FoodSafeSum is a machine-actionable dataset for NLP in food safety. It contains human-written and LLM-generated summaries and titles of 2,091 food-safety documents, plus manually curated topics, document types, and automatically extracted hazard annotations. Documents were gathered by SGS Digicomply from news, regulatory/legal sources, guidance portals, and scientific outlets (years 2002–2023; ~58% originally in English; the rest translated and curated). The dataset enables research on classification, retrieval, RAG-style QA, and event clustering in food-safety monitoring and policy. (See Section 3 and Table 7 for schema and fields; Figures 1–3 for source/type statistics. In the paper) What’s included?Manual summary and manual title (by domain experts)LLM summary and LLM title (generated with meta.llama3-70b-instruct via Bedrock)Document type (News, Regulation, Guidance, Scientific) and topic labels (12 high-level categories, e.g., Policies & Laws; Contaminants, residues & contact materials)Hazard annotations auto-extracted from a controlled vocabulary derived from prior workSource name and original titleFor each source item: Note: The full original documents are not included in the public release (used internally for analysis only). Format & schemaPrimary release as CSV/JSON with columns (see Table 7): manual_summary, manual_title, llama70b_summary, llama70b_title, source_name, doc_type, topics, plus hazards (list) and any auxiliary metadata used for experiments.Multilingual inputs were translated (Google Translate/DeepL) and curated; see paper for details.

FoodSafeSum is a machine-actionable dataset for NLP in food safety. It contains human-written and LLM-generated summaries and titles of 2,091 food-safety documents, plus manually curated topics, document types, and automatically extracted hazard annotations. Documents were gathered by SGS Digicomply from news, regulatory/legal sources, guidance portals, and scientific outlets (years 2002–2023; ~58% originally in English; the rest translated and curated). The dataset enables research on classification, retrieval, RAG-style QA, and event clustering in food-safety monitoring and policy. (See Section 3 and Table 7 for schema and fields; Figures 1–3 for source/type statistics. In the paper) What’s included?Manual summary and manual title (by domain experts)LLM summary and LLM title (generated with meta.llama3-70b-instruct via Bedrock)Document type (News, Regulation, Guidance, Scientific) and topic labels (12 high-level categories, e.g., Policies & Laws; Contaminants, residues & contact materials)Hazard annotations auto-extracted from a controlled vocabulary derived from prior workSource name and original titleFor each source item: Note: The full original documents are not included in the public release (used internally for analysis only). Format & schemaPrimary release as CSV/JSON with columns (see Table 7): manual_summary, manual_title, llama70b_summary, llama70b_title, source_name, doc_type, topics, plus hazards (list) and any auxiliary metadata used for experiments.Multilingual inputs were translated (Google Translate/DeepL) and curated; see paper for details.

In 2022 a request to participate in a research interview about people's experiences of using Natural Cycles (a mobile phone application and digital thermometer) as a form of contraception was sent via Natural Cycles to English speaking, Spanish speaking, Swedish speaking and Finnish speaking subscribers of Natural Cycles who had been using Natural Cycles in Prevent mode for more than 6 months. Approx. 300 people responded to this request which resulted in 134 people scheduling and attending an online semi-structured interview. The interviews focus on three core themes - people's reasons for choosing Natural Cycles as a form of contraception and their history of use; people's experiences of using Natural Cycles and reasons they have for trusting or not Natural Cycles as a form of contraception; and people's experiences of sharing Natural Cycles and the data it collects with others (intimate partners, friends, healthcare professionals).The data consists of 134 transcribed interviews in form of .doc files. 108 transcripts are in English, 1 Finnish, 12 Spanish and 13 in Swedish.

In 2022 a request to participate in a research interview about people's experiences of using Natural Cycles (a mobile phone application and digital thermometer) as a form of contraception was sent via Natural Cycles to English speaking, Spanish speaking, Swedish speaking and Finnish speaking subscribers of Natural Cycles who had been using Natural Cycles in Prevent mode for more than 6 months. Approx. 300 people responded to this request which resulted in 134 people scheduling and attending an online semi-structured interview. The interviews focus on three core themes - people's reasons for choosing Natural Cycles as a form of contraception and their history of use; people's experiences of using Natural Cycles and reasons they have for trusting or not Natural Cycles as a form of contraception; and people's experiences of sharing Natural Cycles and the data it collects with others (intimate partners, friends, healthcare professionals).The data consists of 134 transcribed interviews in form of .doc files. 108 transcripts are in English, 1 Finnish, 12 Spanish and 13 in Swedish.

Dataset that contains of all reviewed research items obtained for the Scoping Literature Review Immersive information seeking - a scoping review of information seeking in virtual reality environments.

Dataset that contains of all reviewed research items obtained for the Scoping Literature Review Immersive information seeking - a scoping review of information seeking in virtual reality environments.