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.

Kantor, Rose · Shakya, Migun · Ruth, Nelson · et al.

2,095 rows · 907 KB · fasta, tsv

A virus genome database representing 21,015 near-complete virus genomes collected from untargeted ultra-deep RNA/DNA combined sequencing of wastewater. Sequence data was provided by the CASPER consortium and raw data may be found on NCBI SRA under bioprojects PRJNA1247874 and PRJNA1198001. Data underwent read trimming, rRNA and human read removal, de novo assembly, and selection of high-quality viral contigs. Contigs were clustered at 95% identity and 85% query coverage to dereplicate. Chimera-checking required at least two independent assemblies of the same viral genome or presence of the genome in another reference database. Annotation made use of RdRpCATCH, geNomad, checkV, BLASTN against NCBI core-nt, and RNAVirHost. The RdRp fasta files contain representative RdRp sequences identified through homology to major RdRp reference databases and clustered at 90% sequence identity over 75% sequence coverage. Included sequences contain all three conserved RdRp motifs (A, B, and C) arranged in either the canonical ABC configuration or the permuted CAB configuration.

Hubbard, Alfred · Solares, Edwin · Hemming-Schroeder, Elizabeth

88 rows · 18 KB · fasta

These are the files needed to run the Broad Institute's malaria amplicon pipeline for the PvGAP Plasmodium vivax panel, described in detail here . They consist of FASTA files containing the forward and reverse primers and another FASTA file containing reference sequences for each target, derived from the PvP01 reference genome.

Yuan, Guangyuan

72 rows × 13 cols · 6.0 KB · csv, fasta

11 numeric · 2 categorical

This dataset supports the findings of the manuscript "Root anatomical traits modulate the assembly and nitrogen transformation potential of root-associated microbiomes in a temperate steppe" (NPH-MS-2026-55667). It contains root traits data, bacterial 16S rRNA gene absolute abundances, functional genes relative abundances, DNA extraction metadata, and phylogenetic marker sequences for 37 plant species from a temperate steppe ecosystem. The dataset includes the following files: 1. root traits.csv - Root traits including average diameter (AD), specific root length (SRL), specific root area (SRA), root tissue density (RTD), root nitrogen content (RNC), root carbon content (RCC), carbon‑nitrogen ratio (RCN), cortex layer number (CLN), cortex thickness (CT), and the ratio of cortex thickness to root diameter (CTRD). The first column lists plant species names. 2. Absolute abundance of 16S rRNA gene.csv - Quantitative PCR (qPCR) derived absolute abundances of bacterial 16S rRNA gene copies (copies/ng DNA) across different root compartments (rhizosphere, rhizoplane, endosphere), host species, root orders, and cotyledon classes (monocot/dicot). 3. DNA extraction sample weight.csv - Fresh weight (grams) of root material used for DNA extraction for each sample, linked by SampleID to the abundance data. 4. DNA extraction concentration.csv - Qubit‑measured DNA concentrations (ng/μL) and the sample volume (μL) used for quality control, together with sample metadata. 5. 37species.fasta - DNA sequences of two chloroplast markers (matK and rbcL) for the 37 plant species included in the study. The sequences are in FASTA format with headers formatted as ">Species". These were used for host phylogeny construction and Pagel's λ analyses. 6. Quantitative PCR results of functional gene.csv - Quantitative PCR (qPCR) derived relative abundances of bacterial 16S rRNA gene and functional genes across different root compartments (rhizosphere, rhizoplane, endosphere), host species, root orders, and cotyledon classes (monocot/dicot). 7. README.md - A detailed description of each file, column headers, abbreviations, units, and any missing value codings (NA). All data are provided to ensure transparency and reproducibility of the analyses. For methodological details, please refer to the Materials and Methods section of the associated publication. These data are under embargo until the associated research article is published. After that date, they will be freely available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. During the embargo period, the metadata (title, authors, abstract) and the DOI remain publicly visible, but the data files are not accessible. For access requests before the embargo expires, please contact the corresponding author.

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.

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)

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)

1 files · 40 KB · netcdf

Sea surface temperature retrievals produced by NOAA/NESDIS/OSPO 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, degree_C) sses_standard_deviation (degree_C) 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 wind_speed (m s-1) sst_dtime (time difference from reference time, seconds) measurement_dtime sst_gradient_magnitude (SST gradient magnitude value, kelvin/km) sst_front_position (Binary SST front position indicator)