cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.hsg94j
title: Occurrence Download

description: A dataset containing 426 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Human Observation"
 ]
 },
 "Geometry POLYGON((-25 35,40 35,40 75,-25 75,-25 35))",
 "HasCoordinate is true",
 "TaxonKey is Salvinia natans (L.) All.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 426 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051864-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4gp6gw
title: Occurrence Download

description: A dataset containing 62651 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Triakidae"
 ]
}

 The dataset includes 62651 records from 304 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058709-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jdqp3h
title: Occurrence Download

description: A dataset containing 265 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Colletes stepheni Timberlake, 1958"
 ]
}

 The dataset includes 265 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071343-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.id95hm
title: Occurrence Download

description: A dataset containing 863 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-125.33203 -51.75,-22.67578 -51.75,-21.49805 43.38281,-125.33203 40.35938,-125.33203 -51.75))",
 "TaxonKey is Opuntia stricta (Haw.) Haw."
 ]
}

 The dataset includes 863 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030422-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ydr8vgg64w.2
title: Annotated Sugarcane Plants

description: Ubaid, M.T.; Javaid, S. Precision Agriculture: Computer Vision-Enabled Sugarcane Plant Counting in the Tillering Phase. Journal of Imaging 2024, 10, 102. https://doi.org/10.3390/jimaging10050102

Description

Plant annotation is the process of identifying and naming certain aspects or characteristics of plant species, usually for research, categorization, or agriculture. This technique is frequently done out manually by specialists or using automated systems that employ picture recognition technologies. Annotations give useful information on plants' morphology, phenology, diseases, and genetic characteristics. They may include labels for anatomical structures. Annotations may also include categorizing plants based on their development stage, health status, or species identification. Plant annotations are used in agriculture to monitor crop development, detect pests and diseases, optimize cultivation practices, and improve production estimates. Additionally, annotated plant datasets are useful resources for training machine learning models for automated plant recognition and analysis tasks.

The images were labeled using the labeling tool "labelImg". The cane under the leaves was labeled. Annotating the images was difficult because the cane section was so little. Labeling needs care and accuracy while drawing a bounded box around the cane. For 175 photos of data, around 18650 bounding boxes were drawn. The bounding boxes were allocated the class name "sugarcane".
DOI: 10.15468/dl.9tawyc
title: Occurrence Download

description: A dataset containing 574 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Uvariastrum pierreanum Engl."
 ]
}

 The dataset includes 574 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0342916-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ydr8vgg64w.1
title: Annotated Sugarcane Plants

description: Plant annotation is the process of identifying and naming certain aspects or characteristics of plant species, usually for research, categorization, or agriculture. This technique is frequently done out manually by specialists or using automated systems that employ picture recognition technologies. Annotations give useful information on plants' morphology, phenology, diseases, and genetic characteristics. They may include labels for anatomical structures. Annotations may also include categorizing plants based on their development stage, health status, or species identification. Plant annotations are used in agriculture to monitor crop development, detect pests and diseases, optimize cultivation practices, and improve production estimates. Additionally, annotated plant datasets are useful resources for training machine learning models for automated plant recognition and analysis tasks.

The images were labeled using the labeling tool "labelImg". The cane under the leaves was labeled. Annotating the images was difficult because the cane section was so little. Labeling needs care and accuracy while drawing a bounded box around the cane. For 175 photos of data, around 18650 bounding boxes were drawn. The bounding boxes were allocated the class name "sugarcane".
DOI: 10.15468/dl.g04waw
title: Occurrence Download

description: A dataset containing 1046 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "TaxonKey is Megaptera novaeangliae (Borowski, 1781)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1046 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002268-180412121330197/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.phkqlu
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Attalea brejinhoensis (Glassman) Zona",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 12 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002221-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9ajjp5
title: Occurrence Download

description: A dataset containing 1061840 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nycticorax nycticorax (Linnaeus, 1758)"
 ]
}

 The dataset includes 1061840 records from 586 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034462-191105090559680/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pi87yf
title: Occurrence Download

description: A dataset containing 3452 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-69.45557 62.46277,-65.92896 61.7926,-65.34668 63.98712,-69.54895 64.54742,-69.45557 62.46277))",
 "TaxonKey is Plantae",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3452 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002268-190307172214381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.semaas
title: Occurrence Download

description: A dataset containing 196443 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Chile",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 196443 records from 429 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002221-180730143533302/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wtxb2k
title: Occurrence Download

description: A dataset containing 3078 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Faidherbia A.Chev."
 ]
}

 The dataset includes 3078 records from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000781-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14711941
title: Flood Susceptibility Map Over Mediterranean area benefiting from the latest high-resolution satellite observations (1km)

description: This dataset comprises high-resolution (1 km) flood susceptibility maps (FSM) for the Mediterranean region in Cloud-Optimized GeoTIFF (COG) format. The maps provide a probabilistic assessment of flood susceptibility based on a combination of dynamic variables, including soil moisture and precipitation, and static variables, such as elevation, slope, land cover, proximity to rivers, and drainage density. These maps are designed to support disaster risk assessment and management efforts by offering detailed spatial information on flood-prone areas across the Mediterranean region.
DOI: 10.5281/zenodo.14711942
title: Flood Susceptibility Map Over Mediterranean area benefiting from the latest high-resolution satellite observations (1km)

description: This dataset comprises high-resolution (1 km) flood susceptibility maps (FSM) for the Mediterranean region in Cloud-Optimized GeoTIFF (COG) format. The maps provide a probabilistic assessment of flood susceptibility based on a combination of dynamic variables, including soil moisture and precipitation, and static variables, such as elevation, slope, land cover, proximity to rivers, and drainage density. These maps are designed to support disaster risk assessment and management efforts by offering detailed spatial information on flood-prone areas across the Mediterranean region.
DOI: 10.5281/zenodo.14711542
title: Monthly drought Soil Moisture Anomalies (SMA) at 1 km from soil moisture data of the TU Wien Copernicus - Sentinel 1 RT1 dataset over Italy, monthly time-series (2017-2022)

description: General Description


Monthly drought Soil Moisture Anomalies (SMA) at 1 km from soil moisture data of the TU Wien Copernicus - Sentinel 1 RT1 dataset over Italy, monthly time-series (2017-2022)The Soil Moisture Anomalies (SMA) dataset is generated using the soil moisture dataset of the TU Wien  Copernicus - Sentinel 1 RT1 dataset. The dataset time spans from 2017 to September 2022. The dataset is applicable in analysing the soil moisture alterations down to the local scale, as well as in evaluating the multiple other processes dependent on the evolution of soil moisture not only in the hydrosphere but also in the atmosphere and biosphere. The dataset includes:



Monthly time-series:


Monthly aggregated SMA generated aggreagating soil moisture anomalies at dekad (10-day period) scale.

Data Details



Time period: Jan 2017 – September 2022

Type of data: Soil moisture anomaly data aggregated at monthly temporal scale

How the data was collected or derived: TU Wien Copernicus - Sentinel 1 RT1 soil moisture dataset obtained using Python.

Statistical methods used: standardized soil moisture anomalies.

Coordinate reference system: EPSG:4326

Bounding box (Xmin, Ymin, Xmax, Ymax): (-60, 20, 70, 88)

Spatial resolution: 0.008333 d.d. (1km)

Image size: 15600 x 8160 pixels

File format: Cloud Optimized Geotiff (COG) format.


Support

If you discover a bug, artefact, or inconsistency, or if you have a question please contact: jaimegaonagarcia@cnr.it

Reference

Brocca, L., Gaona, J., Bavera, D., Fioravanti, G., Puca, S., Ciabatta, L., ... & Wagner, W. (2024). Exploring the actual spatial resolution of 1 km satellite soil moisture products. Science of the Total Environment, 945, 174087. https://doi.org/10.1016/j.scitotenv.2024.174087

Name convention

To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way, users can search files, prepare data analysis etc, without needing to open files. The fields are:



Generic variable name: ssma= Surface Soil Moisture Anomaly

Variable procedure combination: s1.rt1 = Sentinel 1 RT1 Copernicus TU-Wien dataset.

Statistical methods used: Mean per month.

Spatial support: 1 km

Depth reference: s5cm = surface soil moisture in the uppermost 5 cm layer

Time reference begin time: 20170101 = 2017-01-01

Time reference end time: 20220930 = 2022-09-30

Bounding box: it = Italy

EPSG code: epsg4326 = EPSG:4326

Version code: v20240310 = 2024-03-10 (creation date)
DOI: 10.5281/zenodo.14711543
title: Monthly drought Soil Moisture Anomalies (SMA) at 1 km from soil moisture data of the TU Wien Copernicus - Sentinel 1 RT1 dataset over Italy, monthly time-series (2017-2022)

description: General Description


Monthly drought Soil Moisture Anomalies (SMA) at 1 km from soil moisture data of the TU Wien Copernicus - Sentinel 1 RT1 dataset over Italy, monthly time-series (2017-2022)The Soil Moisture Anomalies (SMA) dataset is generated using the soil moisture dataset of the TU Wien  Copernicus - Sentinel 1 RT1 dataset. The dataset time spans from 2017 to September 2022. The dataset is applicable in analysing the soil moisture alterations down to the local scale, as well as in evaluating the multiple other processes dependent on the evolution of soil moisture not only in the hydrosphere but also in the atmosphere and biosphere. The dataset includes:



Monthly time-series:


Monthly aggregated SMA generated aggreagating soil moisture anomalies at dekad (10-day period) scale.

Data Details



Time period: Jan 2017 – September 2022

Type of data: Soil moisture anomaly data aggregated at monthly temporal scale

How the data was collected or derived: TU Wien Copernicus - Sentinel 1 RT1 soil moisture dataset obtained using Python.

Statistical methods used: standardized soil moisture anomalies.

Coordinate reference system: EPSG:4326

Bounding box (Xmin, Ymin, Xmax, Ymax): (-60, 20, 70, 88)

Spatial resolution: 0.008333 d.d. (1km)

Image size: 15600 x 8160 pixels

File format: Cloud Optimized Geotiff (COG) format.


Support

If you discover a bug, artefact, or inconsistency, or if you have a question please contact: jaimegaonagarcia@cnr.it

Reference

Brocca, L., Gaona, J., Bavera, D., Fioravanti, G., Puca, S., Ciabatta, L., ... & Wagner, W. (2024). Exploring the actual spatial resolution of 1 km satellite soil moisture products. Science of the Total Environment, 945, 174087. https://doi.org/10.1016/j.scitotenv.2024.174087

Name convention

To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way, users can search files, prepare data analysis etc, without needing to open files. The fields are:



Generic variable name: ssma= Surface Soil Moisture Anomaly

Variable procedure combination: s1.rt1 = Sentinel 1 RT1 Copernicus TU-Wien dataset.

Statistical methods used: Mean per month.

Spatial support: 1 km

Depth reference: s5cm = surface soil moisture in the uppermost 5 cm layer

Time reference begin time: 20170101 = 2017-01-01

Time reference end time: 20220930 = 2022-09-30

Bounding box: it = Italy

EPSG code: epsg4326 = EPSG:4326

Version code: v20240310 = 2024-03-10 (creation date)
DOI: 10.5281/zenodo.14627717
title: The potential change on Land Surface Temperature during the diurnal cycle produced by changes on Land Cover Classes

description: Summary

The potential change on Land Surface Temperature (LST) diurnal cycle produced by changes on Land Cover Classes was estimated for Africa and Europe using LST from EUMESAT LSA SAF (MLST [LSA-004], [1]), and land cover classes from ESA-CCI for the year 2019 [2]. The estimation of the potential change was estimated using the space for time substitution workflow proposed by Duveiller et al., 2018 [3].  The current product is a temporary aggregate for the month of August 2019. Where a value every 15 minutes was estimated as the average during the month for that hour.

Dimensions



lat: [-35.0, 80.0]

lon: [-20.0, 52.0]

time_of_day: ['00:00:00', '23:45:00']

transitions: ['BUILT to BARE', 'WATER_INLAND to BARE', 'TREES to BARE',  'GRASS to BARE', 'SHRUB to BARE', 'WATER_INLAND to BUILT', 'TREES to BUILT', 'GRASS to BUILT', 'SHRUB to BUILT', 'TREES to WATER_INLAND', 'GRASS to WATER_INLAND', 'SHRUB to WATER_INLAND', 'GRASS to TREES', 'SHRUB to TREES', 'SHRUB to GRASS'].

GRASS: Included ESA-CCI classes: 'GRASS-MAN' + 'GRASS-NAT'

TREES: Included ESA-CCI classes: 'TREES-BD' + 'TREES-BE' + 'TREES-ND' + 'TREES-NE'

SHRUB: Included ESA-CCI classes: 'SHRUBS-BD' + 'SHRUBS-BE' + 'SHRUBS-ND' + 'SHRUBS-NE'


Data Variables:



delta: The potential change of land surface temperature for a specific transition.

delta_error: The error associated to the estimation of delta for a specific transition.

co-occurrence: Co-occurrence of two land use classes in the local moving window.


 

Post-processing:



Pixels where co-occurrence < 0.4 were set as NaNs. Pixels with delta values lower than -20 and higher than 20 were set as NaNs.


 

Disclaimer:



The current product is in beta state. Future versions of the product will be based on a new version of the YAXArraysToolbox package.


Source code:

https://github.com/dpabon/space4time_bdap_products

References:

[1] Ermida, S.L., Trigo, I.F., DaCamara, C.C., Pires, A.C., 2018. A Methodology to Simulate LST Directional Effects Based on Parametric Models and Landscape Properties. Remote Sens. 10, 1114. https://doi.org/10.3390/rs10071114 [2] Harper, K. L., Lamarche, C., Hartley, A., Peylin, P., Ottlé, C., Bastrikov, V., San Martín, R., Bohnenstengel, S. I., Kirches, G., Boettcher, M., Shevchuk, R., Brockmann, C., and Defourny, P.: A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models, Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, 2023. [3] Duveiller, Gregory, Josh Hooker, and Alessandro Cescatti. “A Dataset Mapping the Potential Biophysical Effects of Vegetation Cover Change.” Scientific Data 5, no. 1 (February 20, 2018): 180014. https://doi.org/10.1038/sdata.2018.14.

 

Acknowledgement:

The Open-Earth-Monitor Cyberinfrastructure project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101059548.
DOI: 10.5281/zenodo.14627718
title: The potential change on Land Surface Temperature during the diurnal cycle produced by changes on Land Cover Classes

description: Summary

The potential change on Land Surface Temperature (LST) diurnal cycle produced by changes on Land Cover Classes was estimated for Africa and Europe using LST from EUMESAT LSA SAF (MLST [LSA-004], [1]), and land cover classes from ESA-CCI for the year 2019 [2]. The estimation of the potential change was estimated using the space for time substitution workflow proposed by Duveiller et al., 2018 [3].  The current product is a temporary aggregate for the month of August 2019. Where a value every 15 minutes was estimated as the average during the month for that hour.

Dimensions



lat: [-35.0, 80.0]

lon: [-20.0, 52.0]

time_of_day: ['00:00:00', '23:45:00']

transitions: ['BUILT to BARE', 'WATER_INLAND to BARE', 'TREES to BARE',  'GRASS to BARE', 'SHRUB to BARE', 'WATER_INLAND to BUILT', 'TREES to BUILT', 'GRASS to BUILT', 'SHRUB to BUILT', 'TREES to WATER_INLAND', 'GRASS to WATER_INLAND', 'SHRUB to WATER_INLAND', 'GRASS to TREES', 'SHRUB to TREES', 'SHRUB to GRASS'].

GRASS: Included ESA-CCI classes: 'GRASS-MAN' + 'GRASS-NAT'

TREES: Included ESA-CCI classes: 'TREES-BD' + 'TREES-BE' + 'TREES-ND' + 'TREES-NE'

SHRUB: Included ESA-CCI classes: 'SHRUBS-BD' + 'SHRUBS-BE' + 'SHRUBS-ND' + 'SHRUBS-NE'


Data Variables:



delta: The potential change of land surface temperature for a specific transition.

delta_error: The error associated to the estimation of delta for a specific transition.

co-occurrence: Co-occurrence of two land use classes in the local moving window.


 

Post-processing:



Pixels where co-occurrence < 0.4 were set as NaNs. Pixels with delta values lower than -20 and higher than 20 were set as NaNs.


 

Disclaimer:



The current product is in beta state. Future versions of the product will be based on a new version of the YAXArraysToolbox package.


Source code:

https://github.com/dpabon/space4time_bdap_products

References:

[1] Ermida, S.L., Trigo, I.F., DaCamara, C.C., Pires, A.C., 2018. A Methodology to Simulate LST Directional Effects Based on Parametric Models and Landscape Properties. Remote Sens. 10, 1114. https://doi.org/10.3390/rs10071114 [2] Harper, K. L., Lamarche, C., Hartley, A., Peylin, P., Ottlé, C., Bastrikov, V., San Martín, R., Bohnenstengel, S. I., Kirches, G., Boettcher, M., Shevchuk, R., Brockmann, C., and Defourny, P.: A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models, Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, 2023. [3] Duveiller, Gregory, Josh Hooker, and Alessandro Cescatti. “A Dataset Mapping the Potential Biophysical Effects of Vegetation Cover Change.” Scientific Data 5, no. 1 (February 20, 2018): 180014. https://doi.org/10.1038/sdata.2018.14.

 

Acknowledgement:

The Open-Earth-Monitor Cyberinfrastructure project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101059548.