Data

This Data Descriptor provides an open, anonymized dataset describing anthropometric and physical fitness outcomes in undergraduate students enrolled in a Physical Activity and Sport Sciences degree program. The dataset included 156 participants (28 females and 128 males) and reported sex, age, body mass, stature, and body mass index, alongside standardized field-based tests covering flexibility, muscular endurance, strength, and jump performance. Hip flexibility was assessed using the Thomas test on both sides. Trunk extensor endurance was measured using the Biering–Sørensen test, and upper-body strength–endurance was assessed using a dead-hang test. Upper limb strength was recorded as elbow flexion strength. Lower limb power was evaluated using vertical jump tests, including Abalakov, squat jump, and countermovement jump, and a derived indicator (IE) was provided to facilitate comparisons across jump modalities. The data are distributed as a machine-readable CSV file accompanied by a detailed data dictionary describing the variables, units, and missingness. The dataset is intended to support the reproducible reporting of normative fitness profiles in sports science students, facilitate teaching and benchmarking in exercise science contexts, and enable secondary analyses exploring associations between anthropometry and physical performance. For reproducible inferential comparisons, users may apply Welch’s two-sample t-test for sex-based differences.

Indoor environmental comfort plays a central role in occupants’ well-being, learning outcomes, and productivity, especially in educational buildings characterized by high occupancy variability and diverse activities. This paper presents a real-world dataset collected at the Cesena Campus of the University of Bologna, aimed at supporting occupant-centric comfort analysis and prediction in classrooms and laboratories. The dataset integrates continuous environmental measurements, such as temperature, humidity, noise, air pressure, and CO₂ concentration, with subjective comfort feedback gathered from students during regular lectures. Data were collected using permanently installed ceiling sensors and additional control sensors placed near occupants, enabling both longitudinal monitoring and validation analyses. Furthermore, the dataset includes both repeated comfort perception reports and a one-time comfort definition phase capturing individual relevance weights for different comfort dimensions. By combining objective and subjective data in realistic academic settings, the dataset provides a valuable resource for developing, benchmarking, and validating data-driven models for smart campus applications, indoor comfort prediction, and human-centered building analytics.

This data descriptor presents two fully synthetic corpora for sentiment analysis and named entity recognition (NER) in Uzbek. The first corpus contains 12,000 hybrid synthetic sentences generated from templates with lexical randomization, automatic insertion of named entities (PER/ORG/LOC), lexicon-based polarity scoring, and a controlled emoji distribution. The second corpus includes 3000 “manual-style” sentences designed to resemble short, naturally structured messages. Although the manual-style subset was initially intended to be emoji-free, the released version includes a 39.6% emoji presence (sentences containing at least one emoji) to maintain comparability in emotional markers across corpora. Both corpora are released in CSV, XLSX, and JSONL formats and share a unified schema (id, text, sentiment, entities, entity_type, polarity_score, polarity_source, token_count, emojis, emoji_position, emoji_sentiment, conflict_flag, sentiment_from_polarity_score, split). The dataset is publicly available via Mendeley Data (DOI: 10.17632/y2d5pcyrzz.3).