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Data protection impact assessment (DPIA) is a tool that can be used to map an organization's readiness in managing personal data. Provisions regarding DPAI are regulated in the GDPR and are also adopted by countries that adhere to the GDPR regulatory model. Under normative provisions, children's personal data is sensitive data that must be protected, it's can be viewed into two point of view, namely as part of sensitive data and stand alone as a subject that must be protected by law. On many assessment models, the methods oftentimes tedious to fill in with the many questions asked. In addition, it also demands to be answered by the managerial level within an organization. This makes the accuracy of the assessment very subjective because it can only be seen at certain levels in the organization. Whereas the impact assessment method must be able to see holistically at every level on the organization. This paper offers a new model of impact assessment by combining the assessment methods of ISO 9001 2015, NIST 800-300 R1, Startup readiness level, and regulations on personal data protection. By merging the models, four approaches were formulated, namely: (1) leadership, (2) process, (3) technology, and (4) regulatory/law. The assessment model offered is made to be simple so that it is easy to fill in, can be filled in by any level of management, is not fatigue to fill out, and can be applied to public institutions and private institutions with different levels of readiness, so that they can be mapped out early on how the conditions for protecting personal data are. in an organization.

Dataset 1 Published Ni isotope data for Jinchuan deposit； Dataset 2 Published Cu isotope data for Jinchuan deposit.

This dataset contains manually annotated flight trajectories recorded in the field for a study comparing birds and multi-rotor drones. It comprises 282 trajectories: 173 bird trajectories and 109 drone trajectories.

This dataset contains refined and pre-processed information derived from the Local Administration License Data (formerly LOCALDATA) portal provided by the Ministry of the Interior and Safety, South Korea. The data was curated specifically to analyze the survival determinants and their interactions of small businesses during 2016-2024.

Digital storytelling (DST) provides an authentic context for developing creative thinking and multimodal expression, yet evidence on generative artificial intelligence (GAI) in K–12 settings remains limited, particularly at the process level. This pretest–posttest randomized controlled study involved 94 seventh-grade students assigned to an AI-supported group (AIG) or a non-AI group (NAIG). The AIG used GAI for planning, text and material generation, and revision, whereas the NAIG used non-AI digital tools. Data included digital stories, creative thinking tests, and screen recordings. ANCOVAs, Mann–Whitney U tests, and lag sequential analysis were conducted. The AIG showed significantly higher overall digital story quality (adjusted M = 22.27 vs. 17.79, partial η² = .195), with advantages in structure, accuracy, completeness, appearance, and innovation, but not interaction. The NAIG showed higher frequencies of writing and multimedia integration, whereas the AIG showed more frequent revising sources. Lag sequential analysis revealed distinctive transitions linking planning, writing, evaluation, and revision in the AIG. The AIG also showed significantly higher overall creative thinking (adjusted M = 9.12 vs. 8.02, partial η² = .110), particularly in text creativity and creative problem solving, but not idea generation. These findings highlight differences in both creative outcomes and process organization under GAI-supported DST.

This dataset provides the technical documentation and data files for a Multi-Regional Social Accounting Matrix (MRSAM) for the United Kingdom, featuring a granular resolution of 12 regions and 42 economic sectors. Developed to support Spatial Computable General Equilibrium (SCGE) modeling specifically the UK UCL Subnational CGE model framework. This MRSAM integrates national input-output tables, national income and product accounts, and regional accounts via a rigorous top-down regionalization approach. The repository serves as a validation and methodological reference for researchers, economists, and policymakers studying regional economic dynamics, industrial decarbonization, and spatial spillovers.

The file includes the full search strategy, Supplementary Table I (patient-level characteristics and outcomes), Supplementary Table II (JBI critical appraisal), and references for the included studies.

This project examines how portrait source, AI-enhancement intensity, and intended use affect perceived bodily animacy, perceived identity continuity, and willingness to use a portrait. Images are organised into four levels: L0 (unedited reference), L1 (mild retouching), L2 (moderate beautification), and L3 (strong AI enhancement). AI-source reference portraits and enhanced images were produced with the Wan 2.7 Pro Image Model. The work combines an online observer study with a personalised portrait study and reports de-identified aggregate ratings. Review Contents — Research Design and Participants 1. Experiment 1 — observer evaluation. A 2 (real-source versus AI-source portrait) × 4 (L0–L3) within-participant design used 12 identities and 48 stimuli. The online Wenjuanxing questionnaire presented 12 randomised comparison trials and 1–7 ratings of bodily animacy and perceived identity continuity. Of 144 complete responses, 103 were retained after the stated completion-time rule was applied. The manuscript does not report Experiment 1 participant demographics, recruitment channel, compensation, or data-collection dates. 2. Experiment 2 — personalised portrait evaluation. Forty-four adult non-professional participants (22 women and 22 men; age 22–55 years) were recruited through workplace and university channels. Each participant evaluated 12 personalised portraits across identification, daily-social, and professional/commercial contexts. The procedure included version ranking, one facial-region selection per context, and 1–5 image ratings, yielding 528 image-level evaluation units and 2,640 analysed item values.

Supplementary methods, figures, and tables

Supplementary materials for Ensemble explainable machine learning links cassiterite provenance analysis to mineral exploration