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Cross-sectional data about the job demands and resources, work capabilities and burnout of Industrial Psychology and human resource management practitioners in South Africa.

The replication dataset (data.dta) contains an unbalanced panel of Chinese listed firms from 2009 to 2021 and includes all variables required to construct the climate risk perception index, the three perception-bias measures, firm characteristics, carbon-risk indicators, and climate-sensitivity classifications. The dataset allows full replication of all tables and figures in “Risk-Shaped Cognition: How Climate-Risk Bias Influences Corporate M&A Decisions”, including baseline results, robustness checks, and heterogeneity analyses. All variables are derived from publicly available sources or standard databases, and detailed construction steps are provided in the Supplementary Material.

Information of antibiotic resistance and transmissible elements genes

Genus-level species composition analysis of the ZS sample.

PCoA analysis based on Bray Curtis distance matrix algorithm of different seasons(A) and different years(B)

Note:HM: Hami region; ZS:Zhaosu region; AS:Akesu region; XY:Xinyuan region; GL:Gongliu region; YN:Yining region

Construct Reliability and Validity

The dataset encompasses systematically extracted biomarker, clinical, and methodological data from 28 studies (2016-2025) evaluating exosomes and extracellular vesicles (EVs) across dermatologic and systemic inflammatory diseases, including melanoma, psoriasis, psoriatic arthritis, vitiligo, pemphigus vulgaris, oral lichen planus, chronic spontaneous urticaria, dermatomyositis/polymyositis, Behçet’s disease, atopic dermatitis, systemic lupus erythematosus, rheumatoid arthritis, and cutaneous squamous cell carcinoma. It integrates demographic characteristics, EV sources (plasma, serum, keratinocytes, fibroblasts), isolation and characterization techniques (ultracentrifugation, size-exclusion chromatography, polymer precipitation, microfluidics), and molecular cargo profiles spanning proteins, miRNAs, mRNAs, lipids, and circRNAs. Quantitative outputs include EV particle sizes, concentrations, canonical marker expression (CD9, CD63, CD81, TSG101), and diagnostic performance metrics such as ROC AUC, sensitivity, and specificity. The dataset also incorporates mechanistic insights (e.g., PD-L1/FasL–mediated immune evasion in melanoma, miR-625-3p correlating with PASI in psoriasis, miR-1469–NK cell axis in vitiligo, Plexin D1 in myositis) and treatment-response modulation following biologic or immunotherapeutic interventions. Additionally, it includes full MINORS methodological quality assessments for comparative and non-comparative studies, PRISMA screening data, and PICO-defined outcome structures, collectively providing a comprehensive, standardized resource supporting evidence synthesis and translational research on EV-based diagnostic and prognostic biomarkers.

This dataset was developed to support research on object detection and recognition, focusing on items forgotten inside vehicles. It captures a diverse range of real-world scenarios under different lighting conditions, both indoors and outdoors, to ensure robustness and applicability in various analytical tasks. The collection contains 971 high-quality images featuring everyday objects such as: 0 - smartphone, 1 - laptop, 2 - card, 3 - suitcase, 4 - wallet, 5 - backpack, 6 - clothing, 7 - keys, 8 - glasses, 9 - handbag. The dataset is organized into two main directories (inside leftincar-data.zip): ➔ images/ – contains all visual samples. ➔ labels/ – includes YOLO-format annotation files (.txt), one per image. Images without annotations correspond to negative samples (no objects present). An additional Python script, yolo_dataset_splitter.py, is provided to automate the division of the dataset into training, validation, and testing subsets. The script ensures that all images are included in the output, creating empty label files where necessary for full YOLO compatibility.

Supplementary Table of Manuscript