BackgroundIndividual participant data (IPD) meta-analyses that obtain “raw” data from studies rather than summary data typically adopt a “two-stage” approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of “one-stage” approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare “two-stage” and “one-stage” models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way. Methods and FindingsWe included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model. ConclusionsFor these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials. Researchers considering undertaking an IPD meta-analysis should not necessarily be deterred by a perceived need for sophisticated statistical methods when combining information from large randomised trials.

Statistical analysis.

BackgroundThere is widespread evidence that statistical methods play an important role in original research articles, especially in medical research. The evaluation of statistical methods and reporting in journals suffers from a lack of standardized methods for assessing the use of statistics. The objective of this study was to develop and evaluate an instrument to assess the statistical intensity in research articles in a standardized way.MethodsA checklist-type measure scale was developed by selecting and refining items from previous reports about the statistical contents of medical journal articles and from published guidelines for statistical reporting. A total of 840 original medical research articles that were published between 2007–2015 in 16 journals were evaluated to test the scoring instrument. The total sum of all items was used to assess the intensity between sub-fields and journals. Inter-rater agreement was examined using a random sample of 40 articles. Four raters read and evaluated the selected articles using the developed instrument.ResultsThe scale consisted of 66 items. The total summary score adequately discriminated between research articles according to their study design characteristics. The new instrument could also discriminate between journals according to their statistical intensity. The inter-observer agreement measured by the ICC was 0.88 between all four raters. Individual item analysis showed very high agreement between the rater pairs, the percentage agreement ranged from 91.7% to 95.2%.ConclusionsA reliable and applicable instrument for evaluating the statistical intensity in research papers was developed. It is a helpful tool for comparing the statistical intensity between sub-fields and journals. The novel instrument may be applied in manuscript peer review to identify papers in need of additional statistical review.

Dataset for the statistical analysis of the article "Empowerment through Participatory Game Creation: A Case Study with Adults with Intellectual Disability".

Overview

This dataset contains 50 articles sourced from Medium, focusing on AI-related content. It is designed for business owners, content creators, and AI developers looking to analyze successful articles, improve engagement, and fine-tune AI language models (LLMs). The data can be used to explore what makes articles perform well, including sentiment analysis, follower counts, and headline effectiveness.

Dataset Contents

• articles_50.db - Sample database with 50 articles(Free Version)

The database includes pre-analyzed data such as sentiment scores, follower counts, and headline metadata, helping users gain insights into high-performing content.

Use Cases

• Content Strategy Optimization: Identify trends in successful AI-related articles to enhance your content approach.
• Headline Crafting: Study patterns in top-performing headlines to create more compelling article titles.
• LLM Fine-Tuning: Utilize the dataset to fine-tune AI models with real-world data on content performance.
• Sentiment-Driven Content: Create content that resonates with readers by aligning with sentiment insights.

This dataset is a valuable tool for anyone aiming to harness the power of data-driven insights to enhance their content or AI models.

Descriptive statistical analysis.

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

This file contains R code for the data analyzed in the paper from Frontiers in Ecology and Evolution

It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. However, these analysis methods can only be applied after a mature release of the code has been developed. But in order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. Previous research into change-prone classes has identified some common aspects, with different studies suggesting that complex and large classes tend to undergo more changes and classes that changed recently are likely to undergo modifications in the near future. Though the guidance provided is helpful, developers need more specific guidance in order for it to be applicable in practice. Furthermore, the information needs to be available at a level that can help in developing tools that highlight and monitor evolution prone parts of a system as well as support effort estimation activities. The specific research questions that we address in this chapter are: (1) What is the likelihood that a class will change from a given version to the next? (a) Does this probability change over time? (b) Is this likelihood project specific, or general? (2) How is modification frequency distributed for classes that change? (3) What is the distribution of the magnitude of change? Are most modifications minor adjustments, or substantive modifications? (4) Does structural complexity make a class susceptible to change? (5) Does popularity make a class more change-prone? We make recommendations that can help developers to proactively monitor and manage change. These are derived from a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The analysis methods that we applied took into consideration the highly skewed nature of the metric data distributions. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

Following Chapter 4 of my thesis, 3 E.coli strains (MG1655, F022 and ELU39) were analysed using untargeted metabolomics after ~700 generations of evolution in 1 of three conditions: Plasmid free, plasmid carrying and plasmid carrying with antibiotic selection.The following data has been analysed using metaboanalyst

Data that was used to train the SVM. As the train-test data were assigned randomly for every training iteration, the individual data used for generating the subfigures b–e are not separately listed, as these cannot be manually recreated but depend on the train-test assignment by the algorithm. (ZIP)

Note. 5-yr IF = five-year Impact Factor in 2011. Articles = number of articles published in 2012. Empirical = number of empirical articles published in 2012.Sample.

Electroencephalogram (EEG) is used to monitor child's brain during coma by recording data on electrical neural activity of the brain. Signals are captured by multiple electrodes called channels located over the scalp. Statistical analyses of EEG data includes classification and prediction using arrays of EEG features, but few models for the underlying stochastic processes have been proposed. For this purpose, a new strictly stationary strong mixing diffusion model with marginal multimodal (three-peak) distribution (MixGGDiff) and exponentially decaying autocorrelation function for modeling of increments of EEG data was proposed. The increments were treated as discrete-time observations and a diffusion process where the stationary distribution is viewed as a mixture of three non-central generalized Gaussian distributions (MixGGD) was constructed.Probability density function of a mixed generalized Gaussian distribution (MixGGD) consists of three components and is described using a total of 12 parameters:\muk, location parameter of each of the components,sk, shape parameter of each of the components, \sigma2k, parameter related to the scale of each of the components andwk, weight of each of the components, where k, k={1,2,3} refers to theindex of the component of a MixGGD. The parameters of this distribution were estimated using the expectation-maximization algorithm, where the added shape parameter is estimated using the higher order statistics approach based on an analytical relationship between the shape parameter and kurtosis.To illustrate an application of the MixGGDiff to real data, analysis of EEG data collected in Uganda between 2008 and 2015 from 78 children within age-range of 18 months to 12 years who were in coma due to cerebral malaria was performed. EEG were recorded using the International 10–20 system with the sampling rate of 500 Hz and the average record duration of 30 min. EEG signal for every child was the result of a recording from 19 channels. MixGGD was fitted to each channel of every child's recording separately, hence for each channel a total of 12 parameter estimates were obtained. The data is presented in a matrix form (dimension 79*228) in a .csv format and consists of 79 rows where the first row is a header row which contains the names of the variables and the subsequent 78 rows represent parameter estimates of one instance (i.e. one child, without identifiers that could be related back to a specific child). There are a total of 228 columns (19 channels times 12 parameter estimates) where each column represents one parameter estimate of one component of MixGGD in the order of the channels, thus columns 1 to 12 refer to parameter estimates on the first channel, columns 13 to 24 refer to parameter estimates on the second channel and so on. Each variable name starts with "chi" where "ch" is an abbreviation of "channel" and i refers to the order of the channel from EEG recording. The rest of the characters in variable names refer to the parameter estimate names of the components of a MixGGD, thus for example "ch3sigmasq1" refers to the parameter estimate of \sigma2 of the first component of MixGGD obtained from EEG increments on the third channel. Parameter estimates contained in the .csv file are all real numbers within a range of -671.11 and 259326.96.Research results based upon these data are published at https://doi.org/10.1007/s00477-023-02524-y

Summary of independent variables used in the statistical analysis: aesthetic judgment of species and informant attributes (personal and household) (n = 191).

This dataset contains article metadata and information about Open Science Indicators for approximately 139,000 research articles published in PLOS journals from 1 January 2018 to 30 March 2025 and a set of approximately 28,000 comparator articles published in non-PLOS journals. This is the tenth release of this dataset, which will be updated with new versions on an annual basis.This version of the Open Science Indicators dataset shares the indicators seen in the previous versions as well as fully operationalised protocols and study registration indicators, which were previously only shared in preliminary forms. The v10 dataset focuses on detection of five Open Science practices by analysing the XML of published research articles:Sharing of research data, in particular data shared in data repositoriesSharing of codePosting of preprintsSharing of protocolsSharing of study registrationsThe dataset provides data and code generation and sharing rates, the location of shared data and code (whether in Supporting Information or in an online repository). It also provides preprint, protocol and study registration sharing rates as well as details of the shared output, such as publication date, URL/DOI/Registration Identifier and platform used. Additional data fields are also provided for each article analysed. This release has been run using an updated preprint detection method (see OSI-Methods-Statement_v10_Jul25.pdf for details). Further information on the methods used to collect and analyse the data can be found in Documentation.Further information on the principles and requirements for developing Open Science Indicators is available in https://doi.org/10.6084/m9.figshare.21640889.Data folders/filesData Files folderThis folder contains the main OSI dataset files PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv, which containdescriptive metadata, e.g. article title, publication data, author countries, is taken from the article .xml filesadditional information around the Open Science Indicators derived algorithmicallyand the OSI-Summary-statistics_v10_Jul25.xlsx file contains the summary data for both PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv.Documentation folderThis file contains documentation related to the main data files. The file OSI-Methods-Statement_v10_Jul25.pdf describes the methods underlying the data collection and analysis. OSI-Column-Descriptions_v10_Jul25.pdf describes the fields used in PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv. OSI-Repository-List_v1_Dec22.xlsx lists the repositories and their characteristics used to identify specific repositories in the PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv repository fields.The folder also contains documentation originally shared alongside the preliminary versions of the protocols and study registration indicators in order to give fuller details of their detection methods.Contact details for further information:Iain Hrynaszkiewicz, Director, Open Research Solutions, PLOS, ihrynaszkiewicz@plos.org / plos@plos.orgLauren Cadwallader, Open Research Manager, PLOS, lcadwallader@plos.org / plos@plos.orgAcknowledgements:Thanks to Allegra Pearce, Tim Vines, Asura Enkhbayar, Scott Kerr and parth sarin of DataSeer for contributing to data acquisition and supporting information.

This article contains consolidated proteomic data obtained from xylem sap collected from tomato plants grown in Fe- and Mn-sufficient control, as well as Fe-deficient and Mn-deficient conditions. Data presented here cover proteins identified and quantified by shotgun proteomics and Progenesis LC-MS analyses: proteins identified with at least two peptides and showing changes statistically significant (ANOVA; p ≤ 0.05) and above a biologically relevant selected threshold (fold ≥ 2) between treatments are listed. The comparison between Fe-deficient, Mn-deficient and control xylem sap samples using a multivariate statistical data analysis (Principal Component Analysis, PCA) is also included. Data included in this article are discussed in depth in "Effects of Fe and Mn deficiencies on the protein profiles of tomato (Solanum lycopersicum) xylem sap as revealed by shotgun analyses", Ceballos-Laita et al., J. Proteomics, 2018. This dataset is made available to support the cited study as well to extend analyses at a later stage. Resources in this dataset:Resource Title: ProteomeExchange submission PXD007517. Xylem sap shotgun proteomics from Fe- and Mn-deficient and Mn-toxic tomato plants. . File Name: Web Page, url: http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD007517 The MS proteomics data have been deposited to the ProteomeXchange Consortium via the Pride partner repository with the data set identifier PXD007517. Also includes FTP location. Files available at https://www.ebi.ac.uk/pride/archive/projects/PXD007517 via HTML, FTP, or Fast (Aspera) download : 1 SEARCH.xml file, 1 Peak file, 24 RAW files, 1 Mascot information.xlsx file. Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.dib.2018.01.034

Although prehospital emergency anesthesia (PHEA), with a specific focus on intubation attempts, is frequently studied in prehospital emergency care, there is a gap in the knowledge on aspects related to adherence to PHEA guidelines. This study investigates adherence to the “Guidelines for Prehospital Emergency Anesthesia in Adults” with regard to the induction of PHEA, including the decision making, rapid sequence induction, preoxygenation, standard monitoring, intubation attempts, adverse events, and administration of appropriate medications and their side effects. This retrospective study examined PHEA interventions from 01/01/2020 to 12/31/2021 in the city of Aachen, Germany. The inclusion criteria were adult patients who met the indication criteria for the PHEA. Data were obtained from emergency medical protocols. A total of 127 patients were included in this study. All the patients met the PHEA indication criteria. Despite having a valid indication, 29 patients did not receive the PHEA. 98 patients were endotracheally intubated. For these patients, monitoring had conformed to the guidelines. The medications were used according to the guidelines. A significant increase in oxygen saturation was reported after anesthesia induction (p < 0.001). The patients were successfully intubated endotracheally on the third attempt. Guideline adherence was maintained in terms of execution of PHEA, rapid sequence induction, preoxygenation, monitoring, selection, and administration of relevant medications. Emergency physicians demonstrated the capacity to effectively respond to cardiorespiratory events. Further investigations are needed on the group of patients who did not receive PHEA despite meeting the criteria. The underlying causes of decision making in these cases need to be evaluated in the future.

Interview and workshop transcripts from EPSRC Digital Transformations Communities and Cultures Network + (http://www.communitiesandculture.org/) project Digital Data Analytics, Public Engagement and the Social Life of Methods (http://www.communitiesandculture.org/projects/digital-data-analysis/). Methodology described in papers available at the above link.

Data used in statistical analyses.

Additional file 1.