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In 2015, the Open Science Collaboration repeated a series of 100 psychological experiments. Since a considerable part of these replications could not confirm the original effects and some of them pointed in the opposite direction, psychological research is said to lack reproducibility. Several general criticisms can explain this finding, such as the standardized use of undirected nil-null hypothesis tests, samples being too small and selective, lack of corrections for multiple testing, but also some widespread questionable research practices and incentives to publish positive results only. A selection of 57,909 articles from 12 renowned journals is processed with the JATSdecoder software to analyze the extent to which several empirical research practices in psychology have changed over the past 12 years. To identify journal- and time-specific changes, the relative use of statistics based on p-values, the number of reported p-values per paper, the relative use of confidence intervals, directed tests, power analysis, Bayesian procedures, non-standard α levels, correction procedures for multiple testing, and median sample sizes are analyzed for articles published between 2010 and 2015 and after 2015, and in more detail for every included journal and year of publication. In addition, the origin of authorships is analyzed over time. Compared to articles that were published in and before 2015, the median number of reported p-values per article has decreased from 14 to 12, whereas the median proportion of significant p-values per article remained constant at 69%. While reports of effect sizes and confidence intervals have increased, the α level is usually set to the default value of .05. The use of corrections for multiple testing has decreased. Although uncommon in each case (4% in total), directed testing is used less frequently, while Bayesian inference has become more common after 2015. The overall median estimated sample size has increased from 105 to 190., Competing Interests: The author has declared that no competing interests exist., (Copyright: © 2023 Ingmar Böschen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper introduces and evaluates the study.character module from the JATSdecoder package which extracts several key methodological study characteristics from NISO-JATS coded scientific articles., Study: character splits the text into sections and applies its heuristic-driven extraction procedures to the text of the method and result section/s. When used individually, study.character's functions can also be applied to any textual input. An externally coded data set of 288 PDF articles serves as an indicator of study.character's capabilities in extracting the number of sub-studies reported per article, the statistical methods applied and software solutions used. Its precision of extraction of the reported [Formula: see text]-level, power, correction procedures for multiple testing, use of interactions, definition of outlier, and mentions of statistical assumptions are evaluated by a comparison to a manually curated data set of the same collection of articles. Sensitivity, specificity, and accuracy measures are reported for each of the evaluated functions., Study: character reliably extracts the methodological study characteristics targeted here from psychological research articles. Most extractions have very low false positive rates and high accuracy ([Formula: see text]). Most non-detections are due to PDF-specific conversion errors and complex text structures, that are not yet manageable., Study: character can be applied to large text resources in order to examine methodological trends over time, by journal and/or by topic. It also enables a new way of identifying study sets for meta-analyzes and systematic reviews., (© 2023. The Author(s).)

The extraction of statistical results in scientific reports is beneficial for checking studies on plausibility and reliability. The R package JATSdecoder supports the application of text mining approaches to scientific reports. Its function get.stats() extracts all reported statistical results from text and recomputes p values for most standard test results. The output can be reduced to results with checkable or computable p values only. In this article, get.stats()'s ability to extract, recompute and check statistical results is compared to that of statcheck, which is an already established tool. A manually coded data set, containing the number of statistically significant results in 49 articles, serves as an initial indicator for get.stats()'s and statcheck's differing detection rates for statistical results. Further 13,531 PDF files by 10 mayor psychological journals, 18,744 XML documents by Frontiers of Psychology and 23,730 articles related to psychological research and published by PLoS One are scanned for statistical results with both algorithms. get.stats() almost replicates the manually extracted number of significant results in 49 PDF articles. get.stats() outperforms the statcheck functions in identifying statistical results in every included journal and input format. Furthermore, the raw results extracted by get.stats() increase statcheck's detection rate. JATSdecoder's function get.stats() is a highly general and reliable tool to extract statistical results from text. It copes with a wide range of textual representations of statistical standard results and recomputes p values for two- and one-sided tests. It facilitates manual and automated checks on consistency and completeness of the reported results within a manuscript., (© 2021. The Author(s).)

JATSdecoder is a general toolbox which facilitates text extraction and analytical tasks on NISO-JATS coded XML documents. Its function JATSdecoder() outputs metadata, the abstract, the sectioned text and reference list as easy selectable elements. One of the biggest repositories for open access full texts covering biology and the medical and health sciences is PubMed Central (PMC), with more than 3.2 million files. This report provides an overview of the PMC document collection processed with JATSdecoder() . The development of extracted tags is displayed for the full corpus over time and in greater detail for some meta tags. Possibilities and limitations for text miners working with scientific literature are outlined. The NISO-JATS-tags are used quite consistently nowadays and allow a reliable extraction of metadata and text elements. International collaborations are more present than ever. There are obvious errors in the date stamps of some documents. Only about half of all articles from 2020 contain at least one author listed with an author identification code. Since many authors share the same name, the identification of person-related content is problematic, especially for authors with Asian names. JATSdecoder() reliably extracts key metadata and text elements from NISO-JATS coded XML files. When combined with the rich, publicly available content within PMCs database, new monitoring and text mining approaches can be carried out easily. Any selection of article subsets should be carefully performed with in- and exclusion criteria on several NISO-JATS tags, as both the subject and keyword tags are used quite inconsistently., Competing Interests: Conflict of interestThe author declares no conflict of interest., (© The Author(s) 2021.)