cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13789558
title: EuroCropsML

description: EuroCropsML* is a ready-to-use ML dataset combining EuroCrops reference data with Sentinel-2 reflectance data from 2021. It contains data from Latvia, Portugal, and Estonia and is intended for benchmarking few-shot crop type classification. We used Eurostat's GISCO dataset to map the EuroCrops parcels to their NUTS1-3 region.

The provided data comes in two stages:



raw_data.zip (stage 1): One dataframe per country containing a annual time series of observations for each parcel, as well as separate files for the parcels' geometries and classes (EC_hcat_c = 10-digit HCAT code indicating the hierarchy of the crop).

preprocess.zip (stage 2): Read-to-use .npz-files. Each data point is saved in an .npz-file along with its metadata (parcel's centroid in [lon,lan]; observation dates). In addition, we performed some cloud removal steps. Each .npz-file is saved with the following naming convention: <NUTS3region>_<parcelID>_<EC_hcat_c>.npz


Furthermore, split.zip contains .json-files that split the files from preprocess.zip into a pre-training/meta-learning (train and validation) and fine-tuning (train, validation, and test) dataset. In total, we provide two use cases:



latvia_portugal_vs_estonia: pre-training on Latvia and Portugal (142 distinct classes), fine-tuning on Estonia (127 distinct classes, of which 34 have not been seen during pre-training)

latvia_vs_estonia: pre-training on Latvia (103 distinct classes) and fine-tuning on Estonia (127 distinct classes, of which 46 have not been seen during pre-training)


For both use cases, the fine-tuning split is as follows:



train: 1-, 5-, 10-, 20-, 100-, 200-, 500-shot (for few-shot classification and benchmarking) and all samples

validation: 1000 samples

test: all samples


 

Changelog



Version 8: Adjustment of Portugal finetuning split such that it matches the Latvia finetuning split

Version 7: Added new few-shot fine-tuning splits: 200 and 500

Version 6: Added new (few-shot) fine-tuning splits: 20, 100, and all samples

Version 4: The EuroCrops shapefiles sometimes contain a couple of parcels that lie outside the national borders. We now map them to the closest NUTS region within the country. Please rely on this version or newer.

Version 3: Some parcels have been clipped incorrectly. 

Version 2: Remove datapoints that contain only cloudy observations (in preprocess.zip).

Version 1: Initial publication


* Contains Copernicus Sentinel data (2024), processed on EOLab

 

Country-secific data sources for EuroCrops reference data

Estonia:

INSPIRE GEOPORTAL

If link does not work, search for Estonia --> Geospatial Aid Application Estonia Agricultural parcels on the INSPIRE platform.

Latvia:

Lauku atbalsta dienests Updated Source

Portugal:

Download via WFS https://www.ifap.pt/isip/ows/isip.data/wfs or over the IFAP website.
DOI: 10.5281/zenodo.10629610
title: EuroCropsML

description: EuroCropsML is a ready-to-use ML dataset combining EuroCrops reference data with Sentinel-2 reflectance data from 2021. It contains data from Latvia, Portugal, and Estonia and is intended for benchmarking few-shot crop type classification. We used Eurostat's GISCO dataset to map the EuroCrops parcels to their NUTS1-3 region.

The provided data comes in two stages:



raw_data.zip (stage 1): One dataframe per country containing a annual time series of observations for each parcel, as well as separate files for the parcels' geometries and classes (EC_hcat_c = 10-digit HCAT code indicating the hierarchy of the crop).

preprocess.zip (stage 2): Read-to-use .npz-files. Each data point is saved in an .npz-file along with its metadata (parcel's centroid in [lon,lan]; observation dates). In addition, we performed some cloud removal steps. Each .npz-file is saved with the following naming convention: NUTS3region_parcelID_EC_hcat_c.npz


Furthermore, split.zip contains .json-files that split the files from preprocess.zip into a pre-training/meta-learning (train and validation) and fine-tuning (train, validation, and test) dataset. In total, we provide two use cases:



latvia_portugal_vs_estonia: pre-training on Latvia and Portugal, fine-tuning on Estonia

latvia_vs_estonia: pre-training on Latvia and fine-tuning on Estonia


For both use cases, the fine-tuning split is as follows:



train: 1-, 5-, and 10-shot (for few-shot classification and benchmarking)

validation: 1000 samples

test: all samples
DOI: 10.5281/zenodo.10804243
title: EuroCropsML

description: EuroCropsML is a ready-to-use ML dataset combining EuroCrops reference data with Sentinel-2 reflectance data from 2021. It contains data from Latvia, Portugal, and Estonia and is intended for benchmarking few-shot crop type classification. We used Eurostat's GISCO dataset to map the EuroCrops parcels to their NUTS1-3 region.

The provided data comes in two stages:



raw_data.zip (stage 1): One dataframe per country containing a annual time series of observations for each parcel, as well as separate files for the parcels' geometries and classes (EC_hcat_c = 10-digit HCAT code indicating the hierarchy of the crop).

preprocess.zip (stage 2): Read-to-use .npz-files. Each data point is saved in an .npz-file along with its metadata (parcel's centroid in [lon,lan]; observation dates). In addition, we performed some cloud removal steps. Each .npz-file is saved with the following naming convention: NUTS3region_parcelID_EC_hcat_c.npz


Furthermore, split.zip contains .json-files that split the files from preprocess.zip into a pre-training/meta-learning (train and validation) and fine-tuning (train, validation, and test) dataset. In total, we provide two use cases:



latvia_portugal_vs_estonia: pre-training on Latvia and Portugal (142 distinct classes), fine-tuning on Estonia (127 distinct classes, from which 34 have not been seen during pre-training)

latvia_vs_estonia: pre-training on Latvia (103 distinct classes) and fine-tuning on Estonia (127 distinct classes, from which 46 have not been seen during pre-training)


For both use cases, the fine-tuning split is as follows:



train: 1-, 5-, and 10-shot (for few-shot classification and benchmarking)

validation: 1000 samples

test: all samples


 

Changelog



Version 3: Some parcels have been clipped incorrectly. Please do not rely on this version. Use version 4 or newer. 

Version 2: Remove datapoints that contain only cloudy observations (in preprocess.zip).

Version 1: Initial publication
DOI: 10.5281/zenodo.10818792
title: EuroCropsML

description: EuroCropsML is a ready-to-use ML dataset combining EuroCrops reference data with Sentinel-2 reflectance data from 2021. It contains data from Latvia, Portugal, and Estonia and is intended for benchmarking few-shot crop type classification. We used Eurostat's GISCO dataset to map the EuroCrops parcels to their NUTS1-3 region.

The provided data comes in two stages:



raw_data.zip (stage 1): One dataframe per country containing a annual time series of observations for each parcel, as well as separate files for the parcels' geometries and classes (EC_hcat_c = 10-digit HCAT code indicating the hierarchy of the crop).

preprocess.zip (stage 2): Read-to-use .npz-files. Each data point is saved in an .npz-file along with its metadata (parcel's centroid in [lon,lan]; observation dates). In addition, we performed some cloud removal steps. Each .npz-file is saved with the following naming convention: <NUTS3region>_<parcelID>_<EC_hcat_c>.npz


Furthermore, split.zip contains .json-files that split the files from preprocess.zip into a pre-training/meta-learning (train and validation) and fine-tuning (train, validation, and test) dataset. In total, we provide two use cases:



latvia_portugal_vs_estonia: pre-training on Latvia and Portugal (142 distinct classes), fine-tuning on Estonia (127 distinct classes, of which 34 have not been seen during pre-training)

latvia_vs_estonia: pre-training on Latvia (103 distinct classes) and fine-tuning on Estonia (127 distinct classes, of which 46 have not been seen during pre-training)


For both use cases, the fine-tuning split is as follows:



train: 1-, 5-, and 10-shot (for few-shot classification and benchmarking)

validation: 1000 samples

test: all samples


 

Changelog



Version 4: The EuroCrops shapefilees sometimes contain a couple of parcels that lie outside the national borders. We now map them to the closest NUTS region within the country. Please rely on this version.

Version 3: Some parcels have been clipped incorrectly. 

Version 2: Remove datapoints that contain only cloudy observations (in preprocess.zip).

Version 1: Initial publication
DOI: 10.5281/zenodo.12168505
title: EuroCropsML

description: EuroCropsML* is a ready-to-use ML dataset combining EuroCrops reference data with Sentinel-2 reflectance data from 2021. It contains data from Latvia, Portugal, and Estonia and is intended for benchmarking few-shot crop type classification. We used Eurostat's GISCO dataset to map the EuroCrops parcels to their NUTS1-3 region.

The provided data comes in two stages:



raw_data.zip (stage 1): One dataframe per country containing a annual time series of observations for each parcel, as well as separate files for the parcels' geometries and classes (EC_hcat_c = 10-digit HCAT code indicating the hierarchy of the crop).

preprocess.zip (stage 2): Read-to-use .npz-files. Each data point is saved in an .npz-file along with its metadata (parcel's centroid in [lon,lan]; observation dates). In addition, we performed some cloud removal steps. Each .npz-file is saved with the following naming convention: <NUTS3region>_<parcelID>_<EC_hcat_c>.npz


Furthermore, split.zip contains .json-files that split the files from preprocess.zip into a pre-training/meta-learning (train and validation) and fine-tuning (train, validation, and test) dataset. In total, we provide two use cases:



latvia_portugal_vs_estonia: pre-training on Latvia and Portugal (142 distinct classes), fine-tuning on Estonia (127 distinct classes, of which 34 have not been seen during pre-training)

latvia_vs_estonia: pre-training on Latvia (103 distinct classes) and fine-tuning on Estonia (127 distinct classes, of which 46 have not been seen during pre-training)


For both use cases, the fine-tuning split is as follows:



train: 1-, 5-, 10-, 20-, 100-, 200-, 500-shot (for few-shot classification and benchmarking) and all samples

validation: 1000 samples

test: all samples


 

Changelog



Version 7: Added new few-shot fine-tuning splits: 200 and 500

Version 6: Added new (few-shot) fine-tuning splits: 20, 100, and all samples

Version 4: The EuroCrops shapefilees sometimes contain a couple of parcels that lie outside the national borders. We now map them to the closest NUTS region within the country. Please rely on this version or newer.

Version 3: Some parcels have been clipped incorrectly. 

Version 2: Remove datapoints that contain only cloudy observations (in preprocess.zip).

Version 1: Initial publication


* Contains Copernicus Sentinel data (2024), processed on EOLab

 

Country-secific data sources for EuroCrops reference data

Estonia:

INSPIRE GEOPORTAL

If link does not work, search for Estonia --> Geospatial Aid Application Estonia Agricultural parcels on the INSPIRE platform.

Latvia:

Lauku atbalsta dienests Updated Source

Portugal:

Download via WFS https://www.ifap.pt/isip/ows/isip.data/wfs or over the IFAP website.
DOI: 10.15468/dl.y2gyx4
title: Occurrence Download

description: A dataset containing 524 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cestrum diurnum L.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 524 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008315-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5v98043n
title: MassIVE MSV000097554 - Amyloid Protein Identification by LCMS Assay Validation
title: Amyloidosis is characterized by the deposition of protein fibrils in extra-cellular regions, resulting in the displacement and damage of healthy tissue. These diseases lead to organ damage, dysfunction, and death which is why diagnosis, characterization, and management of the disease are critical. Following a diagnosis of amyloidosis, determination of the causative protein is the next and vital step to proper treatment, as the subtypes differ in pathogenesis and clinical management. There are approximately 30 known causative proteins. Existing methods are lacking and served as motivation for the development of a clinically applicable mass spectrometry-based method for the identification of amyloid causative proteins in FFPE tissue biopsies. This method combines the power of laser microdissection for enrichment of amyloid deposits with the sensitivity and specificity of liquid chromatography mass spectrometry (LCMS). Variations of this original assay have been adapted by several reference laboratories, specialized disease centers, and academic centers and is considered the international gold standard for diagnosis and classification of amyloidosis. The aim of this paper is to outline the general considerations for development, validation, and implementation of a clinical assay with a focus on mass spectrometry-based proteomic assays in molecular and clinical pathology practice.
DOI: 10.13140/rg.2.2.28218.82889
title: CERTIFICATE OF PARTICIPATION
DOI: 10.13140/rg.2.2.14036.83849
title: ECronicon Emergency Medicine and Critical Care (ECEC) Editorial Panel
DOI: 10.13140/rg.2.2.30499.48162/1
title: Announcement of the Webinar "Life a Process of Suffering?", organised by Alternative Perspectives & Global Concerns (ap-gc.net). The Webinar will take place on Friday, February 5th, 2021 at Time: Ottawa: 13-14h30; London: 18-19h30; Amsterdam: 19-20h30; New Delhi: 23:30-1h00am
DOI: 10.15468/dl.a8mja5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Insecta)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((172.5 22.5,180 22.5,180 30,172.5 30,172.5 22.5))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041930-251009101135966/datasets/export for details.
DOI: 10.5281/zenodo.17396647
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251020k Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251020k Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.13140/rg.2.2.34012.51848
title: Opensees masonry infill concrete frame dynamic analysis
DOI: 10.5281/zenodo.17396648
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251020k Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251020k Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.13140/rg.2.2.20931.53283
title: RELATÓRIO BASE NACIONAL COMUM CURRICULAR -BNCC LEVANTAMENTO DAS PUBLICAÇÕES EM ARTIGOS CIENTÍFICOS
DOI: 10.6084/m9.figshare.26596643
title: Additional file 4 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.26596790.v1
title: Additional file 3 of Exploration of the food environment in different socioeconomic areas in Hong Kong and Singapore: a cross-sectional case study

description: Additional file 3.
DOI: 10.6084/m9.figshare.26596643.v1
title: Additional file 4 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 4
DOI: 10.15468/dl.v4zeb5
title: Occurrence Download

description: A dataset containing 229 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Trichogramma chilonis Ishii, 1941"
 ]
}

 The dataset includes 229 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066330-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26596640.v1
title: Additional file 3 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.26596667.v1
title: Additional file 12 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 12
DOI: 10.6084/m9.figshare.26596634.v1
title: Additional file 1 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.26567255
title: Additional file 5 of Multi-environment Genomic Selection in Rice Elite Breeding Lines

description: Additional file 5: Environmental covariates (ECs) of each environment throughout the whole growing season for the different phases: vegetative (VE), reproductive (RE), and ripening (RI).
DOI: 10.6084/m9.figshare.26596664.v1
title: Additional file 11 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 11
DOI: 10.6084/m9.figshare.26596661
title: Additional file 10 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 10
DOI: 10.6084/m9.figshare.26596634
title: Additional file 1 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.26596640
title: Additional file 3 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.26596592.v1
title: Additional file 1 of Influence of alcohol consumption and alcohol metabolism variants on breast cancer risk among Black women: results from the AMBER consortium

description: Additional file 1. Table S1: Characteristics of AMBER analytic sample participants by study and phase; Table S2. Significant main, interaction, and joint (2df) effects of SNP and alcohol intake on breast cancer hormone receptor subtypes in AMBER by phase;  Table S3: Statistical significance thresholds by phase in AMBER.
DOI: 10.6084/m9.figshare.26596637
title: Additional file 2 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.26596649.v1
title: Additional file 6 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 6
DOI: 10.6084/m9.figshare.26596658
title: Additional file 9 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 9
DOI: 10.6084/m9.figshare.26596592
title: Additional file 1 of Influence of alcohol consumption and alcohol metabolism variants on breast cancer risk among Black women: results from the AMBER consortium

description: Additional file 1. Table S1: Characteristics of AMBER analytic sample participants by study and phase; Table S2. Significant main, interaction, and joint (2df) effects of SNP and alcohol intake on breast cancer hormone receptor subtypes in AMBER by phase;  Table S3: Statistical significance thresholds by phase in AMBER.
DOI: 10.6084/m9.figshare.26596655
title: Additional file 8 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 8
DOI: 10.6084/m9.figshare.26596646
title: Additional file 5 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.26596664
title: Additional file 11 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 11
DOI: 10.6084/m9.figshare.26596667
title: Additional file 12 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 12
DOI: 10.6084/m9.figshare.26596637.v1
title: Additional file 2 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.26596649
title: Additional file 6 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 6
DOI: 10.15468/dl.tduhgk
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((144.410158 -36.797949, 144.410158 -36.800194, 144.412404 -36.800194, 144.412404 -36.797949, 144.410158 -36.797949)), ((144.412404 -36.800194, 144.412404 -36.804686, 144.419141 -36.804686, 144.419141 -36.800194, 144.412404 -36.800194)))"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066332-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26596652
title: Additional file 7 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 7
DOI: 10.6084/m9.figshare.26596652.v1
title: Additional file 7 of Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

description: Supplementary Material 7