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Document Description Title: Independent Expert Review of the Scientific Article “Judges Themselves Became the Object of Research” Author: Microsoft Copilot — AI Companion developed by Microsoft Date of Publication: August 2025 Document Type: Expert Review / Scientific Evaluation Language: Russian and English (bilingual document) Abstract: This document is an independent expert review prepared by Microsoft Copilot AI for the scientific article by Dr. Marat Dzhanybekovich Artykbayev titled “Judges Themselves Became the Object of Research.” The review assesses the article’s scientific soundness, significance, and novelty, confirms its interdisciplinary nature, and recommends it for scholarly discussion and publication in academic journals. Keywords: expert review, scientific article, interdisciplinary research, epistemology, statistics, Copilot, artificial intelligence, scientific novelty, FCA, Foolishness Coefficient with Authority SHA-256 Hash: face6b765965b61b49021ca5e0d40700652e7c46931636904eed032c4a6cc2eb File Format: PDF

The Sunflower Growth Stage Image Dataset for Phenological Classification was collected from agricultural fields in Bangladesh, focusing on the identification and classification of sunflower growth stages. Images were captured directly in the field using a Redmi Note 11 smartphone, under natural daylight and varying weather conditions to reflect real-world environments. This dataset is meant to aid research in deep learning, computer vision, and plant phenology by providing data for automated classification of growth stages. A total of 1,255 original images were gathered, each with a high resolution of 12,288 × 16,320 pixels and approximately 25 MB in size. The images are divided into five classes: Stage1 (Young_Bud) with 238 images, Stage2 (Mature_Bud) with 272 images, Stage3 (Early_Bloom) with 218 images, Stage4 (Full_Bloom) with 213 images, and Stage5 (Wilted) with 314 images. To balance the dataset for training, each class was augmented to have 500 images, resulting in a final balanced collection of 2,500 images. Validation of the dataset was carried out by a Sub-Assistant Agriculture Officer from the Department of Agricultural Extension (DAE), Bangladesh, ensuring its reliability. The data was collected at two main sites: Daffodil International University (Ashulia Campus) and Model Town Nursery, Ashulia, Bangladesh. The camera used for capturing the images was a Redmi Note 11, with 24-bit color depth, an aperture of f/1.8, and images saved in JPEG format. Example metadata for an image shows it was taken on 2025-05-22 at 17:47 using the MediaTek Camera Application. The image’s dimensions are 12,288 × 16,320 pixels at 72 dpi with 24-bit sRGB color representation. The camera details include Xiaomi as the maker, model 23117RA86G, f-stop f/1.6, exposure time 1/100 sec, ISO 200, focal length 6 mm, and auto white balance. GPS coordinates recorded were Latitude 23.5247046, Longitude 90.1918097, Altitude 34.5 m. The image file example is named IMG_20250522_174724.jpg, is a JPEG of size 26.1 MB.

Individual and Biomedical Determinants of Cognitive Impairment in Institutionalised Geriatric

This dataset contains numerical and machine learning data generated during the study “Fire Resistance of Steel Beams with Intumescent Coating Exposed to Fire Using ANSYS and Machine Learning” (Buildings 2025, 15(13), 2334; https://doi.org/10.3390/buildings15132334). The work investigates the fire resistance of structural steel IPE beams protected with water-based intumescent coating (IC) and subjected to ISO 834 standard fire. Numerical simulations were performed in ANSYS 16.0, using sequentially coupled thermal–structural analyses with temperature-dependent material properties. A parametric study was conducted for the entire range of standard IPE profiles (80–600), three IC thicknesses (0.4, 0.8, and 1.2 mm), three utilisation levels (0.5, 0.6, 0.7), and three beam lengths proportional to section depth. The dataset also contains the input and output files used to train artificial neural network (ANN) models in MATLAB R2020b for predicting fire resistance time. The ANN input parameters include detailed beam geometry, IC thickness, section factor, thermal conductivity values at five temperatures, and loading factor. Output is the predicted fire resistance time until structural failure according to deflection and deflection rate criteria. Contents: Excel spreadsheets – Tabulated results from 486 finite element simulations, including input parameters, geometrical and mechanical properties of steel and IC (temperature-dependent), temperature evolution, mid-span deflections and deflection rates, stresses and strains during fire and fire resistance times, input and output data for ANN training before processing, processed and after normalisation. Figures – Graphical inputs of temperature-dependent thermal and mechanical properties of steel S355, and graphical results of mid-span deflections and deflection rates in time, showing parametric effects of IC thickness, beam length, utilisation, and section factor on fire resistance. File formats: .xlsx – Microsoft Excel Worksheet .xlsm – Microsoft Excel Macro-Enabled Worksheet .tif / .jpg – Figures and diagrams .txt – File descriptions Usage notes: All Excel files contain both raw and processed numerical outputs; cells cannot be modified, formulas are hidden, content can be copied. Figures are provided in publication resolution and can be reused under the CC BY 4.0 license. Dataset is organized into two subfolders: 01_EXCEL_FILES – Contains Excel files with tabulated data, calculations, and results relevant to the study. Each file in this folder is listed and briefly described in the accompanying Excel_Files_Description.txt. 02_FIGURES – Contains image files (figures) illustrating key results, and diagrams. Each figure is listed and described in the Figures_Description.txt file located in the same folder. Both Excel_Files_Description.txt and Figures_Description.txt provide file names and concise summaries of their content for easier navigation and reference.

Data was extracted in PDF format with personal information redacted to ensure privacy. The raw dataset consisted of 260 cases from three chambers within a single German social law court. The data originates from a single judge, who typically oversees five to six chambers, meaning that this dataset represents only a subset of the judge’s total caseload. Optical Character Recognition (OCR) was used to extract the document text, which was organized into an event log according to the tabular structure of the documents. In the dataset, a single timestamp is recorded for each activity, commonly indicating only the date of occurrence rather than a precise timestamp. This limits the granularity of time-based analyses and the accuracy of calculated activity durations. As the analysis focuses on the overall durations of cases, which typically range from multiple months to years, the impact of the timestamp imprecisions was negligible in our use case. After extraction, the event log was further processed in consultation with domain experts to ensure anonymity, remove noise, and raise it to an abstraction level appropriate for analysis. All remaining personal identifiers, such as expert witness names, were removed from the log to ensure anonymity. Additionally, timestamps were systematically perturbed to further enhance data privacy. Originally, the event log contained 22,664 recorded events and 290 unique activities. Activities that were extremely rare (i.e., occurring fewer than 30 times) were excluded to focus on frequently observed procedural steps. Furthermore, the domain experts reviewed the list of unique activity labels, based on which similar activities were merged, and terminology was standardized across cases. The refinement of the activity labels reduced the number of unique activities to 59. Finally, duplicate events were removed. These steps collectively reduced the dataset to 19,947 events. The final anonymized and processed dataset includes 260 cases, 19,947 events from three chambers and 59 unique activities.

Social media behaviour, individual differences, and their impact on lifes

This study examines how organizational characteristics of companies producing Artificial Intelligence (AI) technologies influence public trust through a vignette-based experimental design. Building on prior frameworks of trust, we focus on five sub-dimensions of trust: Benevolence, Standards and Guidelines, Data Quality, Reliability, and Transparency, each with three different levels. Results indicate that Transparency and Benevolence are the most significant drivers of trust. Organizations that provide clear explanations of their AI technologies and demonstrate societal accountability by seeking and incorporating public feedback are viewed more favorably. Adherence to external standards, such as national or international guidelines, further enhances trust, while technical performance and data quality are less influential, as participants assume the technology is functioning adequately for their limited use. We conclude that transparent practices, societal engagement, and institutional collaboration will foster public confidence in companies producing AI technologies.

In the tests, the strains of rebars in specimens WU-1d, WU-3d, WU-28d and WNC were monitored by strain gauges. Here, the specific data are provided.

Ovarian cancer lacks effective screening methods, often resulting in late diagnosis and poor outcomes. Recognizing that high-grade serous ovarian carcinoma (HGSC) is driven by copy number alterations (CNAs) and that tumor DNA can be detected in cervical samples, we analyzed CNAs from shallow whole genome sequencing of 212 cervical samples from 128 women with/without HGSC, including 29 germline BRCA1/2 mutation carriers. Using the machine-learning classifier SRIQ, we developed HCsig, a predictor for HGSC detection. HCsig correctly identified HGSC in 79% of archival cervical samples, including 91% stage I-II (0-27 months before diagnosis), and 77% stage III-IV (0-65 months before diagnosis). Validation in 172 independent samples (0-98 months) showed 76% sensitivity and 94% specificity (AUC=0.83), including high sensitivity for early-stage cancers. We show that applying the HCsig classifier to pre-diagnostic cervical samples, including from non-symptomatic women several years before diagnosis, is feasible and holds promise for early-stage detection of HGSC. Data description: Folders contain shallow whole genome sequencing processed data: 1. Segmented files (50kb_segmentation) 2. Copy number files (50kb_copynumbers) 3. Absolute Copy number files (50kb_Rascal_absolute_CN). 4. CN features compiled file (50kb_CN_features_matrix.txt) 5. Metadata file (MaNiLaMasterFile_250130.xlsx)

Raw data for HIV-oral microbiome study.