S., Maurer, B., Coffin, S., Hampton, L. M. T., Hale, T., Nelson, R., et al. (2024) · The Journal of Open Source Software · doi:10.21105/joss.06715 · Cited by 1
Critical reflections on the nature of the scientific process for physiologists
Nell, T. A., Joseph, D., Rambharose, S., & Sishi, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-23655-6.00002-2 · Cited by 1
Bhardwaj, R. K., Nazim, M., & Verma, M. K. (2025) · Data Technologies and Applications · doi:10.1108/dta-12-2022-0477 · Cited by 1
Four Replications of a User-Defined Gesture Study with Smart Rings
Vanderdonckt, J. M., & Vatavu, R.-D. (2025) · International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (MobileHCI Adjunct) · doi:10.1145/3737821.3749582 · Cited by 1
Metadata
- Authors
- Peng, R. D.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Peng, Roger D.
- ORCID
- Peng, Roger D.
- Venue
- Science
- Published
- 2011-12-02
- Volume
- 334
- Issue
- 6060
- Pages
- 1226-1227
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC3383002
- OpenAlex
- W2008985174
- Semantic Scholar
- e4625641d1072542315c14083befb43cd7096aba
- Cited by
- 1,449
- References
- 11
- Citations by year
- 2026: 162 · 2025: 75 · 2024: 63 · 2023: 74 · 2022: 85 · 2021: 121 · 2020: 139 · 2019: 162 · 2018: 138 · 2017: 113 · 2016: 106 · 2015: 82 · 2014: 51 · 2013: 46 · 2012: 29
- FWCI
- 98.43
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Environmental Health Sciences (R01ES019560)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
- Keywords
- reproducible research, scientific computing, reproducibility, scientific claims, replication
- MeSH terms
- Computers, Publishing, Research, Reproducibility of Results, Computational Biology, Editorial Policies, Biomedical Research, Biostatistics
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 ›
Are you an author, or do you see a problem with this page? Request a review