@CONTROL{REVTEX41Control} @CONTROL{apsrev41Control,pages="1",title="1",year="1"} @article{aagaard_considerations_2017, title = {Some considerations about causes and effects in studies of performance-based research funding systems}, volume = {11}, issn = {1751-1577}, doi = {10.1016/j.joi.2017.05.018}, number = {3}, journal = {J. Informetr.}, author = {Aagaard, Kaare and Schneider, Jesper W}, month = aug, year = {2017}, pages = {923--926} } @article{altman2002, title = {Poor-quality medical research: what can journals do?}, author = {Altman, Douglas G.}, year = {2002}, month = {06}, date = {2002-06-05}, journal = {JAMA}, pages = {2765--2767}, volume = {287}, number = {21}, doi = {10.1001/jama.287.21.2765}, langid = {eng} } @article{arif2023, title = {Applying the structural causal model framework for observational causal inference in ecology}, author = {Arif, Suchinta and MacNeil, M. Aaron}, year = {2023}, date = {2023}, journal = {Ecological Monographs}, pages = {e1554}, volume = {93}, number = {1}, doi = {10.1002/ecm.1554}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ecm.1554} } @article{azoulay2019, title = {Public R\&D Investments and Private-sector Patenting: Evidence from NIH Funding Rules}, author = {Azoulay, Pierre and Graff Zivin, Joshua S and Li, Danielle and Sampat, Bhaven N}, year = {2019}, month = {01}, date = {2019-01-01}, journal = {The Review of Economic Studies}, pages = {117--152}, volume = {86}, number = {1}, doi = {10.1093/restud/rdy034}, langid = {en} } @article{balke2012, title = {Bounds on Treatment Effects from Studies with Imperfect Compliance}, author = {Balke, Alexander and Pearl, Judea}, year = {2012}, month = {02}, date = {2012-02-17}, journal = {Journal of the American Statistical Association}, url = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1997.10474074} } @article{berrie_depicting_2025, title = {Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional data}, volume = {194}, issn = {0002-9262}, shorttitle = {Depicting deterministic variables within directed acyclic graphs}, doi = {10.1093/aje/kwae153}, number = {2}, urldate = {2025-08-13}, journal = {American Journal of Epidemiology}, author = {Berrie, Laurie and Arnold, Kellyn F and Tomova, Georgia D and Gilthorpe, Mark S and Tennant, Peter W G}, month = feb, year = {2025}, pages = {469--479} } @article{bol2018, title = {The Matthew effect in science funding}, author = {Bol, Thijs and de Vaan, Mathijs and van de Rijt, Arnout}, year = {2018}, month = {05}, date = {2018-05}, journal = {Proceedings of the National Academy of Sciences}, pages = {4887{\textendash}4890}, volume = {115}, number = {19}, doi = {10.1073/pnas.1719557115} } @article{bornmann2005, title = {Selection of research fellowship recipients by committee peer review. Reliability, fairness and predictive validity of Board of Trustees' decisions}, author = {Bornmann, Lutz and Daniel, Hans-Dieter}, year = {2005}, month = {04}, date = {2005-04}, journal = {Scientometrics}, pages = {297--320}, volume = {63}, number = {2}, doi = {10.1007/s11192-005-0214-2}, langid = {en} } @article{bornmann2011, title = {Scientific peer review}, author = {Bornmann, Lutz}, year = {2011}, date = {2011}, journal = {Annual Review of Information Science and Technology}, pages = {197--245}, volume = {45}, number = {1}, doi = {10.1002/aris.2011.1440450112}, langid = {en} } @article{brembs2019, title = {Reliable novelty: New should not trump true}, author = {Brembs, {Björn}}, year = {2019}, month = {02}, date = {2019-02-12}, journal = {PLOS Biology}, pages = {e3000117}, volume = {17}, number = {2}, doi = {10.1371/journal.pbio.3000117}, url = {http://dx.doi.org/10.1371/journal.pbio.3000117} } @inproceedings{cinelli_sensitivity_2019, title = {Sensitivity {Analysis} of {Linear} {Structural} {Causal} {Models}}, url = {https://proceedings.mlr.press/v97/cinelli19a.html}, abstract = {Causal inference requires assumptions about the data generating process, many of which are unverifiable from the data. Given that some causal assumptions might be uncertain or disputed, formal methods are needed to quantify how sensitive research conclusions are to violations of those assumptions. Although an extensive literature exists on the topic, most results are limited to specific model structures, while a general-purpose algorithmic framework for sensitivity analysis is still lacking. In this paper, we develop a formal, systematic approach to sensitivity analysis for arbitrary linear Structural Causal Models (SCMs). We start by formalizing sensitivity analysis as a constrained identification problem. We then develop an efficient, graph-based identification algorithm that exploits non-zero constraints on both directed and bidirected edges. This allows researchers to systematically derive sensitivity curves for a target causal quantity with an arbitrary set of path coefficients and error covariances as sensitivity parameters. These results can be used to display the degree to which violations of causal assumptions affect the target quantity of interest, and to judge, on scientific grounds, whether problematic degrees of violations are plausible.}, urldate = {2023-12-18}, booktitle = {Proceedings of the 36th {International} {Conference} on {Machine} {Learning}}, publisher = {PMLR}, author = {Cinelli, Carlos and Kumor, Daniel and Chen, Bryant and Pearl, Judea and Bareinboim, Elias}, month = may, year = {2019}, pages = {1252--1261} } @book{cunningham_causal_2021, title = {Causal {Inference}}, isbn = {978-0-300-25168-5}, publisher = {Yale University Press}, author = {Cunningham, Scott}, month = jan, year = {2021} } @article{davis_open_2008, title = {Open access publishing, article downloads, and citations: {Randomised} controlled trial}, volume = {337}, issn = {0959-8138, 0959-8146}, doi = {10.1136/bmj.a568}, number = {7665}, journal = {BMJ}, author = {Davis, Philip M and Lewenstein, Bruce V and Simon, Daniel H and Booth, James G and Connolly, Mathew J L}, month = aug, year = {2008}, pages = {343--345} } @misc{davis_reanalysis_2020, title = {Reanalysis of {Tweeting} {Study} {Yields} {No} {Citation} {Benefit}}, url = {https://scholarlykitchen.sspnet.org/2020/07/13/tweeting-study-yields-no-benefit/}, abstract = {Scientific authorship comes with benefits, but also responsibilities. If authors are unwilling to explain their work, editors must step up to defend their journal.}, urldate = {2023-12-16}, journal = {The Scholarly Kitchen}, author = {Davis, Philip M}, month = jul, year = {2020} } @article{deffner2022, title = {A Causal Framework for Cross-Cultural Generalizability}, author = {Deffner, Dominik and Rohrer, Julia M. and McElreath, Richard}, year = {2022}, month = {07}, date = {2022-07-01}, journal = {Advances in Methods and Practices in Psychological Science}, pages = {25152459221106366}, volume = {5}, number = {3}, doi = {10.1177/25152459221106366}, url = {https://doi.org/10.1177/25152459221106366} } @article{dong_beyond_2022, title = {Beyond correlation: {Towards} matching strategy for causal inference in {Information} {Science}}, volume = {48}, issn = {0165-5515}, shorttitle = {Beyond correlation}, url = {https://doi.org/10.1177/0165551520979868}, doi = {10.1177/0165551520979868}, number = {6}, urldate = {2024-01-07}, journal = {Journal of Information Science}, author = {Dong, Xianlei and Xu, Jiahui and Bu, Yi and Zhang, Chenwei and Ding, Ying and Hu, Beibei and Ding, Yang}, month = dec, year = {2022}, pages = {735--748} } @inbook{elwert2013, title = {Graphical Causal Models}, author = {Elwert, Felix}, editor = {Morgan, Stephen L.}, year = {2013}, date = {2013}, publisher = {Springer Netherlands}, pages = {245--273}, series = {Handbooks of Sociology and Social Research}, doi = {10.1007/978-94-007-6094-3_13}, url = {https://doi.org/10.1007/978-94-007-6094-3_13}, address = {Dordrecht} } @article{esterling_necessity_2025, title = {The necessity of construct and external validity for deductive causal inference}, volume = {13}, copyright = {De Gruyter expressly reserves the right to use all content for commercial text and data mining within the meaning of Section 44b of the German Copyright Act.}, issn = {2193-3685}, url = {https://www.degruyter.com/document/doi/10.1515/jci-2024-0002/html}, doi = {10.1515/jci-2024-0002}, number = {1}, urldate = {2025-02-22}, journal = {Journal of Causal Inference}, author = {Esterling, Kevin M. and Brady, David and Schwitzgebel, Eric}, month = jan, year = {2025} } @inbook{fecher2014, title = {Open Science: One Term, Five Schools of Thought}, author = {Fecher, Benedikt and Friesike, Sascha}, editor = {Bartling, {Sönke} and Friesike, Sascha}, year = {2014}, date = {2014}, publisher = {Springer International Publishing}, pages = {17--47}, doi = {10.1007/978-3-319-00026-8_2}, url = {http://link.springer.com/10.1007/978-3-319-00026-8_2}, address = {Cham} } @article{glaser_governing_2016, title = {Governing {Science}: {How} {Science} {Policy} {Shapes} {Research} {Content}}, volume = {57}, issn = {0003-9756, 1474-0583}, shorttitle = {Governing {Science}}, url = {https://www.cambridge.org/core/product/identifier/S0003975616000047/type/journal_article}, doi = {10.1017/S0003975616000047}, abstract = {This review explores contributions by science policy studies and the sociology of science to our understanding of the impact of governance on research content. Contributions are subsumed under two perspectives, namely an “impact of”—perspective that searches for effects of specific governance arrangements and an “impact on”—perspective that asks what factors contribute to the construction of research content and includes governance among them. Our review shows that little is known so far about the impact of governance on knowledge content. A research agenda does not necessarily need to include additional empirical phenomena but must address the macro-micro-macro link inherent to the question in its full complexity, and systematically exploit comparative approaches in order to establish causality. This requires interdisciplinary collaboration between science policy studies, the sociology of science, and bibliometrics, which all can contribute to the necessary analytical toolbox.}, number = {1}, urldate = {2023-03-14}, journal = {European Journal of Sociology}, author = {Gläser, Jochen and Laudel, Grit}, month = apr, year = {2016}, pages = {117--168} } @article{goodman_manuscript_1994, title = {Manuscript {Quality} before and after {Peer} {Review} and {Editing} at {Annals} of {Internal} {Medicine}}, volume = {121}, issn = {0003-4819}, url = {https://www.acpjournals.org/doi/full/10.7326/0003-4819-121-1-199407010-00003}, doi = {10.7326/0003-4819-121-1-199407010-00003}, number = {1}, urldate = {2023-12-16}, journal = {Annals of Internal Medicine}, author = {Goodman, Steven N. and Berlin, Jesse and Fletcher, Suzanne W. and Fletcher, Robert H.}, month = jul, year = {1994}, pages = {11--21} } @article{goodman1994, title = {Manuscript Quality before and after Peer Review and Editing at Annals of Internal Medicine}, author = {Goodman, Steven N. and Berlin, Jesse and Fletcher, Suzanne W. and Fletcher, Robert H.}, year = {1994}, month = {07}, date = {1994-07}, journal = {Annals of Internal Medicine}, pages = {11--21}, volume = {121}, number = {1}, doi = {10.7326/0003-4819-121-1-199407010-00003} } @article{hardwicke2018, title = {Data availability, reusability, and analytic reproducibility: evaluating the impact of a mandatory open data policy at the journal {\emph{Cognition}}}, author = {Hardwicke, Tom E. and Mathur, Maya B. and MacDonald, Kyle and Nilsonne, Gustav and Banks, George C. and Kidwell, Mallory C. and Hofelich Mohr, Alicia and Clayton, Elizabeth and Yoon, Erica J. and Henry Tessler, Michael and Lenne, Richie L. and Altman, Sara and Long, Bria and Frank, Michael C.}, year = {2018}, month = {08}, date = {2018-08}, journal = {Royal Society Open Science}, pages = {180448}, volume = {5}, number = {8}, doi = {10.1098/rsos.180448}, url = {https://royalsocietypublishing.org/doi/10.1098/rsos.180448} } @article{hardwicke2021, title = {Analytic reproducibility in articles receiving open data badges at the journal Psychological Science: an observational study}, author = {Hardwicke, Tom E. and Bohn, Manuel and MacDonald, Kyle and Hembacher, Emily and Nuijten, {Michèle B.} and Peloquin, Benjamin N. and deMayo, Benjamin E. and Long, Bria and Yoon, Erica J. and Frank, Michael C.}, year = {2021}, month = {01}, date = {2021-01-06}, journal = {Royal Society Open Science}, pages = {201494}, volume = {8}, number = {1}, doi = {10.1098/rsos.201494}, url = {https://royalsocietypublishing.org/doi/10.1098/rsos.201494} } @article{healy2017, title = {Fuck Nuance}, author = {Healy, Kieran}, year = {2017}, month = {06}, date = {2017-06-01}, journal = {Sociological Theory}, pages = {118--127}, volume = {35}, number = {2}, doi = {10.1177/0735275117709046}, url = {https://doi.org/10.1177/0735275117709046} } @misc{hunermund_causal_2023, title = {Causal {Inference} and {Data} {Fusion} in {Econometrics}}, url = {http://arxiv.org/abs/1912.09104}, doi = {10.48550/arXiv.1912.09104}, abstract = {Learning about cause and effect is arguably the main goal in applied econometrics. In practice, the validity of these causal inferences is contingent on a number of critical assumptions regarding the type of data that has been collected and the substantive knowledge that is available. For instance, unobserved confounding factors threaten the internal validity of estimates, data availability is often limited to non-random, selection-biased samples, causal effects need to be learned from surrogate experiments with imperfect compliance, and causal knowledge has to be extrapolated across structurally heterogeneous populations. A powerful causal inference framework is required to tackle these challenges, which plague most data analysis to varying degrees. Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the artificial intelligence (AI) literature has developed a wide array of techniques for causal learning that allow to leverage information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016). In this paper, we discuss recent advances in this literature that have the potential to contribute to econometric methodology along three dimensions. First, they provide a unified and comprehensive framework for causal inference, in which the aforementioned problems can be addressed in full generality. Second, due to their origin in AI, they come together with sound, efficient, and complete algorithmic criteria for automatization of the corresponding identification task. And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the strengths of both structural econometrics as well as the potential outcomes framework, and thus offer an effective middle ground between these two literature streams.}, urldate = {2023-12-16}, publisher = {arXiv}, author = {Hünermund, Paul and Bareinboim, Elias}, month = mar, year = {2023} } @book{huntington-klein_effect_2021, title = {The {Effect}: {An} {Introduction} to {Research} {Design} and {Causality}}, isbn = {978-1-00-050914-4}, shorttitle = {The {Effect}}, abstract = {The Effect: An Introduction to Research Design and Causality is about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject. The first half goes through the concepts of causality, with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams. Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we “add a control variable” what does that actually do? Key Features: • Extensive code examples in R, Stata, and Python • Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions • An easy-to-read conversational tone • Up-to-date coverage of methods with fast-moving literatures like difference-in-differences}, publisher = {CRC Press}, author = {Huntington-Klein, Nick}, month = dec, year = {2021} } @book{imbens_causal_2015, address = {Cambridge}, title = {Causal {Inference} for {Statistics}, {Social}, and {Biomedical} {Sciences}: {An} {Introduction}}, isbn = {978-0-521-88588-1}, shorttitle = {Causal {Inference} for {Statistics}, {Social}, and {Biomedical} {Sciences}}, url = {https://www.cambridge.org/core/books/causal-inference-for-statistics-social-and-biomedical-sciences/71126BE90C58F1A431FE9B2DD07938AB}, publisher = {Cambridge University Press}, author = {Imbens, Guido W. and Rubin, Donald B.}, year = {2015}, doi = {10.1017/CBO9781139025751} } @article{jacob2011, title = {The impact of research grant funding on scientific productivity}, author = {Jacob, Brian A. and Lefgren, Lars}, year = {2011}, month = {10}, date = {2011-10-01}, journal = {Journal of Public Economics}, pages = {1168--1177}, series = {Special Issue: The Role of Firms in Tax Systems}, volume = {95}, number = {9}, doi = {10.1016/j.jpubeco.2011.05.005} } @article{jefferson2002, title = {Effects of Editorial Peer Review: A Systematic Review}, author = {Jefferson, Tom and Alderson, Philip and Wager, Elizabeth and Davidoff, Frank}, year = {2002}, month = {06}, date = {2002-06-05}, journal = {JAMA}, pages = {2784}, volume = {287}, number = {21}, doi = {10.1001/jama.287.21.2784}, langid = {en} } @software{klebel_code, title = {Code for "Introduction to structural causal models in science studies"}, publisher = {Zenodo}, author = {Klebel, Thomas and Traag, Vincent}, date = {2024-01-28}, doi = {10.5281/zenodo.17550972}, year = {2024} } @article{klebel2023, title = {{PathOS - D1.2 Scoping Review of Open Science Impact}}, author = {Klebel, Thomas and Cole, Nicki Lisa and Tsipouri, Lena and Kormann, Eva and Karasz, Istvan and Liarti, Sofia and Stoy, Lennart and Traag, Vincent and Vignetti, Silvia and Ross-Hellauer, Tony}, year = {2023}, month = {05}, date = {2023-05-01}, url = {https://zenodo.org/record/7883699} } @article{kwon2021, title = {Incentive or disincentive for research data disclosure? {A} large-scale empirical analysis and implications for open science policy}, author = {Kwon, Seokbeom and Motohashi, Kazuyuki}, year = {2021}, month = {10}, date = {2021-10}, journal = {International Journal of Information Management}, pages = {102371}, volume = {60}, doi = {10.1016/j.ijinfomgt.2021.102371}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0268401221000645} } @article{liénard2018, title = {Intellectual synthesis in mentorship determines success in academic careers}, author = {{Liénard}, Jean F. and Achakulvisut, Titipat and Acuna, Daniel E. and David, Stephen V.}, year = {2018}, month = {11}, date = {2018-11-27}, journal = {Nature Communications}, pages = {4840}, volume = {9}, number = {1}, doi = {10.1038/s41467-018-07034-y}, langid = {en} } @article{liu_data_2023, title = {Data, measurement and empirical methods in the science of science}, volume = {7}, copyright = {2023 Springer Nature Limited}, issn = {2397-3374}, url = {https://www.nature.com/articles/s41562-023-01562-4}, doi = {10.1038/s41562-023-01562-4}, number = {7}, urldate = {2023-11-17}, journal = {Nature Human Behaviour}, author = {Liu, Lu and Jones, Benjamin F. and Uzzi, Brian and Wang, Dashun}, month = jul, year = {2023}, pages = {1046--1058} } @article{luc_does_2021, title = {Does {Tweeting} {Improve} {Citations}? {One}-{Year} {Results} {From} the {TSSMN} {Prospective} {Randomized} {Trial}}, volume = {111}, issn = {0003-4975}, shorttitle = {Does {Tweeting} {Improve} {Citations}?}, url = {https://www.sciencedirect.com/science/article/pii/S0003497520308602}, doi = {10.1016/j.athoracsur.2020.04.065}, number = {1}, urldate = {2023-12-16}, journal = {The Annals of Thoracic Surgery}, author = {Luc, Jessica G. Y. and Archer, Michael A. and Arora, Rakesh C. and Bender, Edward M. and Blitz, Arie and Cooke, David T. and Hlci, Tamara Ni and Kidane, Biniam and Ouzounian, Maral and Varghese, Thomas K. and Antonoff, Mara B.}, month = jan, year = {2021}, pages = {296--300} } @article{lundberg2021, title = {What Is Your Estimand? {Defining} the Target Quantity Connects Statistical Evidence to Theory}, author = {Lundberg, Ian and Johnson, Rebecca and Stewart, Brandon M.}, year = {2021}, month = {06}, date = {2021-06-01}, journal = {American Sociological Review}, pages = {532--565}, volume = {86}, number = {3}, doi = {10.1177/00031224211004187}, url = {https://doi.org/10.1177/00031224211004187} } @article{ma2020, title = {Mentorship and protégé success in STEM fields}, author = {Ma, Yifang and Mukherjee, Satyam and Uzzi, Brian}, year = {2020}, month = {06}, date = {2020-06-23}, journal = {Proceedings of the National Academy of Sciences}, pages = {14077--14083}, volume = {117}, number = {25}, doi = {10.1073/pnas.1915516117} } @article{malmgren_role_2010, title = {The role of mentorship in protégé performance}, volume = {465}, issn = {1476-4687}, url = {https://www.nature.com/articles/nature09040}, doi = {10.1038/nature09040}, abstract = {It is clear that mentors, in academia and elsewhere, influence the future success of their protégés, but it is unclear to what extent they influence future mentorship skills and career choices of their protégés. The records of the Mathematics Genealogy Project, which track the careers of 114,666 mathematicians since 1637, provide a data set with sufficient detail for those questions to be addressed. Malmgren et al. determine that career success of academic mathematicians was correlated with how many protégés they mentored, and the protégés of mentors with small trainee pools went on to have significantly larger than expected mentorship pools themselves.}, number = {7298}, urldate = {2023-12-16}, journal = {Nature}, author = {Malmgren, R. Dean and Ottino, Julio M. and Nunes Amaral, Luís A.}, month = jun, year = {2010}, pages = {622--626} } @book{mcelreath2020, title = {Statistical rethinking: a Bayesian course with examples in R and Stan}, author = {McElreath, Richard}, year = {2020}, date = {2020}, publisher = {Taylor and Francis, CRC Press}, series = {CRC texts in statistical science}, edition = {2}, address = {Boca Raton} } @article{molloy2011, title = {The {Open Knowledge Foundation}: Open Data Means Better Science}, author = {Molloy, Jennifer C.}, year = {2011}, month = {12}, date = {2011-12-06}, journal = {PLoS Biology}, pages = {e1001195}, volume = {9}, number = {12}, doi = {10.1371/journal.pbio.1001195}, url = {https://dx.plos.org/10.1371/journal.pbio.1001195} } @article{munafò2018, title = {Robust research needs many lines of evidence}, author = {{Munafò}, Marcus R. and Smith, George Davey}, year = {2018}, month = {01}, date = {2018-01}, journal = {Nature}, pages = {399--401}, volume = {553}, number = {7689}, doi = {10.1038/d41586-018-01023-3}, url = {https://www.nature.com/articles/d41586-018-01023-3} } @misc{nettle2023, title = {It probably is that bad}, author = {Nettle, Author Daniel}, year = {2023}, month = {11}, date = {2023-11-09}, url = {https://www.danielnettle.org.uk/2023/11/09/it-probably-is-that-bad/} } @article{nosek2022, title = {Replicability, Robustness, and Reproducibility in Psychological Science}, author = {Nosek, Brian A. and Hardwicke, Tom E. and Moshontz, Hannah and Allard, {Aurélien} and Corker, Katherine S. and Dreber, Anna and Fidler, Fiona and Hilgard, Joe and Kline Struhl, Melissa and Nuijten, {Michèle B.} and Rohrer, Julia M. and Romero, Felipe and Scheel, Anne M. and Scherer, Laura D. and {Schönbrodt}, Felix D. and Vazire, Simine}, year = {2022}, date = {2022}, journal = {Annual Review of Psychology}, pages = {719--748}, volume = {73}, number = {1}, doi = {10.1146/annurev-psych-020821-114157}, url = {https://doi.org/10.1146/annurev-psych-020821-114157} } @article{nuijten2017, title = {Journal Data Sharing Policies and Statistical Reporting Inconsistencies in Psychology}, author = {Nuijten, {Michèle B.} and Borghuis, Jeroen and Veldkamp, Coosje L. S. and Dominguez-Alvarez, Linda and van Assen, Marcel A. L. M. and Wicherts, Jelte M.}, editor = {Vazire, Simine and Chambers, Chris}, year = {2017}, month = {01}, date = {2017-01-01}, journal = {Collabra: Psychology}, pages = {31}, volume = {3}, number = {1}, doi = {10.1525/collabra.102}, url = {https://online.ucpress.edu/collabra/article/3/1/31/112350/Journal-Data-Sharing-Policies-and-Statistical} } @book{pearl_causality_2009, edition = {2}, title = {Causality: {Models}, {Reasoning}, and {Inference}}, isbn = {978-0-511-80316-1 978-0-521-89560-6 978-0-521-74919-0}, shorttitle = {Causality}, url = {https://www.cambridge.org/core/product/identifier/9780511803161/type/book}, abstract = {Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.}, urldate = {2023-03-14}, publisher = {Cambridge University Press}, author = {Pearl, Judea}, month = sep, year = {2009}, doi = {10.1017/CBO9780511803161} } @article{piwowar2007, title = {Sharing Detailed Research Data Is Associated with Increased Citation Rate}, author = {Piwowar, Heather and Day, Roger and Fridsma, Douglas}, editor = {Ioannidis, John}, year = {2007}, month = {03}, date = {2007-03-21}, journal = {PLoS ONE}, pages = {e308}, volume = {2}, number = {3}, doi = {10.1371/journal.pone.0000308}, url = {https://dx.plos.org/10.1371/journal.pone.0000308} } @techreport{piwowar2013, title = {Data reuse and the open data citation advantage}, author = {Piwowar, Heather and Vision, Todd J.}, year = {2013}, month = {04}, date = {2013-04-04}, doi = {10.7287/peerj.preprints.1v1}, url = {https://peerj.com/preprints/1v1} } @article{rohrer2018, title = {Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data}, author = {Rohrer, Julia M.}, year = {2018}, month = {03}, date = {2018-03-01}, journal = {Advances in Methods and Practices in Psychological Science}, pages = {27--42}, volume = {1}, number = {1}, doi = {10.1177/2515245917745629}, url = {https://doi.org/10.1177/2515245917745629} } @article{rohrer2022, title = {That{\textquoteright}s a Lot to Process! {Pitfalls} of Popular Path Models}, author = {Rohrer, Julia M. and {Hünermund}, Paul and Arslan, Ruben C. and Elson, Malte}, year = {2022}, month = {04}, date = {2022-04-01}, journal = {Advances in Methods and Practices in Psychological Science}, pages = {25152459221095827}, volume = {5}, number = {2}, doi = {10.1177/25152459221095827}, url = {https://doi.org/10.1177/25152459221095827} } @article{ross-hellauer2022, title = {{TIER2}: {Enhancing} Trust, Integrity and Efficiency in Research through next-level Reproducibility}, author = {Ross-Hellauer, Tony and Klebel, Thomas and Bannach-Brown, Alexandra and Horbach, Serge P. J. M. and Jabeen, Hajira and Manola, Natalia and Metodiev, Teodor and Papageorgiou, Haris and Reczko, Martin and Sansone, Susanna-Assunta and Schneider, Jesper and Tijdink, Joeri and Vergoulis, Thanasis}, year = {2022}, month = {12}, date = {2022-12-08}, journal = {Research Ideas and Outcomes}, pages = {e98457}, volume = {8}, doi = {10.3897/rio.8.e98457}, url = {https://riojournal.com/article/98457/} } @article{rowhani-farid2017, title = {What incentives increase data sharing in health and medical research? A systematic review}, author = {Rowhani-Farid, Anisa and Allen, Michelle and Barnett, Adrian G.}, year = {2017}, month = {05}, date = {2017-05-05}, journal = {Research Integrity and Peer Review}, pages = {4}, volume = {2}, number = {1}, doi = {10.1186/s41073-017-0028-9} } @article{schmal2023, title = {The role of gender and coauthors in academic publication behavior}, author = {Schmal, W. Benedikt and Haucap, Justus and Knoke, Leon}, year = {2023}, month = {12}, date = {2023-12-01}, journal = {Research Policy}, pages = {104874}, volume = {52}, number = {10}, doi = {10.1016/j.respol.2023.104874} } @article{simsek2024, title = {Do grant proposal texts matter for funding decisions? A field experiment}, author = {Simsek, {Müge} and de Vaan, Mathijs and van de Rijt, Arnout}, year = {2024}, month = {05}, date = {2024-05-01}, journal = {Scientometrics}, pages = {2521--2532}, volume = {129}, number = {5}, doi = {10.1007/s11192-024-04968-7}, url = {https://doi.org/10.1007/s11192-024-04968-7}, langid = {en} } @book{smaldino2023, title = {Modeling social behavior: mathematical and agent-based models of social dynamics and cultural evolution}, author = {Smaldino, Paul E.}, year = {2023}, date = {2023}, publisher = {Princeton University Press}, address = {Princeton} } @article{smith2006, title = {Peer review: a flawed process at the heart of science and journals}, author = {Smith, Richard}, year = {2006}, month = {04}, date = {2006-04-01}, journal = {Journal of the Royal Society of Medicine}, pages = {178--182}, volume = {99}, number = {4}, doi = {10.1258/jrsm.99.4.178} } @article{sugimoto2011, title = {Academic genealogy as an indicator of interdisciplinarity: An examination of dissertation networks in Library and Information Science}, author = {Sugimoto, Cassidy R. and Ni, Chaoqun and Russell, Terrell G. and Bychowski, Brenna}, year = {2011}, date = {2011}, journal = {Journal of the American Society for Information Science and Technology}, pages = {1808--1828}, volume = {62}, number = {9}, doi = {10.1002/asi.21568}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.21568} } @book{tashakkori2021, title = {Foundations of mixed methods research: integrating quantitative and qualitative approaches in the social and behavioral sciences}, author = {Tashakkori, Abbas and Johnson, R. Burke and Teddlie, Charles}, year = {2021}, date = {2021}, publisher = {SAGE}, edition = {Second edition}, address = {Los Angeles London New Delhi Singapore Washington DC Melbourne} } @techreport{tennant2016, title = {The academic, economic and societal impacts of Open Access: an evidence-based review}, author = {Tennant, Jonathan P. and Waldner, {François} and Jacques, Damien C. and Masuzzo, Paola and Collister, Lauren B. and Hartgerink, Chris H. J.}, year = {2016}, month = {09}, date = {2016-09-21}, doi = {10.12688/f1000research.8460.3}, url = {https://f1000research.com/articles/5-632} } @article{textor_robust_2016, title = {Robust causal inference using directed acyclic graphs: the {R} package ‘dagitty’}, volume = {45}, issn = {0300-5771}, shorttitle = {Robust causal inference using directed acyclic graphs}, url = {https://doi.org/10.1093/ije/dyw341}, doi = {10.1093/ije/dyw341}, number = {6}, urldate = {2025-02-13}, journal = {International Journal of Epidemiology}, author = {Textor, Johannes and van der Zander, Benito and Gilthorpe, Mark S and Liśkiewicz, Maciej and Ellison, George TH}, month = dec, year = {2016}, pages = {1887--1894} } @article{tomkins_reviewer_2017, title = {Reviewer bias in single- versus double-blind peer review}, volume = {114}, issn = {0027-8424, 1091-6490}, doi = {10.1073/pnas.1707323114}, number = {48}, journal = {Proc. Natl. Acad. Sci. U. S. A.}, author = {Tomkins, Andrew and Zhang, Min and Heavlin, William D}, month = nov, year = {2017}, pmid = {29138317}, pages = {12708--12713} } @misc{traag_causal_2022, title = {Causal foundations of bias, disparity and fairness}, url = {http://arxiv.org/abs/2207.13665}, doi = {10.48550/arXiv.2207.13665}, urldate = {2023-04-10}, publisher = {arXiv}, author = {Traag, V. 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