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Content of the dataset: raw data - docking and molecular dynamics calculation files, experimental spectra of myoglobin interaction with ligands (UV-vis, fluorescence, proton NMR) Supported manuscript: Spectroscopic Evaluation of the Interaction of Myoglobin with Biomedically Relevant Compounds, by Alexandra Mânzat, Nicoleta Andrian, Cezara Zăgrean-Tuza, Dan Porumb, Radu Silaghi-Dumitrescu

This dataset contains the analytical data supporting the study on resource-efficiency profiles and circular-economy transition in Europe and Ukraine. It includes country-level indicators for 30 European countries and regional indicators for 24 Ukrainian oblasts, together with cluster-validation statistics, regional regression inputs and estimates, variance-inflation diagnostics, leave-one-out sensitivity checks, and a three-indicator country-clustering robustness analysis.

Introduction: Measures of muscle strength and/or torque, limb symmetry (SI) and the ischium/quadriceps ratio (R-I/Q) are widely used in clinical and sports settings. It is crucial to ensure their reproducibility. Objective: To determine reproducibility and responsiveness, using the Dinabang, for aspects of knee flexor-extensor muscle function measured isometrically. Methods: An observational test-retest study with 20 volunteers aged 18 to 59. A Dinabang portable traction dynamometer was used to obtain maximum isometric torque (MIT) measurements. The volunteers were assessed bilaterally for the MITflexors and MITextensors outcomes using the test-retest approach, and the SI and R-I/Q were then calculated. There was a total of one test and two retest measurements, and the assessment procedures were identical for all tests. Relative reproducibility was assessed using the intraclass correlation coefficient (ICC) and absolute reproducibility using the standard error of measurement (SEM). Responsiveness was assessed by the minimum detectable change (MDC). Results: No differences were observed between MIT at different times, neither for MITextensors nor for MITflexors. The SI ICCs of the extensors were moderate and those of the flexors poor; the SEM values indicated high variability (around 85%) and low responsiveness (high MDC values). For R-I/Q, although the ICC was moderate, the SEM was low (with variability of 10.8%) and the responsiveness was acceptable. Conclusions: The MITextensors and R-I/Q measurements showed better reproducibility and responsiveness than those measured by MITflexors and SI, especially for the SI of the flexors

In pharmaceutical manufacturing, terminal high-efficiency particulate air (HEPA) filters are a critical barrier against airborne microbiota, yet maintenance is often performed on fixed schedules. This case study evaluates statistical process control (SPC) rules for low-count airborne Colony Forming Unit (CFU) data from Class C cleanrooms and assesses whether they can support HEPA filter maintenance decisions beyond a time-based program. Using 813 quarterly monitoring records collected over three and a half years from 79 HEPA filters, a distribution-based alert rule was developed by fitting a negative binomial model to discrete, overdispersed counts, combining discrete outlier diagnostics, a 95th-percentile control limit, and the Class C specification limit. The attached documents include the CFU data set, a Demo file data set, the Discrete SPC app and spc_core codes in R. Also a Readme file with the instructions on how to use the R code is included.

Python code that exactly reproduces every quantitative result, table and figure in the associated manuscript, submitted to the International Journal of Production Research (special issue "Innovations in Production Planning: Emerging Problems and Modern Solution Paradigms"). The manuscript extends the Quality Scheduling Index (QSI), a managerial tool from production scheduling, into a Bayesian Quality Scheduling Index (BQSI) for planning recurrent tasks of a household service robot under uncertainty. The code has four parts. Part 1: household-level net present value Monte Carlo of robot acquisition across three price tiers (Table 1). Part 2: value of Bayesian learning, comparing a Thompson-sampling BQSI scheduler against a static allocator over 90 days (Section 3.3). Part 3: sensitivity analysis — Spearman rank correlations, prior-perturbation scenarios and a one-at-a-time tornado analysis (Table 3, Figure 1). Part 4, new in version 2: hierarchical empirical-Bayes fleet-level prior, forgetting factor for drifting task environments, and the trial period valued as expected value of sample information (Table 2, Figure 2). Two identical implementations are provided: bqsi_sim.py (plain Python script) and bqsi_sim.ipynb (Jupyter/Google Colab notebook). All random draws use fixed seeds, so outputs are deterministic. No empirical data are used or included; all results are simulation outputs under the priors documented in the manuscript. Requirements: Python 3 with NumPy; matplotlib only for the two optional figure blocks.

This table includes data on the cybersecurity professional certification industry from the 1970s through 2025. Each of 466 certifications is listed with the certifying organization, year in which the organization was founded, year in which the certification was founded (both earliest and latest possible), year in which the certification retired (if applicable) and total lifetime of the certification, and hyperlinks to current or historical webpages that form the basis for the data shown.

This paper and the accompanying dataset introduce and formally define the Sundiata Post Model (SPM), an institutional model for media-based knowledge production in which an independent newsroom systematically integrates journalism, original research, conceptual innovation, and scholarly dissemination to produce original analytical constructs that contribute to public understanding, academic inquiry, and policy discourse within the global knowledge ecosystem. Developed from the operational experience of Sundiata Post, an independent Nigerian newsroom, and first articulated across a seven-part series in its weekly column, ‘The Sunday Stew’, the Model proposes a Dual Engine Architecture in which a Media Operations Engine and a Knowledge Operations Engine function as mutually reinforcing components of a single institution. Building on this internal architecture, the paper develops the Realm of the Long Term and its Seven Pillars as the conditions for institutional endurance; a four-level framework of institutional success culminating in Legacy; a proposed conceptual vocabulary for twenty-first-century journalism; and a Triple Institutional Architecture spanning media, knowledge, and social-impact functions. The Model is situated within Nigeria's own tradition of the newspaper as intellectual institution, associated with Nnamdi Azikiwe and Obafemi Awolowo, and within a complementary global tradition linking journalism and disciplined inquiry, associated with Walter Lippmann and Hannah Arendt. It is grounded empirically in the newsroom's own production of three original analytical constructs—the Insecurity Triad, the Trinity of Sovereignty Decay (initially developed as Trinity of State Decay), and the Decoupling Sovereignty Index—within a documented ninety-one-day period, offered here as an initial proof of concept rather than a generalisable finding. The paper situates the Model at the intersection of journalism and media studies, organisational theory, and strategic management, and closes with a discussion of its evidentiary limitations and a research agenda for testing, replicating, and adapting the framework across other newsrooms and institutional contexts, particularly within Africa and the wider Global South.

Record-level screening data of a systematic mapping of the 2026 literature on artificial intelligence in Chinese education, companion to the paper "Digital transformation and systemic architecture of Chinese education: trajectory and the AI + Education plan toward 2030" (SAMYRAD 2026, Seville, 5-6 October 2026, IEEE Xplore proceedings). The corpus was retrieved from Scopus on 19 May 2026 with the search string TITLE-ABS-KEY (China AND education AND "artificial intelligence") AND PUBYEAR = 2026 (N = 363 records: 265 articles, 40 conference papers, 24 reviews, 18 book chapters, 16 other document types; 35 in press). Records were screened at title and abstract level against two inclusion criteria (setting in the Chinese education system; at least one pedagogical, attitudinal or competency outcome) and four hierarchically applied exclusion criteria (document type; AI not the object or outside an instructional setting; no primary outcome data; non-Chinese or unspecified setting). Screening was performed in two passes: a first pass by a large language model (Claude, Anthropic) applying the criteria, and an independent verification of every record by one author. Retained records were assigned a primary and, where relevant, a secondary theme against a codebook (acceptance models and cognitive friction; socio-territorial stratification; AI literacy frameworks; disciplinary specialization and local language models; and an open category). Files: search protocol (query, date, export settings, record composition); screening workbook with one row per record (identifiers, title, document type, publication stage, decisions and reasons of both passes, themes, borderline flags), a Summary sheet (counts, percent agreement and Cohen's kappa between passes) and a README sheet with the codebook; a CSV of record identifiers (Scopus ID, EID, DOI); Scopus advanced-search queries that reconstruct the corpus exactly; and the codebook as a separate file. The raw Scopus export (abstracts, author lists, keywords) is not redistributed, in line with Scopus terms of use; every record keeps its Scopus ID, EID and DOI, so full metadata can be retrieved from Scopus or the publisher. Screening decisions, protocol and codebook are released under CC BY 4.0. Bibliographic identifiers and titles are factual metadata of third-party works provided for identification only.

This dataset contains 5,941 pre-processed, leakage-free T1-weighted contrast-enhanced MRI brain images, categorized into four classes: Glioma, Meningioma, Pituitary, and No Tumor. Images were sourced from Epic & CSCR Hospital, Bangladesh, and rebuilt from an earlier release of this dataset through a rigorous cleaning pipeline: removal of exact and near-duplicate images, followed by a stratified 70% training / 15% validation / 15% testing split, with every class preserved in the same proportion across all three splits. A programmatic integrity check confirms zero image overlap between the training, validation, and test sets — eliminating a common source of inflated benchmark results in brain tumor MRI datasets. Images are organized into subfolders by class and split. The dataset is suitable for machine learning and deep learning research in brain tumor classification, tumor detection, and computer-aided diagnosis. **🧪 Applications:** --------------------- - Brain tumor classification - Deep learning (CNN, ViT, transfer learning) - Computer-aided diagnosis (CAD) - Radiology research and teaching - Benchmarking dataset-integrity / leakage-detection methodology The full preprocessing, deduplication, splitting, and leakage-verification pipeline — along with a 7-model baseline benchmark (CNN, ViT, Hybrid CNN–ViT, ResNet50, and DenseNet121, from-scratch/frozen/fine-tuned) — is publicly available and fully reproducible on GitHub: 🔗 **Code**: https://github.com/irfanulkabirhira/A-Bangladeshi-Clinical-Brain-Tumor-MRI-Dataset-for-Four-Class-Deep-Learning-Classification

Sample of dataset used for data analysis and train