cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25921/fddx-gq76
title: GHRSST NOAA/STAR ACSPO v2.81 0.02 degree L3S Dataset from Afternoon LEO Satellites (GDS version 2)

description: The L3S_LEO_PM-STAR-v2.81 dataset produced by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system derives the Subskin Sea Surface Temperature (SST) from the VIIRSs (Visible Infrared Imaging Radiometer Suite) onboard the Suomi-NPP, NOAA-20 and NOAA-21 satellites and MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the Aqua satellite. The L3S-LEO is a family of multi-sensor super-collated (L3S) gridded 0.02º resolution SST products from low earth orbit (LEO) satellites. The L3S-LEO-PM and AM data include SSTs from afternoon (~1:30 am/pm) and mid-morning (~9:30 am/pm) satellites, respectively. The PM and AM SSTs, for both day (D) and night (N), and Terra MODIS SSTs, are further aggregated into a daily L3S-LEO-DY SST product.

This PM SST product is derived by collating individual satellite ACSPO L3U data (https://doi.org/10.5067/GHVRS-3UO61, https://doi.org/10.5067/GHV20-3UO61 and https://doi.org/10.5067/GHN21-3U280). It covers from 2002-07-04 to present and is reported in 2 files daily, day and night, at 1:30am/pm local time. The SST is in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The v2.81 is updated from the previous v2.80: (1) v2.81 includes 3 VIIRSs (NPP, N20, and N21 from 2023-03-19 - on); (2) Aqua MODIS SST included from 2002-07-04 to 2022-12-31; (3) Time series in v2.81 extended back to 2002-07-04 (from 2012-02-01 in v2.80); (4) recently uncovered VIIRS daytime SST drifts in NPP and N20 SSTs of approximately -0.1 K/decade mitigated.

The Near Real Time (NRT) data are available with 6h latency, and replaced by the Re-ANalysis (RAN) files in 2 months, with identical file names. They can be differentiated by the file creation time and ancillary inputs. The data are validated against quality controlled in situ data from the NOAA in situ SST Quality Monitor (iQuam), and monitored in another NOAA system, SST Quality Monitor (SQUAM) Version Description: Advanced Clear Sky Processor for Ocean version 2.81.
DOI: 10.25921/xkhb-5e96
title: GHRSST Level 3U NOAA ACSPO SST v2.80 from VIIRS on NOAA-21 Satellite (GDS version 2)

description: The N21-VIIRS-L3U-ACSPO-v2.80 dataset produced by the NOAA ACSPO system derives the Subskin Sea Surface Temperature (SST) from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System (JPSS)-2 satellite, renamed as NOAA-21 (N21). N21 was launched on Nov. 10, 2022, the 3rd satellite in the US NOAA latest JPSS series.

The ACSPO N21 VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N21 VIIRS L2P product, also available at PO.DAAC (10.5067/GHV21-2P280). The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). The ACSPO N21 VIIRS SST record is available back to 19 Mar 2023. There are 144 granules per 24 hour interval, with a total data volume of 0.6GB/day. Fill values are reported at all invalid pixels, including pixels &gt;5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SST, a subset of variable l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and the SST minus reference CMC SST (Canadian Met Centre 0.1deg L4 SST, 10.5067/GHCMC-4FM03). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST.

The ACSPO VIIRS L3U product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM).
DOI: 10.25921/sfs7-9688
title: GHRSST Level 2P NOAA STAR SST v2.80 from VIIRS on NOAA-20 Satellite (GDS version 2)

description: NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). SSTs are derived from Brightness Temperatures (BTs) using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3U product is also available, where gridded L2P SSTs with QL=5 only are reported. The v2.80 is an updated version from the v2.61 with several algorithm improvements including two added thermal front layers, reduced L2P SST data size, mitigated warm biases in the high latitudes, and improved clear-sky mask.
DOI: 10.25921/7c1m-rw73
title: GHRSST Level 3U NOAA STAR SST v2.80 from VIIRS on NOAA-20 Satellite (GDS version 2)

description: NOAA-20 (N20/JPSS-1/J1) is the second satellite in the US NOAA latest generation Joint Polar Satellite System (JPSS), launched on November 18, 2017. The ACSPO N20/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N20/VIIRS L2P product. The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 0.5GB/day. Fill values are reported at all invalid pixels, including pixels with &gt;5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, a subset of l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data in SQUAM. The v2.80 is an updated version from the v2.61 with several L2P algorithm improvements including two added thermal front layers, mitigated warm biases in the high latitudes, and improved clear-sky mask.
DOI: 10.7289/v5pr7sx5
title: GHRSST Level 2P NOAA STAR SST v2.80 from VIIRS on S-NPP Satellite (GDS version 2)

description: The Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). NOAA is responsible for all JPSS products, including SST from the Visible Infrared Imaging Radiometer Suite (VIIRS). The L2P SST product is derived at the native sensor resolution (~0.75 km at nadir, ~1.5 km at swath edge) using NOAA's Advanced Clear-Sky Processor for Ocean (ACSPO) system, and reported in 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). SSTs are derived from Brightness Temperatures (BTs) using the Non-Linear SST (NLSST) algorithms (Petrenko et al., 2014). An ACSPO clear-sky mask (ACSM) is provided in each pixel as part of variable l2p_flags, which also includes day/night, land, ice, twilight, and glint flags (Petrenko et al., 2010). Only ACSM confidently clear pixels are recommended (equivalent to GDS2 quality level=5). Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with QL=5. The ACSPO VIIRS L2P product is monitored and validated against quality controlled in situ data provided by NOAA in situ SST Quality Monitor system (iQuam) using another NOAA system, SST Quality Monitor (SQUAM). A reduced size (0.5GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3U product is also available, where gridded L2P SSTs with QL=5 only are reported. The v2.80 is an updated version from the v2.61 with several algorithm improvements including two added thermal front layers, reduced L2P SST data size, mitigated warm biases in the high latitudes, and improved clear-sky mask.
DOI: 10.7289/v5kk98s8
title: GHRSST Level 3U NOAA STAR SST v2.80 from VIIRS on S-NPP Satellite (GDS version 2)

description: The Joint Polar Satellite System (JPSS), starting with S-NPP launched on 28 October 2011, is the new generation of the US Polar Operational Environmental Satellites (POES). The ACSPO SNPP/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO NPP/VIIRS L2P product. The L3U output files are 10-minute granules in netCDF4 format, compliant with the Group for High Resolution Sea Surface Temperature (GHRSST) Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 0.5GB/day. Fill values are reported at all invalid pixels, including pixels with &gt;5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, a subset of l2p_flags (including day/night, land, ice, twilight, and glint flags), wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data in SQUAM. The v2.80 is an updated version from the v2.61 with several L2P algorithm improvements including two added thermal front layers, mitigated warm biases in the high latitudes, and improved clear-sky mask.
DOI: 10.6084/m9.figshare.28723247
title: DATA - Activity patterns, feeding habits and water consumption in food in Verreaux’s sifakas (<i>Propithecus verreauxi</i>) and red-fronted lemurs (<i>Eulemur rufifrons</i>) during the transition between wet and dry season

description: In seasonal habitats, animals may have difficulties accessing enough food and water year-round because environmental factors change massively. In the dry forests of Madagascar, lemurs adapt their behavior and diet to cope with resource scarcity, but their water requirements remain poorly understood. This study compared the activity patterns, feeding habits, and water consumption in food of Verreaux’s sifakas (<i>Propithecus verreauxi</i>) and red-fronted lemurs (<i>Eulemur rufifrons</i>) in Kirindy Forest during the transition from the wet to the dry season. We hypothesized that both species would reduce their activity levels to conserve energy and water, adapt their diets to resource availability, and consume foods with higher water content. From March to May 2024, we recorded behavior and food preferences of 6 lemur groups and collected food item samples to analyze their water content. We found that <i>E. rufifrons</i> rested more and fed less than sifakas, but both species rested less when humidity was higher. Sifakas preferred leaves, while red-fronted lemurs fed more on fruits. The two species consumed food with a similar water content that was unaffected by seasonal transitions, and their diets remained consistent despite seasonal climatic variations. By exploring these behavioral adaptations, this research provides insight into how these lemur species cope with seasonal resource dynamics.
DOI: 10.5281/zenodo.15128170
title: Integrating Ecological Networks and Multi-Scenario Optimization: A Novel Framework for Constructing Ecological Security Patterns

description: This dataset is utilized to simulate ecological networks and perform multi-scenario optimization, thereby constructing a new framework for ecological security patterns.

The dataset support the findings and conclusions of the paper:

Scenario-Specific Insights: The ecological priority scenario (SSP119-2030) enhances landscape connectivity, while intensive development scenarios (SSP245-2030, SSP545-2030) increase habitat fragmentation, highlighting economic-ecological trade-offs.

Policy Implications: The study emphasizes integrating ecological network optimization into land-use planning, offering practical guidance for sustainable regional development.
DOI: 10.5281/zenodo.15128171
title: Integrating Ecological Networks and Multi-Scenario Optimization: A Novel Framework for Constructing Ecological Security Patterns

description: This dataset is utilized to simulate ecological networks and perform multi-scenario optimization, thereby constructing a new framework for ecological security patterns.

The dataset support the findings and conclusions of the paper:

Scenario-Specific Insights: The ecological priority scenario (SSP119-2030) enhances landscape connectivity, while intensive development scenarios (SSP245-2030, SSP545-2030) increase habitat fragmentation, highlighting economic-ecological trade-offs.

Policy Implications: The study emphasizes integrating ecological network optimization into land-use planning, offering practical guidance for sustainable regional development.
DOI: 10.71795/9mz6-vt55
title: Lithologic Classification of Geologic Map Units
DOI: 10.6084/m9.figshare.28723285
title: Supplementary material from The Genomics of T'ef and Finger Millet Domestication and Spread

description: Electronic Supplementary Materials.zip
DOI: 10.6084/m9.figshare.28723285.v1
title: Supplementary material from The Genomics of T'ef and Finger Millet Domestication and Spread

description: Electronic Supplementary Materials.zip
DOI: 10.6084/m9.figshare.28723292
title: Bibliometric Results from Testing for Normality in Regression Models: Mistakes Abound (But May Not Matter)

description: Data used in the bibliometric portion of the study. Journal articles are listed by their category, DOI, journal title, Impact Factor (IF), and whether they should be included in the study based on whether their usage was discernible. Comments follow that describe the usage.
DOI: 10.6084/m9.figshare.28723253
title: Spermatogenesis

description: Dataset of scRNA-seq measurements from spermatogenesis cells, including cell state annotation from the original study, and spliced and unspliced reads estimated with velocyto.Please cite the original publication: https://doi.org/10.1016/j.celrep.2018.10.026.
DOI: 10.6084/m9.figshare.28723292.v1
title: Bibliometric Results from Testing for Normality in Regression Models: Mistakes Abound (But May Not Matter)

description: Data used in the bibliometric portion of the study. Journal articles are listed by their category, DOI, journal title, Impact Factor (IF), and whether they should be included in the study based on whether their usage was discernible. Comments follow that describe the usage.
DOI: 10.25921/cwws-6141
title: National Coral Reef Monitoring Program: Water temperature data from subsurface temperature recorders (STRs) deployed at coral reef sites in the Flower Gardens National Marine Sanctuary

description: This collection contains water temperature data collected using subsurface temperature recorders (STRs) that aid in the monitoring of seawater temperature variability at permanent coral reef sites in Flower Garden Banks National Marine Sanctuary as part of the NOAA Coral Reef Conservation Program’s (CRCP’s) National Coral Reef Monitoring Program (NCRMP). High-accuracy STRs are made by SeaBird Electronics (SBE) and deployed on the reef for a period of 3 years at a variety of depths from 21 to 38 meters at Ocean and Climate Change monitoring survey sites. When the STR is recovered, typically another STR is deployed in the same location. Raw data contains temperatures collected in 5-minute intervals each hour. Temperature data are archived by region, location and year recovered. For analysis purposes, temperature data is divided by region then by location.

The temperature data provided in this dataset were collected from STRs deployed at existing long-term monitoring sites by the AOML Coral Program based at NOAA’s Atlantic Oceanographic and Meteorological Laboratory
DOI: 10.15468/dl.j3en8r
title: Occurrence Download

description: A dataset containing 198 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((3.23898 43.20126,4.72932 43.355,4.72032 43.97504,3.7113 44.08946,3.11012 43.77746,3.23898 43.20126))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Schedonorus arundinaceus (Schreb.) Dumort.",
 "Year 2015-2025"
 ]
}

 The dataset includes 198 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002003-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14193338
title: Modelled gridded population estimates for the Kasaï-Oriental Province in the Democratic Republic of Congo (2024) version 4.2

description: Content

This repository contains the input data and scripts used to create the modeled gridded population estimates for Kasaï-Oriental Province in the Democratic Republic of Congo. It also includes the grid-cell posterior distributions and scripts to aggregate them within user-defined geographic boundaries.

 In particular, this repository contains two compressed files (.zip):

1. population_estimates.zip



Includes raster files (.tif) with summaries of population count posterior predictions at the grid-cell level, specifically the mean, median, lower credible interval, and upper credible interval.

Includes spatial files (.gpkg) with summaries of population count posterior predictions at the health-area and health-zone levels, specifically the mean, median, lower credible interval, and upper credible interval.


2. population_model.zip

This directory comprises five subdirectories with scripts, input data, and output data necessary to replicate the population model:



01_model_stan: Contains the Stan model, input data, and an R script (01_model_stan.R) with a function to run the model.

02_model_run: Includes an R script (02_model_run.R) for running the model, along with output data.

03_model_evaluate: Features a Quarto report template (03_model_evaluate.qmd) and model evaluation summary files(.pdf).

04_predict_posterior: Provides R scripts (04_predict_posterior.R and 04_predict_run.R) for generating predictions, along with input and output data, namely the posterior predictions files (.rds).

05_aggregate_posterior: Contains R scripts (05_aggregate_posterior.R and 05_aggregate_run.R) and associated input and output data, namely the population count posterior summaries as presented in the file population_estimates.zip .


The work was carried out in R (version 4.4.0), with the packages tidyverse (version 2.0.0), terra (version 1.7-78), sf (version 1.0-16), furrr (version 0.3.1), doParallel (version 1.0.17), foreach (version 1.5.2), rstudioapi (version 0.16.0), and rstan (version 2.32.6), on macOS Sequoia (version 15.1.1). While the scripts are designed to be portable, minor adjustments may be required for compatibility with other operating systems.

Important

This version includes changes in the STAN model 10h_survey_survey_covariate_building_random_effect_hierarchy_building_covariate_density_fixed_effect_hierarchy_density.stan. Consequentely, all the files generated in the previous versions are now changed.

 

For inquiries regarding the model and the data, please contact Gianluca Boo at gianluca.boo@soton.ac.uk.
DOI: 10.5281/zenodo.14193339
title: Modelled gridded population estimates for the Kasaï-Oriental Province in the Democratic Republic of Congo (2024) version 4.2

description: This repository contanins the inputa data and scripts used to create the modelled gridded population estimates for the Kasaï-Oriental Province in the Democratic Republic of Congo (2024) version 4.2. It also includes the grid-cell posterior distributions and scripts to aggregate them within user-defined geographic boundaries. In particular, this repository contains two compresssed files (.zip):

1. population_estimates.zip



Includes raster files (.tif) with summaries of population count posterior predictions at the grid-cell level, namely the mean, median, lower credible interval, and upper credible interval.

Includes spatial files (.gpkg) with summaries of population count posterior predictions at the health-area and health-zone levels, namely the mean, median, lower credible interval, and upper credible interval.


2. population_model.zip

This directory comprises five subdirectories with scripts, input data, and output data necessary to replicate the population model:



01_model_stan: Contains the Stan model), input data, and an R script (01_model_stan.R) with a function to run the model.

02_model_run: Includes an R script (02_model_run.R) for running the model, along with output data.

03_model_evaluate: Features a Quarto report template (03_model_evaluate.qmd) and model evaluation summary files(.pdf).

04_predict_posterior: Provides R scripts (04_predict_posterior.R and 04_predict_run.R) for generating predictions, along with input and output data, namely the posterior predictions files (.rds).

05_aggregate_posterior: Contains R scripts (05_aggregate_posterior.R and 05_aggregate_run.R) and associated input and output data, namely the population count posterior summaries as presented in the file population_estimate.zip .


The work was carried out in R (version 4.4.0), with the packages tidyverse (version 2.0.0), terra (version 1.7-78), sf (version 1.0-16), furrr (version 0.3.1), doParallel (version 1.0.17), foreach (version 1.5.2), rstudioapi (version 0.16.0), and rstan (version 2.32.6), on MacOS Sequoia (version 15.1.1). While the scripts are designed to be portable, minor adjustments may be required for compatibility with other operating systems.

For inquiries regarding the model and the data, please contact Gianluca Boo at gianluca.boo@soton.ac.uk.