cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1115859.10fu
title: SH1115859.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1115862.10fu
title: SH1115862.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1115864.10fu
title: SH1115864.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1115865.10fu
title: SH1115865.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.14391871
title: Dataset blockage report

description: The following database was used in the research SPATIOTEMPORAL ANALYSIS AND PREDICTION OF SEWER BLOCKAGES DUE TO FOG ACCUMULATION USING BINARY CLASSIFICATION MODELS
DOI: 10.5281/zenodo.14391872
title: Dataset blockage report

description: The following database was used in the research SPATIOTEMPORAL ANALYSIS AND PREDICTION OF SEWER BLOCKAGES DUE TO FOG ACCUMULATION USING BINARY CLASSIFICATION MODELS
DOI: 10.15468/dl.ra7v6a
title: Occurrence Download

description: A dataset containing 535 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Nardostachys jatamansi (D.Don) DC."
 ]
}

 The dataset includes 535 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030381-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dbk6vf
title: Occurrence Download

description: A dataset containing 9283055 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Flora von Deutschland (Phanerogamen)"
 ]
}

 The dataset includes 9283055 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030347-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5523/bris.3nt2av52pzf8h1y93y4b8bonas
title: Daly_Dee_Org_Lett_2024_v3

description: Data for Daley-Dee Org Lett 2024

N.b. This dataset supercedes and replaces those data previously published as Daly_Dee_Org_Lett_2024 v2 (https://data.bris.ac.uk/data/dataset/bris.3nt2av52pzf8h1y93y4b8bonas).
DOI: 10.5281/zenodo.14223152
title: SEN4LDN National Demonstration Products on Trends in Land Cover for Land Degradation Neutrality Monitoring

description: This dataset contains the National Demonstration products that were generated within the ESA SEN4LDN project: "High resolution Land Degradation Neutrality Monitoring" for the sub-indicator on Trends in Land Cover over Colombia, Portugal and Uganda.

Trends in land cover between 2018 and 2023 are evaluated based on an automated algorithm to map land cover dynamics at 10 m resolution that combines deep learning and a pixel classifier on pre-processed Sentinel-2 imagery and ancillary input layers. Post-processing is performed to mitigate class fluctuations, resulting in consistent annual land cover maps. Land cover probabilities are used to generate land cover transition (probability) layers, that are further processed to discrete and continuous land degradation products.

The dataset includes:



Annual Land Cover Maps (LCM) for the years 2018 to 2023, with file naming LCM_<country>_<year>.zip

Land Cover Transition (LCT) for 2018-2023, with file naming LCT_<country>_2018-2023.zip

Land Cover Degradation (LCD) classes for 2018-2023, with file naming LCD_<country>_2018-2023.zip

Land Cover Degradation Probabilities (LCD-PROB) for 2018-2023, with file naming LCD-PROB_<country>_2018-2023.zip


Products are distributed as 3x3° tiles with 1/12000° resolution (~10m) in Cloud Optimized Geotiff format. More information on product format and content can be found in the Product User Guide, available on the SEN4LDN Deliverables web page.

The SEN4LDN project aimed to develop, demonstrate and validate a robust and scientifically-sound EO methodology that exploits the high frequency and spatial resolution of open and free-of-charge satellite imagery to increase the spatial details of national assessments of land degradation and restoration, and provide synoptic information for countries to plan LDN interventions at appropriate scales. More information on http://esa-sen4ldn.org/ .

Click here to view the maps in an interactive Google Earth Engine application.
DOI: 10.5281/zenodo.14223153
title: SEN4LDN National Demonstration Products on Trends in Land Cover for Land Degradation Neutrality Monitoring

description: This dataset contains the National Demonstration products that were generated within the ESA SEN4LDN project: "High resolution Land Degradation Neutrality Monitoring" for the sub-indicator on Trends in Land Cover over Colombia, Portugal and Uganda.

Trends in land cover between 2018 and 2023 are evaluated based on an automated algorithm to map land cover dynamics at 10 m resolution that combines deep learning and a pixel classifier on pre-processed Sentinel-2 imagery and ancillary input layers. Post-processing is performed to mitigate class fluctuations, resulting in consistent annual land cover maps. Land cover probabilities are used to generate land cover transition (probability) layers, that are further processed to discrete and continuous land degradation products.

The dataset includes:



Annual Land Cover Maps (LCM) for the years 2018 to 2023, with file naming LCM_<country>_<year>.zip

Land Cover Transition (LCT) for 2018-2023, with file naming LCT_<country>_2018-2023.zip

Land Cover Degradation (LCD) classes for 2018-2023, with file naming LCD_<country>_2018-2023.zip

Land Cover Degradation Probabilities (LCD-PROB) for 2018-2023, with file naming LCD-PROB_<country>_2018-2023.zip


Products are distributed as 3x3° tiles with 1/12000° resolution (~10m) in Cloud Optimized Geotiff format. More information on product format and content can be found in the Product User Guide, available on the SEN4LDN Deliverables web page.

The SEN4LDN project aimed to develop, demonstrate and validate a robust and scientifically-sound EO methodology that exploits the high frequency and spatial resolution of open and free-of-charge satellite imagery to increase the spatial details of national assessments of land degradation and restoration, and provide synoptic information for countries to plan LDN interventions at appropriate scales. More information on http://esa-sen4ldn.org/ .

Click here to view the maps in an interactive Google Earth Engine application.
DOI: 10.15468/dl.5d7abn
title: Occurrence Download

description: A dataset containing 376 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Amazophrynella Fouquet, Recoder, Teixeira, Cassimiro, Amaro, Camacho, Damasceno, Carnaval, Moritz et al., 2012"
 ]
}

 The dataset includes 376 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030382-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14230324
title: SEN4LDN National Demonstration Products on Trends in Land Productivity for Land Degradation Neutrality Monitoring

description: This dataset contains the National Demonstration products that were generated within the ESA SEN4LDN project: "High resolution Land Degradation Neutrality Monitoring" for the sub-indicator on Trends in Land Productivity over Colombia, Portugal and Uganda.

To evaluate trends in land productivity, the seasonal accumulated production of green biomass is estimated from a Sentinel-2 derived index, which is an indicator for photosynthetic activity and overall ecosystem functionality. The trend of vegetation productivity is estimated for the period 2018-2023 at 10 m spatial resolution. The performance of vegetation productivity is based on comparison of the local productivity to similar land units over a larger area. Discrete and continuous land productivity degradation maps are generated based on the combination of the former two.

The dataset includes:



Values of trend coefficient of productivity over the period 2018-2023, with file naming LPROD_<country>_trendval.zip

Classes indicating trend / no trend, with file naming LPROD_<country>_trendclass.zip

Maximum performance 2021-2023 over the land cover class reference, with file naming LPROD_<country>_perfval.zip

Classes of performance indicating degradation / no degradation, with file naming LPROD_<country>_perfclass.zip

Land productivity degradation classes, with file naming LPROD_<country>_LPD.zip


Products are distributed as 3x3° tiles with 1/12000° resolution (~10m) in Cloud Optimized Geotiff format. More information on product format and content can be found in the Product User Guide, available on the SEN4LDN Deliverables web page. 

The SEN4LDN project aimed to develop, demonstrate and validate a robust and scientifically-sound EO methodology that exploits the high frequency and spatial resolution of open and free-of-charge satellite imagery to increase the spatial details of national assessments of land degradation and restoration, and provide synoptic information for countries to plan LDN interventions at appropriate scales. More information on http://esa-sen4ldn.org/ .

Click here to view the maps in an interactive Google Earth Engine application.
DOI: 10.5281/zenodo.14230325
title: SEN4LDN National Demonstration Products on Trends in Land Productivity for Land Degradation Neutrality Monitoring

description: This dataset contains the National Demonstration products that were generated within the ESA SEN4LDN project: "High resolution Land Degradation Neutrality Monitoring" for the sub-indicator on Trends in Land Productivity over Colombia, Portugal and Uganda.

To evaluate trends in land productivity, the seasonal accumulated production of green biomass is estimated from a Sentinel-2 derived index, which is an indicator for photosynthetic activity and overall ecosystem functionality. The trend of vegetation productivity is estimated for the period 2018-2023 at 10 m spatial resolution. The performance of vegetation productivity is based on comparison of the local productivity to similar land units over a larger area. Discrete and continuous land productivity degradation maps are generated based on the combination of the former two.

The dataset includes:



Values of trend coefficient of productivity over the period 2018-2023, with file naming LPROD_<country>_trendval.zip

Classes indicating trend / no trend, with file naming LPROD_<country>_trendclass.zip

Maximum performance 2021-2023 over the land cover class reference, with file naming LPROD_<country>_perfval.zip

Classes of performance indicating degradation / no degradation, with file naming LPROD_<country>_perfclass.zip

Land productivity degradation classes, with file naming LPROD_<country>_LPD.zip


Products are distributed as 3x3° tiles with 1/12000° resolution (~10m) in Cloud Optimized Geotiff format. More information on product format and content can be found in the Product User Guide, available on the SEN4LDN Deliverables web page. 

The SEN4LDN project aimed to develop, demonstrate and validate a robust and scientifically-sound EO methodology that exploits the high frequency and spatial resolution of open and free-of-charge satellite imagery to increase the spatial details of national assessments of land degradation and restoration, and provide synoptic information for countries to plan LDN interventions at appropriate scales. More information on http://esa-sen4ldn.org/ .

Click here to view the maps in an interactive Google Earth Engine application.
DOI: 10.15468/dl.anvqa8
title: Occurrence Download

description: A dataset containing 122 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Morocco",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Caretta caretta (Linnaeus, 1758)"
 ]
}

 The dataset includes 122 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030383-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14274475
title: SEN4LDN National Demonstration Products on Trends in Carbon Stocks for Land Degradation Neutrality Monitoring

description: This dataset contains the National Demonstration products that were generated within the ESA SEN4LDN project: "High resolution Land Degradation Neutrality Monitoring" for the sub-indicator on Trends in Carbon Stocks over Colombia, Portugal and Uganda.

The concept of carbon stocks in terms of LDN assessments according to the United Nations Convention to Combat Desertification (UNCCD) Good Practice Guidance is primarily related to the soil carbon pool and its changes. However, since soil organic carbon (SOC) stock change estimates from remote sensing data are not globally readily available (yet), SEN4LDN explored the use of above-ground biomass (AGB) changes as a proxy for carbon stock changes to provide an estimate independent of the other two sub-indicators (i.e. trends in land cover and trends in land productivity). Two approaches were combined (averaged) to quantify trends in carbon stocks (Araza et al., 2023): a stock change approach based on European Space Agency (ESA) Climate Change Initiative (CCI) biomass maps (version 5), and a gain-loss approach based on the World Resource Institute (WRI) carbon flux model. Results from our hybrid approach provide the estimate of AGB evolution between 2010 and 2018 as well as the standard deviation, indicating the absolute uncertainty of the modeling results. Maps at 100 m spatial resolution have been generated for three countries (Colombia, Portugal and Uganda) as a feasibility assessment.

The dataset includes:



Hybrid AGB Average 2010-2018, with file naming SEN4LDN_Hybrid-Avg_V100_2010-2018_<country>_MAP.tif

Hybrid AGB Standard Deviation 2010-2018, with file naming SEN4LDN_Hybrid-Stdev_V100_2010-2018_<country>_MAP.tif


Products are distributed as country-wide Geotiff files with 0.00088888° resolution (~100m). More information on product format and content can be found in the Product User Guide, available on the SEN4LDN Deliverables web page.

The SEN4LDN project aimed to develop, demonstrate and validate a robust and scientifically-sound EO methodology that exploits the high frequency and spatial resolution of open and free-of-charge satellite imagery to increase the spatial details of national assessments of land degradation and restoration, and provide synoptic information for countries to plan LDN interventions at appropriate scales. More information on http://esa-sen4ldn.org/ .

Click here to view the maps in an interactive Google Earth Engine application.
DOI: 10.5281/zenodo.14274476
title: SEN4LDN National Demonstration Products on Trends in Carbon Stocks for Land Degradation Neutrality Monitoring

description: This dataset contains the National Demonstration products that were generated within the ESA SEN4LDN project: "High resolution Land Degradation Neutrality Monitoring" for the sub-indicator on Trends in Carbon Stocks over Colombia, Portugal and Uganda.

The concept of carbon stocks in terms of LDN assessments according to the United Nations Convention to Combat Desertification (UNCCD) Good Practice Guidance is primarily related to the soil carbon pool and its changes. However, since soil organic carbon (SOC) stock change estimates from remote sensing data are not globally readily available (yet), SEN4LDN explored the use of above-ground biomass (AGB) changes as a proxy for carbon stock changes to provide an estimate independent of the other two sub-indicators (i.e. trends in land cover and trends in land productivity). Two approaches were combined (averaged) to quantify trends in carbon stocks (Araza et al., 2023): a stock change approach based on European Space Agency (ESA) Climate Change Initiative (CCI) biomass maps (version 5), and a gain-loss approach based on the World Resource Institute (WRI) carbon flux model. Results from our hybrid approach provide the estimate of AGB evolution between 2010 and 2018 as well as the standard deviation, indicating the absolute uncertainty of the modeling results. Maps at 100 m spatial resolution have been generated for three countries (Colombia, Portugal and Uganda) as a feasibility assessment.

The dataset includes:



Hybrid AGB Average 2010-2018, with file naming SEN4LDN_Hybrid-Avg_V100_2010-2018_<country>_MAP.tif

Hybrid AGB Standard Deviation 2010-2018, with file naming SEN4LDN_Hybrid-Stdev_V100_2010-2018_<country>_MAP.tif


Products are distributed as country-wide Geotiff files with 0.00088888° resolution (~100m). More information on product format and content can be found in the Product User Guide, available on the SEN4LDN Deliverables web page.

The SEN4LDN project aimed to develop, demonstrate and validate a robust and scientifically-sound EO methodology that exploits the high frequency and spatial resolution of open and free-of-charge satellite imagery to increase the spatial details of national assessments of land degradation and restoration, and provide synoptic information for countries to plan LDN interventions at appropriate scales. More information on http://esa-sen4ldn.org/ .

Click here to view the maps in an interactive Google Earth Engine application.
DOI: 10.15468/dl.hgmgpw
title: Occurrence Download

description: A dataset containing 99629 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (United States of America, Canada, Mexico)",
 "TaxonKey is Chrysemys picta (Schneider, 1783)"
 ]
}

 The dataset includes 99629 records from 117 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030384-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14283566
title: SEN4LDN National Demonstration Products on Land Degradation Neutrality Indicator SDG15.3.1

description: This dataset contains the National Demonstration products that were generated within the ESA SEN4LDN project: "High resolution Land Degradation Neutrality Monitoring" for the integrated SDG15.3.1 indicator on Land Degradation Neutrality over Colombia, Portugal and Uganda.

The SEN4LDN national demonstration products consist of a series of output products on the three sub-indicators of land degradation as defined by the UNCCD – trends in land cover, trends in land productivity, and trends in carbon stocks – and a combined integrated LDN indicator. The outputs of the trends in land cover and trends in land productivity sub-indicators are integrated to generate a product that allows to calculate the extent of land degradation for reporting on UN SDG indicator 15.3.1, expressed as the proportion (percentage) of land that is degraded over total land area. This database contains the result of the so-called one-out-all-out in which a significant reduction or negative change in any one of the sub-indicators is considered to comprise land degradation.

The dataset includes the Land Degradation Neutrality indicator 2018-2023, with file naming LD_<country>_2018-2023.zip.

Products are distributed as 3x3° tiles with 1/12000° resolution (~10m) in Cloud Optimized Geotiff format. More information on product format and content can be found in the Product User Guide, available on the SEN4LDN Deliverables web page.

The SEN4LDN project aimed to develop, demonstrate and validate a robust and scientifically-sound EO methodology that exploits the high frequency and spatial resolution of open and free-of-charge satellite imagery to increase the spatial details of national assessments of land degradation and restoration, and provide synoptic information for countries to plan LDN interventions at appropriate scales. More information on http://esa-sen4ldn.org/ .

Click here to view the maps in an interactive Google Earth Engine application.