Lopez, Annalaura · Greco, Margherita · Marcolli, Beatrice · et al.

4 files · 17 KB · docx, xlsx

This dataset originates from a study aiming to valorise Ciuta sheep, a local breed native from the Italian Central Alps, through the characterization of nutritional quality and chemical composition of fresh meat (loins) and one traditional dry-cured product. Specifically, the research focused on determining the chemical composition of Ciuta sheep meat and on identifying key changes in its chemical profile during dry curing process, hypothesizing that such chemical fingerprint may suggest some markers linked to the production system, geographical origin, and traditional processing techniques. For this reason, for bthe dry-cured product, both an aliquot of fresh meat before and after transformation and dry-curing was sampled and analysed. Regarding loins, three commercial categories (lambs, hoggets and mutton) were considered, in order to define any possible difference induced by age of the sheep (and physiological factors, such as rumen development). The dataset includes chemical data regarding the proximate composition (moisture, protein, fat, ash, salt content for the dry-cured product) and energy content of fresh and dry-cured meat; the fatty acids content of fresh and dry-cured meat product; the volatile profile of fresh and dry-cured meat product. Results from analysis performed in our study suggested that the development of high-quality dry-cured products could provide a strategy to valorise Ciuta sheep meat, especially from adult animals (culled ewes and rams), while fresh meat production could focus on lambs. The complex volatile profile detected was influenced by both the farming system and traditional processing methods.

Budzinski, Lisa · Beenken, Anne Elisabeth · Sempert, Toni · et al.

9 rows × 1 cols · 743 B · csv, docx, zip

1 categorical

We have investigated an IgG4-RD (IgG4-RD) cohort by our multi-parameter microbiota flow cytometry approach to characterise the microbiota on single-cell level for attributes of the disease. The microbiota is isolated from stool samples and stained according to the published protocol for (a) host immunoglobulins IgA1, IgA2, IgM, IgG and (b) agglutinin binding to mannose, galactose or N-Acetyl-glucosamine surface sugar moieties. For all samples we also determined the microbiome composition by 16S rRNA (V3-V4) sequencing on the illumina MiSeq platform. We provide the raw .fcs and FASTQ files of 40 IgG4-RD patients. For comparison we additionally analysed 36 healthy donors. All .fcs files were generated on BD Influx®. The metadata is collected in the provided meta.csv. The staining parameters are summarized in provided panel.csv.

Li, Zhiyao · Wang, Ningbo · Zhong, Jiahao

6 files · 48 MB · docx, zip

GIFT-BDS is a regional ionospheric total electron content (TEC) and TEC-gradient dataset over China derived from BeiDou geostationary Earth orbit (GEO) observations and a dense ground-based GNSS receiver network. The dataset is designed to provide high-resolution observations of ionospheric TEC variability and horizontal TEC-gradient structures over China and adjacent regions. The versioned release covers the period from 19 July 2024 to 31 December 2025, corresponding to DOY 201 of 2024 to DOY 365 of 2025. The geographical coverage is 15°N-50°N and 95°E-135°E. The dataset is provided in daily NetCDF files and contains two product levels. Level-1 products provide observation-level GEO-derived slant TEC (STEC) and rate of TEC index (ROTI) records for individual receiver-GEO satellite lines of sight, with a temporal resolution of 30 s. Level-2 products provide gridded regional TEC and TEC-gradient variables, including VTEC, VTEC t , ROTI, GIX, GIX std , GIX x , GIX y , GIX t,x , and GIX t,y , with a temporal resolution of 15 min. IPP-based variables are provided on a 1° × 1° grid, while inter-IPP-gradient variables are provided on a 0.25° × 0.25° grid. The main processing steps include observation screening, cycle-slip and data-gap detection, continuous-arc segmentation, carrier-to-code leveling, satellite and receiver DCB correction, IPP calculation, inter-IPP pair selection, gradient estimation, and gridding. Quality control is applied before release. Missing values may occur because of station outages, data gaps, quality-control exclusions, or insufficient valid samples within a grid cell. Users should check the NetCDF variable attributes, including units and fill values, before analysis. The dataset is suitable for regional ionospheric studies, TEC-gradient monitoring, space-weather-related analyses, and investigations of ionospheric effects on GNSS positioning applications.

Jungmyoung, Son · Sihoon, Lee · Jiyeon, Hong

3 files · 20 KB · docx, xlsx

This dataset provides the complete double-coding matrix, PRISMA 2020 checklist, and search strategy supporting the systematic review "Representation-to-AI Transformation in K-12 Generative AI Learning: A Theory-Building Systematic Review of Semantic Transformation Mechanisms." It includes: (1) study-level tier classification (Core/Supporting/Context) for two independent coders and consensus tier for all 18 included studies; (2) the full semantic transformation unit (STU) coding matrix (18 studies x 10 STUs = 180 cells) with pre-consensus and consensus scores; (3)evidence-weighting consensus scores; (4) inter-rater reliability statistics (Cohen's kappa); (5) the completed PRISMA 2020 checklist; and (6) the full database-specific Boolean search strategy.

guo, na · pan, jiachen · li, tiantian · et al.

14 files · 100 MB · csv, rar, tsv

ADM_LSIR is a physics-inspired laparoscopic aerosol degradation dataset for aerosol-aware surgical image analysis and image restoration. The v1.0.0 release contains: - 21,916 clean clinical laparoscopic frames (clean/) - 9,562 real intraoperative aerosol-degraded frames (degraded/) - 36,052 simulated aerosol masks, including 19,701 smoke-like masks and 16,351 trajectory masks (mask/) - Blender simulation/cache materials (ADM_LSIR_Blender_simulation_files_v1.0.rar) - metadata_quality_report_v1.0.csv - recommended_splits_v1.0.csv - video_mapping_v1.0.csv - parts_manifest.txt - checksums_v1.0.tsv - release_manifest_v1.0.json All released clinical frames are de-identified and stored as lossless PNG files. Filenames use anonymized video identifiers, e.g., C-V##-####.png for clean frames and D-V##-####.png for degraded frames. The recommended split is defined at the source_video_id/public_video_label level to reduce leakage across frames from the same source video. The public video labels in video_mapping_v1.0.csv provide privacy-safe source-video identifiers (video1-video19). The Blender archive documents the smoke and trajectory mask simulation setup and supports reuse, but it is not a guaranteed exact per-mask reproduction package. The released pre-rendered mask library is the primary reusable dataset component. Source code for synthesis and quality screening is available at: https://github.com/SweetDeathh/ADM_LSIR

Xu, Tao

3 files · 100 MB · rar, xlsx

The deepmd_data dataset comprises energy data for 375,781 structures and force data for more than 45 million atoms, generated from six hierarchical active learning iterations. The init directory stores the non-periodic molecular configurations established at the initialization stage. The iter directories contain structures labeled by both AIMD and DPMD-FP methods for each iteration. Each folder is named according to the scheme of solvent molecule index followed by lithium salt designation. The finetuned_data folder contains SEI reaction simulation data used for fine-tuning the MLFFs. Comprehensive details regarding the solvent molecules are available in the solvent_with_smiles.xlsx file.

Yuezong Wang · Yu Niu · Jiqiang Chen

2 files · 100 MB · rar, zip

This dataset contains raw experimental images, video sequences of real test samples, simulated microscopic image data from the manuscript " A robust method for microscopic 3D shape restoration via shape-from-focus" , as well as focal volume datasets collected before vibration simulation, after vibration simulation, and post anti-vibration processing. All provided data enable the validation of conclusions and reproducibility of experimental results obtained via the computational pipeline proposed in this paper.

Kovaliov, Michael

1 files · 100 MB · parquet

Madhusudan, Gujral

6 files · 29 MB · parquetdeclared

Large language models (LLMs) are trained on massive, publicly available text datasets comprising trillions of tokens, enabling them to excel at general language tasks like next-token prediction. However, LLMs often struggle with domain-specific prompts, exhibiting reduced accuracy or generating inaccurate information (hallucinations). This is because they lack sufficient subject matter expertise. Two primary approaches exist to address this limitation for augmenting LLMs knowledge: Retrieval-Augmented Generation (RAG) and fine-tuning. This presentation focuses on fine-tuning smaller LLMs with domain-specific instruct datasets using the LoRA (Low-Rank Adaptation) technique on Gaudi hardware. We will leverage publicly available LLMs and datasets from the Hugging Face Hub for this demonstration. Though it is possible to fine tune LLMs with plain text data - sourced from documents, articles, and other materials.

Azziz, Ricardo

1 files · 1.6 MB · docxdeclared

Supplemental Table 1. Meeting Agenda of the 2024 PCOS Challenge-CDC Stakeholder Meeting on Testosterone Reference Interval Standardization. August 26, 2024. Centers for Disease Control and Prevention, Atlanta, Georgia. Supplemental Table 2. Proposed Four-Stage Workflow for Developing Standardized Testosterone Reference Intervals Using Existing Study Data.

Austin, Timothy · do Valle Chagas Azaneu, Marina · Roughan, Moninya

26 files · 29 MB · netcdf, pdf, pngdeclared

Data collected from a temperature mooring at Lord Howe Island maintained by UNSW Sydney and funded by Parks Australia. The mooring position is longitude = 158.97°E and latitude = -31.51°, and local depth of approximately 52 m. The data were sampled using a series of thermistors (aqualogger 520PTs) deployed on a mooring line at 4m intervals through the water column, with shallowest instrument at 13 m and deepest at 53 m. The time period spans between 14-05-2025 and 22-04-2026. IMOS standard data quality assurance and quality control processes have been followed and the data formatted following IMOS conventions. Data quality control includes automated routines and visual inspection (expert QC) and flagging of obvious errors. File are c.f. compliant NetCDF files, and file name format follows IMOS conventions and includes sampling period in the format: UNSW_Lord_Howe_Marine_Park_TZ_ yyyymmddThhmmss Z_LH050_FV01_ LH050-2511-Aqualogger-AQUAlogger-520PT16-max160m-13_END- yyyymmddThhmmssZ.

Wang, Chang · Lu, Xingcheng

2 files · 222 MB · netcdfdeclared

Machine-learning-inferred monthly anthropogenic NOx emission over the 2026 Strait-of-Hormuz disruption (global, 0.1 degree, January 2025 - May 2026). This dataset is the top-down NOx emission product underlying the companion manuscript on the 2026 Strait-of-Hormuz shipping-emission collapse. A LightGBM estimator trained on the CAMS-GLOB-ANT v6.2 inventory (2018-2024), with the observed TROPOMI NO2 column and GEOS-CF chemistry/meteorology as predictors, is applied month by month to 2025-01 through 2026-05 to infer the anthropogenic NOx emission flux. Provided as a single self-describing CF-1.8 NetCDF containing: the total anthropogenic NOx flux (kg m-2 s-1, reported as NO) and a per-pixel cross-validation uncertainty (log-space). The estimator resolves the total emission only; no sector decomposition is distributed, because over open ocean the total is essentially ship emission while on land a sector split would only re-apply the CAMS-GLOB-ANT prior shares and is not constrained by the observations. Coverage is land and ocean within +/-60 degrees latitude on a regular 0.1-degree global grid. All units and coordinates are embedded in the file.

Daugavietis, Jānis

6 files · 3.3 GB · jpeg, rardeclared

Skala Bünte, Süki Vagonu Hallē [2026.07.08.] Koncerta beigu telefona foto/ video. SKALA BÜNTE X SÜKI FACE2FACE https://fb.me/e/7dycfN2gI Details Event by John Dow Vagonu Hall Public · Anyone on or off Facebook 8TH OF JULY VAGONU HALLE TWO BANDS TWO BACKLINES MOSHPIT IN THE MIDDLE ONCE IN A LIFETIME FACE2FACE MASSACRE PROVIDED BY SKALA BÜNTE & SÜKI 🔪🔪🔪 DOORS 19:00 7€

García Mina, Janeth Elizabeth · Rodríguez Garófalo, Napoleón Hernán · Rumbaut-Rangel, Dayron · et al.

6 files · 737 KB · docx, pdfdeclared

El objetivo de este estudio es evaluar la importancia de la inteligencia artificial (IA) en el proceso de formación académica de estudiantes de educación básica, específicamente en la enseñanza de ciencias naturales. La investigación destaca cómo la IA puede personalizar el aprendizaje, adaptándose a los estilos individuales de cada alumno, lo que resulta en una experiencia educativa más efectiva y significativa. Se utilizó un diseño cuasiexperimental con una muestra de 60 estudiantes de sexto año, distribuidos en un grupo control (30 estudiantes) que recibió instrucción tradicional y un grupo experimental (30 estudiantes) que utilizó herramientas de IA, como chatbots educativos y plataformas de aprendizaje adaptativo. Los resultados mostraron que el 70% de los estudiantes del grupo experimental alcanzaron calificaciones superiores a 8, en comparación con solo el 30% del grupo control. Este hallazgo sugiere que la implementación de herramientas de IA puede transformar la educación básica, mejorando tanto el rendimiento académico como la motivación de los estudiantes. En conclusión, el estudio resalta la necesidad de seguir investigando y desarrollando nuevas estrategias educativas que integren la IA para maximizar su potencial en la educación.

Makhado, Langanani Christinah · Raliphaswa, Ndidzulafhi Selina

9 files · 157 KB · docxdeclared

This dataset contains anonymized transcripts derived from face-to-face interviews conducted with pregnant women and breastfeeding mothers as part of the research study. The interviews were audio-recorded and transcribed verbatim to capture participants' experiences, perceptions, and insights related to the study topic. All personally identifiable information has been removed to protect participant confidentiality. The transcripts are provided to support transparency, reproducibility, and further research related to the findings reported in the associated article

Fontelo, Paul

1 files · 26 KB · docxdeclared

Auto-Brewery Syndrome (ABS), also known as gut fermentation syndrome, is a condition in which microbial fermentation of dietary carbohydrates generates endogenous ethanol, causing signs and symptoms of alcohol intoxication without alcohol consumption. Misdiagnosis as alcohol use disorder (AUD) is common and carries severe medical, psychosocial, legal, and forensic consequences. The objective of thus narrative review is to synthesize the clinical literature on ABS for practicing clinicians and to present the first systematic analysis of ABS susceptibility in Asian populations - including communities of Asian descent in Western countries.

Hackl, Jürgen

6 files · 132 MB · parquet, tiffdeclared

EuroFlood is an open, cloud-native index over the JRC/Copernicus CEMS-EFAS Satellite-Derived Flood Depth Maps for Europe (Betterle & Salamon, 2025; CC-BY-4.0) - ~3,280 satellite-derived observed flood-depth maps across Europe, 2015-2024. The bundle is a sparse Cloud-Optimized GeoTIFF encoding, per pixel, the set of flood events that inundated it, plus a combo_id -sorted GeoParquet dictionary and a small events table. Query by region and time via HTTP range reads (GDAL /vsicurl + DuckDB) to retrieve matching events, then fetch only the source depth rasters needed. Built with the open-source EuroFlood Python package ( pip install euroflood ).

Petitpierre, Remi · di Lenardo, Isabella · Hudson, Polly · et al.

4 files · 1.4 GB · geopackage, zipdeclared

The dataset provides a large-scale vector layer of historical building footprints for the Greater London area at the end of the 19th century. It comprises 1,299,029 individual building footprints, extracted automatically from the Ordnance Survey five-feet-to-the-mile (1:1,056) map. The original maps were surveyed between 1891 and 1895 and published between 1893 and 1896, covering approximately 450 km² of urban and suburban London. The dataset was produced using a deep learning-based semantic segmentation pipeline, achieving approximately 97% precision and 95% recall in building detection. Source The source maps were digitised by the National Library of Scotland at 400 ppi and manually georeferenced in collaboration with the David Rumsey Map Collection. The extraction relies on 753 georeferenced map sheets, provided that six sheets are missing from the original archive. Extraction methodology The dataset was generated through an automated workflow: 262 image patches were annotated manually with three classes: (i) regular buildings; (ii) compound buildings; (iii) building boundaries A Mask2Former [1] semantic segmentation model is used. For training and inference, we followed the approach described by [2]. At inference time, boundary predictions are supplemented by the predictions of a specialist model, trained on cadastral plans [3]. Polygon geometries are extracted, based on predicted building contours, and vectorised. Compound buildings components are merged. Content Two versions of the dataset are provided: london_buildings_1891-96_raw.gpkg Raw output from the automated extraction pipeline (after vectorisation and merging) london_buildings_1891-96_corr_v1.gpkg Minimally corrected version including manual adjustments for major structures (e.g. railway stations, monuments, large buildings) Each geometry has a field buil_class , taking values in ['regular','compound'] , relative to the cartographic representation of building classes and the associated processing approach. In corr_v1, manually added geometries take the value NULL . In addition, we relase the labeled images used for training the segmentation model: annotations.zip ZIP folders containing the training and validation data The archive contains subfolders images , containing the tif image samples and labels , corresponding to the semantic class indexes, in png format. labelmap.txt details the class index codes. Data format Format: Geopackage Coordinate reference system: WGS 84 / Pseudo-Mercator (EPSG:3857) Temporal coverage Survey period: 1891-1895 Publication period: 1893-1896 Data creation: 2024-2026 Descriptive statistics Number of building footprints: 1,300,831 (raw), 1,299,040 (corr_v1) Coverage area: 450 km² Detection performance (raw): Precision: 97% Recall: 95% Use and reuse potential This dataset supports research in: urban history historical GIS urban morphology economic and social history It is particularly suited for studying long-term urban change and fine-grained spatial patterns in industrial-era cities. Related publication This dataset is described in a data paper submitted to the Journal of Open Humanities Data Corresponding author Remi Petitpierre Email: remi.petitpierre@epfl.ch Funding The research was supported by the College of Humanities at EPFL and the European Union Horizon Europe Programme (Grant No. 101233051). License This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Citation If you use this dataset, please cite: @misc{london_footprints_petitpierre_2026, author = {Petitpierre, R{\'{e}}mi and di Lenardo, Isabella and Hudson, Polly and Herold Hendrik and McDonough, Katherine and Hecht, Robert and Vaienti, Beatrice and Fleet, Christopher}, title = {{A Layer of Late Victorian London: The Building Footprints from the 1:1,056 Ordnance Survey Map (1891-1896)}}, year = {2026}, publisher = {EPFL}, url = {https://doi.org/10.5281/zenodo.19497434}} Limitations Lower accuracy for very small buildings Occasional segmentation errors in dense or complex areas Minor inconsistencies near map sheet boundaries Liability The authors assume no liability for the use of this dataset. References Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., & Girdhar, R. (2022). Masked-attention Mask Transformer for Universal Image Segmentation . arXiv. https://doi.org/10.48550/arXiv.2112.01527 Petitpierre R. (2026) Generalizable Multiscale Segmentation of Heterogeneous Map Collections . arXiv. https://doi.org/10.48550/arXiv.2603.05037 Petitpierre, R., di Lenardo, I., & Rappo, L. (2024). Revealing the Structure of Land Ownership through the Automatic Vectorisation of Swiss Cadastral Plans . Digital History Switzerland. https://doi.org/10.13140/RG.2.2.26632.33281

Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services

2 files · 1.5 MB · docx, pdfdeclared

This is the report on the indigenous and local knowledge (ILK) dialogue workshop for the first order draft of the summary for policymakers and the second order draft of the IPBES assessment of the sustainable use of wild species (the "sustainable use assessment"). It was held from 17-21 May 2021, online, due to the ongoing COVID-19 pandemic. The report aims to provide a written record of the dialogue workshop, which can be used by assessment authors to inform their work on the sustainable use assessment, and also by all dialogue participants who may wish to review and contribute to the work of the assessment moving forward. The report is not intended to be comprehensive or give final resolution to the many interesting discussions and debates that took place during the workshop. Instead, it is intended as a written record of the discussions, and this conversation will continue to evolve in the course of the assessment process. For this reason, clear points of agreement are discussed, but diverging views among participants are also presented for further attention and discussion.