OSC Bibliography: Static

242 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, as determined by OpenAlex. OSC members are printed in bold in the co-authors’ list.

Articles

Art

  • 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

Biology

  • 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
  • Gailus‐Durner V, Naton B, Adler T, Afonso L, Aguilar-Pimentel J, Becker L, Calzada‐Wack J, Cohrs C, Silva‐Buttkus P, Hans W, Horsch M, Kahle M, Lengger C, Ludwig T, Maier H, Micklich K, Möller G, Neff F, Neschen S, Prehn C, Rathkolb B, Rozman J, Schiller E, Schrewe A, Scheerer M, Schöfer F, Steinkamp R, Stoeger C, Thiele F, Tost M, Treise I, Willershäuser M, Zeh R, Adamski J, Bekeredjian R, Beckers J, Esposito I, Höfler H, Katus H, Klingenspor M, Klopstock T, Ollert M, Wolf E, Busch D, Fuchs H & Angelis M (2011). The german mouse clinic – running an open access platform. Springer eBooks. https://doi.org/10.1007/978-94-007-0750-4_2
  • 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-Martı́nez 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

  • 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
  • Fuchs S & Sarstedt M (2010). 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

Data science

  • Stefan A & Schönbrodt F (2023). Big little lies: a compendium and simulation ofp-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
  • 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/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
  • 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
  • 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
  • Hoffmann S, Schönbrodt F, Elsas R, Wilson R, Strasser U & Boulesteix A (2021). The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines. Royal Society open science. https://doi.org/10.1098/rsos.201925
  • Weber T & Kranzlmüller D (2019). Methods to evaluate lifecycle models for research data management. Bibliothek. https://doi.org/10.1515/bfp-2019-2016
  • 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/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
  • 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
  • Boulesteix A (2009). Over-optimism in bioinformatics research. Bioinformatics. https://doi.org/10.1093/bioinformatics/btp648

Internet privacy

  • 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

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 (2023). Improving software engineering in biostatistics: challenges and opportunities. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2301.11791

Information retrieval

  • 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

Artificial intelligence

  • 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 data structures. 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

Management science

  • Heinze G, Boulesteix A, Kammer M, Morris T & White I (2023). Phases of methodological research in biostatistics—building the evidence base for new methods. Biometrical journal. https://doi.org/10.1002/bimj.202200222
  • Stefan A, Evans N & Wagenmakers E (2022). Practical challenges and methodological flexibility in prior elicitation.. Psychological methods. https://doi.org/10.1037/met0000354
  • Leising D, Thielmann I, Glöckner A, Gärtner A & Schönbrodt F (2022). Ten steps toward a better personality science – how quality may be rewarded more in research evaluation. Personality science. https://doi.org/10.5964/ps.6029
  • 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/Wiley interdisciplinary reviews. Data mining and knowledge discovery. https://doi.org/10.1002/widm.1441

Data mining

  • Wunsch 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
  • Bun M, Gaboardi M, Neunhoeffer M & Zhang W (2023). Continual release of differentially private synthetic data. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2306.07884
  • Schulze P, Wiegrebe S, Thurner P, Heumann C & Aßenmacher M (2023). A bayesian approach to modeling topic-metadata relationships. AStA. Advances in statistical analysis. https://doi.org/10.1007/s10182-023-00485-9
  • Klau S, Felix F, Patel C, Ioannidis J, Boulesteix A & Hoffmann S (2023). Comparing the vibration of effects due to model, data pre-processing and sampling uncertainty on a large data set in personality psychology. Meta-psychology. https://doi.org/10.15626/mp.2020.2556
  • Landes J & Williamson J (2022). Objective bayesian nets for integrating consistent datasets. Journal of artificial intelligence research/˜The œjournal 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/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 œAmerican 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

Engineering ethics

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/Computational Brain & Behavior. https://doi.org/10.1007/s42113-019-00070-x

Machine learning

  • 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

Statistics

Operations research

Virology

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. JMIR. Journal of medical internet research/Journal of medical internet research. https://doi.org/10.2196/16533

Cognitive science

Knowledge management

  • 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

Computational biology

Library science

  • 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

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

Mathematics

  • 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
  • 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
  • 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

Medicine

  • Drude N, Martínez-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 G, Tenbusch M, 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. Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-1855244/v1
  • 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
  • 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
  • Ewers M, Ioannidis J & Plesnila N (2021). Access to data from clinical trials in the covid-19 crisis: open, flexible, and time-sensitive. Journal of clinical epidemiology. https://doi.org/10.1016/j.jclinepi.2020.10.008
  • 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
  • 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 and metabolism. https://doi.org/10.1177/0271678x16647396
  • 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
  • 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

  • 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
  • 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
  • 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. 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

  • Bühner M, Schubert A, Bermeitinger C, Bölte J, Fiebach C, Renner K & Schulz‐Hardt S (2022). Dgps-vorstand. Der kulturwandel in unserer forschung muss in der ausbildung unserer studierenden beginnen. Psychologische Rundschau. https://doi.org/10.1026/0033-3042/a000563
  • Gollwitzer M, Abele-Brehm 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

Statistics

  • 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
  • Oberlader V, Quinten L, Banse R, Volbert R, Schmidt A & Schönbrodt F (2021). 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
  • 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
  • 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

Applied psychology

  • 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

Social psychology

  • 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 (2022). Replicability, robustness, and reproducibility in psychological science. Annual review of psychology. https://doi.org/10.1146/annurev-psych-020821-114157
  • 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
  • Altenmüller M & Gollwitzer M (2022). Prosociality in science. Current opinion in psychology. https://doi.org/10.1016/j.copsyc.2021.08.011
  • 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, Edelmann A, 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 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 of the United States of America. https://doi.org/10.1073/pnas.2203150119
  • 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
  • Abele-Brehm 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 (2019). 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
  • Auspurg K & Hinz T (2017). Social dilemmas in science: detecting misconduct and finding institutional solutions. De Gruyter eBooks. https://doi.org/10.1515/9783110472974-010
  • 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

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

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 (2020). A series of meta-analytic tests of the efficacy of long-term psychoanalytic psychotherapy. European psychologist. https://doi.org/10.1027/1016-9040/a000385

Data science

  • Auspurg K & Brüderl J (2021). Has the credibility of the social sciences been credibly destroyed? Reanalyzing the “many analysts, one data set” project. Socius. 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

Cognitive psychology

  • Gollwitzer M & Schwabe J (2021). Context dependency as a predictor of replicability. Review of general psychology. https://doi.org/10.1177/10892680211015635
  • Ekhtiari H, Ghobadi‐Azbari P, Thielscher A, Antal A, Li L, Shereen A, Cabral‐Calderín Y, Keeser D, Bergmann T, Jamil A, Violante I, Almeida J, Meinzer M, Siebner H, Woods A, Stagg C, Abend R, Antonenko D, Auer T, Bächinger M, Baeken C, Barron H, Chase H, Crinion J, Datta A, Davis M, Ebrahimi M, Esmaeilpour Z, Falcone B, Fiori V, Ghodratitoostani I, Gilam G, Grabner R, Greenspan J, Groen G, Hartwigsen G, Hauser T, Herrmann C, Juan C, Krekelberg B, Lefebvre S, Liew S, Madsen K, Mahdavifar-Khayati R, Malmir N, Marangolo P, Martin A, Meeker T, Ardabili H, Moisa M, Momi D, Mulyana B, Opitz A, Orlov N, Ragert P, Ruff C, Ruffini G, Ruttorf M, Sangchooli A, Schellhorn K, Schlaug G, Sehm B, Soleimani G, Tavakoli H, Thompson B, Timmann D, Tsuchiyagaito A, Martin U, Vosskuhl J, Weinrich C, Zare-Bidoky M, Zhang X, Zoefel B, Nitsche M & Bikson M (2020). A checklist for assessing the methodological quality of concurrent tes-fmri studies (contes checklist): a consensus study and statement. medRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2020.12.23.20248579
  • 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

Physical medicine and rehabilitation

  • 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

Medical education

Law

Engineering ethics

Epistemology

Positive economics

Sociology

  • 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
  • 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

  • Schalk D, Hoffmann V, Bischl B & Mansmann U (2023). Dsbinval: conducting distributed roc analysis using datashield. Journal of open source software. https://doi.org/10.21105/joss.04545

Software engineering

Econometrics

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 (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

Programming language

Artificial intelligence

  • Pfisterer F, Kern C, Dandl S, Sun M, Kim M & Bischl B (2021). Mcboost: multi-calibration boosting for r. 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. Journal of open source software. https://doi.org/10.21105/joss.01903
  • Schalk D, Thomas J & Bischl B (2018). Compboost: modular framework for component-wise boosting. Journal of open source software. https://doi.org/10.21105/joss.00967

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 and data 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. 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

Data science

Multimedia

Computer security

Algorithm

  • Boulesteix A, Bin R, Jiang X & Fuchs M (2017). Ipf-lasso: integrative l1-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
  • Kutyniok G, Lim W & Reisenhofer R (2016). Shearlab 3d. ACM transactions on mathematical software. https://doi.org/10.1145/2740960

Distributed computing

  • Krieger M, Torreño Ó, Trelles O & Kranzlmüller D (2017). 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

Mechanical 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

World Wide Web

  • 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

Mathematics

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

Psychology

Open data and material

Business

Computer science

General

Data mining

  • Ringle C, Sarstedt M, Sinkovics N & Sinkovics R (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in brief. https://doi.org/10.1016/j.dib.2023.109074
  • Gollwitzer M, Skitka L, Wisneski D, Sjöström A, Liberman P, Nazir S & Bushman B (2014). Desire for revenge for 9/11 measure. PsycTESTS Dataset. https://doi.org/10.1037/t33975-000
  • Gollwitzer M, Skitka L, Wisneski D, Sjöström A, Liberman P, Nazir S & Bushman B (2014). Message to al qaeda measure. PsycTESTS Dataset. https://doi.org/10.1037/t33980-000

Natural language processing

  • Georgiou E, Skondra M, Charalampopoulou M, 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 M, 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

Data science

Real-time computing

Mathematics education

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

Information retrieval

  • Lindoerfer D & Mansmann U (2017). Data for the elaboration of the cipros checklist with items for a patient registry software system: examples and explanations. Data in brief. https://doi.org/10.1016/j.dib.2017.07.075

Mathematical economics

Combinatorics

Artificial intelligence

Economics

  • Seelkopf L, Bubek M, Eihmanis E, Ganderson J, Limberg J, Mnaili Y, Zuluaga P & Genschel P (2019). The rise of modern taxation: a new comprehensive dataset of tax introductions worldwide. ˜The œreview 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
  • Povey D, Prime J & Taylor P (1997). Notifiable offences: england and wales, 1996. PsycEXTRA Dataset. https://doi.org/10.1037/e422872008-001
  • Povey D, Prime J & Taylor P (1997). Notifiable offences: england and wales, july 1996 to june 1997. PsycEXTRA Dataset. https://doi.org/10.1037/e422862008-001

History

  • 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

Linguistics

Mathematics

Medicine

  • 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, Gudmundsson Ó, Hanon C, Heinz A, Santos M, Hedlund Å, Ismayilov F, Ismayilov N, Isometsä E, Izáková L, Kleinberg A, Kozma 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, Neznanov N, 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
  • Takahashi S, Keeser D, Rauchmann B, Schneider‐Axmann T, 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, Öztürk 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
  • Mansmann U, Taylor W, Porter P, Bernarding J, Jäger 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

  • Mehltretter A, Pamp O, Thurner P & Binder P (2023). Introducing the rebels armament dataset (rad): collecting evidence on rebel military capabilities. Social Science Research Network. 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

Psychology

General

Social psychology

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

Developmental psychology

  • 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

Criminology

Cartography

Cognitive psychology

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

Data mining

  • 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

Law

Psychoanalysis

Econometrics

Medical education

Clinical psychology

  • 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

Organic chemistry

Psychotherapist

Applied psychology

Psychiatry