Reproducible Research in Computational Science

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

Replication, Replication

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

Reproducible research and Biostatistics

Peng, R. D. (2009) · Biostatistics · doi:10.1093/biostatistics/kxp014 · Cited by 245

Making 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

Replication, Replication

King, G. (1995) · PS Political Science & Politics · doi:10.2307/420301 · Cited by 145

Towards a credibility revolution in bilingualism research: Open data and materials as stepping stones to more reproducible and replicable research

Bolibaugh, C., Vanek, N., & Marsden, E. J. (2021) · Bilingualism Language and Cognition · doi:10.1017/s1366728921000535 · Cited by 30

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Leman, J. K., Lyskov, S., Lewis, S. M., Adolf‐Bryfogle, J., Alford, R. F., Barlow, K. A., Ben-Aharon, Z., Farrell, D. P., Fell, J. S., Hansen, W. A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27222-7 · Cited by 25

Assessing and Improving Robustness of Psychological Research Findings in Four Steps

Nuijten, M. B. (2022) · Springer eBooks · doi:10.1007/978-3-031-04968-2_17 · Cited by 26

Modflow-setup: Robust automation of groundwater model construction

Leaf, A. T., & Fienen, M. N. (2022) · Frontiers in Earth Science · doi:10.3389/feart.2022.903965 · Cited by 26

Computer model and code sharing practices in healthcare discrete-event simulation: a systematic scoping review

Monks, T., & Harper, A. L. (2023) · Journal of Simulation · doi:10.1080/17477778.2023.2260772 · Cited by 27

A Simple Graphical Method for Quantification of Disaster Management Surge Capacity Using Computer Simulation and Process-control Tools

Franc, J. M., Ingrassia, P. L., Verde, M., Colombo, D., & Corte, F. D. (2014) · Prehospital and Disaster Medicine · doi:10.1017/s1049023x1400123x · Cited by 26

A proposal for an integrated modelling framework to characterise habitat pattern

Estreguil, C., de Rigo, D., & Caudullo, G. (2013) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2013.10.011 · Cited by 26

Science Deserves Better: The Imperative to Share Complete Replication FilesPreprint

Dafoe, A. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2318223 · Cited by 26

Non-cognate translation priming effects in the same–different task: evidence for the impact of “higher level” information

Lupker, S. J., Perea, M., & Nakayama, M. (2015) · Language Cognition and Neuroscience · doi:10.1080/23273798.2015.1015430 · Cited by 25

Advancements in RNASeqGUI towards a Reproducible Analysis of RNA-Seq Experiments

Russo, F., Righelli, D., & Angelini, C. (2016) · BioMed Research International · doi:10.1155/2016/7972351 · Cited by 27

Tool-Driven Revolutions in Archaeological Science

Schmidt, S. C., & Marwick, B. (2020) · Journal of Computer Applications in Archaeology · doi:10.5334/jcaa.29 · Cited by 30

Reproducible and transparent research practices in published neurology research

Rauh, S. L., Torgerson, T., Johnson, A., Pollard, J., Tritz, D., & Vassar, M. B. (2020) · Research Integrity and Peer Review · doi:10.1186/s41073-020-0091-5 · Cited by 24

Reproducibility in Computing Research: An Empirical Study

Raghupathi, W., Raghupathi, V., & Ren, J. (2022) · IEEE Access · doi:10.1109/access.2022.3158675 · Cited by 33

Computational science: shifting the focus from tools to models

Hinsen, K. (2014) · F1000Research · doi:10.12688/f1000research.3978.2 · Cited by 25

Structuring research methods and data with the research object model: genomics workflows as a case study

Hettne, K. M., Dharuri, H. K., Zhao, J., Wolstencroft, K., Belhajjame, K., Soiland‐Reyes, S., Mina, E., Thompson, M., Cruickshank, D. G., Verdes-Montenegro, L. L., et al. (2014) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-5-41 · Cited by 25

Systems Approaches to Cancer Biology

Archer, T. C., Fertig, E. J., Gosline, S. J. C., Hafner, M. A., Hughes, S. K., Joughin, B. A., Meyer, A. S., Piccolo, S., & Shajahan‐Haq, A. N. (2016) · Cancer Research · doi:10.1158/0008-5472.can-16-1580 · Cited by 25

Closed‐form variance estimator for weighted propensity score estimators with survival outcome

Hajage, D., Chauvet, G., Belin, L., Lafourcade, A., Tubach, F., & Rycke, Y. D. (2018) · Biometrical Journal · doi:10.1002/bimj.201700330 · Cited by 25

refellips: A Python package for the analysis of variable angle spectroscopic ellipsometry data

Robertson, H., Gresham, I. J., Prescott, S. W., Webber, G. B., Wanless, E. J., & Nelson, A. R. J. (2022) · SoftwareX · doi:10.1016/j.softx.2022.101225 · Cited by 25

An algorithm to detect and communicate the differences in computational models describing biological systems

Scharm, M., Wolkenhauer, O., & Waltemath, D. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv484 · Cited by 24

vortex R: an R package for post Vortex simulation analysis

Pacioni, C., & Mayer, F. (2017) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12786 · Cited by 24

Statistical Significance Testing for Natural Language Processing

Dror, R., Peled-Cohen, L., Shlomov, S., & Reichart, R. (2020) · Synthesis lectures on human language technologies · doi:10.2200/s00994ed1v01y202002hlt045 · Cited by 55

Visualizing Digital Traces for Sustainable Urban Management: Mapping Tourism Activity on the Virtual Public Space

Dalmau, F. V., & Cladera, J. R. (2021) · Sustainability · doi:10.3390/su13063159 · Cited by 25

Kinds of Replicability: Different Terms and Different Functions

Matarese, V. (2022) · Axiomathes · doi:10.1007/s10516-021-09610-2 · Cited by 28

On the challenges of using black-box APIs for toxicity evaluation in research

Pozzobon, L., Ermis, B., Lewis, P., & Hooker, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.472 · Cited by 29

Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers

Knoth, C., & Nüst, D. (2017) · Remote Sensing · doi:10.3390/rs9030290 · Cited by 24

Geospatial Data Science Techniques and Applications

Karimi, B. (2017) · CRC Press eBooks · doi:10.1201/b22052 · Cited by 24

Unlocking data to improve public policy

Hastings, J., Howison, M., Lawless, T., Ucles, J., & White, P. (2019) · Communications of the ACM · doi:10.1145/3335150 · Cited by 24

Replication and generalization in applied neuroimaging

Lerma‐Usabiaga, G., Mukherjee, P., Ren, Z., Perry, L. M., & Wandell, B. A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116048 · Cited by 23

Do oncology researchers adhere to reproducible and transparent principles? A cross-sectional survey of published oncology literature

Walters, C. G., Harter, Z. J., Wayant, C., Vo, N. H., Warren, M., Chronister, J., Tritz, D., & Vassar, M. B. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-033962 · Cited by 23

The missing pieces of open design enablement

Ansell, T., & Saligane, M. (2020) · doi:10.1145/3400302.3415736 · Cited by 23

AURORA, a multi-sensor dataset for robotic ocean exploration

Bernardi, M. L., Hosking, B., Chiara Petrioli, B., Bett, B. J., Jones, D., Huvenne, V. A. I., Marlow, R., Furlong, M. E., McPhail, S., & Munafò, A. (2022) · The International Journal of Robotics Research · doi:10.1177/02783649221078612 · Cited by 25

Social network structure and the trade-off between social utility and economic performance

Growiec, K., Growiec, J., & Kamiński, B. (2018) · Social Networks · doi:10.1016/j.socnet.2018.05.002 · Cited by 23

Cyber Literacy for GIScience: Toward Formalizing Geospatial Computing Education

Shook, E., Bowlick, F. J., Kemp, K. K., Ahlqvist, O., Carbajeles-Dale, P., DiBiase, D., Kim, E., Lathrop, S. A., Ricker, B. A., Rickles, P., et al. (2019) · The Professional Geographer · doi:10.1080/00330124.2018.1518720 · Cited by 24

uap: reproducible and robust HTS data analysis

Kämpf, C., Specht, M., Scholz, A., Puppel, S.-H., Doose, G., Reiche, K., Schor, J., & Hackermüller, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3219-1 · Cited by 24

Reproducible Research Using Biomodels

Mendes, P. (2018) · Bulletin of Mathematical Biology · doi:10.1007/s11538-018-0498-z · Cited by 20

Parallel cartographic modeling: a methodology for parallelizing spatial data processing

Shook, E., Hodgson, M. E., Wang, S., Behzad, B., Soltani, K., Hiscox, A. L., & Ajayakumar, J. (2016) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2016.1172714 · Cited by 22

Comparing perturbation models for evaluating stability of neuroimaging pipelines

Kiar, G., Castro, P. D. O., Rioux, P., Petit, E., Brown, S. T., Evans, A. C., & Tristan Glatard, B. (2020) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342020926237 · Cited by 22

RepliGES and GEStory: Visual Tools for Systematizing and Consolidating Knowledge on User-Defined Gestures

Gheran, B.-F., Villarreal-Narvaez, S., Vatavu, R.-D., & Vanderdonckt, J. M. (2022) · International Conference on Advanced Visual Interfaces (AVI) · doi:10.1145/3531073.3531112 · Cited by 22

Relevance relations for the concept of reproducibility

Harald Atmanspacher, A., Lambert, L. B., Folkers, G., & Schubiger, P. A. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.1030 · Cited by 23

Limitations of Medical Research and Evidence at the Patient-Clinician Encounter Scale

Morris, A. H., & Ioannidis, J. P. A. (2013) · CHEST Journal · doi:10.1378/chest.12-1908 · Cited by 23

Use it or lose it: citations predict the continued online availability of published bioinformatics resources

Wren, J. D., Georgescu, C., Giles, C. B., & Hennessey, J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx182 · Cited by 22

“Reproducible” Research in Mathematical Sciences Requires Changes in our Peer Review Culture and Modernization of our Current Publication Approach

Schnell, S. D. (2018) · Bulletin of Mathematical Biology · doi:10.1007/s11538-018-0500-9 · Cited by 25

Does open data boost journal impact: evidence from Chinese economics

Zhang, L., & Ma, L. (2021) · Scientometrics · doi:10.1007/s11192-021-03897-z · Cited by 24

Introducing maxent.ot : an R package for Maximum Entropy constraint grammars

Mayer, C., Tan, A., & Zuraw, K. (2024) · Phonological Data and Analysis · doi:10.3765/pda.v6art4.88 · Cited by 21

Realities of data sharing using the genome wars as case study - an historical perspective and commentary

Jasny, B. R. (2013) · EPJ Data Science · doi:10.1140/epjds13 · Cited by 21

Scientific research in the age of omics: the good, the bad, and the sloppy

Witten, D., & Tibshirani, R. J. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000972 · Cited by 21

On the techniques we create, the tools we build, and their misalignments

Rizzi, E. F., Elbaum, S. G., & Dwyer, M. B. (2016) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2884781.2884835 · Cited by 21

Citations for Software: Providing Identification, Access and Recognition for Research Software

Soito, L., & Hwang, L. (2016) · International Journal of Digital Curation · doi:10.2218/ijdc.v11i2.390 · Cited by 21

Ultra-Chaos: An Insurmountable Objective Obstacle of Reproducibility and Replication

Liao, S., & Qin, S. (2022) · Advances in Applied Mathematics and Mechanics · doi:10.4208/aamm.oa-2021-0364 · Cited by 22

Jenkins-CI, an Open-Source Continuous Integration System, as a Scientific Data and Image-Processing Platform

Moutsatsos, I. K., Hossain, I., Agarinis, C., Harbinski, F., Abraham, Y., Dobler, L., Zhang, X., Wilson, C. J., Jenkins, J. L., Holway, N., et al. (2016) · SLAS DISCOVERY · doi:10.1177/1087057116679993 · Cited by 21

Variability in the analysis of a single neuroimaging dataset by many teamsPreprint

Botvinik‐Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, R. A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/843193 · Cited by 21

Reproducible machine learning research in mental workload classification using EEG

Demirezen, G., Temizel, T. T., & Brouwer, A.-M. (2024) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2024.1346794 · Cited by 27

Scientific literature on carbon dioxide removal revealed as much larger through AI-enhanced systematic mapping

Lück, S., Callaghan, M., Borchers, M., Cowie, A., Fuss, S., Gidden, M., Hartmann, J., Kammann, C. I., Keller, D. P., Kraxner, F., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61485-8 · Cited by 36

Trails of Data: Three Cases for Collecting Web Information for Social Science Research

Li, F., Zhou, Y., & Cai, T. (2019) · Social Science Computer Review · doi:10.1177/0894439319886019 · Cited by 20

Amines-mediated β-glucose pentaacetate to generate photoluminescent polymer-carbon nanodots for visual monitoring the freshness of shrimp

Zhang, Y., Dong, S., Chen, J., Gao, W., & Shi, X. (2022) · Talanta · doi:10.1016/j.talanta.2022.123706 · Cited by 20

A white paper on good research practices in benchmarking: The case of cluster analysis

Mechelen, I. V., Boulesteix, A., Dangl, R., Dean, N., Hennig, C. M., Leisch, F., Steinley, D., & Warrens, M. J. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1511 · Cited by 56

Possible Directions for Improving Dependency Versioning in R

Ooms, J. C. L. (2013) · The R Journal · doi:10.32614/rj-2013-019 · Cited by 24

An Italian version of the Ottawa Crisis Resource Management Global Rating Scale: a reliable and valid tool for assessment of simulation performance

Franc, J. M., Verde, M., Gallardo, A. R., Carenzo, L., & Ingrassia, P. L. (2016) · Internal and Emergency Medicine · doi:10.1007/s11739-016-1486-7 · Cited by 21

OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia

Ayzel, G., Varentsova, N., Erina, O. N., Sokolov, D., Kurochkina, L., & Михайлович, М. В. (2019) · Water · doi:10.3390/w11081546 · Cited by 20

Guidelines for evaluating bug‐assignment research

Badashian, A. S., & Stroulia, E. (2020) · Journal of Software Evolution and Process · doi:10.1002/smr.2250 · Cited by 20

SFRmaker and Linesink‐Maker: Rapid Construction of Streamflow Routing Networks from Hydrography Data

Leaf, A. T., Fienen, M. N., & Reeves, H. W. (2021) · Ground Water · doi:10.1111/gwat.13095 · Cited by 20

Producing and perceiving gestures conveying height or shape

Hassemer, J., & Winter, B. (2016) · Gesture · doi:10.1075/gest.15.3.07has · Cited by 20

Eleven quick tips for architecting biomedical informatics workflows with cloud computing

Cole, B. S., & Moore, J. H. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005994 · Cited by 17

Jagged competencies: Measuring the reliability of generative AI in academic research

Thomas, L. D. W., Romasanta, A. K. S., & Priego, L. P. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115804 · Cited by 22

Experiences in integrated data and research object publishing using GigaDB

Edmunds, S., Li, P., Hunter, C., Zhe, X. S., Davidson, R., Nogoy, N., & Goodman, L. (2016) · International Journal on Digital Libraries · doi:10.1007/s00799-016-0174-6 · Cited by 18

Standing on the Feet of Giants — Reproducibility in AI

Gundersen, O. E. (2019) · AI Magazine · doi:10.1609/aimag.v40i4.5185 · Cited by 19

Toward replication study types for design science research

Brendel, B., Lembcke, T.-B., Muntermann, J., & Kolbe, L. M. (2021) · Journal of Information Technology · doi:10.1177/02683962211006429 · Cited by 16

Four level provenance support to achieve portable reproducibility of scientific workflows

Banati, A., Kacsuk, P., & Kozlovszky, M. (2015) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro.2015.7160272 · Cited by 19

Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System

Arifin, S. M. N., Arifin, R. R., Pitts, D. A. D. A., Rahman, M. A., Nowreen, S., Madey, G. R., & Collins, F. (2015) · Land · doi:10.3390/land4020378 · Cited by 22

ActivePapers: a platform for publishing and archiving computer-aided research

Hinsen, K. (2015) · F1000Research · doi:10.12688/f1000research.5773.3 · Cited by 19

Data Science in Environmental Health Research

Choirat, C., Braun, D., & Kioumourtzoglou, M. (2019) · Current Epidemiology Reports · doi:10.1007/s40471-019-00205-5 · Cited by 19

CartAGen: an Open Source Research Platform for Map Generalization

Touya, G., Lokhat, I., & Duchêne, C. (2019) · Proceedings of the ICA · doi:10.5194/ica-proc-2-134-2019 · Cited by 19

The State of Reproducible Research in Computer Science

Cacho, J. R. F., & Taghva, K. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-43020-7_68 · Cited by 19

Ten simple rules to power drug discovery with data science

Ferrero, E., Brachat, S., Jenkins, J. L., Marc, P., Skewes-Cox, P., Altshuler, R. C., Keller, C. G., Kauffmann, A., Sassaman, E. K., Laramie, J. M., et al. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008126 · Cited by 21

Software Must be Recognised as an Important Output of Scholarly Research

Jay, C., Haines, R., & Katz, D. S. (2021) · International Journal of Digital Curation · doi:10.2218/ijdc.v16i1.745 · Cited by 19

ARPIR: automatic RNA-Seq pipelines with interactive report

Spinozzi, G., Tini, V., Adorni, A., Falini, B., & Martelli, M. P. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03846-2 · Cited by 19

DataCite as a novel bibliometric source: Coverage, strengths and limitations

Robinson‐García, N., Mongeon, P., Jeng, W., & Costas, R. (2017) · RePEc: Research Papers in Economics · Cited by 19

On the genome base composition of teleosts: the effect of environment and lifestyle

Tarallo, A., Angelini, C., Sanges, R., Yagi, M., Agnisola, C., & D’Onofrio, G. (2016) · BMC Genomics · doi:10.1186/s12864-016-2537-1 · Cited by 20

Statistical controversies in clinical research: overlap and errors in the meta-analyses of microRNA genetic association studies in cancers

Park, J. H., Eisenhut, M., van der Vliet, H. J., & Shin, J. I. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx024 · Cited by 20

Computational reproducibility of scientific workflows at extreme scales

Pouchard, L. C., Baldwin, S., Elsethagen, T., Jha, S., Raju, B., Stephan, E., Tang, L., & van Dam, K. K. (2019) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342019839124 · Cited by 19

Seq: a high-performance language for bioinformatics

Shajii, A., Numanagić, I., Baghdadi, R., Berger, B., & Amarasinghe, S. P. (2019) · Proceedings of the ACM on Programming Languages · doi:10.1145/3360551 · Cited by 19

Machine learning for multi-omics data integration in crop improvement: a systematic review

Tsega, A., & Mullualem, D. (2026) · BMC Bioinformatics · doi:10.1186/s12859-026-06438-8 · Cited by 22

annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions

Gaonkar, K. S., Marini, F., Rathi, K. S., Jain, P., Zhu, Y., Chimicles, N. A., Brown, M., Naqvi, A. S., Zhang, B., Storm, P. B., et al. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03922-7 · Cited by 18

Nine best practices for research software registries and repositories

Garijo, D., Ménager, H., Hwang, L., Trišović, A., Hucka, M., Morrell, T. E., Allen, A., & Task Force on Best Practices for Software Registries (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1023 · Cited by 20

Making complex prediction rules applicable for readers: Current practice in random forest literature and recommendations

Boulesteix, A., Janitza, S., Hornung, R., Probst, P., Marchi, H., & Hapfelmeier, A. (2018) · Biometrical Journal · doi:10.1002/bimj.201700243 · Cited by 18

Replication studies considered harmful

Shepperd, M. (2018) · IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) · doi:10.1145/3183399.3183423 · Cited by 19

Understanding node-link and matrix visualizations of networks: A large-scale online experiment

Ren, D., Marusich, L. R., O’Donovan, J., Bakdash, J. Z., Schaffer, J., Cassenti, D. N., Kase, S. E., Roy, H., Lin, W.-Y., & Höllerer, T. (2019) · Network Science · doi:10.1017/nws.2019.6 · Cited by 18

The influence of regional hydrometric data incorporation on the accuracy of gridded reconstruction of monthly runoff

Ayzel, G., Kurochkina, L., & Zhuravlev, S. (2020) · Hydrological Sciences Journal · doi:10.1080/02626667.2020.1762886 · Cited by 18

The R Language as a Tool for Biometeorological Research

Charalampopoulos, I. (2020) · Atmosphere · doi:10.3390/atmos11070682 · Cited by 18

The Factor Structure of Social Cognition in Schizophrenia: A Focus on Replication With Confirmatory Factor Analysis and Machine Learning

Riedel, P., Horan, W. P., Lee, J., Hellemann, G., & Green, M. F. (2020) · Clinical Psychological Science · doi:10.1177/2167702620951527 · Cited by 18

Open science and conflict of interest policies of medical and health sciences journals before and during the COVID-19 pandemic: A repeat cross-sectional study

Gardener, A. D., Hick, E. J., Jacklin, C., Tan, G., Cashin, A. G., Lee, H., Nunan, D., Toomey, E. C., & Richards, G. C. (2022) · JRSM Open · doi:10.1177/20542704221132139 · Cited by 22

Extrinsic site defensibility and landscape-based archaeological inference: An example from the Northwest Coast

Bocinsky, R. K. (2014) · Journal of Anthropological Archaeology · doi:10.1016/j.jaa.2014.05.003 · Cited by 18

Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park

Thomer, A. K., Wickett, K. M., Baker, K. S., Fouke, B. W., & Palmer, C. L. (2018) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24039 · Cited by 18

Matrix exponential in C/C++ version of vector radiative transfer code IPOL

Korkin, S. V., & Lyapustin, A. (2019) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2019.02.009 · Cited by 18

Ten Simple Rules for Reproducible Research in Jupyter NotebooksPreprint

Rule, A., Birmingham, A., Zúñiga, C., Altıntaş, İ., Huang, S.-C., Knight, R. D., Moshiri, N., Nguyen, M. H., Rosenthal, S. B., Pérez, F., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08055 · Cited by 18

Computational Reproducibility via Containers in Social PsychologyPreprint

Clyburne-Sherin, A., Fei, X., & Green, S. A. (2018) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/mf82t · Cited by 21

QMachine: commodity supercomputing in web browsers

Wilkinson, S. R., & Almeida, J. S. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-176 · Cited by 19

Metrics for openness

Nichols, D. M., & Twidale, M. B. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23741 · Cited by 17

AquaFlux: Rapid, transparent and replicable analyses of plant transpiration

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