OSC Bibliography: Static

469 works from the overall bibliography of OSC members have been selected as related to “meta-science” or “open science”. We then created three bibliographic sections: articles, open source software, and open datasets and material. Within each category, resources are shown by fields and subfields. These categories are automatically determined by OpenAlex and might not fit in every case. OSC members are printed in bold in the co-authors’ list.

Articles

Biology

  • Gärtner A, Leising D & Schönbrodt F (2024). Towards responsible research assessment: how to reward research quality. PLoS Biology. https://doi.org/10.1371/journal.pbio.3002553
  • Stadler M, Lukauskas S, Bartke T & Müller C (2024). asteria enables robust interaction modeling between chromatin modifications and epigenetic readers. Nucleic Acids Research. https://doi.org/10.1093/nar/gkae361
  • Gaona‐Gordillo I, Holtmann B, Mouchet A, Hutfluss A, Sánchez‐Tójar A & Dingemanse N (2023). Are animal personality, body condition, physiology and structural size integrated? A comparison of species, populations and sexes, and the value of study replication. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13966
  • Sorbie A, Jiménez R & Benakis C (2022). Increasing transparency and reproducibility in stroke-microbiota research: a toolbox for microbiota analysis. iScience. https://doi.org/10.1016/j.isci.2022.103998
  • O’Dea R, Parker T, Chee Y, Čulina A, Drobniak S, Duncan D, Fidler F, Gould E, Ihle M, Kelly C, Lagisz M, Roche D, Sánchez‐Tójar A, Wilkinson D, Wintle B & Nakagawa S (2021). Towards open, reliable, and transparent ecology and evolutionary biology. BMC Biology. https://doi.org/10.1186/s12915-021-01006-3
  • Ihle M, Winney I, Krystalli A & Croucher M (2017). Striving for transparent and credible research: practical guidelines for behavioral ecologists. Behavioral Ecology. https://doi.org/10.1093/beheco/arx003
  • Albl B, Haesner S, Braun-Reichhart C, Streckel E, Renner S, Seeliger F, Wolf E, Wanke R & Blutke A (2016). Tissue sampling guides for porcine biomedical models. Toxicologic Pathology. https://doi.org/10.1177/0192623316631023
  • Mills J, Teplitsky C, Arroyo B, Charmantier A, Becker P, Birkhead T, Bize P, Blumstein D, Bonenfant C, Boutin S, Bushuev A, Cam E, Cockburn A, Côté S, Coulson J, Daunt F, Dingemanse N, Doligez B, Drummond H, Espie R, Festa‐Bianchet M, Frentiu F, Fitzpatrick J, Furness R, Gauthier G, Grant P, Griesser M, Gustafsson L, Hansson B, Harris M, Jiguet F, Kjellander P, Korpimäki E, Krebs C, Lens L, Linnell J, Low M, McAdam A, Margalida A, Merilä J, Møller A, Nakagawa S, Nilsson J, Nisbet I, Noordwijk A, Oró D, Pärt T, Pelletier F, Potti J, Pujol B, Réale D, Rockwell R, Ropert‐Coudert Y, Roulin A, Thébaud C, Sedinger J, Swenson J, Visser M, Wanless S, Westneat D, Wilson A & Zedrosser A (2016). Solutions for archiving data in long-term studies: a reply to whitlock et al.. Trends in Ecology & Evolution. https://doi.org/10.1016/j.tree.2015.12.004
  • Beniston M, Stoffel M, Harding R, Kernan M, Ludwig R, Moors E, Samuels P & Tockner K (2012). Obstacles to data access for research related to climate and water: implications for science and eu policy-making. Environmental Science & Policy. https://doi.org/10.1016/j.envsci.2011.12.002
  • Bauersachs S, Blum H, Krebs S, Fröhlich T, Arnold G & Wolf E (2010). Creating new knowledge for ruminant reproduction from rapidly expanding and evolving scientific databases. Reproduction in Domestic Ruminants. https://doi.org/10.5661/rdr-vii-29
  • Eravci M, Mansmann U, Broedel O, Weist S, Buetow S, Wittke J, Brunkau C, Hummel M, Eravci S & Baumgartner A (2009). Strategies for a reliable biostatistical analysis of differentially expressed spots from two-dimensional electrophoresis gels. Journal of Proteome Research. https://doi.org/10.1021/pr800532f
  • Gailus‐Durner V, Fuchs H, Becker L, Bolle I, Brielmeier M, Calzada‐Wack J, Elvert R, Ehrhardt N, Dalke C, Franz T, Grundner‐Culemann E, Hammelbacher S, Hölter S, Hölzlwimmer G, Horsch M, Javaheri A, Kalaydjiev S, Klempt M, Kling E, Kunder S, Lengger C, Lisse T, Mijalski T, Naton B, Pedersen V, Prehn C, Przemeck G, Rácz I, Reinhard C, Reitmeir P, Schneider I, Schrewe A, Steinkamp R, Zybill C, Adamski J, Beckers J, Behrendt H, Favor J, Graw J, Heldmaier G, Höfler H, Ivandic B, Katus H, Kirchhof P, Klingenspor M, Klopstock T, Lengeling A, Müller W, Ohl F, Ollert M, Quintanilla‐Martinez L, Schmidt J, Schulz H, Wolf E, Wurst W, Zimmer A, Busch D & Angelis M (2005). Introducing the german mouse clinic: open access platform for standardized phenotyping. Nature Methods. https://doi.org/10.1038/nmeth0605-403

Business

  • Furrer C, Sieh D, Jank A, Bras G, Herrmann M, Reguant-Closa A & Nemecek T (2024). Interlinking environmental and food composition databases: an approach, potential and limitations. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2024.143198
  • Deer L, Adler S, Datta H, Mizik N & Sarstedt M (2024). Toward open science in marketing research. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2024.12.005
  • Gerdon F, Nissenbaum H, Bach R, Kreuter F & Zins S (2021). Privacy attitudes in times of crisis: acceptance of data sharing for public health?. MADOC (University of Mannheim). https://doi.org/10.5281/zenodo.5607689
  • Fuchs S & Sarstedt M (2009). Is there a tacit acceptance of student samples in marketing and management research?. International Journal of Data Analysis Techniques and Strategies. https://doi.org/10.1504/ijdats.2010.030011

Computer science

General

Data science

  • Krähmer D, Schächtele L & Auspurg K (2026). Code sharing and reproducibility in survey-based social research: evidence from a large-scale audit. Royal Society Open Science. https://doi.org/10.1098/rsos.251997
  • Bové D, Seibold H, Boulesteix A, Manitz J, Gasparini A, Günhan B, Boix O, Schüler A, Fillinger S, Weimer K, Jacob A & Jaki T (2026). The statistical software revolution in pharmaceutical development: challenges and opportunities in open source.. Apollo (University of Cambridge). https://doi.org/10.17863/cam.125122
  • Short C, Inceler Y, Frank M & Hildebrandt A (2025). The systematic multiverse analysis registration tool for defining multiverse analyses. Royal Society Open Science. https://doi.org/10.1098/rsos.250800
  • Dongen N, Finnemann A, Ron J, Tiokhin L, Wang S, Algermissen J, Altmann E, Bahník Š, Chuang L, Dumbravă A, Fuenderich J, Geiger S, Gerasimova D, Golan A, Herbers J, Jekel M, Kunnari A, Lin Y, Moreau D, Oberholzer Y, Peetz H, Rohrer J, Rothers A, Schönbrodt F, Seetahul Y, Szabelska A, Tonge N, Walasek N, Werner M & Borsboom D (2025). Practicing theory building in a many modelers hackathon. Meta-Psychology. https://doi.org/10.15626/mp.2023.3688
  • Ball S, Allmendinger S, Kreuter F & Kühl N (2025). Human preferences in large language model latent space: a technical analysis on the reliability of synthetic data in voting outcome prediction. ArXiv.org. https://doi.org/10.48550/arxiv.2502.16280
  • Lemaréchal Y, Couture G, Pelletier F, Lefol R, Asselin P, Ouellet S, Bernard J, Ebrahimpour L, Manem V, Topalis J, Schachtner B, Jodogne S, Joubert P, Jeblick K, Ingrisch M & Després P (2025). Paradim: a platform to support research at the interface of data science and medical imaging. Journal of Imaging Informatics in Medicine. https://doi.org/10.1007/s10278-025-01554-y
  • Langener A, Siepe B, Elsherif M, Niemeijer K, Andresen P, Akre S, Bringmann L, Cohen Z, Choukas N, Drexl K, Fassi L, Green J, Hoffmann T, Jagesar R, Kas M, Kurten S, Schoedel R, Stulp G, Turner G & Jacobson N (2024). A template and tutorial for preregistering studies using passive smartphone measures. Behavior Research Methods. https://doi.org/10.3758/s13428-024-02474-5
  • Viglia G, Adler S, Miltgen C & Sarstedt M (2024). The use of synthetic data in tourism. Annals of Tourism Research. https://doi.org/10.1016/j.annals.2024.103819
  • Auspurg K & Brüderl J (2024). Toward a more credible assessment of the credibility of science by many-analyst studies. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2404035121
  • Mandl M, Hoffmann S, Bieringer S, Jacob A, Kraft M, Lemster S & Boulesteix A (2024). Raising awareness of uncertain choices in empirical data analysis: a teaching concept toward replicable research practices. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1011936
  • Stefan A & Schönbrodt F (2023). Big little lies: a compendium and simulation of p -hacking strategies. Royal Society Open Science. https://doi.org/10.1098/rsos.220346
  • Locher C, Goff G, Louarn A, Mansmann U & Naudet F (2023). Making data sharing the norm in medical research. BMJ. https://doi.org/10.1136/bmj.p1434
  • Krähmer D, Schächtele L & Schneck A (2023). Care to share? Experimental evidence on code sharing behavior in the social sciences. PLoS ONE. https://doi.org/10.1371/journal.pone.0289380
  • Lebmeier E, Aßenmacher M & Heumann C (2023). On the current state of reproducibility and reporting of uncertainty for aspect-based sentiment analysis. Lecture notes in computer science. https://doi.org/10.1007/978-3-031-26390-3_31
  • Henninger F (2023). Born-fair data projects using cookiecutter templates. Proceedings of the Conference on Research Data Infrastructure. https://doi.org/10.52825/cordi.v1i.331
  • Mechelen I, Boulesteix A, Dangl R, Dean N, Hennig C, Leisch F, Steinley D & Warrens M (2023). A white paper on good research practices in benchmarking: the case of cluster analysis. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1511
  • Drechsler J & Haensch A (2023). 30 years of synthetic data. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2304.02107
  • Sarstedt M & Adler S (2023). An advanced method to streamline p-hacking. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2023.113942
  • Ludwig T, Altenmüller M, Schramm L & Twardawski M (2023). Evading open science: the black box of student data collection. Social Psychological Bulletin. https://doi.org/10.32872/spb.9411
  • Shrestha Y, Krogh G & Feuerriegel S (2023). Building open-source ai. Nature Computational Science. https://doi.org/10.1038/s43588-023-00540-0
  • Ulmer D, Bassignana E, Müller-Eberstein M, Varab D, Zhang M, Goot R, Plank B & Plank B (2022). Experimental standards for deep learning in natural language processing research. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2204.06251
  • Betz D, Biniossek C, Blanchi C, Henninger F, Lauer T, Wieder P, Wittenburg P & Zünkeler M (2022). Canonical workflow for experimental research. Data Intelligence. https://doi.org/10.1162/dint_a_00123
  • Rigdon E, Sarstedt M & Becker J (2022). Managing uncertainty in consumer research: replicability and the elephant in the lab: an abstract. Developments in marketing science: proceedings of the Academy of Marketing Science. https://doi.org/10.1007/978-3-030-89883-0_36
  • Seibold H, Czerny S, Decke S, Dieterle R, Eder T, Fohr S, Hahn N, Hartmann R, Heindl C, Kopper P, Lepke D, Loidl V, Mandl M, Musiol S, Peter J, Piehler A, Rojas E, Schmid S, Schmidt H, Schmoll M, Schneider L, To X, Tran V, Völker A, Wagner M, Wagner J, Waize M, Wecker H, Yang R, Zellner S & Nalenz M (2022). Correction: a computational reproducibility study of plos one articles featuring longitudinal data analyses. PLoS ONE. https://doi.org/10.1371/journal.pone.0269047
  • Breznau N, Rinke E, Wuttke A, Nguyen H, Adem M, Adriaans J, Álvarez-Benjumea A, Andersen H, Auer D, Azevedo F, Bahnsen O, Balzer D, Bauer G, Bauer P, Baumann M, Baute S, Benoit V, Bernauer J, Berning C, Berthold A, Bethke F, Biegert T, Blinzler K, Blumenberg J, Bobzien L, Bohman A, Bol T, Bostic A, Brzozowska Z, Burgdorf K, Burger K, Busch K, Castillo J, Chan N, Christmann P, Connelly R, Czymara C, Damian E, Ecker A, Kellogg S, Eger M, Ellerbrock S, Forke A, Förster A, Gaasendam C, Gavras K, Gayle V, Gessler T, Gnambs T, Godefroidt A, Grömping M, Groß M, Gruber S, Gummer T, Hadjar A, Heisig J, Hellmeier S, Heyne S, Hirsch M, Hjerm M, Hochman O, Hövermann A, Hunger S, Hunkler C, Huth-Stöckle N, Ignácz Z, Jacobs L, Jacobsen J, Jaeger B, Jungkunz S, Jungmann N, Kauff M, Kleinert M, Klinger J, Kolb J, Kołczyńska M, Kuk J, Kunißen K, Sinatra D, Langenkamp A, Lersch P, Löbel L, Lutscher P, Mader M, Madia J, Malancu N, Maldonado L, Marahrens H, Martin N, Martinez P, Mayerl J, Mayorga O, McManus P, McWagner K, Meeusen C, Meierrieks D, Mellon J, Merhout F, Merk S, Meyer D & (2022). Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2203150119
  • Nießl C, Herrmann M, Wiedemann C, Casalicchio G & Boulesteix A (2021). Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results. arXiv (Cornell University). https://doi.org/10.1002/widm.1441
  • Seibold H, Czerny S, Decke S, Dieterle R, Eder T, Fohr S, Hahn N, Hartmann R, Heindl C, Kopper P, Lepke D, Loidl V, Mandl M, Musiol S, Peter J, Piehler A, Rojas E, Schmid S, Schmidt H, Schmoll M, Schneider L, To X, Tran V, Völker A, Wagner M, Wagner J, Waize M, Wecker H, Yang R, Zellner S & Nalenz M (2021). A computational reproducibility study of plos one articles featuring longitudinal data analyses. PLoS ONE. https://doi.org/10.1371/journal.pone.0251194
  • Arnold C & Neunhoeffer M (2020). Really useful synthetic data – a framework to evaluate the quality of differentially private synthetic data. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2004.07740
  • Hoffmann S, Schönbrodt F, Elsas R, Wilson R, Strasser U & Boulesteix A (2020). The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines. Open access LMU (Ludwid Maxmilian’s Universitat Munchen). https://doi.org/10.5282/ubm/epub.74850
  • Wuttke A, Rinke E, Connelly R & Wang Y (2019). Bitss-mzes open science research transparency and reproducibility workshop - mannheim, 27 january 2019. OSF Preprints (OSF Preprints). https://doi.org/10.17605/osf.io/pcft5
  • Weber T & Kranzlmüller D (2019). Methods to evaluate lifecycle models for research data management. BIBLIOTHEK Forschung und Praxis. https://doi.org/10.1515/bfp-2019-2016
  • Weber T, Kranzlmüller D, Fromm M & Sousa N (2019). Using supervised learning to classify metadata of research data by discipline of research. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1910.09313
  • Schönbrodt F, Mellor D, Bergmann C, Penfold N, Westwood S, Lautarescu A, Kowalczyk O, Dall’Aglio L, Blok E, Schettino A & Florentin S (2018). Academic job offers that mentioned open science. OSF Preprints (OSF Preprints). https://doi.org/10.17605/osf.io/7jbnt
  • Boulesteix A, Stierle V & Hapfelmeier A (2015). Publication bias in methodological computational research. Cancer Informatics. https://doi.org/10.4137/cin.s30747
  • Boulesteix A (2015). Ten simple rules for reducing overoptimistic reporting in methodological computational research. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1004191
  • Mills J, Teplitsky C, Arroyo B, Charmantier A, Becker P, Birkhead T, Bize P, Blumstein D, Bonenfant C, Boutin S, Bushuev A, Cam E, Cockburn A, Côté S, Coulson J, Daunt F, Dingemanse N, Doligez B, Drummond H, Espie R, Festa‐Bianchet M, Frentiu F, Fitzpatrick J, Furness R, Garant D, Gauthier G, Grant P, Griesser M, Gustafsson L, Hansson B, Harris M, Jiguet F, Kjellander P, Korpimäki E, Krebs C, Lens L, Linnell J, Low M, McAdam A, Margalida A, Merilä J, Møller A, Nakagawa S, Nilsson J, Nisbet I, Noordwijk A, Oró D, Pärt T, Pelletier F, Potti J, Pujol B, Réale D, Rockwell R, Ropert‐Coudert Y, Roulin A, Sedinger J, Swenson J, Thébaud C, Visser M, Wanless S, Westneat D, Wilson A & Zedrosser A (2015). Archiving primary data: solutions for long-term studies. Trends in Ecology & Evolution. https://doi.org/10.1016/j.tree.2015.07.006
  • Boulesteix A (2009). Over-optimism in bioinformatics research. Bioinformatics. https://doi.org/10.1093/bioinformatics/btp648

Software engineering

  • Bové D, Seibold H, Boulesteix A, Manitz J, Gasparini A, Guünhan B, Boix O, Schuüler A, Fillinger S, Nahnsen S, Jacob A & Jaki T (2026). The statistical software revolution in pharmaceutical development: challenges and opportunities in open source. Drug Discovery Today. https://doi.org/10.1016/j.drudis.2026.104613
  • Schmitt C, Kuhr T & Thomas K (2022). A workflow management system guide. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2212.01422
  • Tennant J, Colomb J, Matthias L, Worthington S, Kohrt F, irrubio i, Allard T, Zumstein P, Katz D, Morley A, Steiner T, Druskat S, Pandovski Z, Smith A, Orlandi G, Vos R, Pazos J, Griffiths P, Streethran N, Marshall H, Johnston L, Camacho L, Förstner K, Seibold H, EricDWilkey E, Álvarez E, Palmer B, Sarretta A, Marocchino A & Mayes A (2019). Opensciencemooc/module-5-open-research-software-and-open-source: 3.1. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.3361509

Engineering ethics

  • Burger V, Besouw M, Fehr J, Minocher R, Moorhead E, Velarde I, Agha-Mir-Salim L, Amann J, Bannach-Brown A, Blumenthal D, Hair K, Heinrichs B, Herrmann M, Hofvenschiold E, Holm S, Hond A, Kijewski S, McLennan S, Minssen T, Nobile M, Pfeifer N, Rohmann J, Ross-Hellauer T, Slavkovik M, Tafur K, Viganò E, Westerlund M, Weissgerber T & Madai V (2026). How meta-research can pave the road towards trustworthy ai in healthcare: catalogue of ideas and roadmap for future research. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2603.13286
  • Haim M & Jungblut M (2023). How open is communication science? Open-science principles in the field. Annals of the International Communication Association. https://doi.org/10.1080/23808985.2023.2201601

Natural language processing

  • Thummerer A, Maspero M, Bijl E, Corradini S, Belka C, Landry G & Kurz C (2025). 2711 harmonizing multi-lingual and multi-institutional structure names using open-source large language models. Radiotherapy and Oncology. https://doi.org/10.1016/s0167-8140(25)01242-3

Management science

  • Boulesteix A, Callahan P, Hanßum L, Gaertner V & Hoster E (2025). Bridging the gap between methodological research and statistical practice: toward “translational simulation research. ArXiv.org. https://doi.org/10.48550/arxiv.2510.05800
  • Sarstedt M, Adler S, Ringle C, Cho G, Diamantopoulos A, Hwang H & Liengaard B (2024). Same model, same data, but different outcomes: evaluating the impact of method choices in structural equation modeling. Journal of Product Innovation Management. https://doi.org/10.1111/jpim.12738
  • Adler S, Sharma P & Radomir L (2023). Toward open science in pls-sem: assessing the state of the art and future perspectives. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2023.114291
  • Nießl C, Herrmann M, Wiedemann C, Casalicchio G & Boulesteix A (2021). Over‐optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1441
  • Stefan A, Evans N & Wagenmakers E (2020). Practical challenges and methodological flexibility in prior elicitation.. Psychological Methods. https://doi.org/10.1037/met0000354

Knowledge management

  • Aßmann C, Bayer S, Blask K, Blaette A, Breidenbach P, Broneske D, Fräßdorf A, Goebel J, Heisig J, Hollstein B, Kleimann B, Keller K, Kühn L, Latif A, Liebig S, Limani F, Sandt A, Meyermann A, Miller B, Mozygemba K, Mutschke P, Niessner C, Raatz P, Rittberger M, Schlücker F, Schneider K, Estevao J, Dittrich G, Wichert S, Wiltshire D, Wolf C, Wenzig K, Wolf C, Wenzig K, Wolf C, Wiltshire D, Wenzig K & Wolf C (2025). Konsortswd – nfdi4society. Consortium proposal national research data infrastructure for the second funding phase. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.18196510
  • Bothmann L, Strickroth S, Casalicchio G, Rügamer D, Lindauer M, Scheipl F & Bischl B (2021). Developing open source educational resources for machine learning and data science. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2107.14330
  • Praßer F, Kohlbacher O, Mansmann U, Bauer B & Kuhn K (2018). Data integration for future medicine (difuture). Methods of Information in Medicine. https://doi.org/10.3414/me17-02-0022

Machine learning

  • Amad H, Qian Z, Frauen D, Piskorz J, Feuerriegel S & Schaar M (2025). Improving the generation and evaluation of synthetic data for downstream medical causal inference. ArXiv.org. https://doi.org/10.48550/arxiv.2510.18768
  • Wünsch M, Sauer C, Herrmann M, Hinske L & Boulesteix A (2024). To tweak or not to tweak. How exploiting flexibilities in gene set analysis leads to overoptimism. Biometrical Journal. https://doi.org/10.1002/bimj.70016
  • Stefan A, Schönbrodt F, Evans N & Wagenmakers E (2022). Efficiency in sequential testing: comparing the sequential probability ratio test and the sequential bayes factor test. Behavior Research Methods. https://doi.org/10.3758/s13428-021-01754-8
  • Sonabend R, Bender A & Vollmer S (2022). Avoiding c-hacking when evaluating survival distribution predictions with discrimination measures. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac451
  • Kühn D, Probst P, Thomas J & Bischl B (2018). Automatic exploration of machine learning experiments on openml. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1806.10961

Internet privacy

  • Neunhoeffer M, Seeman J & Drechsler J (2025). On the formal privacy guarantees of synthetic data (generated without formal privacy guarantees). Harvard Data Science Review. https://doi.org/10.1162/99608f92.1af82b35
  • König L, Altenmüller M, Fick J, Crusius J, Genschow O & Sauerland M (2024). How to communicate science to the public?. Zeitschrift für Psychologie. https://doi.org/10.1027/2151-2604/a000572
  • Hase V, Ausloos J, Boeschoten L, Pfiffner N, Janssen H, Araujo T, Carrière T, Vreese C, Haßler J, Löecherbach F, Kmetty Z, Möller J, Ohme J, Schmidbauer E, Struminskaya B, Trilling D, Welbers K & Haim M (2024). Fulfilling data access obligations: how could (and should) platforms facilitate data donation studies?. Internet Policy Review. https://doi.org/10.14763/2024.3.1793
  • Weiss C, Kreuter F & Habernal I (2023). To share or not to share: what risks would laypeople accept to give sensitive data to differentially-private nlp systems?. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2307.06708
  • Oberski D & Kreuter F (2020). Differential privacy and social science: an urgent puzzle. Harvard Data Science Review. https://doi.org/10.1162/99608f92.63a22079
  • Morey R, Chambers C, Etchells P, Harris C, Hoekstra R, Lakens D, Lewandowsky S, Morey C, Newman D, Schönbrodt F, Vanpaemel W, Wagenmakers E & Zwaan R (2016). The peer reviewers’ openness initiative: incentivizing open research practices through peer review. Royal Society Open Science. https://doi.org/10.1098/rsos.150547
  • Lane J, Lane J, Lane J, Strandburg K, Barocas S, Acquisti A, Ohm P, Stodden V, Koonin S, Goerge R, Elias P, Greenwood D, Landwehr C, Wilbanks J, Kreuter F, Karr A & Dwork C (2014). Privacy, big data, and the public good. Cambridge University Press eBooks. https://doi.org/10.1017/cbo9781107590205

Computer security

  • Haim M, Knöpfle P & Breuer J (2025). Contextual changes, credible conclusions? A direct and conceptual replication of shen et al.’s (2019) study on online image credibility. Media Psychology. https://doi.org/10.1080/15213269.2025.2595452
  • Latner J, Neunhoeffer M & Drechsler J (2024). Generating synthetic data is complicated: know your data and know your generator. Lecture notes in computer science. https://doi.org/10.1007/978-3-031-69651-0_8

Medical physics

  • Schwarz M, Hinske L, Mansmann U & Albashiti F (2025). Ml auditing and reproducibility: applying a core criteria catalog to an early sepsis onset detection system. IEEE Access. https://doi.org/10.1109/access.2025.3579631

Statistics

  • Breznau N, Rinke E, Wuttke A, Adem M, Adriaans J, Akdeniz E, Álvarez-Benjumea A, Andersen H, Auer D, Azevedo F, Bahnsen O, Bai L, Balzer D, Bauer P, Bauer G, Baumann M, Baute S, Benoit V, Bernauer J, Berning C, Berthold A, Bethke F, Biegert T, Blinzler K, Blumenberg J, Bobzien L, Bohman A, Bol T, Bostic A, Brzozowska Z, Burgdorf K, Burger K, Busch K, Castillo J, Chan N, Christmann P, Connelly R, Czymara C, Damian E, Rooij E, Ecker A, Kellogg S, Eder C, Eger M, Ellerbrock S, Forke A, Förster A, Freire D, Gaasendam C, Gavras K, Gayle V, Gessler T, Gnambs T, Godefroidt A, Grömping M, Gross M, Gruber S, Gummer T, Hadjar A, Halbherr V, Heisig J, Hellmeier S, Heyne S, Hirsch M, Hjerm M, Hochman O, Höffler J, Hövermann A, Hunger S, Hunkler C, Huth-Stöckle N, Ignácz Z, Israel S, Jacobs L, Jacobsen J, Jaeger B, Jungkunz S, Jungmann N, Kanjana J, Kauff M, Khan S, Khatua S, Kleinert M, Klinger J, Kolb J, Kołczyńska M, Kuk J, Kunißen K, Sinatra D, Langenkamp A, Lee R, Lersch P, Liu D, Löbel L, Lutscher P, Mader M, Madia J, Malancu N, Maldonado L, Marahrens H & (2025). The reliability of replications: a study in computational reproductions. Royal Society Open Science. https://doi.org/10.1098/rsos.241038
  • Sauerbrei W, Ambrogi F, Bin R, Boulesteix A, Goetghebeur E & Huebner M (2025). Commentary: regression models—efforts are required to improve statistical practice and teaching. Statistics in Medicine. https://doi.org/10.1002/sim.10341
  • Pargent F, Koch T, Kleine A, Lermer E & Gaube S (2024). A tutorial on tailored simulation-based sample-size planning for experimental designs with generalized linear mixed models. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/25152459241287132
  • Mandl M, Becker-Pennrich A, Hinske L, Hoffmann S & Boulesteix A (2024). Addressing researcher degrees of freedom through minp adjustment. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2401.11537
  • Schenk P & Kern C (2024). Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2402.09328
  • Heinze G, Boulesteix A, Kammer M, Morris T, White I & initiative t (2023). Phases of methodological research in biostatistics—building the evidence base for new methods. Biometrical Journal. https://doi.org/10.1002/bimj.202200222
  • Gaona‐Gordillo I, Holtmann B, Mouchet A, Hutfluss A, Sánchez‐Tójar A & Dingemanse N (2023). Penothypic integration: assessing the value of study replication. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.7874728
  • Boulesteix A, Hoffmann S, Charlton A & Seibold H (2020). A replication crisis in methodological research?. Significance. https://doi.org/10.1111/1740-9713.01444
  • Altmejd A, Dreber A, Forsell E, Huber J, Imai T, Johannesson M, Kirchler M, Nave G & Camerer C (2019). Predicting the replicability of social science lab experiments. PLoS ONE. https://doi.org/10.1371/journal.pone.0225826
  • Franke G & Sarstedt M (2019). Heuristics versus statistics in discriminant validity testing: a comparison of four procedures. Internet Research. https://doi.org/10.1108/intr-12-2017-0515
  • Schneck A (2017). Examining publication bias—a simulation-based evaluation of statistical tests on publication bias. PeerJ. https://doi.org/10.7717/peerj.4115
  • Schönbrodt F, Wagenmakers E, Zehetleitner M & Perugini M (2015). Sequential hypothesis testing with bayes factors: efficiently testing mean differences.. Psychological Methods. https://doi.org/10.1037/met0000061

Data mining

  • Wünsch M, Sauer C, Callahan P, Hinske L & Boulesteix A (2024). From rna sequencing measurements to the final results: a practical guide to navigating the choices and uncertainties of gene set analysis. Wiley Interdisciplinary Reviews Computational Statistics. https://doi.org/10.1002/wics.1643
  • Bun M, Gaboardi M, Neunhoeffer M & Zhang W (2023). Continual release of differentially private synthetic data from longitudinal data collections. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2306.07884
  • Wünsch M, Sauer C, Callahan P, Hinske L & Boulesteix A (2023). From rna sequencing measurements to the final results: a practical guide to navigating the choices and uncertainties of gene set analysis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2308.15171
  • Landes J & Williamson J (2022). Objective bayesian nets for integrating consistent datasets. Journal of Artificial Intelligence Research. https://doi.org/10.1613/jair.1.13363
  • Ullmann T, Hennig C & Boulesteix A (2021). Validation of cluster analysis results on validation data: a systematic framework. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1444
  • Weber T, Kranzlmüller D, Fromm M & Sousa N (2020). Using supervised learning to classify metadata of research data by field of study. Quantitative Science Studies. https://doi.org/10.1162/qss_a_00049
  • Boulesteix A, Hable R, Lauer S & Eugster M (2015). A statistical framework for hypothesis testing in real data comparison studies. The American Statistician. https://doi.org/10.1080/00031305.2015.1005128
  • Boulesteix A & Slawski M (2009). Stability and aggregation of ranked gene lists. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbp034
  • Ruschhaupt M, Huber W, Poustka A & Mansmann U (2004). A compendium to ensure computational reproducibility in high-dimensional classification tasks. Statistical Applications in Genetics and Molecular Biology. https://doi.org/10.2202/1544-6115.1078

Computational biology

Epistemology

  • Herrmann M, Lange F, Eggensperger K, Casalicchio G, Wever M, Feurer M, Rügamer D, Hüllermeier E, Boulesteix A & Bischl B (2024). Position: why we must rethink empirical research in machine learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2405.02200

Artificial intelligence

  • Sharma P, Sarstedt M, Ringle C, Cheah J, Herfurth A & Hair J (2024). A framework for enhancing the replicability of behavioral mis research using prediction oriented techniques. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2024.102805
  • Kohrt F, Smaldino P, McElreath R & Schönbrodt F (2023). Replication of the natural selection of bad science. Royal Society Open Science. https://doi.org/10.1098/rsos.221306
  • Behnke L, Mizutani-Tiebel Y, Chang K, Thielscher A, Bulubas L, Karali T, Papazov B, Kumpf U, Stöcklein S, Campana M, Soldini A, Dechantsreiter E, Tagnin L, Burkhardt G, Takahashi S, Padberg F & Keeser D (2023). Good news for data sharing: defacing of mr scans using simnibs 4.0. Brain stimulation. https://doi.org/10.1016/j.brs.2023.01.773
  • Stefan A, Lengersdorff L & Wagenmakers E (2022). A two-stage bayesian sequential assessment of exploratory hypotheses. Collabra Psychology. https://doi.org/10.1525/collabra.40350
  • Vasey B, Nagendran M, Campbell B, Clifton D, Collins G, Denaxas S, Denniston A, Faes L, Geerts B, Ibrahim M, Liu X, Mateen B, Mathur P, McCradden M, Morgan L, Ordish J, Rogers C, Saria S, Ting D, Watkinson P, Weber W, Wheatstone P, McCulloch P, Lee A, Fraser A, Connell A, Vira A, Esteva A, Althouse A, Beam A, Hond A, Boulesteix A, Bradlow A, Ercole A, Páez A, Tsanas A, Kirby B, Glocker B, Velardo C, Park C, Hehakaya C, Baber C, Paton C, Johner C, Kelly C, Vincent C, Yau C, McGenity C, Gatsonis C, Faivre‐Finn C, Simon C, Sent D, Bzdok D, Treanor D, Wong D, Steiner D, Higgins D, Benson D, O’Regan D, Gunasekaran D, Danks D, Neri E, Kyrimi E, Schwendicke F, Magrabi F, Ives F, Rademakers F, Fowler G, Frau G, Hogg H, Marcus H, Chan H, Xiang H, McIntyre H, Harvey H, Kim H, Habli I, Fackler J, Shaw J, Higham J, Wohlgemut J, Chong J, Bibault J, Cohen J, Kers J, Morley J, Krois J, Monteiro J, Horovitz J, Fletcher J, Taylor J, Yoon J, Singh K, Moons K, Karpathakis K, Catchpole K, Hood K, Balaskas K, Kamnitsas K, Militello L & (2022). Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: decide-ai. Nature Medicine. https://doi.org/10.1038/s41591-022-01772-9
  • Weber T, Ingrisch M, Fabritius M, Bischl B & Rügamer D (2021). Survival-oriented embeddings for improving accessibility to complex datastructures. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2110.11303
  • Littmann M, Selig K, Cohen-Lavi L, Frank Y, Hönigschmid P, Kataka E, Mösch A, Qian K, Ron A, Schmid S, Sorbie A, Szlak L, Dagan‐Wiener A, Ben‐Tal N, Niv M, Razansky D, Schuller B, Ankerst D, Hertz T & Rost B (2020). Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. Nature Machine Intelligence. https://doi.org/10.1038/s42256-019-0139-8
  • Aßenmacher M & Heumann C (2020). On the comparability of pre-trained language models. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2001.00781
  • Pargent F, Schoedel R & Stachl C (2018). An introduction to machine learning in r (workshop). OSF Preprints (OSF Preprints). https://doi.org/10.17605/osf.io/mnfbd
  • Schönbrodt F, Wagenmakers E, Zehetleitner M & Perugini M (2016). Sequential hypothesis testing with bayes factors. OSF Preprints (OSF Preprints). https://doi.org/10.17605/osf.io/dgmzf

Information retrieval

  • Wang Y, Leutner S, Ingrisch M, Klein C, Hinske L & Danhauser K (2024). Optimizing data extraction: harnessing rag and llms for german medical documents. Studies in health technology and informatics. https://doi.org/10.3233/shti240567
  • Kratzwald B, Yue X, Sun H & Feuerriegel S (2020). Practical annotation strategies for question answering datasets. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2003.03235

Database

  • Steinebach Y, Hinterleitner M, Knill C & Fernández‐i‐Marín X (2024). A review of national climate policies via existing databases. npj Climate Action. https://doi.org/10.1038/s44168-024-00160-y
  • Wood L, Bračko M, Elsethagen T, Fox K, Gamboa C, Kuhr T & Ritter M (2019). Performance of the belle ii conditions database. EPJ Web of Conferences. https://doi.org/10.1051/epjconf/201921404050
  • Skripcak T, Belka C, Bosch W, Brink C, Brunner T, Budach V, Büttner D, Debus J, Dekker A, Grau C, Gulliford S, Hurkmans C, Just U, Krause M, Lambin P, Langendijk J, Lewensohn R, Lühr A, Maingon P, Masucci M, Niyazi M, Poortmans P, Simon M, Schmidberger H, Spezi E, Stuschke M, Valentini V, Verheij M, Whitfield G, Zackrisson B, Zips D & Baumann M (2014). Creating a data exchange strategy for radiotherapy research: towards federated databases and anonymised public datasets. Radiotherapy and Oncology. https://doi.org/10.1016/j.radonc.2014.10.001
  • Fischer A & Mansmann U (2011). A metadata-based patient register for cooperative clinical research: a case study in acute myeloid leukemia. Studies in health technology and informatics. https://doi.org/10.3233/978-1-60750-806-9-857
  • Ahn S, Cho K, Hwang S, Kim J, Bračko M, Drásal Z, Fifield T, Frühwirth R, Grzymkowski R, Hara T, Heck H, Iida Y, Itoh R, Iwai G, Jang H, Katayama N, Kawai Y, Kiesling C, Kim B, Kuhr T, Lee S, Mitaroff W, Moll A, Nakazawa H, Nishida S, Pałka H, Prothmann K, Röhrken M, Sasaki T, Sevior M, Sitarz M, Stanič S, Watase Y & Yoon H (2010). Design of the advanced metadata service system with amga for the belle ii experiment. Journal of the Korean Physical Society. https://doi.org/10.3938/jkps.57.715

Operations research

  • Lebmeier E, Aßenmacher M & Heumann C (2023). Correction to: on the current state of reproducibility and reporting of uncertainty for aspect-based sentiment analysis. Lecture notes in computer science. https://doi.org/10.1007/978-3-031-26390-3_44
  • Seibold H, Charlton A, Boulesteix A & Hoffmann S (2021). Statisticians, roll up your sleeves! There’s a crisis to be solved. Significance. https://doi.org/10.1111/1740-9713.01554

Econometrics

  • Sarstedt M & Moisescu O (2023). Quantifying uncertainty in pls-sem-based mediation analyses. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-023-00231-9
  • Ly A, Stefan A, Doorn J, Dablander F, Bergh D, Sarafoglou A, Kucharský Š, Derks K, Gronau Q, Raj A, Boehm U, Kesteren E, Hinne M, Matzke D, Marsman M & Wagenmakers E (2020). The bayesian methodology of sir harold jeffreys as a practical alternative to the p value hypothesis test. Computational Brain & Behavior. https://doi.org/10.1007/s42113-019-00070-x

World Wide Web

  • Nasseh D, Schneiderbauer S, Lange M, Schweizer D, Heinemann V, Belka C, Cadenovic R, Buysse L, Erickson N, Mueller M, Kortuem K, Niyazi M, Marschner S & Fey T (2020). Optimizing the analytical value of oncology-related data based on an in-memory analysis layer: development and assessment of the munich online comprehensive cancer analysis platform. Journal of Medical Internet Research. https://doi.org/10.2196/16533

Cognitive science

Public relations

  • Nissim M, Abzianidze L, Evang K, Goot R, Haagsma H, Plank B & Wieling M (2017). Sharing is caring: the future of shared tasks. Computational Linguistics. https://doi.org/10.1162/coli_a_00304

Chromatography

Operating system

  • Ahn S, Kim J, Huh T, Hwang S, Cho K, Jang H, Kim B, Yoon H, Yu J, Drásal Z, Hara T, Iida Y, Itoh R, Iwai G, Katayama N, Kawai Y, Nishida S, Sasaki T, Watase Y, Uglov T, Frühwirth R, Mitaroff W, Grzymkowski R, Sitarz M, Zdybał M, Heck M, Kuhr T, Röhrken M, Bračko M, Pestotnik R, Petric R, Santelj L, Starič M, LEE S, Kiesling C, Koblitz S, Moll A, Prothmann K, Nakazawa H, Fifield T, Sevior M & Stanič S (2011). The embedment of a metadata system at grid farms at the belle ii experiment. Journal of the Korean Physical Society. https://doi.org/10.3938/jkps.59.2695

Parallel computing

  • Schmidberger M, Tierney L, Eddelbuettel D, Yu H, Mansmann U & Morgan M (2009). State-of-the-art in parallel computing with r. Open access LMU (Ludwid Maxmilian’s Universitat Munchen). https://doi.org/10.5282/ubm/epub.8991

Economics

  • Lin P, Brown A, Imai T, Wang J, Wang S & Camerer C (2020). Evidence of general economic principles of bargaining and trade from 2,000 classroom experiments. Nature Human Behaviour. https://doi.org/10.1038/s41562-020-0916-8
  • Camerer C, Dreber A, Forsell E, Ho T, Huber J, Johannesson M, Kirchler M, Almenberg J, Altmejd A, Chan T, Heikensten E, Holzmeister F, Imai T, Isaksson S, Nave G, Pfeiffer T, Razen M & Wu H (2016). Evaluating replicability of laboratory experiments in economics. Science. https://doi.org/10.1126/science.aaf0918

Engineering

History

  • Bischl B, Casalicchio G, Das T, Feurer M, Fischer S, Gijsbers P, Mukherjee S, Müller A, Németh L, Oala L, Purucker L, Ravi S, Rijn J, Singh P, Vanschoren J, Velde J & Wever M (2025). Openml: insights from 10 years and more than a thousand papers. Patterns. https://doi.org/10.1016/j.patter.2025.101317

Medicine

General

  • Seibold H, Schönbrodt F & Plesnila N (2019). Workshop: wissenschaftliches arbeiten in der medizin - reproduzierbarkeit, data sharing und open science. OSF Preprints (OSF Preprints). https://doi.org/10.17605/osf.io/hgy49
  • Schönbrodt F, Baumert A, Glöckner A, Back M, Arslan R, Voracek M, Horstmann K, Rohrer J, Mueller E, Stahl C, C. S, Frank R, Laura B, Tina S, Samuel T, Philipp R, Felix S, Claudia N, Erik M, Nadine J, Andreas G, Larissa S, T. H, M. S, Ina F, Tobias H, Lydia R, Birgit S, M. R, Sibylle M, Schmidt N, Marcel W, Lena R, Lasse W, Ramona S, Mathias K, Markus G, Marie H, Christoph S, Manuel R, Alexander H, Mitja B, Hilmar B, Jürgen S, Henrik B, Lukas R, H. H, Alexandra Z, Lieneke J, Martin V & A. B (2016). Netzwerk der open-science-initiativen (nosi). OSF Preprints (OSF Preprints). https://doi.org/10.17605/osf.io/tbkzh

Management science

  • Rotter S, Arroyo-Araujo M, Drude N, Pellegrini P, Kobold S, Richter G, Müller O, Bruder D, Riecken L, Gerlach B, Schuler L, Juliane S, Dalloul I, Kühn S, Wilcke J & Toelch U (2026). Strengthening translational preclinical research through confirmatory multi-laboratory studies. Frontiers in Medicine. https://doi.org/10.3389/fmed.2025.1715361
  • Drude N, Martinez‐Gamboa L, Danziger M, Collazo A, Kniffert S, Wiebach J, Nilsonne G, Konietschke F, Piper S, Pawel S, Micheloud C, Held L, Frommlet F, Segelcke D, Pogatzki‐Zahn E, Voelkl B, Friede T, Brunner E, Dempfle A, Haller B, Jung M, Riecken L, Kuhn H, Tenbusch M, Serna-­Higuita L, Remarque E, Grüninger S, Manske K, Kobold S, Rivalan M, Wedekind L, Wilcke J, Boulesteix A, Meinhardt M, Spanagel R, Hettmer S, Lüttichau I, Regina C, Dirnagl U & Toelch U (2022). Planning preclinical confirmatory multicenter trials to strengthen translation from basic to clinical research – a multi-stakeholder workshop report. Translational Medicine Communications. https://doi.org/10.1186/s41231-022-00130-8

Intensive care medicine

  • Mansmann U, Jong V, König F, Sax U, Naudet F & Held L (2026). Reply to trinquart and stockler’s comments on clinical trial data sharing.. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.18196665

Artificial intelligence

  • Gesierich B, Sander L, Pirpamer L, Meier D, Ruberte E, Amann M, Sinnecker T, Huck A, Leeuw F, Maillard P, Moy S, Helmer K, Levin J, Höglinger G, Kühne M, Bonati L, Kühle J, Cattin P, Granziera C, Schlaeger R, Duering M & Duering M (2025). Extended technical and clinical validation of deep learning‐based brainstem segmentation for application in neurodegenerative diseases. Human Brain Mapping. https://doi.org/10.1002/hbm.70141

Knowledge management

Data science

  • Tai K, Müller M, Mansmann U, Armond A, Decullier É, Louarn A, Munung N, Naudet F, Praßer F & Sax U (2025). Fairification of biomedical research data. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2025.111920
  • Mansmann U, Locher C, Praßer F, Weissgerber T, Sax U, Posch M, Decullier É, Cristea I, Debray T, Held L, Moher D, Ioannidis J, Ross J, Ohmann C & Naudet F (2023). Implementing clinical trial data sharing requires training a new generation of biomedical researchers. Nature Medicine. https://doi.org/10.1038/s41591-022-02080-y

Neuroscience

  • Georgopoulos M, Pavlopoulos A, Zerva N, Kokkonakis A, Mourouzis I, Plesnila N, Pantos C & Lourbopoulos A (2025). The mouse stroke unit protocol with standardized neurological scoring for translational mouse stroke studies. Journal of Visualized Experiments. https://doi.org/10.3791/66847

Pathology

  • Perneczky R, Darby D, Frisoni G, Hyde R, Iwatsubo T, Mummery C, Park K, Beek J, Flier W & Jessen F (2025). Real-world datasets for the international registry for alzheimer’s disease and other dementias (inrad) and other registries: an international consensus. The Journal of Prevention of Alzheimer s Disease. https://doi.org/10.1016/j.tjpad.2025.100096

Internet privacy

  • Ballhausen H, Corradini S, Belka C, Bogdanov D, Boldrini L, Bono F, Goelz C, Landry G, Panza G, Parodi K, Talviste R, Tran H, Gambacorta M & Marschner S (2024). Privacy-friendly evaluation of patient data with secure multiparty computation in a european pilot study. npj Digital Medicine. https://doi.org/10.1038/s41746-024-01293-4

Radiology

  • Ibrahim A, Widaatalla Y, Refaee T, Primakov S, Miclea R, Öcal O, Fabritius M, Ingrisch M, Ricke J, Hustinx R, Mottaghy F, Woodruff H, Seidensticker M & Lambin P (2021). Reproducibility of ct-based hepatocellular carcinoma radiomic features across different contrast imaging phases: a proof of concept on soramic trial data. Cancers. https://doi.org/10.3390/cancers13184638
  • Guio F, Jouvent É, Biessels G, Black S, Brayne C, Chen C, Cordonnier C, Leeuw F, Dichgans M, Doubal F, Duering M, Dufouil C, Düzel E, Fazekas F, Hachinski V, Ikram M, Linn J, Matthews P, Mazoyer B, Mok V, Norrving B, O’Brien J, Pantoni L, Ropele S, Sachdev P, Schmidt R, Seshadri S, Smith E, Sposato L, Stephan B, Swartz R, Tzourio C, Buchem M, Lugt A, Oostenbrugge R, Vernooij M, Viswanathan A, Werring D, Wollenweber F, Wardlaw J & Chabriat H (2016). Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease. Journal of Cerebral Blood Flow & Metabolism. https://doi.org/10.1177/0271678x16647396

Medical physics

  • Naudet F, Siebert M, Pellen C, Gaba J, Axfors C, Cristea I, Danchev V, Mansmann U, Ohmann C, Wallach J, Moher D & Ioannidis J (2021). Medical journal requirements for clinical trial data sharing: ripe for improvement. PLoS Medicine. https://doi.org/10.1371/journal.pmed.1003844

Data mining

  • Samaga D, Hornung R, Braselmann H, Heß J, Zitzelsberger H, Belka C, Boulesteix A & Unger K (2020). Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study. Radiation Oncology. https://doi.org/10.1186/s13014-020-01543-1

Virology

Genetics

  • Vries B, Anttila V, Freilinger T, Wessman M, Kaunisto M, Kallela M, Artto V, Vijfhuizen L, Göbel H, Dichgans M, Kubisch C, Ferrari M, Palotie A, Terwindt G & Maagdenberg A (2015). Systematic re-evaluation of genes from candidate gene association studies in migraine using a large genome-wide association data set. Cephalalgia. https://doi.org/10.1177/0333102414566820

Internal medicine

  • Llovera G, Hofmann K, Roth S, Salas-Perdomo A, Ferrer-Ferrer M, Perego C, Zanier E, Mamrak U, Rex A, Party H, Agin V, Fauchon C, Orset C, Haelewyn B, Simoni M, Dirnagl U, Grittner U, Planas A, Plesnila N, Vivien D & Liesz A (2015). Results of a preclinical randomized controlled multicenter trial (prct): anti-cd49d treatment for acute brain ischemia. Science Translational Medicine. https://doi.org/10.1126/scitranslmed.aaa9853

Statistics

  • Peters S, Switzer A, Patil S, McCreary C, Dichgans M, Wardlaw J & Smith E (2015). Abstract t p408: suboptimal quality of reporting of neuroimaging methods for studies of cerebral small vessel disease. Stroke. https://doi.org/10.1161/str.46.suppl_1.tp408
  • Clark T, Berger U & Mansmann U (2013). Sample size determinations in original research protocols for randomised clinical trials submitted to uk research ethics committees: review. BMJ. https://doi.org/10.1136/bmj.f1135

Philosophy

Political science

  • Brachem J, Frank M, Kvetnaya T, Schramm L & Volz L (2022). Replikationskrise, p-hacking und open science. Psychologische Rundschau. https://doi.org/10.1026/0033-3042/a000562
  • Ross R, Sulik J, Buczny J & Schivinski B (2022). Many analysts and few incentives. Religion Brain & Behavior. https://doi.org/10.1080/2153599x.2022.2070248
  • Brembs B, Huneman P, Schönbrodt F, Nilsonne G, Susi T, Siems R, Perakakis P, Trachana V, Ma L & Rodríguez‐Cuadrado S (2021). Replacing academic journals. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.5526635
  • Herklotz M & Oberländer L (2021). Iva – ein interaktiver virtueller assistent von berd@bw zur aufbereitung von rechtsfragen im bereich open science. heiDOK (Heidelberg University). https://doi.org/10.11588/heidok.00029644
  • Rahal R, Fiebach C, Fiedler S, Schönbrodt F, Plesnila N, Gräf J, Fritzsch B, Tochtermann K & Dirnagl U (2021). The german reproducibility network: a strategic community effort to promote transparent research practices in the scientific system. Helmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut). https://doi.org/10.5281/zenodo.4684663
  • Beaudry J, Chen D, Cook B, Errington T, Fortunato L, Given L, Hahn K, Ihle M, Mellor D, Nosek B, Lisa G, Huajin W, K. S, Thomas M, M. E, Nicole P, L B & Andrew T (2020). The open scholarship survey (oss). OSF Preprints (OSF Preprints). https://doi.org/10.17605/osf.io/nsbr3
  • Dienlin T, Johannes N, Bowman N, Masur P, Engesser S, Kümpel A, Lukito J, Bier L, Zhang R, Johnson B, Huskey R, Schneider F, Breuer J, Parry D, Vermeulen I, Fisher J, Banks J, Weber R, Ellis D, Smits T, Ivory J, Trepte S, McEwan B, Rinke E, Neubaum G, Winter S, Carpenter C, Krämer N, Utz S, Unkel J, Wang X, Davidson B, Kim N, Won A, Domahidi E, Lewis N & Vreese C (2020). An agenda for open science in communication. Journal of Communication. https://doi.org/10.1093/joc/jqz052
  • Haim M & Zamith R (2019). Open-source trading zones and boundary objects: examining github as a space for collaborating on “news”. Media and Communication. https://doi.org/10.17645/mac.v7i4.2249
  • Krefeld T & Lücke S (2019). 54 monate verbaalpina – auf dem weg zur fairness. Ladinia. https://doi.org/10.54218/ladinia.42.139-155
  • Wuttke A (2018). Why too many political science findings cannot be trusted and what we can do about it: a review of meta-scientific research and a call for academic reform. Politische Vierteljahresschrift. https://doi.org/10.1007/s11615-018-0131-7
  • Fox E & Rau H (2017). Disengaging citizens? Climate change communication and public receptivity. Irish Political Studies. https://doi.org/10.1080/07907184.2017.1301434
  • Schönbrodt F & Scheel A (2017). Faq zu open data und open science in der sportpsychologie. Zeitschrift für Sportpsychologie. https://doi.org/10.1026/1612-5010/a000217
  • Auspurg K, Hinz T & Schneck A (2014). Ausmaß und risikofaktoren des publication bias in der deutschen soziologie. KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie. https://doi.org/10.1007/s11577-014-0284-3
  • Calise M, Rosa R & Fernández‐i‐Marín X (2010). Electronic publishing, knowledge sharing and open access: a new environment for political science. European Political Science. https://doi.org/10.1057/eps.2010.35

Psychology

General

  • Gollwitzer M, Abele A, Fiebach C, Ramthun R, Scheel A, Schönbrodt F & Steinberg U (2021). Management und bereitstellung von forschungsdaten in der psychologie: überarbeitung der dgps-empfehlungen. Psychologische Rundschau. https://doi.org/10.1026/0033-3042/a000514
  • Benjamin D, Berger J, Johannesson M, Nosek B, Wagenmakers E, Berk R, Bollen K, Brembs B, Brown L, Camerer C, Cesarini D, Chambers C, Clyde M, Cook T, Boeck P, Dienes Z, Dreber A, Easwaran K, Efferson C, Fehr E, Fidler F, Field A, Forster M, George E, Gonzalez R, Goodman S, Green E, Green D, Greenwald A, Hadfield J, Hedges L, Held L, Ho T, Hoijtink H, Hruschka D, Imai K, Imbens G, Ioannidis J, Jeon M, Jones J, Kirchler M, Laibson D, List J, Little R, Lupia A, Machery É, Maxwell S, McCarthy M, Moore D, Morgan S, Munafò M, Nakagawa S, Nyhan B, Parker T, Pericchi L, Perugini M, Rouder J, Rousseau J, Savalei V, Schönbrodt F, Sellke T, Sinclair B, Tingley D, Zandt T, Vazire S, Watts D, Winship C, Wolpert R, Xie Y, Young C, Zinman J & Johnson V (2017). Redefine statistical significance. Nature Human Behaviour. https://doi.org/10.1038/s41562-017-0189-z

Cognitive psychology

  • Leung A, Logvinenko T & Schmalz X (2025). Dyslexia research and replicability: should we be worried?. Mind Brain and Education. https://doi.org/10.1111/mbe.70031
  • Ziegler J & Gollwitzer M (2025). Overspecification – an overlooked but essential aspect of psychological theory development. Zeitschrift für Psychologie. https://doi.org/10.1027/2151-2604/a000607
  • Gollwitzer M & Schwabe J (2021). Context dependency as a predictor of replicability. Review of General Psychology. https://doi.org/10.1177/10892680211015635
  • Maier M, Büechner V, Dechamps M, Pflitsch M, Kurzrock W, Tressoldi P, Rabeyron T, Cardeña E, Marcusson‐Clavertz D & Martsinkovskaja T (2020). A preregistered multi-lab replication of maier et al. (2014, exp. 4) testing retroactive avoidance. PLoS ONE. https://doi.org/10.1371/journal.pone.0238373

Data science

  • Lange F, Wilcke J, Hoffmann S, Herrmann M & Boulesteix A (2025). On “confirmatory” methodological research in statistics and related fields. Statistics in Medicine. https://doi.org/10.1002/sim.70303
  • Grüning D & Frank M (2023). Open science events: a best practice overview. Psychology Teaching Review. https://doi.org/10.53841/bpsptr.2023.29.2.19
  • Auspurg K & Brüderl J (2021). Has the credibility of the social sciences been credibly destroyed? Reanalyzing the “many analysts, one data set” project. Socius Sociological Research for a Dynamic World. https://doi.org/10.1177/23780231211024421
  • Carter E, Schönbrodt F, Gervais W & Hilgard J (2019). Correcting for bias in psychology: a comparison of meta-analytic methods. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245919847196
  • Marsman M, Schönbrodt F, Morey R, Yao Y, Gelman A & Wagenmakers E (2017). Correction to ‘a bayesian bird’s eye view of ‘replications of important results in social psychology’. Royal Society Open Science. https://doi.org/10.1098/rsos.170085

Engineering ethics

  • Schönbrodt F, Gärtner A, Frank M, Gollwitzer M, Ihle M, Mischkowski D, Phan L, Schmitt M, Scheel A, Schubert A, Steinberg U & Leising D (2025). Responsible research assessment i: implementing dora and coara for hiring and promotion in psychology. Meta-Psychology. https://doi.org/10.15626/mp.2024.4601

Applied psychology

  • Uher J, Arnulf J, Barrett P, Heene M, Heine J, Martin J, Mazur L, McGann M, Mislevy R, Speelman C, Toomela A & Weber R (2025). Psychology’s questionable research fundamentals (qrfs): key problems in quantitative psychology and psychological measurement beyond questionable research practices (qrps). Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2025.1553028
  • Siepe B, Bartos̆ F, Morris T, Boulesteix A, Heck D & Pawel S (2024). Simulation studies for methodological research in psychology: a standardized template for planning, preregistration, and reporting.. Psychological Methods. https://doi.org/10.1037/met0000695
  • Niemeyer H, Knaevelsrud C, Aert R & Ehring T (2023). Research into evidence-based psychological interventions needs a stronger focus on replicability. Clinical Psychology in Europe. https://doi.org/10.32872/cpe.9997
  • Leising D, Thielmann I, Glöckner A, Gärtner A & Schönbrodt F (2021). Ten steps toward a better personality science – how quality may be rewarded more in research evaluation. Europe PMC (PubMed Central). https://doi.org/10.23668/psycharchives.4963

Statistics

  • Hoffmann J, Twardawski M, Höhs J, Gast A, Pohl S & Sengewald M (2025). The design of current replication studies: a systematic literature review on the variation of study characteristics. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/25152459251328273
  • Hahn L, Glöckner A, Gollwitzer M, Hellmann J, Lange J, Schindler S & Sassenberg K (2025). A cross-sectional study of the completeness of preregistrations by psychological authors from german-speaking institutions. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/25152459251357568
  • Schneck A (2023). Are most published research findings false? Trends in statistical power, publication selection bias, and the false discovery rate in psychology (1975–2017). PLoS ONE. https://doi.org/10.1371/journal.pone.0292717
  • Maier M & Dechamps M (2022). A pre-registered test of a correlational micro-pk effect: efforts to learn from a failure to replicate. Journal of Scientific Exploration. https://doi.org/10.31275/20222235
  • Doorn J, Bergh D, Böhm U, Dablander F, Derks K, Draws T, Etz A, Evans N, Gronau Q, Haaf J, Hinne M, Kucharský Š, Ly A, Marsman M, Matzke D, Gupta A, Sarafoglou A, Stefan A, Voelkel J & Wagenmakers E (2020). The jasp guidelines for conducting and reporting a bayesian analysis. Psychonomic Bulletin & Review. https://doi.org/10.3758/s13423-020-01798-5
  • Oberlader V, Quinten L, Banse R, Volbert R, Schmidt A & Schönbrodt F (2020). Validity of content‐based techniques for credibility assessment—how telling is an extended meta‐analysis taking research bias into account?. Applied Cognitive Psychology. https://doi.org/10.1002/acp.3776
  • Camerer C, Dreber A, Holzmeister F, Ho T, Huber J, Johannesson M, Kirchler M, Nave G, Nosek B, Pfeiffer T, Altmejd A, Buttrick N, Chan T, Chen Y, Forsell E, Gampa A, Heikensten E, Hummer L, Imai T, Isaksson S, Manfredi D, Rose J, Wagenmakers E & Wu H (2018). Evaluating the replicability of social science experiments in nature and science between 2010 and 2015. Nature Human Behaviour. https://doi.org/10.1038/s41562-018-0399-z
  • Anderson C, Bahník Š, Barnett‐Cowan M, Bosco F, Chandler J, Chartier C, Cheung F, Christopherson C, Cordes A, Cremata E, Penna N, Estel V, Fedor A, Fitneva S, Frank M, Grange J, Hartshorne J, Hasselman F, Henninger F, Hulst M, Jonas K, Lai C, Levitan C, Miller J, Moore K, Meixner J, Munafò M, Neijenhuijs K, Nilsonne G, Nosek B, Plessow F, Prenoveau J, Ricker A, Schmidt K, Spies J, Stieger S, Strohminger N, Sullivan G, Aert R, Assen M, Vanpaemel W, Vianello M, Voracek M & Zuni K (2016). Response to comment on “estimating the reproducibility of psychological science”. Science. https://doi.org/10.1126/science.aad9163

Social psychology

  • Glöckner A, Gollwitzer M, Hahn L, Lange J, Sassenberg K & Unkelbach C (2024). Quality, replicability, and transparency in research in social psychology. Social Psychology. https://doi.org/10.1027/1864-9335/a000548
  • Silber H, Gerdon F, Bach R, Kern C, Keusch F & Kreuter F (2022). A preregistered vignette experiment on determinants of health data sharing behavior. Politics and the Life Sciences. https://doi.org/10.1017/pls.2022.15
  • Nosek B, Hardwicke T, Moshontz H, Allard A, Corker K, Dreber A, Fidler F, Hilgard J, Struhl M, Nuijten M, Rohrer J, Romero F, Scheel A, Scherer L, Schönbrodt F & Vazire S (2021). Replicability, robustness, and reproducibility in psychological science. Annual Review of Psychology. https://doi.org/10.1146/annurev-psych-020821-114157
  • Altenmüller M, Nuding S & Gollwitzer M (2021). No harm in being self-corrective: self-criticism and reform intentions increase researchers’ epistemic trustworthiness and credibility in the eyes of the public. Public Understanding of Science. https://doi.org/10.1177/09636625211022181
  • Altenmüller M & Gollwitzer M (2021). Prosociality in science. Current Opinion in Psychology. https://doi.org/10.1016/j.copsyc.2021.08.011
  • Schönbrodt F (2020). How to share psychological research data: recommendations from the german psychological society. Psychology Archives. https://doi.org/10.23668/psycharchives.4473
  • Abele A, Gollwitzer M, Steinberg U & Schönbrodt F (2019). Attitudes toward open science and public data sharing. Social Psychology. https://doi.org/10.1027/1864-9335/a000384
  • Pargent F, Hilbert S, Eichhorn K & Bühner M (2018). Can’t make it better nor worse. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000471
  • Silberzahn R, Uhlmann E, Martin D, Anselmi P, Aust F, Awtrey E, Bahník Š, Bai F, Bannard C, Bonnier E, Carlsson R, Cheung F, Christensen G, Clay R, Craig M, Rosa A, Dam L, Evans M, Cervantes I, Fong N, Gamez-Djokic M, Glenz A, Gordon-McKeon S, Heaton T, Hederos K, Heene M, Mohr A, Högden F, Hui K, Johannesson M, Kalodimos J, Kaszubowski E, Kennedy D, Lei R, Lindsay T, Liverani S, Madan C, Molden D, Molleman E, Morey R, Mulder L, Nijstad B, Pope N, Pope B, Prenoveau J, Rink F, Robusto E, Roderique H, Sandberg A, Schlüter E, Schönbrodt F, Sherman M, Sommer S, Sotak K, Spain S, Spörlein C, Stafford T, Stefanutti L, Täuber S, Ullrich J, Vianello M, Wagenmakers E, Witkowiak M, Yoon S & Nosek B (2018). Many analysts, one data set: making transparent how variations in analytic choices affect results. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245917747646
  • Greiff S & Heene M (2017). Why psychological assessment needs to start worrying about model fit. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000450

Econometrics

  • Holzmeister F, Johannesson M, Camerer C, Chen Y, Ho T, Hoogeveen S, Huber J, Imai N, Imai T, Jin L, Kirchler M, Ly A, Mandl B, Manfredi D, Nave G, Nosek B, Pfeiffer T, Sarafoglou A, Schwaiger R, Wagenmakers E, Walden V & Dreber A (2024). Examining the replicability of online experiments selected by a decision market. Nature Human Behaviour. https://doi.org/10.1038/s41562-024-02062-9
  • Schweinsberg M, Feldman M, Staub N, Akker O, Aert R, Assen M, Liu Y, Althoff T, Heer J, Kale A, Mohamed Z, Amireh H, Prasad V, Bernstein A, Robinson E, Snellman K, Sommer S, Otner S, Robinson D, Madan N, Silberzahn R, Goldstein P, Tierney W, Murase T, Mandl B, Viganola D, Strobl C, Schaumans C, Kelchtermans S, Naseeb C, Garrison S, Yarkoni T, Chan C, Adie P, Alaburda P, Albers C, Alspaugh S, Alstott J, Nelson A, Rubia E, Adbi A, Bahník Š, Baik J, Balling L, Banker S, Baranger D, Barr D, Barros-Rivera B, Bauer M, Enuh B, Boelen L, Carbonell K, Briers R, Burkhard O, Canela M, Castrillo L, Catlett T, Chen O, Clark M, Cohn B, Coppock A, Cugueró-Escofet N, Curran P, Cyrus-Lai W, Dai D, Riva G, Danielsson H, Russo R, Silva N, Derungs C, Dondelinger F, Souza C, Dube B, Dubova M, Dunn B, Edelsbrunner P, Finley S, Fox N, Gnambs T, Gong Y, Grand E, Greenawalt B, Dan H, Hanel P, Hong A, Hood D, Hsueh J, Huang L, Hui K, Hultman K, Javaid A, Jiang L, Jong J, Kamdar J, Kane D, Kappler G, Kaszubowski E, Kavanagh C, Khabsa M, Kleinberg B & (2021). Same data, different conclusions: radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis. Organizational Behavior and Human Decision Processes. https://doi.org/10.1016/j.obhdp.2021.02.003

Medical education

  • Kohrs F, Auer S, Bannach‐Brown A, Fiedler S, Haven T, Heise V, Holman C, Azevedo F, Bernard R, Bleier A, Bössel N, Cahill B, Castro L, Ehrenhofer A, Eichel K, Frank M, Frick C, Friese M, Gärtner A, Gierend K, Grüning D, Hahn L, Hülsemann M, Ihle M, Illius S, König L, König M, Kulke L, Kutlin A, Lammers F, Mehler D, Miehl C, Mueller‐Alcazar A, Neuendorf C, Niemeyer H, Pargent F, Peikert A, Pfeuffer C, Reinecke R, Röer J, Rohmann J, Sánchez‐Tójar A, Scherbaum S, Sixtus E, Spitzer L, Straßburger V, Weber M, Whitmire C, Zerna J, Zorbek D, Zumstein P & Weissgerber T (2023). Eleven strategies for making reproducible research and open science training the norm at research institutions. eLife. https://doi.org/10.7554/elife.89736
  • Schönbrodt F (2019). Training students for the open science future. Nature Human Behaviour. https://doi.org/10.1038/s41562-019-0726-z

Psychoanalysis

Humanities

  • Lange J, Unkelbach C, Glöckner A, Gollwitzer M, Kaiser F & Sassenberg K (2022). Fachgruppe sozialpsychologie. Task force “qualitätssicherung sozialpsychologischer forschung” der fachgruppe sozialpsychologie. Das zusammenspiel von theorie und methodik. Psychologische Rundschau. https://doi.org/10.1026/0033-3042/a000565
  • Gollwitzer M, Antoni C, Bermeitinger C, Bühner M, Elsner B, Gärtner A, König C, Spinath B, Schulz‐Hardt S & Tuschen‐Caffier B (2022). Dgps-kommission „studium und lehre“ der dgps. Die lehre von heute ist die forschung von morgen. Psychologische Rundschau. https://doi.org/10.1026/0033-3042/a000564

Psychotherapist

  • Ehring T, Limburg K, Kunze A, Wittekind C, Werner G, Wolkenstein L, Guzey M & Cludius B (2022). (When and how) does basic research in clinical psychology lead to more effective psychological treatment for mental disorders?. Clinical Psychology Review. https://doi.org/10.1016/j.cpr.2022.102163
  • Woll C & Schönbrodt F (2019). A series of meta-analytic tests of the efficacy of long-term psychoanalytic psychotherapy. European Psychologist. https://doi.org/10.1027/1016-9040/a000385

Public relations

  • Gerdon F, Nissenbaum H, Bach R, Kreuter F & Zins S (2021). Individual acceptance of using health data for private and public benefit: changes during the covid-19 pandemic. Harvard Data Science Review. https://doi.org/10.1162/99608f92.edf2fc97

Epistemology

Statistical physics

  • Dechamps M, Maier M, Pflitsch M & Duggan M (2021). Observer dependent biases of quantum randomness. Journal of Anomalous Experience and Cognition. https://doi.org/10.31156/jaex.23205

Management science

  • Gollwitzer M (2020). Dfg priority program spp 2317 proposal: a meta-scientific program to analyze and optimize replicability in the behavioral, social, and cognitive sciences (meta-rep). Psychology Archives. https://doi.org/10.23668/psycharchives.3010

Neuroscience

  • Fonteneau C, Mondino M, Arns M, Baeken C, Bikson M, Brunoni A, Burke M, Neuvonen T, Padberg F, Pascual‐Leone Á, Poulet E, Ruffini G, Santarnecchi E, Sauvaget A, Schellhorn K, Suaud‐Chagny M, Palm U & Brunelin J (2019). Sham tdcs: a hidden source of variability? Reflections for further blinded, controlled trials. Brain stimulation. https://doi.org/10.1016/j.brs.2018.12.977

Law

Artificial intelligence

  • Marsman M, Schönbrodt F, Morey R, Yao Y, Gelman A & Wagenmakers E (2017). A bayesian bird’s eye view of ‘replications of important results in social psychology’. Royal Society Open Science. https://doi.org/10.1098/rsos.160426

Positive economics

Sociology

  • Breuer J & Haim M (2024). Are we replicating yet? Reproduction and replication in communication research. Media and Communication. https://doi.org/10.17645/mac.8382
  • Auspurg K & Brüderl J (2022). How to increase reproducibility and credibility of sociological research. Edward Elgar Publishing eBooks. https://doi.org/10.4337/9781789909432.00037
  • Edelsbrunner P, Ruggeri K, Damnjanović K, Greiff S, Lemoine J & Ziegler M (2022). Generalizability, replicability, and new insights derived from registered reports within understudied populations. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000743
  • Wuttke A (2020). Naomi oreskes, why trust science? (Princeton, nj: princeton university press, 2019). 376 pages. Isbn: 9780691179001. Hardcover $24.95. - garret christensen, jeremy freese, and edward miguel, transparent and reproducible social science research: how to do open science (berkeley: university of california press, 2019). 272 pages. Isbn: 9780520296954. Paperback $34.95.. Politics and the Life Sciences. https://doi.org/10.1017/pls.2020.13
  • Auspurg K & Recker A (2020). mehr offenheit in der forschung? Eine evaluation von open science maßnahmen bei der zeitschrift für soziologie . Zeitschrift für Soziologie. https://doi.org/10.1515/zfsoz-2020-0001

Open source software

Biology

  • Rivera‐Vicéns R, García‐Escudero C, Conci N, Eitel M & Wörheide G (2022). Transpi—a comprehensive transcriptome analysis pipeline for de novo transcriptome assembly. Molecular Ecology Resources. https://doi.org/10.1111/1755-0998.13593

Business

  • Balogh A, Harman A & Kreuter F (2022). Real-time analysis of predictors of covid-19 infection spread in countries in the european union through a new tool. International Journal of Public Health. https://doi.org/10.3389/ijph.2022.1604974

Computer science

General

Environmental resource management

  • Fischer S, Zobolas J, Sonabend R, Becker M, Lang M, Binder M, Schneider L, Burk L, Schratz P, Jaeger B, Lauer S, Kapsner L, Muecke M, Wang Z, Pulatov D, Ganz K, Funk H, Harutyunyan L, Camilleri P, Kopper P, Bender A, Zhou B, German N, Koers L, Nazarova A & Bischl B (2025). Mlr3extralearners: expanding the mlr3 ecosystem with community-driven learner integration. The Journal of Open Source Software. https://doi.org/10.21105/joss.08331

Simulation

Library science

Programming language

  • Gkolemis V, Diou C, Ntoutsi E, Dalamagas T, Bischl B, Herbinger J, Casalicchio G, Loukas K, Maximilian M, Theodore D, Eirini N, Bernd B & Giuseppe C (2024). Effector: a python package for regional explanations. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2404.02629
  • Schaipp F, Vlasovets O & Müller C (2021). Gglasso - a python package for general graphical lasso computation. The Journal of Open Source Software. https://doi.org/10.21105/joss.03865
  • Hair J, Hult G, Ringle C, Sarstedt M, Danks N & Ray S (2021). Overview of r and rstudio. Classroom companion: business. https://doi.org/10.1007/978-3-030-80519-7_2
  • Hair J, Hult G, Ringle C, Sarstedt M, Danks N & Ray S (2021). The seminr package. Classroom companion: business. https://doi.org/10.1007/978-3-030-80519-7_3
  • Bischl B, Lang M, Mersmann O, Rahnenführer J & Weihs C (2015). batchjobsandbatchexperiments: abstraction mechanisms for usingrin batch environments. Journal of Statistical Software. https://doi.org/10.18637/jss.v064.i11
  • Schönbrodt F, Back M & Schmukle S (2011). Tripler: an r package for social relations analyses based on round-robin designs. Behavior Research Methods. https://doi.org/10.3758/s13428-011-0150-4

Artificial intelligence

  • Ávila C, Bott F, Tiemann L, Hohn V, May E, Nickel M, Zebhauser P, Groß J & Ploner M (2023). Discover-eeg: an open, fully automated eeg pipeline for biomarker discovery in clinical neuroscience. Scientific Data. https://doi.org/10.1038/s41597-023-02525-0
  • Pfisterer F, Kern C, Dandl S, Sun M, Kim M & Bischl B (2021). Mcboost: multi-calibration boosting for r. The Journal of Open Source Software. https://doi.org/10.21105/joss.03453
  • Vivar G, Strobl R, Grill E, Navab N, Zwergal A & Ahmadi S (2021). Using base-ml to learn classification of common vestibular disorders on dizzyreg registry data. Frontiers in Neurology. https://doi.org/10.3389/fneur.2021.681140
  • Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, Au Q, Casalicchio G, Kotthoff L & Bischl B (2019). Mlr3: a modern object-oriented machine learning framework in r. The Journal of Open Source Software. https://doi.org/10.21105/joss.01903
  • Pröllochs N & Feuerriegel S (2019). Reinforcementlearning: a package to perform model-free reinforcement learning in r. The Journal of Open Source Software. https://doi.org/10.21105/joss.01087
  • Schalk D, Thomas J & Bischl B (2018). Compboost: modular framework for component-wise boosting. The Journal of Open Source Software. https://doi.org/10.21105/joss.00967
  • Probst P, Au Q, Casalicchio G, Stachl C & Bischl B (2017). Multilabel classification with r package mlr. The R Journal. https://doi.org/10.32614/rj-2017-012

Software engineering

Data science

Econometrics

  • Dandl S, Hofheinz A, Binder M, Bischl B & Casalicchio G (2023). Counterfactuals: an r package for counterfactual explanation methods. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2304.06569

Machine learning

  • Gijsbers P, Bueno M, Coors S, LeDell E, Poirier S, Thomas J, Bischl B & Vanschoren J (2022). Amlb: an automl benchmark. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2207.12560
  • Sonabend R, Király F, Bender A, Bischl B & Lang M (2021). Mlr3proba: an r package for machine learning in survival analysis. Bioinformatics. https://doi.org/10.1093/bioinformatics/btab039
  • Casalicchio G, Bossek J, Lang M, Kirchhoff D, Kerschke P, Hofner B, Seibold H, Vanschoren J & Bischl B (2019). Openml: an r package to connect to the networked machine learning platform openml. Zurich Open Repository and Archive (University of Zurich). https://doi.org/10.5167/uzh-130579
  • Casalicchio G, Bossek J, Lang M, Kirchhoff D, Kerschke P, Hofner B, Seibold H, Vanschoren J & Bischl B (2017). Openml: an r package to connect to the machine learning platform openml. Computational Statistics. https://doi.org/10.1007/s00180-017-0742-2
  • Slawski M, Däumer M & Boulesteix A (2008). Cma – a comprehensive bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-9-439

World Wide Web

  • Henninger F, Shevchenko Y, Mertens U, Kieslich P & Hilbig B (2021). Lab.js: a free, open, online study builder. Behavior Research Methods. https://doi.org/10.3758/s13428-019-01283-5
  • Boulesteix A, Bin R, Jiang X & Fuchs M (2017). Ipf-lasso: integrativel1-penalized regression with penalty factors for prediction based on multi-omics data. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/2017/7691937
  • Rijn J, Bischl B, Torgo L, Gao B, Umaashankar V, Fischer S, Winter P, Wiswedel B, Berthold M & Vanschoren J (2013). Openml: a collaborative science platform. Lecture notes in computer science. https://doi.org/10.1007/978-3-642-40994-3_46

Knowledge management

  • Bothmann L, Strickroth S, Casalicchio G, Rügamer D, Lindauer M, Scheipl F & Bischl B (2021). Developing open source educational resources for machine learning anddata science. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2107.14330

Data mining

  • Huang M, Müller C & Gaynanova I (2021). Latentcor: an r package for estimating latent correlations from mixed data types. The Journal of Open Source Software. https://doi.org/10.21105/joss.03634
  • Debus C, Floca R, Ingrisch M, Kompan I, Maier‐Hein K, Abdollahi A & Nolden M (2019). Mitk-modelfit: a generic open-source framework for model fits and their exploration in medical imaging – design, implementation and application on the example of dce-mri. BMC Bioinformatics. https://doi.org/10.1186/s12859-018-2588-1
  • Schmidberger M, Vicedo E & Mansmann U (2009). Affypara—a bioconductor package for parallelized preprocessing algorithms of affymetrix microarray data. Bioinformatics and Biology Insights. https://doi.org/10.4137/bbi.s3060
  • Boulesteix A (2007). Wilcoxcv: an r package for fast variable selection in cross-validation. Bioinformatics. https://doi.org/10.1093/bioinformatics/btm162

Computer security

Database

  • Ritter M, Wood L, Kuhr T, Bračko M, Elsethagen T, Fox K, Hall J, Pulvermacher C, Raju B, Schram M & Stephan E (2018). Belle ii conditions database. Journal of Physics Conference Series. https://doi.org/10.1088/1742-6596/1085/3/032032

Multimedia

Mechanical engineering

Human–computer interaction

Distributed computing

  • Krieger M, Torreño Ó, Trelles O & Kranzlmüller D (2016). Building an open source cloud environment with auto-scaling resources for executing bioinformatics and biomedical workflows. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2016.02.008

Reliability engineering

Theoretical computer science

  • Horn D, Wagner T, Biermann D, Weihs C & Bischl B (2015). Model-based multi-objective optimization: taxonomy, multi-point proposal, toolbox and benchmark. Lecture notes in computer science. https://doi.org/10.1007/978-3-319-15934-8_5

Mathematics

  • Simpson L, Combettes P & Müller C (2021). C-lasso - a python package for constrained sparse and robust regression and classification. The Journal of Open Source Software. https://doi.org/10.21105/joss.02844

Psychology

  • Voß M, Ehring T, Timpano K, Joormann J & Wolkenstein L (2019). Responses to positive affect questionnaire–german version. PsycTESTS Dataset. https://doi.org/10.1037/t73668-000
  • Stas L, Schönbrodt F & Loeys T (2015). Getting the most out of family data with the r package fsrm.. Journal of Family Psychology. https://doi.org/10.1037/fam0000058

Sociology

  • Hufe P, Kanbur R & Peichl A (2021). Replication package for: measuring unfair inequality: reconciling equality of opportunity and freedom from poverty. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.5772718

Open data and material

Art

Biology

  • Wuttke A, Harald S, Agatha K & Maria P (2018). Replication data for: ein umschwung in den letzten wochen des landtagswahlkampfes: befunde einer mehrwelligen wiederholungsbefragung zur niedersächsischen landtagswahl 2017. Harvard Dataverse. https://doi.org/10.7910/dvn/e2hfkn

Business

  • Sarstedt M, Ringle C & Iuklanov D (2023). Antecedents and consequences of corporate reputation: a dataset. Data in Brief. https://doi.org/10.1016/j.dib.2023.109079
  • Seelkopf L & Bastiaens I (2022). Replication data for: achieving sustainable development goal 17? An empirical investigation of the effectiveness of aid given to boost developing countries’ tax revenue and capacity. Harvard Dataverse. https://doi.org/10.7910/dvn/x7nzc9
  • Fuest C, Peichl A & Siegloch S (2018). Replication data for: do higher corporate taxes reduce wages? Micro evidence from germany. ICPSR Data Holdings. https://doi.org/10.3886/e112920v1
  • Kuppelwieser V & Sarstedt M (2014). Customers’ satisfaction–loyalty questionnaire. PsycTESTS Dataset. https://doi.org/10.1037/t62050-000

Computer science

General

Natural language processing

  • Wu C, Ma B, Liu Y, Zhang Z, Deng N, Li Y, Chen B, Zhang Y, Plank B & Xue Y (2025). M-absa: a multilingual dataset for aspect-based sentiment analysis. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2502.11824
  • Georgiou E, Skondra M, Charalampopoulou Μ, Felemegkas P, Pachi A, Stafylidou G, Papazachariou D, Perneczky R, Thomopoulos V, Politis A, Leroi I, Εconomou P & Alexopoulos P (2023). Test for finding word retrieval deficits–greek version. PsycTESTS Dataset. https://doi.org/10.1037/t89670-000
  • Georgiou E, Skondra M, Charalampopoulou Μ, Felemegkas P, Pachi A, Stafylidou G, Papazachariou D, Perneczky R, Thomopoulos V, Politis A, Leroi I, Εconomou P & Alexopoulos P (2023). Test for finding word retrieval deficits–greek version; brief version. PsycTESTS Dataset. https://doi.org/10.1037/t89671-000

Artificial intelligence

  • Senger E, Campbell Y, Goot R & Plank B (2024). Karrierewege: a large scale career path prediction dataset. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2412.14612
  • Maarouf A, Bär D, Geissler D & Feuerriegel S (2023). Hqp: a human-annotated dataset for detecting online propaganda. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2304.14931
  • Neunhoeffer M, Arnold C, Biedebach L & Küpfer A (2023). Replication data for: the role of hyperparameters in machine learning models and how to tune them. Harvard Dataverse. https://doi.org/10.7910/dvn/hljw1q
  • Weber T, Ingrisch M, Bischl B & Rügamer D (2023). Cascaded latent diffusion models for high-resolution chest x-ray synthesis. Lecture notes in computer science. https://doi.org/10.1007/978-3-031-33380-4_14
  • Becker M, Bögner M, Bross F, Bry F, Campanella C, Commare L, Cramerotti S, Jakob K, Josko M, Kneißl F, Kohle H, Krefeld T, Levushkina E, Lücke S, Puglisi A, Regner A, Riepl C, Schefels C, Schemainda C, Schmidt E, Schneider S, Schön G, Schulz K, Siglmüller F, Steinmayr B, Störkle F, Teske I & Wieser C (2018). Artigo – social image tagging [dataset and images]. Universitätsbibliothek der LMU. https://doi.org/10.5282/ubm/data.136
  • Greenstein M, Franklin N, Martins M & Maier M (2011). Threat extends implied motion in remembered scenes. PsycEXTRA Dataset. https://doi.org/10.1037/e520602012-622

Data science

  • Bry F, Kohle H, Krefeld T, Riepl C, Schneider S, Schön G & Schulz K (2024). Artigo: social image tagging (aggregated data). Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.10448345
  • Bry F, Kohle H, Krefeld T, Riepl C, Schneider S, Schön G & Schulz K (2023). Artigo: social image tagging (raw data). Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.8103664
  • Schoedel R, Oldemeier M, Bonauer L & Sust L (2022). Systematic categorisation of 3,091 smartphone applications from a large-scale smartphone sensing dataset. Journal of Open Psychology Data. https://doi.org/10.5334/jopd.59
  • Ullmann T, Müller C, Peschel S, Finger P & Boulesteix A (2022). Agp data for the project “over-optimism in unsupervised microbiome analysis”. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.6652711
  • Bry F, Kohle H, Krefeld T, Riepl C, Schneider S, Schön G & Schulz K (2021). Artigo: social image tagging. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.7763019
  • Breznau N, Rinke E & Wuttke A (2020). The crowdsourced replication initiative participant survey. Harvard Dataverse. https://doi.org/10.7910/dvn/uup8cx
  • Hendrikoff L, Kambeitz‐Ilankovic L, Pryss R, Senner F, Falkai P, Pogarell O, Hasan A & Peters H (2019). Attitudes towards mhealth applications questionnaire. PsycTESTS Dataset. https://doi.org/10.1037/t73464-000
  • Leiner D, Kobilke L, Rueß C & Brosius H (2018). Facebook features use measure. PsycTESTS Dataset. https://doi.org/10.1037/t67526-000
  • Steinberg U, Abele A, Gollwitzer M & Schönbrodt F (2018). Attitudes towards dgps data management recommendations and public data sharing. Psychology Archives. https://doi.org/10.23668/psycharchives.856
  • Zetsche U, Ehring T & Ehlers A (2009). Memory integration processing questionnaire. PsycTESTS Dataset. https://doi.org/10.1037/t13998-000

Data mining

Algorithm

  • Claussen E, Renfrew P, Müller C & Drew K (2023). Scaffold matcher: a cma-es based algorithm for identifying hotspot aligned peptidomimetic scaffolds (datasets). Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.8422475

Virology

Cognitive psychology

  • Rudolph L, Freitag M & Thurner P (2023). Replication data for: ordering effects vs. Cognitive burden: how should we structure attributes in conjoint experiments?. Harvard Dataverse. https://doi.org/10.7910/dvn/hrekq9

Statistics

  • Neunhoeffer M, Rittmann O & Gschwend T (2023). Replication data for: how to improve the substantive interpretation of regression results when the dependent variable is logged. Harvard Dataverse. https://doi.org/10.7910/dvn/kzwkt6
  • Sjöström A & Gollwitzer M (2015). Regret about revenge measure. PsycTESTS Dataset. https://doi.org/10.1037/t38507-000

Database

  • Hogg B, Amann B, Moreno‐Alcázar A, Valiente-Gómez A, Gardoki-Souto I, Fontana-McNally M, Lupo W, Redolar D, Jiménez E, Madre M, Reinares M, Cortizo R, Rodríguez A, Castaño J, Argila I, Presas L, Castro-Rodríguez J, Comes M, Doñate M, Herrería E, Macias C, Mur-Milà E, Novo P, Rosa A, Vieta E, Raduà J, Padberg F & Pérez V (2022). Bipolar database with data including current ptsd diagnosis. Figshare. https://doi.org/10.6084/m9.figshare.21163030
  • Ballhause H, Li M & Belka C (2019). Metadata record for: the promotion lmu dataset, prostate intra-fraction motion recorded by transperineal ultrasound. Figshare. https://doi.org/10.6084/m9.figshare.9988691

Programming language

  • Landauer M, Skopik F, Frank M, Hotwagner W, Wurzenberger M & Rauber A (2022). Ait log data set v2.0. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.5789064

Real-time computing

Mathematics education

Epistemology

  • Fernández‐i‐Marín X, Knill C & Steinebach Y (2021). Replication material for “studying policy design quality in comparative perspective”. Harvard Dataverse. https://doi.org/10.7910/dvn/m5sdch

Social psychology

  • Molho C, Fan L & Twardawski M (2020). Dataset and codebook for: what motivates direct and indirect punishment? Extending the ‘intuitive retributivism’ hypothesis. Psychology Archives. https://doi.org/10.23668/psycharchives.4374

Information retrieval

Econometrics

  • Wuttke A, Schimpf C & Schoen H (2019). Replication data for: ‘when the whole is greater than the sum of its parts: on the concept and measurement of populist attitudes and other multi-dimensional constructs’. Harvard Dataverse. https://doi.org/10.7910/dvn/kps1ky
  • Neunhoeffer M, F. S, Thomas G, Simon M & Sebastian S (2018). Replication data for: forecasting elections in multi-party systems: a bayesian approach combining polls and fundamentals. Harvard Dataverse. https://doi.org/10.7910/dvn/mlynx0

World Wide Web

  • Haim M & Zamith R (2019). Replication data for: open-source trading zones and boundary objects: examining github as a space for collaborating on “news”. Harvard Dataverse. https://doi.org/10.7910/dvn/luozjl

Neuroscience

  • Kirsch V, Boegle R, Keeser D, Kierig E, Ertl‐Wagner B, Brandt T & Dieterich M (2019). Beyond binary parcellation of the vestibular cortex – a dataset. Data in Brief. https://doi.org/10.1016/j.dib.2019.01.014

Advertising

Internet privacy

  • Haim M (2018). Replication data for: equal access to online information? Google’s suicide-prevention disparities may amplify a global digital divide. Harvard Dataverse. https://doi.org/10.7910/dvn/pcrg1d

Aesthetics

  • Aguilar‐Raab C, Heene M, Grevenstein D & Weinhold J (2015). Heidelberger drogenbogen–consumption behavior modules. PsycTESTS Dataset. https://doi.org/10.1037/t60234-000

Cartography

Mathematical economics

Economics

  • Genschel P, Limberg J & Seelkopf L (2023). Replication data for: revenue, redistribution, and the rise and fall of inheritance taxation. Harvard Dataverse. https://doi.org/10.7910/dvn/euvcsp
  • Seelkopf L, Bubek M, Eihmanis E, Ganderson J, Limberg J, Mnaili Y, Borrero P & Genschel P (2019). The rise of modern taxation: a new comprehensive dataset of tax introductions worldwide. The Review of International Organizations. https://doi.org/10.1007/s11558-019-09359-9

Environmental science

Geography

  • Čulina A, Adriaensen F, Bailey L, Burgess M, Charmantier A, Cole E, Eeva T, Matthysen E, Nater C, Sheldon B, Sæther B, Vriend S, Zajková Z, Adamík P, Aplin L, Angulo E, Artemyev A, Barba E, Barišić S, Belda E, Bilgin C, Bleu J, Both C, Bouwhuis S, Branston C, Broggi J, Burke T, Bushuev A, Camacho C, Campobello D, Cañal D, Cantarero A, Samuel P, Cauchoix M, Chaine A, Cichoń M, Ćiković D, Cusimano C, Deimel C, Dhondt A, Dingemanse N, Doligez B, Dominoni D, Doutrelant C, Drobniak S, Dubiec A, Eens M, Erikstad K, Espín S, Farine D, Figuerola J, Gülbeyaz P, Grégoire A, Hartley I, Hau M, Hegyi G, Hille S, Hinde C, Holtmann B, Ilyina T, Isaksson C, Iserbyt A, Иванкина Е, Kania W, Kempenaers B, Керимов А, Komdeur J, Korsten P, Král M, Krist M, Lambrechts M, Lara C, Leivits A, Liker A, Lodjak J, Mägi M, Mainwaring M, Mänd R, Massa B, Massemin S, Martínez‐Padilla J, Mazgajski T, Mennerat A, Moreno J, Mouchet A, Nakagawa S, Nilsson J, Nilsson J, Norte A, Oers K, Orell M, Potti J, Quinn J, Réale D, Reiertsen T, Rosivall B, Russell A, Rytkönen S, Sánchez‐Virosta P, Santos E & (2020). Connecting the data landscape of long‐term ecological studies: the spi‐birds data hub. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13388
  • Stan K, Sánchez‐Azofeifa A, Calvo-Rodríguez S, Castro-Magnani M, Chen J, Ludwig R & Zou L (2019). Replication data for: climate change scenarios and projected impacts for the forest productivity in the guanacaste province: lessons for tropical forest regions. Harvard Dataverse. https://doi.org/10.7910/dvn/g8q7zg
  • Santostefano F, Wilson A, Niemelä P & Dingemanse N (2017). Dataset santostefano et al. Rspb-2017-1567. DRYAD. https://doi.org/10.5061/dryad.s8820/1
  • Heurich M, Zeiss K, Küchenhoff H, Müller J, Belotti E, Bufka L & Woelfing B (2016). S2dataset_distance_moved_red_deer_5to7hours.csv. Figshare. https://doi.org/10.6084/m9.figshare.3545012.v1

History

  • Schönbrodt F, Hagemeyer B, Brandstätter V, Czikmantori T, Gröpel P, Hennecke M, Israel L, Janson K, Kemper N, Köllner M, Kopp P, Mojzisch A, Müller-Hotop R, Prüfer J, Quirin M, Scheidemann B, Schiestel L, Schulz‐Hardt S, Sust L, Zygar C & Schultheiss O (2020). Database of expert-coded german pse stories. Psychology Archives. https://doi.org/10.23668/psycharchives.2738
  • Schmalz X, Marinus E, Robidoux S, Castles A & Coltheart M (2013). Quantifying the reliance on sublexical strategies in german and english reading. PsycEXTRA Dataset. https://doi.org/10.1037/e636952013-089

Mathematics

Medicine

  • Steinbuechel N, Zeldovich M, Holloway I, Mayer A, Rojczyk P, Krenz U, Koerte I, Bonfert M, Berweck S, Kieslich M, Brockmann K, Roediger M, Lendt M, Staebler M, Auer C, Neu A, Kaiser A, Driemeyer J, Wartemann U, Pinggera D, Thomé C, Schoen V, Geiger P, Suß J, Buchheim A, Muehlan H & Cunitz K (2025). Quality of life after brain injury in children and adolescents questionnaire–proxy version. PsycTESTS Dataset. https://doi.org/10.1037/t96536-000
  • Kálmán J, Burkhardt G, Adorjan K, Barton B, Jonge S, Eser-Valeri D, Falter‐Wagner C, Heilbronner U, Jobst A, Keeser D, Koenig C, Koller G, Koutsouleris N, Kurz C, Landgraf D, Merz K, Musil R, Nelson A, Padberg F, Papiol S, Pogarell O, Perneczky R, Raabe F, Reinhard M, Richter A, Rüther T, Simon M, Schmitt A, Slapakova L, Scheel N, Schüle C, Wagner E, Wichert S, Zill P, Falkai P, Schulze T & Schulte E (2022). Biobanking in everyday clinical practice in psychiatry—the munich mental health biobank. Frontiers in Psychiatry. https://doi.org/10.3389/fpsyt.2022.934640
  • Wasserman D, Apter G, Baeken C, Bailey S, Balázs J, Bec C, Bieńkowski P, Bobes J, Bravo‐Ortiz M, Brunn H, Böke Ö, Camilleri N, Carpiniello B, Chihai J, Chkonia E, Courtet P, Cozman D, David M, Dom G, Andrei E, Falkai P, Flannery W, Gasparyan K, Gerlinger G, Gorwood P, Guðmundsson O, Hanon C, Heinz A, Heitor M, Hedlund Å, Ismayilov F, Ismayilov N, Isometsä E, Izáková Ľ, Kleinberg A, Kurimay T, Reitan S, Lečić-Toševski D, Lehmets A, Lindberg N, Lundblad K, Lynch G, Maddock C, Malt U, Martin L, Martynikhin I, Maruta N, Matthys F, Mazaliauskienė R, Mihajlović G, Peleš A, Miklavic V, Mohr P, Ferrandis M, Musalek M, Незнанов Н, Ostorharics-Horvath G, Pajević I, Popova A, Pregelj P, Prinsen E, Rados C, Roig A, Kuzman M, Samochowiec J, Sartorius N, Savenko Y, Skugarevsky O, Slodecki E, Soghoyan A, Stone D, Taylor-East R, Tērauds E, Tsopelas C, Tudose C, Tyano S, Vallon P, Gaag R, Varandas P, Vavrušová L, Voloshyn P, Wancata J, Wise J, Zemishlany Z, Öncü F & Vahip S (2020). European psychiatric association survey on involuntary psychiatric admissions. PsycTESTS Dataset. https://doi.org/10.1037/t83101-000
  • Riva F, Ponzoni M, Supino D, MTS B, Polentarutti N, Massara M, Pasqualini F, Carriero R, Innocenzi A, Anselmo A, Veliz‐Rodriguez T, Simonetti G, Anders H, Caligaris‐Cappio F, Mantovani A, Muzio M & Garlanda C (2020). Dataset related to article “il1r8 deficiency drives autoimmunity-associated lymphoma development.”. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.3708984
  • Takahashi S, Keeser D, Rauchmann B, Thomas N, Keller-Varady K, Maurus I, Dechent P, Wobrock T, Hasan A, Schmitt A, Ertl‐Wagner B, Malchow B & Falkai P (2020). Effect of aerobic exercise on cortical thickness in patients with schizophrenia—a dataset. Data in Brief. https://doi.org/10.1016/j.dib.2020.105517
  • Ballhausen H, Li M & Belka C (2019). The promotion lmu dataset, prostate intra-fraction motion recorded by transperineal ultrasound. Scientific Data. https://doi.org/10.1038/s41597-019-0280-6
  • Blutke A, Renner S, Flenkenthaler F, Backman M, Haesner S, Kemter E, Ländström E, Braun-Reichhart C, Albl B, Streckel E, Rathkolb B, Prehn C, Palladini A, Grzybek M, Krebs S, Bauersachs S, Bähr A, Brühschwein A, Deeg C, Monte E, Dmochewitz M, Eberle C, Emrich D, Fux R, Groth F, Gumbert S, Heitmann A, Hinrichs A, Kessler B, Kurome M, Leipig‐Rudolph M, Matiasek K, GÜRGEN H, Otzdorff C, Reichenbach M, Reichenbach H, Rieger A, Rieseberg B, Rosati M, Saucedo M, Schleicher A, Schneider M, Simmet K, Steinmetz J, Übel N, Zehetmaier P, Jung A, Adamski J, Coskun Ü, Angelis M, Simmet C, Ritzmann M, Meyer‐Lindenberg A, Blum H, Arnold G, Fröhlich T, Wanke R & Wolf E (2017). The munich midy pig biobank – a unique resource for studying organ crosstalk in diabetes. Molecular Metabolism. https://doi.org/10.1016/j.molmet.2017.06.004
  • Ma Q, Steiger S & Anders H (2017). Dataset for: sodium glucose transporter-2 inhibition has no renoprotective effects in non-diabetic chronic kidney disease induced by hyperoxaluria. Figshare. https://doi.org/10.6084/m9.figshare.4818385
  • Mansmann U, Taylor W, Porter P, Bernarding J, J�ger H, Lasjaunias P, terBrugge K & Meisel J (2001). Concepts and data model for a co-operative neurovascular database. Acta Neurochirurgica. https://doi.org/10.1007/s007010170032

Political science

General

  • Brachem J, Frank M, Kvetnaya T, Schramm L & Volz L (2018). Replikationskrise, p-hacking und open science - eine umfrage zu fragwürdigen forschungspraktiken in studentischen projekten und impulse für die lehre. OSF Preprints (OSF Preprints). https://doi.org/10.17605/osf.io/t3mak

Law

  • Pamp O, Rudolph L, Thurner P, Mehltretter A & Primus S (2026). Replication material for paper “pamp, rudolph, thurner, mehltretter, primus (2018): the build-up of coercive capacities. Journal of peace research. Doi: 10.1177/0022343317740417”. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.18144505
  • Fernández‐i‐Marín X, Knill C & Steinebach Y (2023). Replication data for “do parties matter for policy accumulation? An analysis of social policy portfolios in 22 countries”. European journal of political research. Harvard Dataverse. https://doi.org/10.7910/dvn/7xjljb
  • Schulte-Cloos J (2021). Replication data for: political potentials, deep-seated nativism, and the success of the german afd. Harvard Dataverse. https://doi.org/10.7910/dvn/uykyil
  • Schulte-Cloos J (2019). Replication.csv. Harvard Dataverse. https://doi.org/10.7910/dvn/5hc6sv/sf2bsb
  • Schulte-Cloos J (2019). Replication_parfe.csv. Harvard Dataverse. https://doi.org/10.7910/dvn/5hc6sv/tgxrmy

Political economy

International trade

Computer security

  • Mehltretter A, Pamp O, Thurner P & Binder P (2023). Introducing the rebels armament dataset (rad): collecting evidence on rebel military capabilities. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4537283
  • Gollwitzer M, Skitka L, Wisneski D, Sjöström A, Liberman P, Nazir S & Bushman B (2014). Support for continued “war on terrorism” measure. PsycTESTS Dataset. https://doi.org/10.1037/t33985-000

Statistics

Virology

  • Rudolph L, Freitag M & Thurner P (2023). Replication data for: deontological and consequentialist preferences towards arms exports: a comparative conjoint experiment in france and germany. Harvard Dataverse. https://doi.org/10.7910/dvn/c6ptyd
  • Neunhoeffer M, Gschwend T, Müller K, Munzert S & Stoetzer L (2021). Replication data for: the zweitstimme model: a dynamic forecast of the 2021 german federal election. Harvard Dataverse. https://doi.org/10.7910/dvn/edtknw
  • Wuttke A, Gavras G & Schoen H (2020). Replication data for: ‘have europeans grown tired of democracy?’. Harvard Dataverse. https://doi.org/10.7910/dvn/y5y6vd

Genealogy

  • Schulte-Cloos J & Bauer P (2021). Replication data for: local candidates, place-based identities, and electoral success. Harvard Dataverse. https://doi.org/10.7910/dvn/5ork7c

Epistemology

  • Köhler L, Steinberg U, Egorov M, Peus C & Gollwitzer M (2021). Dataset for: perspective-specific moral foundations sensitivity and political orientation. Psychology Archives. https://doi.org/10.23668/psycharchives.4845

Demography

Cartography

Public administration

  • Schulte-Cloos J (2018). Replication data for: do european parliament elections foster challenger parties’ success on the national level?. Harvard Dataverse. https://doi.org/10.7910/dvn/5hc6sv

Psychology

General

Data science

  • Mede N, Cologna V, Berger S, Besley J, Brick C, Joubert M, Maibach E, Mihelj S, Орескес Н, Schäfer M, Linden S, Aziz N, Abdulsalam S, Shamsi N, Aczél B, Adinugroho I, Alabrese E, Aldoh A, Alfano M, Ali I, Alsobay M, Altenmüller M, Alvarez R, Amoako R, Amollo T, Ansah P, Apriliawati D, Azevedo F, Bajrami A, Bardhan R, Bati K, Bertsou E, Betsch C, Bhatiya A, Bhui R, Białobrzeska O, Bilewicz M, Bouguettaya A, Breeden K, Bret A, Buchel O, Cabrera‐Álvarez P, Cagnoli F, Valdez A, Callaghan T, Cases R, Çoksan S, Czarnek G, Peuter S, Debnath R, Delouvée S, Stefano L, Díaz‐Catalán C, Doell K, Dohle S, Douglas K, Dries C, Dubrov D, Dzimińska M, Ecker U, Mitkidis P, Elsherif M, Enke B, Étienne T, Facciani M, Fage‐Butler A, Faisal M, Fan X, Farhart C, Feldhaus C, Ferreira M, Feuerriegel S, Fischer H, Freundt J, Friese M, Fuglsang S, Gallyamova A, Garrido‐Vásquez P, Vásquez M, Gatua W, Genschow O, Ghasemi O, Gkinopoulos T, Gloor J, Goddard E, Gollwitzer M, González-Brambila C, Gordon H, Grigoryev D, Grimshaw G, Guenther L, Haarstad H, Harari D, Hawkins L, Hensel P, Hernández‐Mondragón A, Herziger A, Huang G, Huff M, Hurley M & (2025). Perceptions of science, science communication, and climate change attitudes in 68 countries – the tisp dataset. Scientific Data. https://doi.org/10.1038/s41597-024-04100-7

Social psychology

  • Doell K, Todorova B, Vlasceanu M, Coleman J, Pronizius E, Schumann P, Azevedo F, Patel Y, Berkebile-Weinberg M, Brick C, Lange F, Grayson S, Pei Y, Chakroff A, Broek K, Lamm C, Vlasceanu D, Constantino S, Rathje S, Goldwert D, Fang K, Aglioti S, Alfano M, Alvarado-Yepez A, Andersen A, Anseel F, Apps M, Asadli C, Awuor F, Basaglia P, Bélanger J, Berger S, Bertin P, Białek M, Białobrzeska O, Blaya-Burgo M, Bleize D, Bø S, Boecker L, Boggio P, Borau S, Borau S, Bos B, Bouguettaya A, Bräuer M, Brik T, Briker R, Brosch T, Buchel O, Buonauro D, Butalia R, Carvacho H, Chamberlain S, Chan H, Chow D, Chung D, Cian L, Cohen-Eick N, Contreras-Huerta L, Contu D, Cristea V, Cutler J, D’Ottone S, keersmaecker J, Delcourt S, Delouvée S, Diel K, Douglas B, Drupp M, Dubey S, Ekmanis J, Mitkidis P, Elsherif M, Engelhard I, Escher Y, Étienne T, Farage L, Farias A, Feuerriegel S, Findor A, Freira L, Friese M, Gains N, Gallyamova A, Geiger S, Genschow O, Gjoneska B, Gkinopoulos T, Goldberg B, Goldenberg A, Gradidge S, Grassini S, Gray K, Grelle S, Griffin S, Grigoryan L, Grigoryan A, Grigoryev D, Gruber J, Guilaran J & (2024). The international climate psychology collaboration: climate change-related data collected from 63 countries. Scientific Data. https://doi.org/10.1038/s41597-024-03865-1
  • Pohl S, Sengewald M, Kondzic D, Hoffmann J & Twardawski M (2024). Osf-materials for the manuscript ‘explaining effect-heterogeneity: adjustment for unintended differences between studies in conceptual replications’. OSF Preprints (OSF Preprints). https://doi.org/10.17605/osf.io/h6zgy
  • Hoppen T, Schlechter P, Arntz A, Rameckers S, Ehring T & Morina N (2022). Guilt and shame questionnaire. PsycTESTS Dataset. https://doi.org/10.1037/t88049-000
  • Sjöström A & Gollwitzer M (2015). Justice-related satisfaction measure. PsycTESTS Dataset. https://doi.org/10.1037/t38483-000
  • Funk F, McGeer V & Gollwitzer M (2014). Justice-related satisfaction scale. PsycTESTS Dataset. https://doi.org/10.1037/t36099-000
  • Kuile H & Ehring T (2014). Changes in religiosity scale. PsycTESTS Dataset. https://doi.org/10.1037/t36301-000
  • Kuile H & Ehring T (2014). Family and environment religiosity questionnaire. PsycTESTS Dataset. https://doi.org/10.1037/t36195-000
  • Keller M, Chang M, Becker E, Goetz T & Frenzel A (2014). State-reported emotional labor measure. PsycTESTS Dataset. https://doi.org/10.1037/t41918-000

Developmental psychology

  • Gerbig P, Reinhard M, Ababu H, Rek S, Amann B, Adorjan K, Abera M, Padberg F & Jobst A (2023). Dataset to “loneliness is associated with retrospective self-reports of adverse childhood experiences – a replication study in ethiopia”. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.7749114
  • Ruiz F, Salazar D, Suárez‐Falcón J, Peña-Vargas A, Ehring T, Barreto-Zambrano M & Gómez-Barreto M (2020). Perseverative thinking questionnaire–child version; spanish version. PsycTESTS Dataset. https://doi.org/10.1037/t82213-000
  • Kami M, Moloodi R, Mazidi M, Ehring T, Mansoori A, Nodooshan M, Mazinani Z, Molavi M & Momeni F (2019). Perseverative thinking questionnaire–persian version. PsycTESTS Dataset. https://doi.org/10.1037/t73222-000
  • Bijttebier P, Raes F, Vasey M, Bastin M & Ehring T (2015). Perseverative thinking questionnaire–child version. PsycTESTS Dataset. https://doi.org/10.1037/t45611-000
  • Zetsche U, Ehring T & Ehlers A (2009). Perseverative thinking questionnaire–state version. PsycTESTS Dataset. https://doi.org/10.1037/t13996-000

Cognitive psychology

Clinical psychology

  • Ballmann C, Kölle M, Bekavac-Günther I, Wolf F, Pargent F, Barzel A, Philipsen A & Gensichen J (2022). Adult attention-deficit/hyperactivity disorder self-report scale for dsm-5–german version. PsycTESTS Dataset. https://doi.org/10.1037/t94665-000
  • Malta L, Karl A, Kleim B, Milad M, Rothbaum B, Davis M, Difede J, Ehlers A, Ehring T, Houry D, Leiberg S, Myers K, Orr S, Pitman R, Rabe S, Rauch S & Shin L (2008). Innovations in experimental psychopathology research. PsycEXTRA Dataset. https://doi.org/10.1037/e517302011-158

Internet privacy

  • Tunçgenç B, Zein M, Sulik J, Newson M, Zhao Y, Dezecache G & Deroy O (2021). Social distancing guidelines measure. PsycTESTS Dataset. https://doi.org/10.1037/t82031-000
  • Haim M, Arendt F & Scherr S (2019). Replication data for: investigating google’s suicide prevention efforts in celebrity suicides using agent-based testing. Harvard Dataverse. https://doi.org/10.7910/dvn/vx6qvb

Virology

  • Rek S, Bühner M, Reinhard M, Freeman D, Keeser D, Adorjan K, Falkai P & Padberg F (2021). Covid-19 pandemic mental health questionnaire. PsycTESTS Dataset. https://doi.org/10.1037/t82123-000

Database

  • Schönbrodt F, Hagemeyer B, Brandstätter V, Czikmantori T, Gröpel P, Hennecke M, Israel L, Janson K, Kemper N, Köllner M, Kopp P, Mojzisch A, Müller-Hotop R, Prüfer J, Quirin M, Scheidemann B, Schiestel L, Schulz‐Hardt S, Sust L, Zygar–Hoffmann C & Schultheiss O (2020). Measuring implicit motives with the picture story exercise (pse): databases of expert-coded german stories, pictures, and updated picture norms. Journal of Personality Assessment. https://doi.org/10.1080/00223891.2020.1726936
  • Soyyılmaz D, Griffin L, Martín M, Kucharský Š, Peycheva E, Vaupotič N & Edelsbrunner P (2017). Educational experiences interview measure. PsycTESTS Dataset. https://doi.org/10.1037/t67498-000

Mathematics education

Cartography

Statistics

  • Soyyılmaz D, Griffin L, Martín M, Kucharský Š, Peycheva E, Vaupotič N & Edelsbrunner P (2017). Statistical misconceptions questionnaire. PsycTESTS Dataset. https://doi.org/10.1037/t64781-000

Criminology

Data mining

  • Dufner M, Arslan R, Hagemeyer B, Schönbrodt F & Denissen J (2015). Affiliative situations measure. PsycTESTS Dataset. https://doi.org/10.1037/t46423-000
  • Dufner M, Arslan R, Hagemeyer B, Schönbrodt F & Denissen J (2015). Progress toward affiliative goals measure. PsycTESTS Dataset. https://doi.org/10.1037/t46469-000
  • Gollwitzer M, Skitka L, Wisneski D, Sjöström A, Liberman P, Nazir S & Bushman B (2014). Continued desire for revenge for 9/11 measure. PsycTESTS Dataset. https://doi.org/10.1037/t33984-000
  • Gollwitzer M, Skitka L, Wisneski D, Sjöström A, Liberman P, Nazir S & Bushman B (2014). Psychological closure measure. PsycTESTS Dataset. https://doi.org/10.1037/t33983-000

Demography

  • Villavicencio‐Chávez C, Monforte‐Royo C, Tomás‐Sábado J, Maier M, Porta-Sales J & Balaguer A (2014). Schedule of attitudes toward hastened death–spanish version. PsycTESTS Dataset. https://doi.org/10.1037/t44335-000

Computer security

Law

Psychoanalysis

Econometrics

  • Wunder C, Wiencierz A, Schwarze J & Küchenhoff H (2013). Replication data for: well-being over the life span: semiparametric evidence from british and german longitudinal data. Harvard Dataverse. https://doi.org/10.7910/dvn/ucvptw
  • Dislich F & Schoenbrodt F (2011). An item response theory analysis of self-report measures of motives. PsycEXTRA Dataset. https://doi.org/10.1037/e523472012-064

Medical education

Psychotherapist

Applied psychology

Psychiatry

Sociology

  • Urchs S, Thurner V, Aßenmacher M, Heumann C & Thiemichen S (2025). Taz2024full: analysing german newspapers for gender bias and discrimination across decades. ArXiv.org. https://doi.org/10.48550/arxiv.2506.05388