cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.as4v59
title: Occurrence Download

description: A dataset containing 1220 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "TaxonKey is Inga vera Willd."
 ]
}

 The dataset includes 1220 records from 111 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0230596-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ierkr7
title: Occurrence Download

description: A dataset containing 9050 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Levantamiento florístico en área del Proyecto GEF Corredores Biológicos de Montaña, Zona Central de Chile"
 ]
}

 The dataset includes 9050 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000964-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.muhfto
title: Occurrence Download

description: A dataset containing 44240 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Olea europaea L."
 ]
}

 The dataset includes 44240 records from 305 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003121-190307172214381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.udf6me
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Enicurus velatus sumatranus Robinson & Kloss, 1923, Enicurus velatus velatus, Enicurus velatus Temminck, 1822)"
 ]
}

 The dataset includes 83 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043380-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13143346
title: MANOLO_DeskResearchResults_Dataset2_20240731_v1

description: The dataset includes the input collected from MANOLO partners through the Desk Research activities in the framework of the project's "D1.1 Trustworthy Efficient AI for the Cloud-edge Deep Dive".
DOI: 10.5281/zenodo.13143347
title: MANOLO_DeskResearchResults_Dataset2_20240731_v1

description: The dataset includes the input collected from MANOLO partners through the Desk Research activities in the framework of the project's "D1.1 Trustworthy Efficient AI for the Cloud-edge Deep Dive".
DOI: 10.5281/zenodo.7435910
title: Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

description: Datasets for PILOT


Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we are still lacking computational methods for the analysis of single-cell and pathomics data at a patient level for finding patient trajectories associated with diseases. This is challenging as a single-cell/pathomics data is represented by clusters of cells/structures, which cannot be compared with other samples. We propose here patient Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two single single-cell experiments. This allows us to perform unsupervised analysis at the sample level and to uncover trajectories associated with disease progression. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression and tissues structures related to the disease trajectories.  We evaluate PILOT and competing approaches in  disease single-cell genomics and pathomics studies with up to 1.000 patients/donors and millions of cells or structures. Results demonstrate that PILOT detects disease-associated samples, cells, and genes from large and complex single-cell and pathomics data.
DOI: 10.5281/zenodo.7435911
title: Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

description: <strong>Datasets for PILOT</strong> Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we are still lacking computational methods for the analysis of single-cell and pathomics data at a patient level for finding patient trajectories associated with diseases. This is challenging as a single-cell/pathomics data is represented by clusters of cells/structures, which cannot be compared with other samples. We propose here patient Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two single single-cell experiments. This allows us to perform unsupervised analysis at the sample level and to uncover trajectories associated with disease progression. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression and tissues structures related to the disease trajectories. We evaluate PILOT and competing approaches in disease single-cell genomics and pathomics studies with up to 1.000 patients/donors and millions of cells or structures. Results demonstrate that PILOT detects disease-associated samples, cells, and genes from large and complex single-cell and pathomics data.
DOI: 10.5281/zenodo.7956950
title: Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

description: Datasets for PILOT


Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we are still lacking computational methods for the analysis of single-cell and pathomics data at a patient level for finding patient trajectories associated with diseases. This is challenging as a single-cell/pathomics data is represented by clusters of cells/structures, which cannot be compared with other samples. We propose here patient Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two single single-cell experiments. This allows us to perform unsupervised analysis at the sample level and to uncover trajectories associated with disease progression. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression and tissues structures related to the disease trajectories.  We evaluate PILOT and competing approaches in  disease single-cell genomics and pathomics studies with up to 1.000 patients/donors and millions of cells or structures. Results demonstrate that PILOT detects disease-associated samples, cells, and genes from large and complex single-cell and pathomics data.
DOI: 10.5281/zenodo.8370081
title: Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

description: Datasets for PILOT


Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we are still lacking computational methods for the analysis of single-cell and pathomics data at a patient level for finding patient trajectories associated with diseases. This is challenging as a single-cell/pathomics data is represented by clusters of cells/structures, which cannot be compared with other samples. We propose here patient Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two single single-cell experiments. This allows us to perform unsupervised analysis at the sample level and to uncover trajectories associated with disease progression. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression and tissues structures related to the disease trajectories.  We evaluate PILOT and competing approaches in  disease single-cell genomics and pathomics studies with up to 1.000 patients/donors and millions of cells or structures. Results demonstrate that PILOT detects disease-associated samples, cells, and genes from large and complex single-cell and pathomics data.
DOI: 10.5281/zenodo.13143375
title: MANOLO_OnlineSurveyQuestionnaire_Dataset3_20240731_v1

description: The dataset includes the questions used to collect feedback from the general public through the Online Survey activity in the framework of the project's "D1.1 Trustworthy Efficient AI for the Cloud-edge Deep Dive".
DOI: 10.5281/zenodo.13143374
title: MANOLO_OnlineSurveyQuestionnaire_Dataset3_20240731_v1

description: The dataset includes the questions used to collect feedback from the general public through the Online Survey activity in the framework of the project's "D1.1 Trustworthy Efficient AI for the Cloud-edge Deep Dive".
DOI: 10.5281/zenodo.13143386
title: MANOLO_OnlineSurveyResults_Dataset4_20240731_v1

description: The dataset includes the results from the Online Survey activity in the framework of the project's "D1.1 Trustworthy Efficient AI for the Cloud-edge Deep Dive".
DOI: 10.5281/zenodo.13143387
title: MANOLO_OnlineSurveyResults_Dataset4_20240731_v1

description: The dataset includes the results from the Online Survey activity in the framework of the project's "D1.1 Trustworthy Efficient AI for the Cloud-edge Deep Dive".
DOI: 10.5281/zenodo.11634768
title: Scherrenberg et al. (2024) supplement (Climate of the past): Ice-sheet model code, and output of Northern Hemisphere ice-sheet evolution of the past 800 kyr

description: Supplement to Scherrenberg et al. (2024), article in Climate of the Past.

This data-set contains ice-sheet model (IMAU-ICE) code (see IMAU_ICE_Code.zip; see https://github.com/IMAU-paleo/IMAU-ICE/tree/main for the most recent version of the model), the model output and configuration files (see Data_output.zip), and scripts to create figures (see Scripts_and_Figures.zip).

Please note that additional input fields are required to run IMAU-ICE and to produce the figures. See Scherrenberg et al., (2024) for more information or contact the corresponding author.

Citation: M.D.W. Scherrenberg, C.J. Berends, R.S.W. van de Wal: Late Pleistocene glacial terminations accelerated by proglacial lakes, climate of the past, special issue "icy landscapes of the past", 2024
DOI: 10.5281/zenodo.11634769
title: Scherrenberg et al. (2024) supplement (Climate of the past): Ice-sheet model code, and output of Northern Hemisphere ice-sheet evolution of the past 800 kyr

description: Supplement to Scherrenberg et al. (2024), article in Climate of the Past.

This data-set contains ice-sheet model (IMAU-ICE) code (see IMAU_ICE_Code.zip; see https://github.com/IMAU-paleo/IMAU-ICE/tree/main for the most recent version of the model), the model output and configuration files (see Data_output.zip), and scripts to create figures (see Scripts_and_Figures.zip).

Please note that additional input fields are required to run IMAU-ICE and to produce the figures. See Scherrenberg et al., (2024) for more information or contact the corresponding author.

Citation: M.D.W. Scherrenberg, C.J. Berends, R.S.W. van de Wal: Late Pleistocene glacial terminations accelerated by proglacial lakes, climate of the past, special issue "icy landscapes of the past", 2024
DOI: 10.5281/zenodo.12699334
title: Multi-modal image analysis for large scale cancer tissue studies within IMMUcan: multiplex immunofluorescence images

description: In cancer research, multiplexed imaging has enabled the in-depth characterization of the tumor microenvironment (TME) and how it relates to patient prognosis. However, standardized, multi-modal data from large numbers of patients to identify robust biomarkers is missing. To provide such data across five cancer indications, the IMMUcan consortium performs broad molecular and cellular spatial profiling of thousands of cancer samples. Two reproducible and scalable workflows have been developed for whole slide multiplexed immunofluorescence (mIF) and imaging mass cytometry (IMC) to overcome challenges of reproducibility and scalability. For mIF we developed IFQuant, a web-based tool optimized for user-friendliness and reproducibility. This Zenodo record contains the mIF images and IFQuant settings to reproduce the results presented in the referenced publication. The companion IMC dataset is available as a joint Zenodo record.
DOI: 10.5281/zenodo.12912567
title: IMMUcan panel 1 cell type classification

description: This repo contains Imaging Mass Cytometry data from 179 cancer patients collected during the IHI2 funded IMMUcan project.

 

Background:

IMMUcan (https://immucan.eu/) collects samples from cancer patients for multimodal analysis. Amongst those Imaging Mass Cytometry (IMC). Samples collection occurs over 6 years. For reproducible cell typing over time, a cell phenotype classifier was built from manually annotated data. The classifier was trained on a first batch of annotated images (V1) and then trained again with a second round of annotated images (V3). A final classifier was build which is used within IMMUcan to classify cell phenotypes for IMC panel 1 for all cancer patients.

 

The dataset contains:



sce_labelled_V1.rds_ is the `SingleCellExperiment` object containing all labelled cells from the first batch of labelling

sce_labelled_V3.rds_ is the `SingleCellExperiment` object containing all labelled cells from the second batch of labelling

rf_images_DCfix.rds_ is the RF model which can be used for cell type predictions

sce_raw.rds_ is the `SingleCellExperiment` object containing all cells from the 179 images. the trained classifier has been applied to this dataset which can be used to view the classification results of cell phenotypes on images using the bioconductor packages cytomapper or cytoviewer. 

all_images.rds a `CytoImageList` object which contains all the images from all images

all_masks.rds a `CytoImageList` object which contains all single-cell segmentation masks from all images

a zip archive `images_masks` containing the tiff images and masks from all 179 images, a CytoImageList Object for images (all_images.rds) and a CytoListImage object for all masks (all_masks.rds)
DOI: 10.5281/zenodo.13143362
title: Mass mortality among colony-breeding seabirds in the German Wadden Sea in 2022 due to distinct genotypes of HPAIV H5N1 clade 2.3.4.4b: data sets on phylogeographic analyses

description: Highly pathogenic avian influenza viruses (HPAIV) of clade 2.3.4.4b of the H5 goose/Guangdong (gs/GD) lineage have repeatedly emerged in Germany since 2016. Both poultry holdings and wild birds have been heavily hit but the 2020-2021 and 2021-2022 HPAI winter seasons exceeded all previously recorded epizootics in Germany in terms of number of wild bird cases recorded, genetic diversity of viruses, and duration of virus activity. In past seasons regional massing of wild bird cases were seen at the German coasts of the Baltic and North Sea, but species mainly affected varied from season to season. In 2022 a new and, in Europe, unprecedented aspect was observed when several cormorant and seabird breeding colonies became affected since May at the Baltic Sea coast and in the Wadden Sea, respectively by HPAI H5N1 viruses.
DOI: 10.5281/zenodo.6901959
title: Mass mortality among colony-breeding seabirds in the German Wadden Sea in 2022 due to distinct genotypes of HPAIV H5N1 clade 2.3.4.4b: data sets on phylogeographic analyses

description: Highly pathogenic avian influenza viruses (HPAIV) of clade 2.3.4.4b of the H5 goose/Guangdong (gs/GD) lineage have repeatedly emerged in Germany since 2016. Both poultry holdings and wild birds have been heavily hit but the 2020-2021 and 2021-2022 HPAI winter seasons exceeded all previously recorded epizootics in Germany in terms of number of wild bird cases recorded, genetic diversity of viruses, and duration of virus activity. In past seasons regional massing of wild bird cases were seen at the German coasts of the Baltic and North Sea, but species mainly affected varied from season to season. In 2022 a new and, in Europe, unprecedented aspect was observed when several cormorant and seabird breeding colonies became affected since May at the Baltic Sea coast and in the Wadden Sea, respectively by HPAI H5N1 viruses.
DOI: 10.5281/zenodo.6901960
title: Mass mortality among colony-breeding seabirds in the German Wadden Sea in 2022 due to distinct genotypes of HPAIV H5N1 clade 2.3.4.4b: data sets on phylogeographic analyses

description: Highly pathogenic avian influenza viruses (HPAIV) of clade 2.3.4.4b of the H5 goose/Guangdong (gs/GD) lineage have repeatedly emerged in Germany since 2016. Both poultry holdings and wild birds have been heavily hit but the 2020-2021 and 2021-2022 HPAI winter seasons exceeded all previously recorded epizootics in Germany in terms of number of wild bird cases recorded, genetic diversity of viruses, and duration of virus activity. In past seasons regional massing of wild bird cases were seen at the German coasts of the Baltic and North Sea, but species mainly affected varied from season to season. In 2022 a new and, in Europe, unprecedented aspect was observed when several cormorant and seabird breeding colonies became affected since May at the Baltic Sea coast and in the Wadden Sea, respectively by HPAI H5N1 viruses.
DOI: 10.5281/zenodo.7257125
title: Primary vine varieties of European wine PDOs

description: Primary varieties are the traditional vine cultivars of a region that are primarily used for making the wine products of a PDO region. In most cases, they are clearly defined in the legal document that regulate each PDO. We extracted primary varieties by analyzing the product specification files of European wine PDOs.
description: Please refer to the following articles when citing the dataset:Candiago, S., Tscholl, S., Bassani, L., Fraga, H. & Egarter Vigl, L. A geospatial inventory of regulatory information for wine protected designations of origin in Europe. Sci Data 9, 394 (2022). https://doi.org/10.1038/s41597-022-01513-0

Tscholl, S., Candiago, S., Marsoner, T., Fraga, H., Giupponi, C., & Egarter Vigl, L.Climate resilience of European wine regions. Nat Commun 15, 6254 (2024). https://doi.org/10.1038/s41467-024-50549-w
DOI: 10.5281/zenodo.13143482
title: CLDF dataset derived from Tolmie and Dawson's "Comparative Vocabulary of the Indigenous Peoples in British Columbia" from 1884

description: Cite the source of the dataset as:




Tolmie, Fraser W. and Dawson, George M. (1884). Comparative vocabularies of the Indian tribes of British Columbia, with a map illustrating distribution. Montreal: Dawson Brothers.
DOI: 10.5281/zenodo.8085892
title: CLDF dataset derived from Tolmie and Dawson's "Comparative Vocabulary of the Indigenous Peoples in British Columbia" from 1884

description: Cite the source of the dataset as: Tolmie, Fraser W. and Dawson, George M. (1884). Comparative vocabularies of the Indian tribes of British Columbia, with a map illustrating distribution. Montreal: Dawson Brothers.
DOI: 10.5281/zenodo.8085891
title: CLDF dataset derived from Tolmie and Dawson's "Comparative Vocabulary of the Indigenous Peoples in British Columbia" from 1884

description: Cite the source of the dataset as:




Tolmie, Fraser W. and Dawson, George M. (1884). Comparative vocabularies of the Indian tribes of British Columbia, with a map illustrating distribution. Montreal: Dawson Brothers.
DOI: 10.15468/dl.c9kvq5
title: Occurrence Download

description: A dataset containing 1011 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Japan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Hyalessa maculaticollis (Motschulsky, 1866)"
 ]
}

 The dataset includes 1011 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043381-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ukrh9e
title: Occurrence Download

description: A dataset containing 53433 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Arabis alpina L."
 ]
}

 The dataset includes 53433 records from 558 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043378-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57680/asep.0583155
title: Soubor primárnich dat klimatických charakteristik a izotopů uhlíku
title: Raw datasets of climate characteristics and stable carbon isotope ratios

description: Soubor primárnich dat klimatických charakteristik a izotopových poměrů uhlíku v letokruzích dubů podél výškového gradientu v letech 1961-2020. Jedná se o primární výchozí data k publikaci Pernicová et al. (2024) Impacts of elevated CO2 levels and temperature on photosynthesis and stomatal closure along an altitudinal gradient are counteracted by the rising atmospheric vapor pressure deficit, který byl publikován v časopise Science of the Total Environment (https://doi.org/10.1016/j.scitotenv.2024.171173).
description: A primary dataset of climate characteristics and carbon isotopic ratios in oak tree rings along an altitudinal gradient in 1961-2020. This dataset contains primary values used in the publication Pernicová et al. (2024) Impacts of elevated CO2 levels and temperature on photosynthesis and stomatal closure along an altitudinal gradient are counteracted by the rising atmospheric vapor pressure deficit, published in the journal Science of the Total Environment (https://doi.org/10.1016/j.scitotenv.2024.171173).
DOI: 10.15468/dl.3prmxk
title: Occurrence Download

description: A dataset containing 67 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Japan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Laccotrephes japonensis Scott, 1874"
 ]
}

 The dataset includes 67 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043382-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10410972
title: Indicators used to calculate the Vulnerability of European wine PDOs to climate change

description: Dataset of the indicators used to define the Vulnerability of European wine PDO to climate change. The database includes the values of Exposure, Sensitivity and Adaptive Capacity for each PDO region. In the case of Exposure we also included the values for the indicators of Cool Night Iindex, Dryness Index and Huglin index. In the case of Adaptive Capacity we also included all the fifteen indicators used for the assessment of financial, natural, physical, social and human capacity. The classification in one of the Vulnerability classes is included in the related field.

Please refer to the following article when citing the dataset:

Tscholl, S., Candiago, S., Marsoner, T., Fraga, H., Giupponi, C., & Egarter Vigl, L.Climate resilience of European wine regions. Nat Commun 15, 6254 (2024). https://doi.org/10.1038/s41467-024-50549-w