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

Repeatability of the searching process in reviews of restoration outcomes

Romanelli, J. P., Silva, L. G. M., Gonçalves, M. C. P., Naves, R. P., de Almeida, D. R. A., de Resende, A. F., & Rodrigues, R. R. (2021) · Restoration Ecology · doi:10.1111/rec.13496 · Cited by 10

Nine quick tips for open meta-analyses

Moreau, D., & Wiebels, K. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012252 · Cited by 11

From field to analysis: strengthening reproducibility and confirmation in research for sustainable agriculture

White, J. W., Boote, K., Kimball, B. A., Porter, C., Salmerón, M., Shelia, V., Thorp, K., & Hoogenboom, G. (2025) · npj Sustainable Agriculture · doi:10.1038/s44264-025-00067-z · Cited by 8

Penerapan Reproducible Research pada RStudio dengan Bahasa R dan Paket Knitr

Budiaji, W. (2019) · Khazanah Informatika Jurnal Ilmu Komputer dan Informatika · doi:10.23917/khif.v5i1.7202 · Cited by 10

Executable Simulation Model of the Liver

König, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11682-4 · Cited by 9

Reproducible Large-Scale Neuroimaging Studies with the OpenMOLE Workflow Management System

Passerat‐Palmbach, J., Reuillon, R., Leclaire, M., Makropoulos, A., Robinson, E. C., Parisot, S., & Rueckert, D. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00021 · Cited by 10

Returns to scale in electricity generation: Replicated and revisited

Bernstein, D. H., & Parmeter, C. F. (2017) · Energy Economics · doi:10.1016/j.eneco.2017.12.024 · Cited by 10

Beyond lexical frequencies: using R for text analysis in the digital humanities

Arnold, T., Ballier, N., Lissón, P., & Tilton, L. (2019) · Language Resources and Evaluation · doi:10.1007/s10579-019-09456-6 · Cited by 14

DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis

Finak, G., Mayer, B. T., Fulp, W. J., Obrecht, P., Sato, A., Chung, E., Holman, D., & Gottardo, R. (2018) · Gates Open Research · doi:10.12688/gatesopenres.12832.1 · Cited by 10

Can You Repeat That? Exploring the Definition of a Successful Model Replication in Health Economics

McManus, E., Turner, D., & Sach, T. (2019) · PharmacoEconomics · doi:10.1007/s40273-019-00836-y · Cited by 10

Separate authorship categories to recognize data collectors and code developers

Ewers, R. M., Barlow, J., Banks-Leite, C., & Rahbek, C. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-1033-9 · Cited by 10

Toward Training and Assessing Reproducible Data Analysis in Data Science Education

Yu, B., & Hu, X. (2019) · Data Intelligence · doi:10.1162/dint_a_00053 · Cited by 10

The Potential of Notebooks for Scientific Publication, Reproducibility and Dissemination

Rowe, F., Maier, G., Arribas‐Bel, D., & Rey, S. J. (2020) · REGION · doi:10.18335/region.v7i3.357 · Cited by 10

European Heart Journal quality standards

Alfonso, F., Torp-Pedersen, C. T., Carter, R. E., & Crea, F. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab324 · Cited by 10

Toward Findable, Accessible, Interoperable and Reusable (FAIR) Photovoltaic System Time Series Data

Nihar, A., Curran, A. J., Karimi, A. M., Braid, J. L., Bruckman, L. S., Koyutürk, M., Wu, Y., & French, R. H. (2021) · Conference record of the IEEE Photovoltaic Specialists Conference · doi:10.1109/pvsc43889.2021.9518782 · Cited by 11

pvsR: An Open Source Interface to Big Data on the American Political Sphere

Matter, U., & Stutzer, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130501 · Cited by 12

Promises and Pitfalls of High-Throughput Biological Assays

Finak, G., & Gottardo, R. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3572-7_12 · Cited by 10

Investigating perceptions and support for transparency and openness in research: Using card sorting in a pilot study with academic librarians

Lyon, L., Mattern, E., Jeng, W., & He, D. (2016) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.2016.14505301114 · Cited by 10

Exploring Reproducibility and FAIR Principles in Data Science Using Ecological Niche Modeling as a Case Study

Mondelli, M. L., Peterson, A. T., & Gadelha, L. M. R., Jr. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34146-6_3 · Cited by 11

File-based localization of numerical perturbations in data analysis pipelines

Salari, A., Kiar, G., Lewis, L. B., Evans, A. C., & Glatard, T. (2020) · GigaScience · doi:10.1093/gigascience/giaa106 · Cited by 10

Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data MiningPreprint

Gardner, J., Yang, Y., Baker, R. S., & Brooks, C. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05208 · Cited by 10

Tagungsband ASIM 2018 – 24. Symposium Simulationstechnik

Sundermeier, J., Gehlhoff, F., & Fay, A. (2018) · doi:10.11128/arep.56 · Cited by 10

VizNet: Towards A Large-Scale Visualization Learning and Benchmarking RepositoryPreprint

Hu, K., Gaikwad, N., Bakker, M. A., Hulsebos, M., Zgraggen, E., Hidalgo, C. A., Kraska, T., Li, G., Satyanarayan, A., & Demiralp, Ç. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04616 · Cited by 15

Data-science ready, multisite, human diffusion MRI white-matter-tract statistics

Lerma‐Usabiaga, G., Mukherjee, P., Perry, L. M., & Wandell, B. A. (2020) · Scientific Data · doi:10.1038/s41597-020-00760-3 · Cited by 9

Machine learning-based evidence and attribution mapping of 100,000 climate impact studiesPreprint

Callaghan, M., Schleussner, C., Nath, S., Lejeune, Q., Knutson, T. R., Reichstein, M., Hansen, G., Theokritoff, E., Andrijevic, M., Brecha, R. J., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-783398/v2 · Cited by 16

AutonoML: Towards an Integrated Framework for Autonomous Machine Learning

Kedziora, D. J., Musiał, K., & Gabryś, B. (2024) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000093 · Cited by 34

A how-to guide for code sharing in biology

Abdill, R. J., Talarico, E., & Grieneisen, L. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002815 · Cited by 13

Characterising reproducibility debt in scientific software: A systematic literature review

Hassan, Z., Treude, C., Norrish, M., Williams, G., & Potanin, A. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112327 · Cited by 12

Code-sharing policies are associated with increased reproducibility potential of ecological findings

Sánchez‐Tójar, A., Bezine, A., Purgar, M., & Čulina, A. (2025) · Peer Community Journal · doi:10.24072/pcjournal.541 · Cited by 11

Tier-based standards for FAIR sequence data and metadata sharing in microbiome research

Kim, L., Lavrinienko, A., Sebechlebská, Z., Stoltenberg, S., & Bokulich, N. A. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf777 · Cited by 10

Reproducibility of machine learning-based fault detection and diagnosis for HVAC systems in buildings: An empirical study

Mukhtar, A., Hadwiger, M., Wotawa, F., & Schweiger, G. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100658 · Cited by 11

On Fishing for Significance and Statistician’s Degree of Freedom in the Era of Big Molecular Data

Boulesteix, A., Hornung, R., & Sauerbrei, W. (2017) · Springer eBooks · doi:10.1007/978-3-658-12153-2_7 · Cited by 9

Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease

Gan, R. W., Sun, D. K., Tatro, A. R., Cohen‐Mekelburg, S., Wiitala, W. L., Zhu, J., & Waljee, A. K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0257520 · Cited by 9

An Open-Source Workflow for Spatiotemporal Studies with COVID-19 as an Example

Malarvizhi, A. S., Liu, Q., Sha, D., Lan, H., & Yang, C. P. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11010013 · Cited by 11

Statistical and Scientific Considerations Concerning the Interpretation, Replicability, and Transportability of Research Findings

Cook, R. J., & Lawless, J. F. (2023) · The Journal of Rheumatology · doi:10.3899/jrheum.2023-0499 · Cited by 9

Gender Bias Detection in Court Decisions: A Brazilian Case Study

Benatti, R. M., Severi, F. C., Avila, S., & Colombini, E. L. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658937 · Cited by 14

Using the Seven Bridges Cancer Genomics Cloud to Access and Analyze Petabytes of Cancer Data

Malhotra, R., Seth, I., Lehnert, E. M., Zhao, J., Kaushik, G. N., Williams, E. H., Sethi, A., & Davis‐Dusenbery, B. N. (2017) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.39 · Cited by 9

WURS: a simulation software for university rankings—software review

Sınıksaran, E., & Satman, M. H. (2019) · Scientometrics · doi:10.1007/s11192-019-03269-8 · Cited by 9

Computational experiment design for operations model simulation

Lara, J. D., Lee, J. T., Callaway, D. S., & Hodge, B. S. (2020) · Electric Power Systems Research · doi:10.1016/j.epsr.2020.106680 · Cited by 10

The Role of Containers in Reproducibility

Canon, R. S. (2020) · IEEE/ACM International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC) · doi:10.1109/canopiehpc51917.2020.00008 · Cited by 10

An open‐source python library for detection of known and novel Kell, Duffy and Kidd variants from exome sequencing

Montemayor, C., Simone, A., Long, J., Montemayor, O. A., Delvadia, B., Rivera, R., Lewis, K. L., Shahsavari, S., Gandla, D., Dura, K., et al. (2021) · Vox Sanguinis · doi:10.1111/vox.13035 · Cited by 9

The biomedical significance of multifunctional nanobiomaterials: The key components for site-specific delivery of therapeutics

Hassanzadeh, P. (2021) · Life Sciences · doi:10.1016/j.lfs.2021.119400 · Cited by 9

Rogeaulito: A World Energy Scenario Modeling Tool for Transparent Energy System Thinking

Benichou, L., & Mayr, S. (2014) · Frontiers in Energy Research · doi:10.3389/fenrg.2013.00013 · Cited by 9

Single image super resolution with guided back-projection and LoG sharpening

Ngocho, B. M., & Mwangi, E. (2016) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melcon.2016.7495419 · Cited by 8

Advantages and Limits in the Adoption of Reproducible Research and R-Tools for the Analysis of Omic Data

Russo, F., Righelli, D., & Angelini, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44332-4_19 · Cited by 9

Distinct Antigen Delivery Systems Induce Dendritic Cells’ Divergent Transcriptional Response: New Insights from a Comparative and Reproducible Computational Analysis

Costa, V., Righelli, D., Russo, F., Berardinis, P. D., Angelini, C., & D’Apice, L. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18030494 · Cited by 9

EnosStack: A LAMP-like stack for the experimenter

Cherrueau, R.-A., Simonin, M., & van Kempen, A. (2018) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2018.8407024 · Cited by 9

INTEREST: INteractive Tool for Exploring REsults from Simulation sTudies

Gasparini, A., Morris, T., & Crowther, M. (2019) · Journal of Data Science Statistics and Visualisation · doi:10.52933/jdssv.v1i4.9 · Cited by 8

Case Studies and Challenges in Reproducibility in the Computational SciencesPreprint

Arabas, S., Bareford, M., de Silva, L. R., Gent, I. P., Gorman, B. M., Hajiarabderkani, M., Henderson, T., Hutton, L., Konovalov, O., Kotthoff, L., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.2123 · Cited by 9

Design and Use of Computational Notebooks

Rule, A. (2018) · eScholarship (California Digital Library) · Cited by 10

Progress toward openness, transparency, and reproducibility in cognitive neurosciencePreprint

Gilmore, R. O., Diaz, M. T., Wyble, B., & Yarkoni, T. (2017) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/5veew · Cited by 11

Detailed Implementation of a Reproducible Machine Learning-Enabled Workflow

Schackart, K., Imker, H. J., & Cook, C. E. (2024) · Data Science Journal · doi:10.5334/dsj-2024-023 · Cited by 8

Guidelines for reproducible analysis of adaptive immune receptor repertoire sequencing data

Peres, A., Klein, V., Frankel, B., Lees, W., Polak, P., Meehan, M., Rocha, A., Lopes, J. C., & Yaari, G. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae221 · Cited by 8

Analysis and Prediction of PM2.5 Pollution in Madrid: The Use of Prophet–Long Short-Term Memory Hybrid Models

Cáceres-Tello, J., & Galán-Hernández, J. J. (2024) · AppliedMath · doi:10.3390/appliedmath4040076 · Cited by 8

From policy to practice: progress towards data- and code-sharing in ecology and evolution

Ivimey‐Cook, E. R., Sánchez‐Tójar, A., Berberi, I., Čulina, A., Roche, D. G., Almeida, R. A., Amin, B., Bairos‐Novak, K. R., Balti, H., Bertram, M. G., et al. (2025) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2025.1394 · Cited by 22

Guidelines for collaborative development of sustainable data treatment software

Wuttke, J., Cottrell, S. P., Gonzalez, M. A., Kaestner, A. P., Markvardsen, A. J., Rod, T. H., Rozyczko, P. B., & Vardanyan, G. (2022) · Journal of Neutron Research · doi:10.3233/jnr-220002 · Cited by 10

Reproducibility challenges in robotic surgery

Faragasso, A., & Bonsignorio, F. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1127972 · Cited by 8

Writing statistical methods for ecologists

Davis, A. J., & Kay, S. L. (2023) · Ecosphere · doi:10.1002/ecs2.4539 · Cited by 8

Predicting coin flips: using resampling and hierarchical models to help untangle the NHL’s shoot-out

Lopez, M. J., & Schuckers, M. E. (2016) · Journal of Sports Sciences · doi:10.1080/02640414.2016.1198046 · Cited by 8

The rocky road to extended simulation frameworks covering uncertainty, inversion, optimization and control

Wirtz, D., & Nowak, W. (2017) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2016.10.003 · Cited by 8

Reproducibility Analysis of Scientific Workflows

Bánáti, A., Kacsuk, P., & Kozlovszky, M. (2017) · Acta Polytechnica Hungarica · doi:10.12700/aph.14.2.2017.2.11 · Cited by 8

Strategies for integrated analysis in imaging genetics studies

Vilor‐Tejedor, N., Alemany, S., Cáceres, A., Bustamante, M., Pujol, J., Sunyer, J., & González, J. R. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.06.013 · Cited by 8

Using the Google Web 1T 5-Gram Corpus for OCR Error Correction

Cacho, J. R. F., Taghva, K., & Alvarez, D. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-14070-0_71 · Cited by 8

From digital hype to analogue reality: Universal simulation beyond the quantum and exascale eras

Coveney, P. V., & Highfield, R. (2020) · Journal of Computational Science · doi:10.1016/j.jocs.2020.101093 · Cited by 8

GeenaR: A Web Tool for Reproducible MALDI-TOF Analysis

Prete, E. D., Facchiano, A. M., Profumo, A., Angelini, C., & Romano, P. D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.635814 · Cited by 8

MadingleyR: An R package for mechanistic ecosystem modelling

Hoeks, S., Tucker, M. A., Huijbregts, M. A. J., Harfoot, M. B. J., Bithell, M., & Santini, L. (2021) · Global Ecology and Biogeography · doi:10.1111/geb.13354 · Cited by 8

Towards Improving Experimentation in Software Engineering

OliveiraJr, E., Furtado, V. R., Vignando, H., Luz, C. D., Cordeiro, A. F. R., Steinmacher, I., & Zorzo, A. F. (2021) · Brazilian Symposium on Software Engineering · doi:10.1145/3474624.3477073 · Cited by 8

Pre-statistical harmonization of behavioral instruments across eight surveys and trials

Chen, D., Jutkowitz, E., Iosepovici, S. L., Lin, J. C. H., & Gross, A. L. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01431-6 · Cited by 8

Towards Better User Studies in Computer Graphics and Vision

Bylinskii, Z., Hertzmann, A., Zhang, Y., Herman, L., & Hutka, S. (2023) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000106 · Cited by 10

Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks–What about specialised metabolism?

Nègre, D., Larhlimi, A., & Bertrand, S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0289757 · Cited by 8

Energy, economics, replication & reproduction

Racine, J. S. (2017) · Energy Economics · doi:10.1016/j.eneco.2017.06.027 · Cited by 8

Detailed Provenance Capture of Data Processing

Meester, B. D., Dimou, A., Verborgh, R., & Mannens, E. (2017) · Ghent University Academic Bibliography (Ghent University) · Cited by 8

Potential use of R-statistical programming in the field of geoscience

Bishwal, R. M. (2017) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct.2017.8226275 · Cited by 8

Mixture Experimental Design

Simmons, J. E., Eide, I., Rice, G. E., & Feder, P. I. (2018) · Springer eBooks · doi:10.1007/978-3-319-56234-6_13 · Cited by 8

Cultural Genomics: Promises and Challenges

Chen, C., & Moyzis, R. K. (2018) · Journal of Cross-Cultural Psychology · doi:10.1177/0022022117736526 · Cited by 8

Modeling population heterogeneity from microbial communities to immune response in cells

Pecht, T., Aschenbrenner, A. C., Ulas, T., & Succurro, A. (2019) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-019-03378-w · Cited by 11

BISTRO

Feygin, S., Lazarus, J. R., Forscher, E. H., Golfier-Vetterli, V., Lee, J., Gupta, A., Waraich, R. A., Sheppard, C., & Bayen, A. M. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3384344 · Cited by 8

Open Science in Machine Learning

Braun, M. L., & Ong, C. S. (2018) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315373461-13 · Cited by 8

Conclusion

(2019) · Converging evidence in language and communication research · doi:10.1075/celcr.20.c18 · Cited by 6

Performance-Based Screening of Porous Materials for Carbon Capture

Farmahini, A. H., Krishnamurthy, S., Friedrich, D., Brandani, S., & Sarkisov, L. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01266 · Cited by 7

Recent Advances in Biological Omics Databases and Tools in Human Health

Kaithal, P., Kanchan, S., & Kesheri, M. (2024) · Springer eBooks · doi:10.1007/978-981-97-1769-9_12 · Cited by 7

Building Trustworthy AI: Transparent AI Systems via Language Models, Ontologies, and Logical Reasoning (TranspNet)

Machot, F. A., Horsch, M., & Ullah, H. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-89274-5_3 · Cited by 7

Integrating the theory of planned behavior in agent-based models: A systematic review of applications of pro-environmental behaviors

Taraghi, M., & Yoder, L. (2025) · Ecological Modelling · doi:10.1016/j.ecolmodel.2025.111231 · Cited by 7

Archiving Software Surrogates on the Web for Future Reference

Holzmann, H., Sperber, W., & Runnwerth, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-43997-6_17 · Cited by 7

Qualitätsdimensionen maschinellen Lernens in der amtlichen Statistik

Saidani, Y., Dumpert, F., Borgs, C., Brand, A., Nickl, A., Rittmann, A., Rohde, J., Salwiczek, C., Storfinger, N., & Straub, S. (2023) · AStA Wirtschafts- und Sozialstatistisches Archiv · doi:10.1007/s11943-023-00329-7 · Cited by 7

Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods

Wirz, C. D., Sutter, C., Demuth, J. L., Mayer, K. J., Chapman, W. E., Cains, M. G., Radford, J. T., Przybylo, V. M., Evans, A., Martin, T., et al. (2024) · Earth and Space Science · doi:10.1029/2023ea003364 · Cited by 7

Bridging consciousness and AI: ChatGPT-assisted phenomenological analysis

Martínez‐Pernía, D., Troncoso, A., Chaigneau, S. E., Marchant, N., Zepeda, A., & Blanco, K. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1520186 · Cited by 8

Towards Automated Event Studies Using High Frequency News and Trading Data

Bohn, N., Rabhi, F., Kundisch, D., Yao, L., & Mutter, T. (2013) · Lecture notes in business information processing · doi:10.1007/978-3-642-36219-4_2 · Cited by 7

Enabling Seamless Execution of Computational and Data Science Workflows on HPC and Cloud with the Popper Container-native Automation Engine

Chakraborty, J., Maltzahn, C., & Jimenez, I. (2020) · IEEE/ACM International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC) · doi:10.1109/canopiehpc51917.2020.00007 · Cited by 7

Reproducible Analyses in Education Research

LeBeau, B. C., Ellison, S., & Aloe, A. M. (2021) · Review of Research in Education · doi:10.3102/0091732x20985076 · Cited by 9

SweetPea: A standard language for factorial experimental design

Musslick, S., Cherkaev, A., Draut, B., Butt, A. S., Darragh, P., Srikumar, V., Flatt, M., & Cohen, J. D. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01598-2 · Cited by 11

StreamingHub

Jayawardana, Y., Ashok, V. G., & Jayarathna, S. (2022) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3529372.3530936 · Cited by 7

Open-source code repository attributes predict impact of computer science research

Bhattarai, P., Ghassemi, M. M., & Alhanai, T. (2022) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3529372.3530927 · Cited by 7

The Challenges of Replicating Volatile Platform-Data Studies: Replicating Schatto-Eckrodt et al. (2020)

Knöpfle, P., & Schatto‐Eckrodt, T. (2024) · Media and Communication · doi:10.17645/mac.7789 · Cited by 7

Analyzing data citation practices according to the Data Citation Index.

Robinson‐García, N., Jiménez‐Contreras, E., & Torres‐Salinas, D. (2015) · Cited by 7

Interval metamodels for the analysis of simulation Input–Output relations

Kamiński, B. (2015) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2015.03.008 · Cited by 7

The Need for Formally Defining “Modern Medicine” by Means of Experimental Design

Emmert‐Streib, F., Tuomisto, L., & Yli-Harja, O. P. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00060 · Cited by 7

DNA methylation studies of depression with onset in the peripartum: A critical systematic review

Braun, S. E., Lapato, D. M., Brown, R., Lancaster, E. E., York, T. P., Amstadter, A. B., & Kinser, P. A. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.04.005 · Cited by 8

Gsmodutils: a python based framework for test-driven genome scale metabolic model development

Gilbert, J., Pearcy, N., Norman, R. O. J., Millat, T., Winzer, K., King, J. R., Hodgman, C. T., Minton, N. P., & Twycross, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz088 · Cited by 7

%svy_logistic_regression: A generic SAS macro for simple and multiple logistic regression and creating quality publication-ready tables using survey or non-survey data

Muthusi, J., Mwalili, S. M., & Young, P. W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214262 · Cited by 7

PERSPECTIVE Reproducible Research in Computational Science

Peng, R. D. (2016)

Publishing Standards for Computational Science: “Setting the Default to Reproducible”

Stodden, V., Borwein, J., & Bailey, D. H. (2013)

Reproducibility in Computational Science

Stodden, V. (2012) · doi:10.7916/d8f76mtz · Cited by 1

for Scientific Computing: Tools and Strategies

Culture, F. C. T. (2013)

ReScience C: A Journal for Reproducible Replications in Computational Science

Rougier, N. P., & Hinsen, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23987-9_14 · Cited by 9