Yu, Hongzheng

3 files · 1006 KB · zip

Data and code for "The Argument of Perigee as a First-Order Control on Tidal Heating Asymmetry in Coupled Eccentricity-Obliquity Regimes"

Bittnar, Petr · Padevět, Pavel

13 files · 1.7 MB · zip

Measured data from experimental tests on concrete specimens with passive confinement during uniaxial compression testing -- various ages, various concrete mixes, and various levels of passive confinement. The data is stored in 13 datasets.

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.

Saha, Sudipto · Raghava, Gajendra

1 files · 186 KB · zip

VICMpred: SVM-Based Prediction of Functional Proteins of Gram-Negative Bacteria Using Amino Acid Patterns and Composition VICMpred is a computational web server developed for predicting the major functional classes of Gram-negative bacterial proteins from amino acid sequences. The tool classifies Gram-negative bacterial proteins into four broad functional categories: virulence factors, information molecules, cellular process proteins, and metabolism-related proteins. VICMpred uses support vector machine-based models trained on amino acid composition, dipeptide composition, and class-specific tetrapeptide patterns. Web Server: https://webs.iiitd.edu.in/raghava/vicmpred/ Citation Saha, S., and Raghava, G. P. S. VICMpred: An SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genomics, Proteomics & Bioinformatics, 4(1), 42-47, 2006. https://doi.org/10.1016/S1672-0229(06)60015-6 About the Research Functional annotation of proteins is one of the major challenges in the post-genomic era. Due to the rapid growth of protein sequence databases, experimental functional characterization of every newly discovered protein is not practical. Traditional methods such as BLAST, FASTA, and PSI-BLAST depend on sequence similarity. However, proteins with similar functions may show poor sequence similarity, making direct function prediction difficult. VICMpred was developed as a direct function prediction method for Gram-negative bacterial proteins. Instead of only predicting subcellular localization, it predicts broad biological functions directly from protein sequence features. Data Compilation: The final dataset contained 670 non-redundant Gram-negative bacterial proteins. These included 255 cellular process proteins, 60 information molecules, 285 metabolism proteins, and 70 virulence factors. Methodology: VICMpred uses support vector machine-based models trained on amino acid composition, dipeptide composition, PSI-BLAST similarity search, class-specific tetrapeptide patterns, and hybrid combinations of these features.

Bilén, Frida · Nahavandizadeh, Nasim · Larsson, Moa · et al.

6 files · 13 MB · zip

NMR datas (raw and processed)

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.

Guastamacchia, Carlo · Piersanti, Roberto · Giardini, Francesco · et al.

3 files · 31 KB · zip

This dataset contains the results of the research presented in: https://doi.org/10.48550/arXiv.2604.00881 In particular, it includes the output of electrophysiology, passive mechanics, and electromechanics simulations performed on a biventricular geometry of a murine heart. The same simulations were run on multiple fiber fields obtained by applying a smoothing procedure to the experimental fiber field, using different regularization radii. The dataset contains the following data: Fiber fields corresponding to regularization radii of 0.0 mm, 0.1 mm, 0.25 mm, 0.5 mm, 1.0 mm, and 1.5 mm. Volume-pressure curves from passive inflation tests. Activation time maps obtained by solving the eikonal problem. Displacement fields from the full electromechanical model. The simulations were run on a mesh with an average edge length of 0.15 mm.

Ahmad, Mushtaq

2 files · 16 MB · zip

This archive is the public reproducibility package for the manuscript "HYPER-Z4c: A Paper-I Reproducible Constraint-Energy-Ledger Framework for Hyperboloidal Z4c Evolution and Direct Scri Waveform Extraction," prepared for submission to the Journal of Scientific Computing. The archive contains the manuscript source, Online Resource 1 source, publication figures, processed CSV/JSON validation records, waveform-level comparison summaries, metric-worldtube readiness audit records, figure-generation scripts, convergence-fit reproduction scripts, environment files, checksum manifests, and a one-command reproduction workflow. The package supports the methods-paper evidence for a stage-synchronous constraint-energy ledger for hyperboloidal Z4c evolution, scri-compatible SAT closure, full-discrete finite-part regularization, evolved-cut BMS waveform extraction, official SXS/SpECTRE Ext-CCE waveform-level comparison, and a separate CceR0207.h5 metric-worldtube input-readiness audit. The archive does not claim a completed same-spacetime HYPER-Z4c-to-SPECTRE-CCE binary-black-hole validation. The manuscript treats the official Ext-CCE waveform-level comparison and the metric-worldtube readiness audit as separate evidence categories. A matched same-spacetime worldtube-to-CCE comparison remains future work. A minimal reproduction workflow verifies the checksum manifest, runs archive smoke tests, recomputes headline convergence-fit quantities, and regenerates the main figures from the archived records.

De Alwis Watuthanthrige, Nethmi

6 files · 5.6 MB · zip

1 files · 29 KB · netcdf

NOAA Coral Reef Watch (v3.1 CoralTemp) Daily Global 5km Satellite Sea Surface Temperature Anomaly. This is a product of NOAA Coral Reef Watch Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][latitude][longitude]): sea_surface_temperature_anomaly (degree_C) mask (pixel characteristics flag array, pixel_classification)

1 files · 15 KB · netcdf

7-day average sea surface temperature (SST) from the AVHRR and VIIRS instruments aboard the NOAA and MetOp satellites, 2006-present, is generated by NOAA/NESDIS/STAR using the Advanced Clear-Sky Processor for Oceans (ACSPO) processing system. Only nighttime overpasses are used and composited into daily mean grids (~830 m), then the daily composite grids are averaged into gridded 7-day temperature averages. More info: https://eastcoast.coastwatch.noaa.gov/cw_avhrr-viirs_sst.php cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][level][latitude][longitude]): sst (sea_surface_subskin_temperature, degree_C)

1 files · 16 KB · netcdf

Climatological 7-day average sea surface temperature (SST) from 2007 to 2025, i.e. there are 52 7-day periods each representing a 2007-2025 mean SST for that period. The 18-year period for averaging begins May 21, 2007 and ends May 20, 2025. Data are merged from the AVHRR and VIIRS instruments aboard the NOAA and MetOp satellites. SST is generated by NOAA/NESDIS/STAR using the Advanced Clear-Sky Processor for Oceans (ACSPO) processing system. Only nighttime overpasses are used and composited into daily mean grids (~830 m), then the daily composite grids are averaged into gridded 7-day temperature averages. Finally, the 18-year climatological average for each 7-day period is averaged from the 7-day files. More info: https://eastcoast.coastwatch.noaa.gov/cw_avhrr-viirs_sst.php cdm_data_type = Grid VARIABLES (all of which use the dimensions [sevenDayPeriodOfYear][level][latitude][longitude]): sst (sea_surface_subskin_temperature, degree_C)

1 files · 16 KB · netcdf

Jet Propulsion Laboratory data from a local source. cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][level][latitude][longitude]): sst (Analysed SST with land and ice masks applied, degree_C)

1 files · 15 KB · netcdf

Jet Propulsion Laboratory SST MUR data from a local source. cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][level][latitude][longitude]): sst (Analysed SST with land and ice masks applied, degree_C)

1 files · 15 KB · netcdf

Jet Propulsion Laboratory data from a local source. cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][level][latitude][longitude]): sst (Analysed SST with land and ice masks applied, degree_C)

1 files · 30 KB · netcdf

NOAA Coral Reef Watch Daily (v3.1) Global 5km Satellite Sea Surface Temperature (CoralTemp). CoralTemp is derived from three different but related 5km daily gap-free SST data sets and provides an internally consistent SST product that stretches from 1985 to present: Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Sea Surface Temperature Reanalysis (1985-2002), Geo-Polar Blended Night-only Sea Surface Temperature Reanalysis (2002-2016), Geo-Polar Blended Night-only Sea Surface Temperature Near Real-Time (2017 to present). cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][latitude][longitude]): analysed_sst (analysed sea surface temperature, degree_C) sea_ice_fraction (1)

1 files · 13 KB · netcdf

Sea surface temperature retrievals produced by NOAA/NESDIS/Office of Satellite and Product Operations (OSPO) office from Visible and Infrared Imager/Radiometer Suite (VIIRS) sensor cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][altitude][rows][cols]): l2p_flags swath_latitude (Latitude, degrees_north) swath_longitude (Longitude, degrees_east) sea_surface_temperature (sea surface subskin temperature, degree_C) sses_bias (SSES bias estimate, degree_C) sses_standard_deviation (degree_C) graphics (graphics overlay planes)

1 files · 17 KB · netcdf

Sea surface temperature retrievals produced by NOAA/NESDIS/STAR office cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][latitude][longitude]): sea_surface_temperature (sea surface sub-skin temperature, degree_C) sses_bias (SSES bias estimate, kelvin) sses_standard_deviation (kelvin) l2p_flags l3s_flags sst_count (Count of input L3U SST pixels) quality_level (quality level of SST pixel) sst_source (Source of major (highest weight) contribution to sst) satellite_zenith_angle (degrees) dt_analysis (deviation from SST reference, kelvin) sea_ice_fraction (1) wind_speed (m s-1) sst_dtime (time difference from reference time, seconds) measurement_dtime (time difference of highest weighted input SST from reference time. Is equal to sst_dtime for pixels where L3S-LEO-PM_N is available, seconds) sst_gradient_magnitude (SST gradient magnitude value, kelvin/km) sst_front_position (Binary SST front position indicator)

1 files · 17 KB · netcdf

Sea surface temperature retrievals produced by NOAA/NESDIS/STAR office cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][latitude][longitude]): sea_surface_temperature (sea surface sub-skin temperature, kelvin) sses_bias (SSES bias estimate, kelvin) sses_standard_deviation (kelvin) l2p_flags l3s_flags sst_count (Count of input L3U SST pixels) quality_level (quality level of SST pixel) sst_source (Source of major (highest weight) contribution to sst) satellite_zenith_angle (degrees) dt_analysis (deviation from SST reference, kelvin) sea_ice_fraction (1) wind_speed (m s-1) sst_dtime (time difference from reference time, seconds) measurement_dtime (time difference of highest weighted input SST from reference time. Is equal to sst_dtime for pixels where L3S-LEO-PM_N is available, seconds) sst_gradient_magnitude (SST gradient magnitude value, kelvin/km) sst_front_position (Binary SST front position indicator)