cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14933647
title: Global Pasture Watch - Annual horse density maps at 1-km for 2000–2022 including prediction interval

description: Global annual maps of horse density at 1-km spatial resolution convering the period of 2000–2022. The maps were produced using harmonized and used as reference data (52,883 census polygons and 678,266 individual data points), random forest predictive models using and a large stack of multi-source harmonized gridded/raster spatial layers (307 individual raster spatial layers harmonized at 1 km spatial resolution).

Pixels values represent heads km-square including:





Mean predicted values (_m_)




Upper prediction interval based on 97.5th percentiles (_p.975_)




Lower prediction interval based on 2.5th percentiles (_p.025_)



Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975
- p.025) by four.

Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.

In line with a request from our funders, livestock maps will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals

Maps of livestock density for 2000—2022 (including prediction interval):Cattle  Goat   Sheep  Horse

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.13891754
title: ICEAGE_NIRCAM_SPECTRAL_DELIVERY_V1

description: This is the initial full spectral delivery (V1) for the ICEAGE NIRCAM WFSS observations of Cha I.

This data is embargoed until 31/12/2025 to this group only. 

The spectra are delivered as a single ZIP file.

 





Please START by reading the READ_ME_ICEAGE_NIRCAM_Sciencereadyspectraldelviery.pdf document. This includes details wrt authorship and arising articles, as well as other very pertinent information that could answer A LOT of your questions and queries. 

Please note though files are given the V1 version of this file delivery does NOT have readme files per 10 spectra yet populated. This will follow before the end of 2024.

 

Until such a time, if there are any queries on a specific spectrum - before using it for science PLEASE contact Helen and / or Zak.

PLEASE do not share outside the NIRCAM team without the express permission of the ICEAGE PIs Adwin and Melissa. 

(If you have been given access here then this data is for your use only – if a student or PDRA is using the data please ask a Zenodo ICEAGE NIRCAM community moderator to add them to this repository so that we are aware whom has access.)

 

Enjoy doing new science!! And don’t forget to check with the ICEAGE PIs and especially Adwin who is leading the NIRCAM team wrt. ongoing papers and work before initiating a project.
DOI: 10.5281/zenodo.13890165
title: ICEAGE_NIRCAM_SPECTRAL_DELIVERY_V1

description: This is the initial full spectral delivery (V1) for the ICEAGE NIRCAM WFSS observations of Cha I.

This data is embargoed until 31/12/2025 to this group only. 

The spectra are delivered as a single ZIP file.

 





Please START by reading the READ_ME_ICEAGE_NIRCAM_Sciencereadyspectraldelviery.pdf document. This includes details wrt authorship and arising articles, as well as other very pertinent information that could answer A LOT of your questions and queries. 

Please note though files are given the V1 version of this file delivery does NOT have readme files per 10 spectra yet populated. This will follow before the end of 2024.

 

Until such a time, if there are any queries on a specific spectrum - before using it for science PLEASE contact Helen and / or Zak.

PLEASE do not share outside the NIRCAM team without the express permission of the ICEAGE PIs Adwin and Melissa. 

(If you have been given access here then this data is for your use only – if a student or PDRA is using the data please ask a Zenodo ICEAGE NIRCAM community moderator to add them to this repository so that we are aware whom has access.)

 

Enjoy doing new science!! And don’t forget to check with the ICEAGE PIs and especially Adwin who is leading the NIRCAM team wrt. ongoing papers and work before initiating a project.
DOI: 10.5281/zenodo.15730622
title: Sea-surface velocity field output from Irish Marine Institute ROMS Model for August and September 2024.

description: The dataset was used in the paper 'Linking small-scale flow patterns to changes in coastal phytoplankton community composition' and was downloaded from https://erddap.marine.ie/erddap/griddap/IMI_NEATL.html. The ocean hydrodynamical model which generated the data was developed by the Irish Marine Institute and is detailed in https://doi.org/10.3390/jmse8090673. The .csv files give the hourly sea-surface velocity fields at 0.0125° horizontal resolution in the grid [50°N,56°N] [-13.5°W,-6°W]. Each file gives the weekly velocity fields.
DOI: 10.5281/zenodo.14843260
title: Supplementary Material for "Effects of mineralogy on ∆47 and ∆48 of carbonate-derived CO2 below analytical resolution "

description: Raw data, processing results as well as X-ray diffractogram supplementary files accompanying our ∆47, ∆48 AFF study.
DOI: 10.5281/zenodo.14645687
title: Dataset for the manuscript entitled "Market-Oriented Flow Allocation for Thermal Solar Plants: An Auction-Based Methodology with Artificial Intelligence"

description: This dataset includes 120 simulations  of 10 loops of a 50 MW parabolic-trough solar plant with varying solar irradiances, optical efficiencies, thermal losses, ambient temperatures, input temperatures, and sector flow rates. The simulations are based on a static version of the concentrated-parameter model, as described in the corresponding article.
DOI: 10.5281/zenodo.14645193
title: Interaction Between Protection Regimes, Linear Infrastructures, and Ecosystem Services: Assessment of Carbon Sequestration in Insular Forest Ecosystems

description: This repository includes the key results, datasets, and scripts used in the study assessing the interaction between protection regimes, linear infrastructures, and ecosystem services, with a focus on carbon sequestration in insular forest ecosystems. While a selection of datasets is available here, certain larger data files are available upon request from the corresponding author due to their size. This collection provides comprehensive resources for replicating the analysis and further exploring the findings presented in the associated paper.
DOI: 10.5281/zenodo.17494177
title: Global Pasture Watch - Annual livestock headcount layers for cattle, goats, sheep, horses, and buffaloes at 1-km 2000–2022 (FAOSTAT-adjusted) (Part-2)

description: Global annual layers of livestock distribution of cattle, goats, sheep, horses and buffaloes at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 939,257 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution).

Raster cell values represent number of livestock animals (headcounts) calibrated according to FAOSTAT database. The adjustment was applied individually to each country, ensuring that the adjusted pixel values accurately reflected the national totals. For applications relying on headcount data from FAOSTAT the provided layers serve as a fully compatible alternative.

In line with a request from our funders, livestock maps will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals (Part-1)  All animals (Part-2)

Maps of livestock density for 2000—2022 (including 95% prediction interval):Cattle   Goats   Sheep   Horses   Buffaloes

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.14048717
title: Rock glacier inventory for Jammu, Kashmir, and Ladakh, western Himalaya, India

description: The data repository contains a comprehensive inventory of 5807 rock glaciers of the western Himalayan regions of Jammu, Kashmir and Ladakh. The rock glacier inventory is characterized by 30 attributes following standards of International Permafrost Association (RGIK 2023) . The inventory is available as polygon as KMZ file.
DOI: 10.5281/zenodo.16421813
title: MONNALISA_WINDTUNNEL_RESULTS

description: Upload of the Wind Tunnel results for the MONNALISA Project. It contains Infrared thermographies, PIV results on steady and unsteady conditions (elevator flapping), surface visualizations via china-clay method and aerodynamic coefficients for all geometries and test conditions.
DOI: 10.5281/zenodo.15485524
title: Global River Sediment Database (GloRivSed)

description: The Global River Sediment Database (GloRivSed) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the world that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of global river reaches using the SWOT River Database (SWORD) centerlines where high quality river water pixels were detected within each Landsat image from 1984-2022. The paper associated with GloRivSed: Prajapati, R., Gardner, J., & Prum, P. (2025). Global trends and drivers of riverine suspended sediment concentration. Geophysical Research Letters.

Please cite the associated paper in Geophysical Research Letters (TExtent of sediment concentration trends associated with climate and human factors across global rivers) and this zenodo database when publishing work using this data. Thank you.
DOI: 10.5281/zenodo.10685536
title: Neuroblastoma copper chelation single-cell

description: To access the data, please send an e-mail to antonietta.salerno@outlook.it specifying a valid reason for your request.
DOI: 10.5281/zenodo.14221886
title: Hourly and daily values of meteorological elements from 40 meteorological stations located in Arizona (USA)

description: The collection includes three text files (Arizona_meteo_hourly_01.txt; Arizona_meteo_hourly_02.txt; Arizona_meteo_daily.txt) with hourly and daily values of measurements from 40 meteorological stations located in Arizona (USA). The data was collected from September to November 2024 by Sylwester Wereski as a part of MINIATURA 5 "The occurrence of strong heat stress in hot and arid climate condition" (2021/05/X/ST10/00587), the research internship project at Arizona State University financed by the National Science Center in Poland. The source of data is website https://cales.arizona.edu/azmet/az-data.htm managed by AZMET (The Arizona Meteorological Network). The collection also includes a Readme.txt file, which contains a description of the location of the meteorological stations and the period of measurements conducted, as well as a description of meteorological variables.
DOI: 10.5281/zenodo.16762250
title: De Novo Annotation Reveals Transcriptomic Complexity Across the Hexaploid Wheat Pan-Genome

description: Wheat is the most widely cultivated crop in the world, with over 215 million hectares grown annually. The 10+ Wheat Genomes Project recently sequenced and assembled to chromosome-level the genomes of nine wheat cultivars, uncovering genetic diversity and selection within the pan-genome of wheat. Here, we provide a wheat pan-transcriptome with de novo annotation and differential expression analysis for these wheat cultivars across multiple tissues. Analysis of these de novo annotations facilitated the identification of cultivar-specific genes and defined the core and dispensable genomes. Expression analysis across cultivars and tissues revealed conservation in expression between a large core set of homeologous genes, but also widespread changes in subgenome homeolog expression bias between cultivars and cultivar-specific expression profiles. In a case study utilising both the newly constructed gene-based wheat pan-genome and pan-transcriptome, we demonstrate variation in the prolamin superfamily and immune-reactive proteins across cultivars.The datasets provided here are detailed in _README_contents.xlsx
DOI: 10.5281/zenodo.14933636
title: Global Pasture Watch - Annual livestock headcount maps for cattle, goats, sheep and horses at 1-km 2000–2022 (FAOSTAT-adjusted)

description: Global annual maps of livestock distribution of cattle, goats, sheep and horses at 1-km spatial resolution convering the period of 2000–2022. The maps were produced using harmonized and used as reference data (52,883 census polygons and 678,266 individual data points), random forest predictive models using and a large stack of multi-source harmonized gridded/raster spatial layers (307 individual raster spatial layers harmonized at 1 km spatial resolution). 

Pixels values represent number of livestock animals (headcounts) calibrated according to FAOSTAT database. The adjustment was applied individually to each country, ensuring that the adjusted pixel values accurately reflected the national totals. For applications relying on headcount data from FAOSTAT the provided maps serve as a fully compatible alternative.

In line with a request from our funders, livestock maps will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals

Maps of livestock density for 2000—2022 (including prediction interval):Cattle  Goat   Sheep  Horse

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.15420474
title: Global River Sediment Database (GloRivSed)

description: The Global River Sediment Database (GloRivSed) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the world that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of global river reaches using the SWOT River Database (SWORD) centerlines where high quality river water pixels were detected within each Landsat image from 1984-2022. The paper associated with GloRivSed: Prajapati, R., Gardner, J., & Prum, P. (2025). Global trends and drivers of riverine suspended sediment concentration. Geophysical Research Letters.

Please cite the associated paper in Geophysical Research Letters (TExtent of sediment concentration trends associated with climate and human factors across global rivers) and this zenodo database when publishing work using this data. Thank you.
DOI: 10.5281/zenodo.16007527
title: Thermal buffering by tidally generated internal bores on the shallows of a remote coral atoll

description: This study investigates the effect of internal bores at Mermaid Reef, a coral reef atoll off the North West Shelf of Australia.
DOI: 10.5281/zenodo.17352150
title: What determines trophic niche breadth? A global analysis of freshwater fishes using isospaces

description: Data and R code repository for "What determines trophic niche breadth? A global analysis of freshwater fishes using isospaces". Reviews in Fish Biology and Fisheries.

Keppeler et al.

Contained is:



R code to conduct the main analyses.

Dataset


Contact information: Friedrich W Keppeler (fkeppeler@gmail.com).
DOI: 10.5281/zenodo.14933641
title: Global Pasture Watch - Annual sheep density maps at 1-km for 2000–2022 including prediction interval

description: Global annual maps of sheep density at 1-km spatial resolution convering the period of 2000–2022. The maps were produced using harmonized and used as reference data (52,883 census polygons and 678,266 individual data points), random forest predictive models using and a large stack of multi-source harmonized gridded/raster spatial layers (307 individual raster spatial layers harmonized at 1 km spatial resolution).

Pixels values represent heads km-square including:





Mean predicted values (_m_)




Upper prediction interval based on 97.5th percentiles (_p.975_)




Lower prediction interval based on 2.5th percentiles (_p.025_)



Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975
- p.025) by four.

Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.

In line with a request from our funders, livestock maps will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals

Maps of livestock density for 2000—2022 (including prediction interval):Cattle  Goat   Sheep  Horse

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.14933660
title: Global Pasture Watch - Annual cattle density maps at 1-km for 2000–2022 including prediction interval

description: Global annual maps of cattle density at 1-km spatial resolution convering the period of 2000–2022. The maps were produced using harmonized and used as reference data (52,883 census polygons and 678,266 individual data points), random forest predictive models using and a large stack of multi-source harmonized gridded/raster spatial layers (307 individual raster spatial layers harmonized at 1 km spatial resolution). 

Pixels values represent heads km-square including:



Mean predicted values (_m_)

Upper prediction interval based on 97.5th percentiles (_p.975_)

Lower prediction interval based on 2.5th percentiles (_p.025_)


 Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.

Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.

In line with a request from our funders, livestock maps will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources



Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):All animals

Maps of livestock density for 2000—2022 (including prediction interval):Cattle  Goat   Sheep  Horse

Livestock reference census data (2000–2022):CSV and GeoPackage files

Maps potential land for livestock production for 2000—2022 (including production systems)Raster files

Data catalogues:OpenLandMap STAC Google Earth Engine
DOI: 10.5281/zenodo.15494597
title: PRC2 gatekeeps the balance between direct and indirect neurogenesis in human corticogenesis

description: The polycomb repressive complex 2 (PRC2) and its catalytic subunit EZH2 are essential for maintaining transcriptional repression at genomic regulatory regions through the methylation of lysine 27 on histone 3 (H3K27me3). PRC2 activity is fundamental during early development, particularly in corticogenesis, with mutations in its subunits leading to various developmental disorders and cancers.

Our knowledge about the impact of PRC2 dysfunction in corticogenesis is still incomplete and largely based on mouse data.

To understand the activity of EZH2 in human early corticogenesis we dissected the molecular mechanisms underlying Weaver syndrome (WVS), a rare autosomal dominant multisystem disorder caused by heterozygous mutations in the EZH2 gene characterized by pre- and post-natal overgrowth, macrocephaly, facial dysmorphisms, and variable intellectual disability.

We performed a comprehensive multimodal omic and functional analysis using longitudinal courses of cortical brain organoidogenesis derived from a uniquely informative cohort of patient-specific models.

This dataset contains: i) raw counts and differential expression analysis table from bulk RNA-seq; ii) peaks and raw counts for ChIP-seq and Cut&Run data; iii) h5ad file containing single-cell RNA-seq at different stages of organoidogenesis; iv) reference data used to build gene-regulatory-networks and data interpretation