fair_data_code.bib

@article{arslan_how_2019,
  title = {How to {{Automatically Document Data With}} the Codebook {{Package}} to {{Facilitate Data Reuse}}},
  author = {Arslan, Ruben C.},
  year = 2019,
  month = jun,
  journal = {Advances in Methods and Practices in Psychological Science},
  volume = {2},
  number = {2},
  pages = {169--187},
  publisher = {SAGE Publications Inc},
  issn = {2515-2459},
  doi = {10.1177/2515245919838783},
  urldate = {2026-07-28},
  abstract = {Data documentation in psychology lags behind not only many other disciplines, but also basic standards of usefulness. Psychological scientists often prefer to invest the time and effort that would be necessary to document existing data well in other duties, such as writing and collecting more data. Codebooks therefore tend to be unstandardized and stored in proprietary formats, and they are rarely properly indexed in search engines. This means that rich data sets are sometimes used only once---by their creators---and left to disappear into oblivion. Even if they can find an existing data set, researchers are unlikely to publish analyses based on it if they cannot be confident that they understand it well enough. My codebook package makes it easier to generate rich metadata in human- and machine-readable codebooks. It uses metadata from existing sources and automates some tedious tasks, such as documenting psychological scales and reliabilities, summarizing descriptive statistics, and identifying patterns of missingness. The codebook R package and Web app make it possible to generate a rich codebook in a few minutes and just three clicks. Over time, its use could lead to psychological data becoming findable, accessible, interoperable, and reusable, thereby reducing research waste and benefiting both its users and the scientific community as a whole.},
  langid = {english},
  file = {C:\Users\dlakens\Zotero\storage\KZ7BPVI4\Arslan - 2019 - How to Automatically Document Data With the codebook Package to Facilitate Data Reuse.pdf}
}

@article{buchanan_getting_2021,
  title = {Getting {{Started Creating Data Dictionaries}}: {{How}} to {{Create}} a {{Shareable Data Set}}},
  shorttitle = {Getting {{Started Creating Data Dictionaries}}},
  author = {Buchanan, Erin M. and Crain, Sarah E. and Cunningham, Ari L. and Johnson, Hannah R. and Stash, Hannah and {Papadatou-Pastou}, Marietta and Isager, Peder M. and Carlsson, Rickard and Aczel, Balazs},
  year = 2021,
  month = jan,
  journal = {Advances in Methods and Practices in Psychological Science},
  volume = {4},
  number = {1},
  pages = {2515245920928007},
  publisher = {SAGE Publications Inc},
  issn = {2515-2459},
  doi = {10.1177/2515245920928007},
  urldate = {2026-07-28},
  abstract = {As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand their data sets' contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a data set. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search-engine indexing to reach a broader audience of interested parties. This Tutorial first explains terminology and standards relevant to data dictionaries and codebooks. Accompanying information on OSF presents a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared data set accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we discuss freely available Web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable.},
  langid = {english},
  file = {C:\Users\dlakens\Zotero\storage\KGXZZYZA\Buchanan et al. - 2021 - Getting Started Creating Data Dictionaries How to Create a Shareable Data Set.pdf}
}

@misc{defossez_structure_2020,
  title = {The Structure of Behavioral Data},
  author = {Defossez, Aur{\'e}lien and Ansarinia, Morteza and Clocher, Brice and Schm{\"u}ck, Emmanuel and Schrater, Paul and {Cardoso-Leite}, Pedro},
  year = 2020,
  month = dec,
  number = {arXiv:2012.12583},
  eprint = {2012.12583},
  primaryclass = {q-bio.NC},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2012.12583},
  urldate = {2026-07-28},
  abstract = {For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend past results. Although behavioral data is fundamental to many scientific fields, there is currently no widely adopted standard for formatting, naming, organizing, describing or sharing such data. This lack of standardization is a major bottleneck for scientific progress. Not only does it prevent the effective reuse of data, it also affects how behavioral data in general are processed, as non-standard data calls for custom-made data analysis code and prevents the development of efficient tools. To address this problem, we develop the Behaverse Data Model (BDM), a standard for structuring behavioral data. Here we focus on major concepts in behavioral data, leaving further details and developments to the project's website (https://behaverse.github.io/data-model/).},
  archiveprefix = {arXiv},
  keywords = {Quantitative Biology - Neurons and Cognition,Statistics - Methodology},
  file = {C\:\\Users\\dlakens\\Zotero\\storage\\2YQSSZAR\\Defossez et al. - 2020 - The structure of behavioral data.pdf;C\:\\Users\\dlakens\\Zotero\\storage\\3CB33IT5\\2012.html}
}

@article{lakens_improving_2021,
  title = {Improving {{Transparency}}, {{Falsifiability}}, and {{Rigor}} by {{Making Hypothesis Tests Machine-Readable}}},
  author = {Lakens, Dani{\"e}l and DeBruine, Lisa M.},
  year = 2021,
  month = apr,
  journal = {Advances in Methods and Practices in Psychological Science},
  volume = {4},
  number = {2},
  pages = {2515245920970949},
  publisher = {SAGE Publications Inc},
  issn = {2515-2459},
  doi = {10.1177/2515245920970949},
  urldate = {2021-07-13},
  abstract = {Making scientific information machine-readable greatly facilitates its reuse. Many scientific articles have the goal to test a hypothesis, so making the tests of statistical predictions easier to find and access could be very beneficial. We propose an approach that can be used to make hypothesis tests machine-readable. We believe there are two benefits to specifying a hypothesis test in such a way that a computer can evaluate whether the statistical prediction is corroborated or not. First, hypothesis tests become more transparent, falsifiable, and rigorous. Second, scientists benefit if information related to hypothesis tests in scientific articles is easily findable and reusable, for example, to perform meta-analyses, conduct peer review, and examine metascientific research questions. We examine what a machine-readable hypothesis test should look like and demonstrate the feasibility of machine-readable hypothesis tests in a real-life example using the fully operational prototype R package scienceverse.},
  langid = {english},
  keywords = {hypothesis testing,machine readability,metadata,scholarly communication},
  file = {C:\Users\dlakens\Zotero\storage\D4MMJJIM\Lakens_DeBruine_2021_Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests.pdf}
}

@book{lewis_data_2024,
  title = {Data {{Management}} in {{Large-Scale Education Research}}},
  author = {Lewis, Crystal},
  year = 2024,
  month = jul,
  publisher = {{Chapman and Hall/CRC}},
  address = {New York},
  doi = {10.1201/9781032622835},
  abstract = {Research data management is becoming more complicated. Researchers are collecting more data, using more complex technologies, all the while increasing the visibility of our work with the push for data sharing and open science practices. Ad hoc data management practices may have worked for us in the past, but now others need to understand our processes as well, requiring researchers to be more thoughtful in planning their data management routines. This book is for anyone involved in a research study involving original data collection. While the book focuses on quantitative data, typically collected from human participants, many of the practices covered can apply to other types of data as well. The book contains foundational context, instructions, and practical examples to help researchers in the field of education begin to understand how to create data management workflows for large-scale, typically federally funded, research studies. The book starts by describing the research life cycle and how data management fits within this larger picture. The remaining chapters are then organized by each phase of the life cycle, with examples of best practices provided for each phase. Finally, considerations on whether the reader should implement, and how to integrate those practices into a workflow, are discussed. Key Features: Provides a holistic approach to the research life cycle, showing how project management and data management processes work in parallel and collaboratively Can be read in its entirety, or referenced as needed throughout the life cycle Includes relatable examples specific to education research Includes a discussion on how to organize and document data in preparation for data sharing requirements Contains links to example documents as well as templates to help readers implement practices},
  isbn = {978-1-032-62283-5}
}

@article{mueller_psychology_2014,
  title = {The {{Psychology Experiment Building Language}} ({{PEBL}}) and {{PEBL Test Battery}}},
  author = {Mueller, Shane T. and Piper, Brian J.},
  year = 2014,
  month = jan,
  journal = {Journal of Neuroscience Methods},
  volume = {222},
  pages = {250--259},
  issn = {0165-0270},
  doi = {10.1016/j.jneumeth.2013.10.024},
  urldate = {2026-07-28},
  abstract = {Background We briefly describe the Psychology Experiment Building Language (PEBL), an open source software system for designing and running psychological experiments. New method We describe the PEBL Test Battery, a set of approximately 70 behavioral tests which can be freely used, shared, and modified. Included is a comprehensive set of past research upon which tests in the battery are based. Results We report the results of benchmark tests that establish the timing precision of PEBL. Comparison with existing method We consider alternatives to the PEBL system and battery tests. Conclusions We conclude with a discussion of the ethical factors involved in the open source testing movement.},
  keywords = {Continuous Performance Test,Executive function,Iowa Gambling Task,Mental rotation,Motor learning,Open science,Trail-making Test,Wisconsin Card Sorting Test}
}

@misc{wilkinson_fair_2016,
  type = {Comments and {{Opinion}}},
  title = {The {{FAIR Guiding Principles}} for Scientific Data Management and Stewardship},
  author = {Wilkinson, Mark D. and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and Santos, Luiz Bonino da Silva and Bourne, Philip E. and Bouwman, Jildau and Brookes, Anthony J. and Clark, Tim and Crosas, Merc{\`e} and Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo, Chris T. and Finkers, Richard and {Gonzalez-Beltran}, Alejandra and Gray, Alasdair J. G. and Groth, Paul and Goble, Carole and Grethe, Jeffrey S. and Heringa, Jaap and 't Hoen, Peter A. C. and Hooft, Rob and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott J. and Martone, Maryann E. and Mons, Albert and Packer, Abel L. and Persson, Bengt and {Rocca-Serra}, Philippe and Roos, Marco and van Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz, Morris A. and Thompson, Mark and van der Lei, Johan and van Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and Mons, Barend},
  year = 2016,
  month = mar,
  journal = {Scientific Data},
  doi = {10.1038/sdata.2016.18},
  urldate = {2018-10-01},
  abstract = {There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders---representing academia, industry, funding agencies, and scholarly publishers---have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.},
  copyright = {2016 Nature Publishing Group},
  howpublished = {https://www.nature.com/articles/sdata201618},
  langid = {english},
  annotation = {00732},
  file = {C:\Users\dlakens\Zotero\storage\B9T8LBA3\sdata201618.html}
}