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Zahid, R · Lázaro, A · Moreno‐Alcántar, G · et al.
hybrid · semantic + lexical · 78 datasets ranked · 0.85s
Zahid, R · Lázaro, A · Moreno‐Alcántar, G · et al.
1 files · 82 KB · tar
Synthetic nanozymes have emerged as promising alternatives to natural enzymes for catalytic and therapeutic applications, yet their limited stability, aqueous compatibility, and catalytic scope impede broader utilization. Here, we report a mild, one-step sol-gel synthesis that yields ultrasmall, water-stable octa-amino silsesquioxanes functioning as metal-free nanozymes. These minimalistic nanostructures exhibit aldolase-like organocatalytic activity in water and enable dynamic, stimuli-responsive modulation of catalysis through reversible supramolecular aggregation and disaggregation triggered by specific chemical inputs, thus forming a multifunctional platform for tunable catalysis and biomedical applications. Structural simplicity, stability, and functional versatility together permit tunable, enzyme-like catalysis in water without auxiliary surfactants or phase-transfer additives. Furthermore, the nanozymes display high biocompatibility and efficient cellular internalization, enabling their use in living cells, for instance, as intracellular prodrug activators via retro-aldol activation of a doxorubicin prodrug in human glioblastoma and metastatic melanoma cells, resulting in selective cytotoxicity. This system provides a cost-effective, sustainable, and scalable platform for water-compatible, metal-free organocatalysis that bridges abiotic catalysis and biological function. These findings demonstrate how rationally designed silsesquioxane frameworks can emulate natural enzyme reactivity while integrating adaptive, stimuli-responsive behavior, broadening the applicability of synthetic nanozymes to catalytic and therapeutic contexts.
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.
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€
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 ).
Modiri, Ehsan · Shrestha, Pallav Kumar · Samaniego Eguiguren, Luis Eduardo
100 files · 49 GB · netcdf, tardeclared
Historical Hydrological Simulations over the South African Domain (1990-2024) The mHM's simulations of the Planet4Health project This dataset contains historical hydrological simulations for the South African domain (domain 1020011530) conducted with the Mesoscale Hydrological Model (mHM) at a spatial resolution of 0.015625°. The simulation period spans 1990-2024 and was part of the Planet4Health (P4H) project, utilising the ERA5 meteorological forcing. This archive is prepared for DOI assignment and ensures long-term reproducibility. It includes relevant clipped NetCDF components for soil moisture layers 4, 5 and 6, consistent with the infrastructure provided within the Helmholtz Centre for Environmental Research (UFZ). The simulations were executed using a specific version of the mHM model with the SCC method for gauges, paired with the mRMv1.0 routing configuration. 🛰️ Simulation Details Model: Mesoscale Hydrological Model (mHM) Codebase: scc_for_gauges branch (https://git.ufz.de/shresthp/mhm/-/tree/scc_for_gauges?ref_type=heads) Spatial resolution: 0.015625° Temporal resolution: Daily Simulation period: 1990-2024 Simulation type: Historical simulation Spin-up: 30-year spin-up using 1990-2019 ERA5 climatology Model version: v1.0 Setup Scope: Model run for domain 1020011530, post-processed and clipped. Configuration & Modules The configuration utilises standard structural components with the SCC methodology. Modules included: Snow processes: Degree-day method Soil moisture: Feddes equation for evapotranspiration reduction Infiltration: Multi-layer Brooks-Corey-like approach Direct runoff: Linear reservoir exceedance method Potential evapotranspiration: Hargreaves-Samani method Interflow: Storage reservoir with nonlinear outflow Groundwater: Linear reservoir Routing: Adaptive time-step routing with mRMv1.0 mechanisms 📥 Input Datasets Meteorological Forcing: ERA5 (Hersbach et al., 2020) at a native input meteorological resolution of 0.25°, dynamically downscaled/mapped to model requirements. Processing Infrastructure: Tracked, processed, and validated under the Planet4Health deployment pipeline (https://git.ufz.de/planet4health/mhm_production/-/tree/main/postproc?ref_type=heads). Data Interfaces: Climate Data Interface version 2.2.4 (CDI) | Climate Data Operators version 2.2.2 (CDO) | NetCDF Operators version 5.1.7 (NCO). 📤 Output Variables sm_l04: Volumetric soil moisture layer 4 (300-500 mm depth) [mm mm-1, fraction between 0 and 1] sm_l05: Volumetric soil moisture layer 5 (500-1000 mm depth) [mm mm-1, fraction between 0 and 1] sm_l06: Volumetric soil moisture layer 6 (1000-2000 mm depth) [mm mm-1, fraction between 0 and 1] 📫 Contact Ehsan Modiri - ehsan.modiri@ufz.de Pallav Kumar Shrestha - pallav-kumar.shrestha@ufz.de Institution Helmholtz Centre for Environmental Research - UFZ, Department of Computational Hydrosystems
Murilo Coelho · de Sousa Amâncio, Francisco Dione · Paixao, Matheus · et al.
3 files · 742 KB · rardeclared
This repository contains the replication package for the paper "More Productive, but at What Cost? Understanding How GenAI Shapes Developers' Work Across the SPACE Dimensions", accepted at the 40th Brazilian Symposium on Software Engineering (SBES 2026), São Paulo, Brazil. The package includes: (i) the complete survey instrument and sanitized participant responses; (ii) qualitative coding artifacts, including the codebook, category consolidation, and classification analysis; (iii) inter-rater reliability materials (Cohen's Kappa = 0.81); (iv) quantitative datasets and statistical analysis outputs (SPACE composite scores, Cronbach's alpha, Kruskal-Wallis, Mann-Whitney U, and Dunn's post-hoc tests); and (v) supporting literature review material. All participant data were anonymized prior to disclosure. The survey materials are in Portuguese, the language of data collection.
Abbas, Syed Hassan
1 files · 86 MB · rardeclared
Abbas H, et al. "GenixRL- " The VUS were extracted from CLinVar database (downloaded [Date: August 2025]) and scored using the GenixRL framework. This dataset provides the foundation for the VUS reclassification analysis presented in the main manuscript. The dataset is provided as a single compressed CSV file: GenixRL_VUS_Scored.csv.gz Key Coulmn Descriptions: [Variant Identifier Columns, e.g., CHROM, POS, REF, ALT]: Standard genomic coordinates for each variant. SYMBOL: The official gene symbol. GenixRL_Score: The continuous pathogenicity score generated by GenixRL, ranging from 0 (most likely benign) to 1 (most likely pathogenic). GenixRL_Classification: The tiered classification based on the manuscript's thresholds: 'Likely Benign': Score < 0.53 'Likely Pathogenic': Score >= 0.53 and < 0.709 'High-Confidence Pathogenic': Score >= 0.709 [Other relevant columns]: The file also includes intermediate scores from other predictors and allele frequencies used for validation.
Cao, Shiyuan · Li, Yaning
8 files · 18 MB · csv, rardeclared
This repository provides the supporting materials for the manuscript "A Confidence-Guided Sports Multi-Object Tracking Method via Jersey Semantic Fusion." The archived materials include evaluation summaries, final tracking outputs, environment records, dataset mapping files, protocol reproduction materials, intermediate summary files, and revision evidence used to support the reported SportsMOT validation results. The reported formal results were obtained from the real detector, real frame-level semantic feature extraction, confidence partitioning, cascaded association, and TrackEval evaluation pipeline. The original public benchmark datasets, including SportsMOT, TeamTrack, SoccerNet Tracking, MOT17, and MOT20, are not redistributed in this repository and should be obtained from their original providers.
Nie, Yong · Huang, Bo
1 files · 1.5 KB · rardeclared
BI tree
Nie, Yong · Huang, Bo
1 files · 9.1 KB · rardeclared
Alignments for phylogen
Njie, Adama · Torkayesh, Ali E · Venghaus, Prof. Dr. Sandra
10 files · 1.6 GB · csv, gzip, parquetdeclared
Structured, speaker-attributed corpus of all German Bundestag plenary session transcripts ( Plenarprotokolle ) from the first legislative period to the present (WP01-WP21, September 1949 - April 2026). Every attributed speech is extracted from the official PDFs published by the Deutscher Bundestag under open data policy and linked to the speaker's name, parliamentary role, party affiliation, and gender. Scale: 4,611 sessions · 1,033,723 speeches · 4,205 identified MdBs · 76 years of parliamentary debate Dataset files speeches.parquet - one row per attributed speech: speaker name, role, party, gender, stammdaten_id, full German text (~1 GB) persons.parquet - one row per MdB: cross-session identity linking all name variants via stammdaten_id; canonical name, birth date, career span, total speeches. Use this - not speakers.parquet - for person-level analysis sessions.parquet - one row per plenary session: date, city, Wahlperiode, source PDF hash, extraction engine speakers.parquet - name-string index: one row per unique name string as extracted from the transcripts. Useful for understanding extraction quality; not suitable for person-level aggregation (the same politician often appears under several name variants across sessions) parties.csv - reference table of 31 German parliamentary parties, 1949-present speeches.csv.gz - CSV fallback for Stata and Excel users (same columns as speeches.parquet) datapackage.json - Frictionless Data schema with column descriptions and foreign key constraints Cross-session identity The same politician often appears under different name strings across sessions (e.g. "Schmidt", "Dr. Schmidt", "Frau Dr. Schmidt"). Cross-session person linkage is provided via stammdaten_id , matched against the official Bundestag Stammdaten biographical XML. The persons.parquet table aggregates all name variants for the same MdB into one row with correctly summed speech counts, career span, and birth date. Coverage: ~98.5% of speeches are linked to a stammdaten_id; the remaining ~1.5% are ambiguous surname-only attributions or speakers not in the Stammdaten. Coverage and sources Source PDFs are the official Stenografische Berichte downloaded from the Bundestag open-data portal (bundestag.de). Party-share normalisation in the corpus statistics uses official seat counts per Wahlperiode sourced from the Federal Returning Officer (Bundeswahlleiter, bundeswahlleiter.de). Two PDF generations are covered: scanned and OCR'd documents (WP01-WP09, Bonn era, 1949-1987) and born-digital documents (WP10-WP21, 1987-present). The engine column in sessions.parquet flags whether pdftotext (born-digital) or pdfminer (OCR fallback) was used; this is the primary data-quality indicator for NLP use. Speaker attribution Each speech is attributed using four patterns extracted from the transcript format: presiding officers (Präsident/in, Vizepräsident/in), regular members (name + party), government officials (name + Bundeskanzler/in, Bundesminister/in, etc.), and procedural roles (Berichterstatter/in, etc.). The party field is null for ~60% of speeches - this is expected, as presiding officers and ministers are not identified by party in the transcript. Gender annotation & distribution Gender is derived by matching speaker names against the official Bundestag Stammdaten biographical XML (all MdBs since 1949), with fallbacks for role title, honorific prefix, manually researched overrides, and a gender_guesser first-name heuristic. The gender_source column distinguishes stammdaten (authoritative, 83%), role_title (gendered job title in attribution, 6.4%), title_prefix (Frau/Herr honorific, 0.5%), manual (historically researched, 2.9%), and inferred (name-based heuristic, 4.7%). Gender distribution: Female 26.6% · Male 73.4% · Unknown 0.0%. Data quality All speeches pass automated validation: zero null speaker names, zero sequence gaps, zero CID artefacts, zero party-misclassified-as-Bundesland errors. Eight sessions with conflicting source PDFs were deduplicated (first lexicographic occurrence retained). 252 non-person names incorrectly accepted by the parser (table headers, legislative terms, agenda fragments) are excluded at build time via a curated exclusion list. OCR sessions (WP01-WP09) may contain Unicode replacement characters (U+FFFD); the engine field identifies these sessions. Licence CC BY 4.0. The underlying Plenarprotokolle are official government documents of the Deutscher Bundestag and are in the public domain.
abdulwahab, samaa · aduallah, mahmood z. · Sallomi, Adheed H.
34 files · 2.7 GB · csv, gzip, parquetdeclared
Intrusion-detection research on Internet Protocol version 6 (IPv6) remains bottlenecked by the scarcity of labelled, protocol-aware flow datasets. Existing machine-learning IDS benchmarks are overwhelmingly IPv4-centric, and the few IPv6 corpora that have been released target narrow attack families or rely on small academic testbeds that cannot be re-created by third parties. We present IPv6-CyberBench, a reproducible eight-phase pipeline that constructs a large, protocol-aware translated-flow corpus by harmonising CIC-IDS-2017, CIC-IDS-2018 and CIC-DDoS-2019, applying deterministic IPv4→IPv6 address translation (6to4, NAT64, Teredo, EUI-64), synthesising 27 IPv6-specific flow features grouped in six protocol families, enforcing nineteen RFC-derived constraint categories together with temporal address dynamics, and rebalancing the long-tailed class distribution with a feature-group-conditioned per-class Wasserstein-GAN-GP augmenter and a SMOTE-KDE fallback selected per class by a formal decision rule. We scope the contribution honestly: because the seed corpora are IPv4 captures, the resulting 2,285,774-record benchmark is a translated-flow corpus suitable for training and evaluating flow-level IPv6 IDS classifiers on flooding, brute-force, scan, web-attack and infiltration traffic under IPv6 protocol-header semantics, and for studying IPv6-specific feature engineering and address dynamics in a reproducible setting. It is not a substitute for protocol-native IPv6 attack capture, and we explicitly exclude ICMPv6 Neighbour-Discovery flooding, SEND flooding, NDP exhaustion and extension-header covert-tunnelling from the threat model. The benchmark is evaluated on four axes - fidelity (Kolmogorov-Smirnov, MMD, Fréchet feature distance), utility (stratified 5×5 nested cross-validation over six classifier families including CNN-LSTM and LightGBM), privacy (Shokri-style membership-inference advantage AUC), and external fidelity against a 24 h anonymised CAIDA IPv6 trace (equinix-chicago, US backbone) and a MAWI samplepoint-F trace (WIDE backbone, Tokyo, Japan). The full pipeline, the hyper-parameter manifest, the RFC-constraint manifest, the reproduction scripts, and the 2,285,774-record benchmark are released unconditionally on Zenodo under CC BY 4.0; the dataset and pipeline are openly available at https://doi.org/10.5281/zenodo.19503446 (CC BY 4.0).
Steinhoff, Daniel · Monaghan, Andrew
1 files · 3.5 MB · tardeclared
WHATCH'EM (Water Height and Temperature in Container Habitats Energy Model) is a physics-based model that simulates water temperature and water height in containers using an energy balance approach. The model uses meteorological inputs together with container characteristics, shading, rainfall, evaporation, and optional manual water additions to simulate container water dynamics across a range of environmental conditions.
Rui, Jin · Cai, Chenfan
1 files · 3.7 MB · rardeclared
This dataset provides a paper-aligned 6-km grid-level perceived informality index for Southeast Asia. It is prepared as the data-release package associated with the manuscript "Unveiling the environmental drivers of urban informality in Southeast Asia through citizen science and explainable machine learning", intended for submission to npj Urban Sustainability. The release retains the ten countries used in the manuscript: Philippines, Indonesia, Vietnam, Cambodia, Myanmar, Thailand, Malaysia, Laos, Brunei and Timor-Leste. Singapore and unassigned grid cells from an earlier draft package were excluded to align the deposited data with the manuscript scope. The primary variable is informality_index, a min-max normalized 0-1 perceived informality index. Higher values indicate stronger predicted perceived urban informality. The original large-valued model output is retained as raw_informality_score for provenance, but informality_index should be used as the main manuscript-ready variable. Normalization was calculated over the retained ten-country grid cells. The package is organized into four main folders. The data/ folder contains the authoritative GeoPackage spatial layer, CSV exports, centroid-coordinate tables, administrative boundaries, country-level summary statistics and whole-dataset summary statistics. The docs/ folder contains a data dictionary, a methodology note and a processing log documenting how the release was generated. The figures/ folder contains quick-look visual summaries, including the informality-index histogram, country-level mean chart and spatial preview map. The checksums/ folder contains SHA-256 checksums for verifying file integrity. The GeoPackage file SEA_Perceived_Informality_6km_Grid.gpkg is the authoritative spatial dataset and includes grid geometries, country assignment, raw_informality_score and informality_index. The CSV files are provided for users who prefer tabular workflows or do not use GIS software. The centroid CSV includes approximate longitude and latitude for each grid cell centroid. Summary-statistics files report country-level and overall distributions of the normalized perceived informality index, with additional raw-score summaries retained for provenance. This release is intended to support reproducibility, spatial visualization and further research on perceived urban informality, environmental exposure and urban sustainability in Southeast Asia. Raw satellite imagery, Google Street View imagery and third-party geospatial layers are not redistributed in this package because they are subject to the access terms and licensing conditions of their original providers.
Haider, Haroon
1 files · 146 MB · rardeclared
This dataset provides global gridded soil nitrous oxide (N₂O) emissions for the period 1961-2019, distributed as a stack of annual NetCDF files ( N2O_soil_emissions_YYYY.nc ), one per year, giving 59 files in total. Each file resolves emissions on a regular 0.5° × 0.5° longitude-latitude mesh (720 longitude columns by 360 latitude rows) covering the full globe. Grid-cell centres are given by the lat (degrees_north) and lon (degrees_east) coordinate variables, with longitude running from -180° to +180°; the environmental-response field was rolled from its native 0-360° convention to -180-180° so that every layer shares this common grid. Ocean and other missing cells are flagged with a fill value of NaN . Within each annual file, emissions are stored as twelve monthly fields ( n2o_january through n2o_december ) together with an annual total ( n2o_annual ). The monthly fields report soil N₂O emission per grid cell in grams of N₂O-N per cell per month, and the annual field, expressed in grams of N₂O-N per cell per year, is the sum of the twelve monthly layers. Because the stored values are absolute per-cell masses rather than per-area fluxes, a regional or global total is obtained by summing cells directly and converting from grams to teragrams (dividing the summed grams by 1 × 10¹²). Grid-cell emissions were computed as the product of a fertiliser-driven emission field, an environmental scaling term, and a mean precipitation modifier. Each file preserves the full provenance of this calculation through its global attributes, which record the dataset title, the reference year, the input and output units, the calculation string, the longitude-fix note, and a UTC creation timestamp, so that every layer can be traced back to its inputs and processing steps.
Gider, Veysel · Ekinci, Serdar · Izci, Davut · et al.
1 files · 96 KB · rardeclared
Experimental dataset (616 Marsh-funnel flow-time and 252 mini-slump measurements; 22 in-house PCE formulations × 4 fly-ash replacement levels × 7 dosages, w/cm = 0.35) and the complete deterministic Python pipeline reproducing every numerical result, table and figure of the associated paper. See README.md for the column dictionary and reproduction instructions.