Fry, Lauren · Seglenieks, Frank · Shrestha, Narayan Kumar · et al.

3 files · 22 MB · sevenzipdeclared

In response to flooding on Lake Ontario and the St. Lawrence River in 2017 and 2019, the U.S. and Canadian governments directed the Great Lakes - St. Lawrence River Adaptive Management (GLAM) Committee to expedite review of Lake Ontario outflow regulation Plan 2014 ahead of its usual 15-year review cycle. The dataset includes (1) a baseline record based on data from 1961-2020, (2) a set of stochastic scenarios that incorporates extremes that may not be in the historical record but are plausible under current hydroclimate conditions, and (3) a set of climate change scenarios to support evaluation of the plan under plausible decadal-scale hydroclimate changes. All data required to simulate Lake Ontario water levels, outflows, and downstream water levels are provided for each scenario. The data have been compressed into the following files: Baseline.7z - Baseline hydroclimate record from 1961-2020 Climate.7z - 8 future climate hydroclimate records Stochastic.7z - 500 stochastic hydroclimate records Within each compressed file are subdirectories that contain the files for each water supply sequence. The following table lists the files that are provided for each of the water supply sequences. For each file there is a description of the variable contained in the file, the location of the data in the file, and the unit of the data in the file if applicable. Filename Variable Location Units dpmi_flw_cms_qm48_na.csv Flow Des Prairies and Mille Iles River cms rich_flw_cms_qm48_na.csv Flow Richelieu River at Rapides Fryer cms stfr_flw_cms_qm48_na.csv Flow St. Francois River at Chut Hemming cms stmc_flw_cms_qm48_na.csv Flow St. Maurice River at La Gabelle cms ont_nbs_cms_qm48_na.csv Net Basin Supply Lake Ontario cms ont_nbs_cms_qm48_na_spinup.csv Net Basin Supply Lake Ontario cms eri_flw_cms_qm48_na.csv Outflow Lake Erie cms eri_flw_cms_qm48_na_spinup.csv Outflow (one year spinup) Lake Erie cms slon_flw_cms_qm48_na.csv SLON flow St. Lawrence River (Lake St. Louis) cms slon_flw_cms_qm48_na_spinup.csv SLON flow (one year spinup) St. Lawrence River (Lake St. Louis) cms stl_icw_cms_qm48_na.csv Ice/weed retardation St. Lawrence River cms stl_tde_m_qm48_na.csv Tidal Signal St. Lawrence River m ont_mlv_m_qm48_na_spinup.csv Water Level (one year spinup) Lake Ontario m stl_ics_xx_qm48_na.csv Ice status indicator St. Lawrence River N/A bati_rgh_xx_qm48_na.csv Ice/weed roughness factor Batiscan N/A card_rgh_xx_qm48_na.csv Ice/weed roughness factor Cardinal N/A corn_rgh_xx_qm48_na.csv Ice/weed roughness factor Cornwall N/A intw_rgh_xx_qm48_na.csv Ice/weed roughness factor International Tail Water N/A irhw_rgh_xx_qm48_na.csv Ice/weed roughness factor Iroquois Head Water N/A irtw_rgh_xx_qm48_na.csv Ice/weed roughness factor Iroquois Tail Water N/A jty1_rgh_xx_qm48_na.csv Ice/weed roughness factor Montreal Jetty No. 1 N/A lspr_rgh_xx_qm48_na.csv Ice/weed roughness factor Lake St. Pierre N/A lstd_rgh_xx_qm48_na.csv Ice/weed roughness factor Long Sault Dam N/A morr_rgh_xx_qm48_na.csv Ice/weed roughness factor Morrisburg N/A ogde_rgh_xx_qm48_na.csv Ice/weed roughness factor Odgensburg N/A ptcl_rgh_xx_qm48_na.csv Ice/weed roughness factor Pointe - Claire N/A sahw_rgh_xx_qm48_na.csv Ice/weed roughness factor Saunders Head Water N/A sorl_rgh_xx_qm48_na.csv Ice/weed roughness factor Sorel N/A summ_rgh_xx_qm48_na.csv Ice/weed roughness factor Summerstown N/A triv_rgh_xx_qm48_na.csv Ice/weed roughness factor Trois Rivières N/A vare_rgh_xx_qm48_na.csv Ice/weed roughness factor Varennes N/A na_fst_xx_qm48_na.csv Forecast indicator N/A N/A

Gomułka, Kamil · Woźniak, Piotr · Krzeszowski, Tomasz

1 files · 6.4 GB · sevenzipdeclared

======================= License ======================= This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. ======================= Summary ======================= DATASET FOR EVALUATING THE EFFECTIVENESS OF AI-GENERATED DATA FOR VIDEO-BASED ACTION RECOGNITION This repository contains the dataset accompanying the paper "Evaluating the Effectiveness of AI-Generated Data for Video-Based Action Recognition". The dataset was curated to investigate the impact of incorporating AI-generated synthetic video data into the training process of deep learning models (such as I3D and TimeSformer) for human action recognition. It is particularly focused on evaluating cross-domain generalization and addressing the synthetic-to-real distribution shift using domain adaptation techniques. It is obligatory to cite the following paper in every work that uses the dataset: K. Gomulka, P. Wozniak, T. Krzeszowski: Evaluating the Effectiveness of AI-Generated Data for Video-Based Action Recognition, Electronics, MDPI, 2026. ======================= Data description ======================= The dataset focuses on 15 target action categories from the HMDB51 benchmark: Draw Sword, Fall, Flick Flack, Handstand, Jump, Kick, Pick, Punch, Run, Shoot Bow, Shoot Gun, Sit, Throw, Walk, and Wave. The repository consists of three main data subsets: * Grok Synthetic Dataset (Grok): Contains 1,500 synthetic video sequences (100 videos per class) generated using the Grok Imagine 1.0 model. Resolution is 560x560 pixels with a duration of 6 seconds per clip. * Meta Synthetic Dataset (Meta): Contains 1,480 synthetic video sequences (80 in the 'throw' class, 100 videos per other classes) generated using the Meta AI Vibes model. Resolution is 624x624 pixels with a duration of 5 seconds per clip. * HMDB51 Subset (hmdb51_org): A specific subset of the real-world HMDB51 dataset containing 2,315 video sequences distributed across the 15 target action classes, providing a direct real-world baseline. The repository also includes sample .csv files defining the train, validation, and test splits. In the example configuration, 60% of the HMDB51 dataset is allocated to training, 20% to validation, and 20% to testing, with all synthetic Grok videos appended exclusively to the training set. ======================= Dataset structure ======================= * Grok/ - directory containing 1,500 synthetic videos generated by Grok Imagine 1.0. * Meta/ - directory containing 1,480 synthetic videos generated by Meta AI Vibes. * hmdb51_org/ - directory containing 2,315 real-world videos from the HMDB51 dataset. * ghtrain_hval/ - directory containing example .csv files defining the train, validation, and test splits for the baseline HMDB + Grok setup. * train.csv - training set paths and labels. * val.csv - validation set paths and labels. * test.csv - testing set paths and labels. * README.md - detailed documentation including dataset overview, configuration details, and instructions for training the TimeSformer model using the provided data splits. ======================= Generation Methodology ======================= The synthetic videos were generated using a structured, combinatorial prompt engineering framework resulting in 100 unique prompts per class. Each prompt combined predefined descriptions of the action, environmental context (e.g., vintage film style, urban outdoor), and camera framing to ensure high scene diversity. While the generated sequences preserve essential motion semantics, they also may contain inherent generative artifacts.

Morais, Thomaz

1 files · 12 GB · sevenzipdeclared

This repository contains a Brazilian Portuguese speech corpus developed as part of the project "Incorporating Regionalisms and Accents into Speech Synthesis Models". The corpus was created to support research on speech synthesis, regional linguistic variation, and accent adaptation in text-to-speech systems, with particular attention to speech varieties from Paraíba and Northeastern Brazil. The corpus was built by organizing, processing, and integrating speech data from multiple sources, including CoLingPB, the Sotaque Brasileiro dataset, and additional collaborative speech recordings collected for this project. The data was curated and standardized to support experiments involving fine-tuning of pre-existing speech synthesis checkpoints, evaluation of regional accent preservation, and comparison between base and adapted text-to-speech models. This dataset is intended for academic and research purposes, especially for studies related to Brazilian Portuguese speech synthesis, regional accents, computational linguistics, speech processing, and deep learning approaches for text-to-speech. Users of this corpus should cite the original data sources and related works used in its construction and contextualization. Suggested citations in BibTeX format: @mastersthesis{morais2026regionalismos, author = {Morais, Thomaz Diniz Pinto de}, title = {{Incorporando Regionalismos e Sotaques em Modelos de Síntese de Fala}}, year = {2026}, school = {Universidade Federal de Campina Grande}, address = {Campina Grande, PB, Brazil}, type = {Master's thesis}, note = {Programa de Pós-Graduação em Ciência da Computação} } @misc{stein2015colingpb, author = {Stein, Cirineu Cecote and others}, title = {{Corpus Linguístico da Paraíba (CoLingPB)}}, year = {2015}, institution = {Universidade Federal da Paraíba}, address = {João Pessoa, PB, Brazil}, url = {https://www.cchla.ufpb.br/colingpb/}, note = {Corpus Linguístico da Paraíba} } @dataset{milan2021sotaquebrasileiro, author = {Milan, Gabriel Gazola}, title = {{Sotaque Brasileiro}}, year = {2021}, publisher = {Zenodo}, version = {2021-09-06}, doi = {10.5281/zenodo.5466945}, url = {https://doi.org/10.5281/zenodo.5466945} } @mastersthesis{batista2019sotaques, author = {Batista, Nathalia Alves Rocha}, title = {{Estudo sobre identificação automática de sotaques regionais brasileiros baseada em modelagens estatísticas e técnicas de aprendizado de máquina}}, year = {2019}, school = {Universidade Estadual de Campinas}, address = {Campinas, SP, Brazil}, type = {Master's thesis}, url = {https://hdl.handle.net/20.500.12733/1636122} }

Majumdar, Sayantan · Smith, Ryan G. · ReVelle, Peter · et al.

3 files · 82 GB · sevenzip, zipdeclared

AZ-Hydro: Historical and Projected Arizona Annual Water Use, 1896-2099 A 2 km-resolution gridded dataset of Arizona groundwater and surface-water withdrawals, irrigation consumptive use, and pumping-induced surface-water capture, spanning 204 years (1896-2099) with quadrature-combined uncertainty bands. Companion data archive for Majumdar et al. (in prep., Nature Scientific Data ) and Majumdar et al. (in prep., AGU Earth's Future ). Graphical Abstract: https://github.com/montimaj/az-hydro/blob/main/docs/images/Graphical_Abstract_Fig1.png Patch release over v1.0.0. The dataset itself is unchanged - every gridded prediction, consumptive-use, surface-water-capture, uncertainty, per-well, and CAP shortage-scenario product in `az-hydro-headline.7z` and `az-hydro-data.7z` is identical to v1.0.0. This version updates only figures, map labeling, and documentation. What changed - Map & figure fixes - corrected map orientations and axis / colorbar / legend labels across the era-mean, σ-attribution, surface-water-capture, and CAP shortage-scenario figures. Withdrawal and consumptive-use HUC12 intercomparison: difference-map orientation/footprint fixes, one-column scatter layout, and decade axis-tick de-cluttering; added public-supply volume-difference maps. Regenerated era-mean and σ-attribution figures and the graphical abstract. - Documentation - README corrections across the source and archive READMEs: basin-count wording (52 basin polygons spanning Arizona's 51 ADWR groundwater basins), disk-space requirements, citation/reference fixes, funding acknowledgment, and data-paper title. Added the pipeline data-harmonization figure. - Source code (`az-hydro-1.0.1.zip`) - matches the tagged GitHub release; now also includes the AZ-Hydro Explorer Earth Engine App (`gee/azhydro-visualizer.js`) and the GEE asset-upload scripts already deployed with v1.0.0. What's in this deposit File Size Contents Audience az-hydro-headline.7z ~8.7 GB Focused subset: published per-pixel/per-year predictions (6-band augmented rasters with σ + CV + SNR + 95 % CI), per-well GeoParquet, SW capture, aggregated time series, CAP shortage scenario outputs, validation against USGS/ADWR/Reitz, era-mean and trend spatial figures. Most users want this. Reviewers, downstream researchers using the published product az-hydro-data.7z ~80 GB Full reproducibility archive: all of the above plus raw inputs (GEE tiles, ADWR meter records, well registry, GW basin / AMA-INA / CAP / SRP / streamflow / USBR vectors, statewide WTD), Step 2 cross-validation outputs, intermediate predictor stacks, per-component σ rasters (σ_MACA / σ_Model / σ_Irr / σ_LULC / σ_GW / σ_USBR / σ_CU). Anyone reproducing the full pipeline from scratch az-hydro-1.0.1.zip ~77 MB Source code release (Python pipeline, GEE export scripts, documentation). Same content as the GitHub repository at the tagged release. Anyone running the pipeline Inside each .7z, see Data/HEADLINE_README.md (headline archive) or Data/README.md (full archive) for a complete per-directory inventory and external-source citations. Important: how to unpack the .7z files The .7z format is not openable by macOS Archive Utility. Use one of: macOS - Keka or The Unarchiver Windows - 7-Zip , WinRAR , or Bandizip Linux - p7zip (e.g. apt install p7zip-full), then 7z x az-hydro-data.7z LZMA2 + solid-block compression yields ~35 % ratio (80 GB compressed from ~224 GB raw; 8.8 GB compressed from ~40 GB raw). Methods overview AZ-Hydro is a four-step physics-constrained ML pipeline: XGBRF prediction of total annual water-use depth per pixel, trained on per-well ADWR meter records (1984-2024) with 16 predictor bands (climate, ET, Peff, irrigation fraction, well density, canal density, water-rights density, etc.). Density-ratio partition decomposing the total prediction into Irrigation/Non-Irrigation × GW/SW × CU using era-mapped factors anchored to USGS Circulars 1950-2015 and ADWR Annual Reports 2016-2024. Six-component quadrature uncertainty quantification : σ_MACA (5 GCMs) + σ_Model (10 XGBRF seeds, t-corrected) + σ_LULC (4 USGS FORE-SCE scenarios) + σ_USBR (5 CMIP3 Upper-Colorado streamflow members, t-corrected) + σ_GW (5 recent ADWR Well Registry snapshots, t-corrected) + σ_CU (analytic propagation through Irrigation Efficiency). Per-pixel SW Capture Fraction and Volume with σ_GW propagation to quantify pumping-induced streamflow depletion. Key validations 2016 ADWR Total : model 6.72 MAF vs ADWR ~7.0 MAF (within -0.28 MAF) 2017 ADWR : 6.81 vs 7.0 MAF; GW share 44.9 % vs 41 % (within 4 pp) 2015 USGS GW pumping : 2.96 vs USGS 3.09 MAF (within -0.13 MAF) 2019-2020 ADWR irrigation share : 73.8 % vs 74 % (essentially exact) WestWater (2026) CAP shortage scenarios : AZ-Hydro Basic Coordination cumulative ΔGW = 7.24 MAF vs WestWater Fig 4 anchor 8.0 MAF (within -9 %); Extreme Shortage = 13.08 MAF vs Fig 4 anchor 8.7 MAF (gap reflects AZ-Hydro's no-regulatory-ceiling framing). CAP delivery shortage scenario sweep Eight scenarios (Baseline_900kAF, DCP Tier 0/1/2a/2b/3, WestWater Basic Coordination, Extreme Shortage) re-partitioned 2026-2099. Cumulative additional GW pumping over 2027-2060: DCP Tier 3 = 10.7 MAF, Basic Coordination = 7.24 MAF, Extreme Shortage = 13.08 MAF. Spatial maps (basin choropleth, per-pixel cumulative ΔGW, σ_cum context, basin/pixel signal-to-noise) included for both 2027-2060 (WestWater anchor) and 2027-2099 windows. External datasets required to reproduce the pipeline (not redistributed here) The pipeline reads four USGS ScienceBase data products that you must download separately: USGS NHM withdrawals ( Haynes et al. 2023 ) - irrigation withdrawals + efficiency by HUC12, 2000-2020 USGS NHM CU / IE reanalysis ( Martin et al. 2023 ; Martin et al. 2025 ) - irrigation consumptive use + Peff by HUC12 USGS public-supply reanalysis ( Luukkonen et al. 2023 ; Alzraiee et al. 2024 ) - public-supply withdrawals by HUC12 USGS Reitz historical ET / Peff ( Reitz et al. 2023 ; Reitz, Sanford & Saxe 2023 ) - 800 m gridded irrigation ET, 1980-2018 Bundled here directly: ADWR Well Registry, ADWR Meter Data, GW basin / AMA-INA / CAP / SRP / streamflow / USBR vectors, HarDWR water-rights shapefile (Lisk et al. 2024) , GRAIN canal network (Suresh et al. 2026) , and the Ma et al. 2026 statewide WTD TIFs . See Data/README.md inside each .7z for exact filename / sub-directory naming required. Citation Data archive (this deposit): Majumdar, S., Smith, R.G., ReVelle, P., Hasan, M.F., & Wogenstahl, C. (2026). AZ-Hydro - Historical and Projected Arizona Annual Water Use: Software, Input Data, Models, Raster and Well Package Predictions, and Validation at 2 km Resolution (1896-2099). Zenodo. https://doi.org/10.5281/zenodo.19057936 Companion papers: Majumdar, S., Smith, R.G., ReVelle, P., Hasan, M.F., & Wogenstahl, C. (2026). Freshwater withdrawals, irrigation consumptive use, and surface water capture for Arizona, 1896-2099 . In prep. for Nature Scientific Data . Majumdar, S., Smith, R.G., ReVelle, P., Hasan, M.F., & Wogenstahl, C. (2026). Where Arizona's Water Goes: Declining Agricultural Dominance and Rising Urban Demand Drive a Two-Century Shift in Withdrawal Patterns (1896-2099). In prep. for AGU Earth's Future . License Data archives (az-hydro-data.7z, az-hydro-headline.7z): CC-BY-4.0 Source code (az-hydro-1.0.0.zip): BSD 3-Clause "Revised" (see LICENSE inside the zip) External datasets bundled here (HarDWR, GRAIN, WTD, ADWR products) retain their original upstream licenses Acknowledgments This work was supported by NASA (Grant numbers 80NSSC21K0979 and 80NSSC23K1453) and U.S. Army Corps of Engineers (Grant number W912HZ25C0016). We thank the open-source software and data communities, the OpenET consortium, and the Arizona Department of Water Resources for making their resources and datasets publicly available, and Google Earth Engine for compute and storage support. S.M. and P.R. acknowledge Dr. Justin L. Huntington, Christopher Pearson, Charles G. Morton, Blake A. Minor, Dr. Samapriya Roy at the Desert Research Institute, and Dr. David Ketchum at the University of Montana for their contributions to related projects that informed this work. We also thank Rahel Pommerenke at Colorado State University for presenting preliminary results from this work at the 2025 ESA Living Planet Symposium. The views expressed herein are those of the authors and do not necessarily reflect those of the funding agencies. Related links Live web app - AZ-Hydro Explorer : https://azhydro.projects.earthengine.app/view/azhydro-explorer (interactive GEE App: year slider 1896-2099, side-by-side category compare, click-driven pixel/basin/sub-basin/well time series with 95 % CI, CAP scenario × window dropdowns) GitHub repository : https://github.com/montimaj/az-hydro Issue tracker / bug reports : https://github.com/montimaj/az-hydro/issues Nature Scientific Data preprint (when available): (link) AGU Earth's Future preprint (when available): (link)

Woodgate, William

1 files · 24 MB · sevenzipdeclared

Light- and CO 2 response curves using the modified LI-6800 were collected from 8 leaves at the long-term Tumbarumba research site in April 2022 (Leuning et al., 2005). Combined gas exchange, PAM fluorescence, reflectance and transmittance (400-650 nm), and spectral fluorescence both forward- and back-scattered (660-850 nm) observations were collected for each light and CO 2 level. See Github repo for processing tools: https://github.com/wwoodgate/li6800_ms

Meekes, Lisa · Tabaro, Francesco · Bexkens, Michiel · et al.

41 files · 8.2 GB · csv, fasta, pdfdeclared

This record contains the Python software for PEPTiGEN, a tool for generating tryptic peptides from prokaryotic gene sequences and their variants, and the associated antimicrobial resistance (AMR) peptide database. The database is provided as an SQL file and a CSV file containing all genes and predicted peptides. The README file contains explanation of the PEPTiGEN tool. The SQL database schema files contains both the database schema of the SQL database used in the PEPTiGEN analysis as the database schema of the AMR peptide datbase.

Hagger, Thomas · Hassanzadeh, Mohammadreza · Urbonavicius, Aidas · et al.

3 files · 24 GB · sevenzip, zipdeclared

Raw experimental data and analysis scripts. The repository includes cathodoluminescence, electron microscopy, photoluminescence, and complementary characterization data organised by figure, together with supporting analysis scripts where applicable. The full electron microscopy data is given in TEM.7z, while the figures used in the manuscript and SI are given in the Raw_Data folder

Huang, Yicong · Shamsnia, Ali · Chen, Mengze · et al.

10 files · 26 GB · sevenzipdeclared

Paper : Intrinsic timing, not temporal prediction, underlies ramping dynamics in visual and parietal cortex, during passive behavior Authors: Huang Y, Shamsnia A, Chen M, Wu S, Stamm T, Medico S, Najafi F The data include two-photon calcium imaging from distinct excitatory and inhibitory cell types (excitatory, VIP, and SST neurons) in the visual (VIS) and parietal (PPC) cortices of mice passively exposed to auditory and visual stimuli. Number of imaged neurons: - Excitatory: ~26,600 - VIP: ~3,900 - SST: ~3,100 File details and code: Details on the contents of each file, how to use the data for analysis, and how to reproduce the paper's results are provided in: 2p_imaging/passive_interval_oddball_202412 at main · najafi-laboratory/2p_imaging Files: PPC data: YH01VT, YH02VT, YH03VT, YH14SC, YH16SC. VIS data: YH17VT, YH18VT, YH19VT, YH20SC, YH21SC. File names ending in: VT contain simultaneous imaging data from excitatory neurons and VIP interneurons. SC contain imaging data from SST interneurons.

Kharyuk, Pavel

4 files · 1.6 GB · sevenzipdeclared

This repository contains data collected under the following study: P.Kharyuk, S.Matveev, I.Oseledets. Exploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentations, arXiv:2503.03283 . Corresponding source code repository: https://github.com/kharyuk/activation_sa CNNs: ILSVRC: 10.5281/zenodo.18097911 Places365: 10.5281/zenodo.18098133 0_models.7z: copy of the visual transformer models (ViT-b-16, Swin-t, MaxViT-t) used in the research (reference: https://docs.pytorch.org/vision/main/models.html ) 1_sensitivity_values.7z: sensitivity values (Sobol indices, Shapley values) computed for the first experimental series (ViT-b-16, Swin-t). In addition, logs and npz-files containing the sampled parameters were packed. To be used within the jupyter notebooks, all .hdf5 files should be placed into the 'results' directory of the source code repository. 2a_predictions.7z: these files include the top-5 class predictions and corresponding classifying layer's outputs used for evaluating Table 1 (ViT-b-16, Swin-t, MaxViT-t). SupplementaryS3.7z : Supplementary material containing visualized sensitivity maps (Vit-b-16, Swin-t).

YANG, YIYAN

56 files · 11 GB · sevenzip, zipdeclared

The data are used in Github Readme and PHILM Step-by-Step Tutorial . The provided archives include: * `example.zip`: Example dataset used in the README. * `tutorial_data.zip`: Healthy human stool sample dataset used in the step-by-step tutorial. * `PHILM_input_data.7z`: Contains the input files required by PHILM. These files should be placed in the `data/` directory. * `PHILM_model_results.7z`: Contains the trained model and prediction metrics, including `PHILM_best_model.pth`, `PHILM_best_params.yaml`, `PHILM_predict_test.ft.metrics`, `PHILM_predict_val.ft.metrics`, and `PHILM_predict_train.ft.metrics`. * `PHILM_interactions.7z`: Contains the inferred interaction results, including `PHILM_interactions.tsv`, `PHILM_interactions.pvalues.tsv`, and `PHILM_interactions.pvalues.filtered.tsv`. * `perm_<start number-end number>_scores.tsv.7z`: * `perm_<start-end>_scores.tsv.7z`: These archives contain the permutation-derived PHILM scores used for empirical p-value calculation. Because storing all 1,000 permutation results in a single file would be very large, the results were divided into 50 compressed files. After decompressing all `.7z` files, organize the permutation results into the required PHILM directory structure by running: `python organize_perm_files.py --input "perm_*-*_scores.tsv" --outdir permutation_null`. This command will generate files with the following structure: `permutation_null/perm_*/PHILM_perm_*.raw_gradient.tsv`. Before running Step 5.3, move or place the generated `permutation_null/` directory under the `results/` directory.

Matuszewska-Mach, Eliza · Konieczny, Igor · Matysiak, Jan · et al.

45 files · 46 GB · sevenzipdeclared

This dataset contains raw and processed mass spectrometry data generated for the study: "Honeybee (Apis mellifera) venom and its bioactive peptides tertiapin and apamin modulate the proteome of SARS‑CoV‑2‑infected cells." The aim of the study was to characterize host proteome remodeling in human ACE2‑expressing HEK293 (hACE2) cells infected with SARS‑CoV‑2 spike‑pseudotyped lentiviral particles and treated with: Whole honeybee venom (HBV) Apamin (APA) Tertiapin (TERT) Proteomic profiling was performed using nano‑liquid chromatography coupled to MALDI‑TOF/TOF tandem mass spectrometry (nanoLC‑MALDI‑TOF/TOF MS/MS). Experimental Design hACE2 cells were: Infected with SARS‑CoV‑2 spike pseudotyped lentiviral particles (MOI 1.33) Treated with: HBV (1 µg/mL and 2 µg/mL) Apamin (0.5, 1, 2 µg/mL) Tertiapin (0.25, 0.5, 1, 2 µg/mL) Compared to infected untreated control cells Each condition was analyzed in: Two biological replicates Two technical replicates Mass Spectrometry Workflow Protein extraction: TAP buffer with protease and phosphatase inhibitors Digestion: Trypsin (in-solution digestion, modified Pierce protocol) Peptide cleanup: ZipTip C18 nanoLC system: Easy‑nLC II (Bruker Daltonics) Fractionation: Proteineer‑fc II fraction collector Matrix: HCCA (α‑cyano‑4‑hydroxycinnamic acid) Target: AnchorChip 384 (Bruker Daltonics) Instrument: UltrafleXtreme MALDI‑TOF/TOF (Bruker Daltonics) Acquisition mode: Reflectron mode MS/MS analysis: TOF/TOF fragmentation Raw Data Files (RAW) The repository includes: Complete Bruker MALDI‑TOF/TOF raw data folders (.mcf format) All nanoLC fractions (384 spots per sample) Raw files were acquired using: FlexControl 3.4 FlexAnalysis 3.4 These files preserve the original vendor format required for reanalysis. Identification / Result Files (RESULT) Protein identification was performed using: Software: Mascot v2.4.1 Database: NCBInr Taxonomy: Homo sapiens The dataset includes: Mascot search result files Protein identification lists Organism Homo sapiens (human HEK293 ACE2-expressing cells) Instrument Bruker UltrafleXtreme MALDI‑TOF/TOF mass spectrometer

Martim Pinto

47 files · 97 GB · sevenzip, zipdeclared

This repository contains an implementation of the optimized Blahut-Arimoto algorithm leveraging CPU parallelization for computing improved upper bounds on the capacity of the binary deletion channel, discussed in "Improved capacity upper bounds for the deletion channel using a parallelized Blahut-Arimoto algorithm", Martim Pinto and Jo&atilde;o Ribeiro. 2026 IEEE International Symposium on Information Theory. Extended version available online at https://arxiv.org/abs/2604.05867. The implementation can be found in the "BDC-Upper-Bounds-GPU-main.zip" compressed folder. Please refer to the README in that folder for a description of the code repository, instructions on how to compile and run the code, and a description of some relevant outputs. As discussed in the README, files "dist_n_k" (compressed txt files) containing the input distributions generated by this algorithm for the exact deletion channels BDC_{n,k} with n=29 and n=31 that were used to obtain the upper bounds reported in the work mentioned above can also be found in this repository. The repository also includes a txt file (capacities.txt) listing approximations of the C_{n,k} capacities for selected smaller values of n and k, and a compressed folder (distributions.7z) containing txt files with the input distributions used to obtain these approximations. For more details on how to interpret and use these files, see the README.

Deng, Zizhuang · Shan, Yiying · Chen, Sanchuan · et al.

8 files · 19 GB · sevenzip, zipdeclared

This is the artifact code and data for the paper "Implementation Bugs as Attacks: Adversarial Neuron Fuzzing and Supply-Chain Backdoors".

Susannah, Heeren · Righetti, Laura · Kramer, Nynke

4 files · 15 GB · sevenzipdeclared

Liu, Renjie

1 files · 1.3 GB · sevenzipdeclared

Hussain, Muhammad Zunnurain

1 files · 56 MB · sevenzipdeclared