cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3955789
title: RNA-seq data from leaf trichomes, stem trichomes, bald stems and leaf primordiums from different tomato genetic backgrounds

description: # Description Various tissues of cultivated and wild tomato genotypes were harvested. # RNA isolation protocol Total RNA was isolated before messenger and small RNA were separated. # RNA sequencing protocol Illumina NextSeq550 # Samples <pre><code class="language-markdown">| tube | accession | tissue | sample | weight (mg) | Small RNA | Messenger RNA | |------|------------|------------------|--------|-------------|------------|----------------| | SQ1 | PI127826 | trichomes | 1 | 49 | 1 | 0 | | SQ2 | PI127826 | trichomes | 2 | 47,1 | 1 | 0 | | SQ3 | PI127826 | trichomes | 3 | 45,2 | 1 | 0 | | SQ4 | Moneymaker | trichomes | 4 | 47,9 | 1 | 0 | | SQ5 | Moneymaker | trichomes | 5 | 48,9 | 1 | 0 | | SQ6 | Moneymaker | trichomes | 6 | 47,2 | 1 | 0 | | SQ7 | Lyc4 | trichomes | 1 | 27,7 | 1 | 0 | | SQ8 | Lyc4 | trichomes | 2 | 26,5 | 1 | 0 | | SQ9 | PIK3 | trichomes | 3 | 50 | 1 | 1 | | SQ10 | LA4024 | trichomes | 1 | 45,5 | 1 | 0 | | SQ11 | LA4024 | trichomes | 2 | 49,5 | 1 | 0 | | SQ12 | LA4024 | trichomes | 3 | 49,9 | 1 | 0 | | SQ13 | LA1777 | trichomes | 1 | 50 | 1 | 0 | | SQ14 | LA1777 | trichomes | 2 | 48,9 | 1 | 0 | | SQ15 | LA1777 | trichomes | 3 | 47,9 | 1 | 0 | | SQ16 | MicroTom | trichomes | 1 | 43,1 | 1 | 1 | | SQ17 | MicroTom | trichomes | 2 | 43,6 | 1 | 1 | | SQ18 | MicroTom | trichomes | 3 | 45,1 | 1 | 1 | | SQ19 | LA0716 | trichomes | 1 | 43 | 1 | 0 | | SQ20 | LA0716 | trichomes | 2 | 46,1 | 1 | 0 | | SQ21 | LA0716 | trichomes | 3 | 49,5 | 1 | 0 | | SQ22 | Myc-1 | trichomes | 1 | 46 | 1 | 1 | | SQ23 | Myc-1 | trichomes | 2 | 37,5 | 1 | 1 | | SQ24 | Myc-1 | trichomes | 3 | 42,5 | 1 | 1 | | SQ25 | Lyc4 | trichomes | 4 | 26,5 | 1 | 0 | | SQ26 | Myc-1 | leaf | 1 | 45 | 1 | 1 | | SQ27 | Myc-1 | bald stem | 1 | 45 | 1 | 1 | | SQ28 | Moneymaker | leaf primordium | 1 | 8,4 | 1 | 1 | | SQ29 | PI127826 | leaf primordium | 1 | 12,8 | 1 | 1 | | SQ30 | LA0716 | leaf primordium | 1 | 9,1 | 1 | 1 | | SQ31 | moneymaker | bald stem | 4 | 45 | 1 | 1 | | SQ32 | moneymaker | bald stem | 5 | 45 | 1 | 1 | | SQ33 | moneymaker | bald stem | 6 | 45 | 1 | 1 | | SQ34 | PI127826 | bald stem | 1 | 45 | 1 | 1 | | SQ35 | PI127826 | bald stem | 2 | 45 | 1 | 1 | | SQ36 | PI127826 | bald stem | 3 | 45 | 1 | 1 | | SQ37 | LA0716 | bald stem | 1 | 45 | 1 | 1 | | SQ38 | LA0716 | bald stem | 2 | 45 | 1 | 1 | | SQ39 | LA0716 | bald stem | 3 | 45 | 1 | 1 | | SQ40 | moneymaker | leaf | 1 | 45 | 1 | 1 | | SQ41 | moneymaker | leaf | 1+2 | 45 | 1 | 1 | | SQ42 | moneymaker | leaf | 2 | 45 | 1 | 1 | | SQ43 | PI127826 | leaf | 1 | 45 | 1 | 1 | | SQ44 | PI127826 | leaf | 2 | 45 | 1 | 1 | | SQ45 | PI127826 | leaf | 3 | 45 | 1 | 1 | | SQ46 | LA0716 | leaf | 1 | 45 | 1 | 1 | | SQ47 | LA0716 | leaf | 2 | 45 | 1 | 1 | | SQ48 | LA0716 | leaf | 3 | 45 | 1 | 1 | | SQ49 | PIK1 | Trichomes | 1 | 25 | 1 | 1 | | SQ50 | PIK2 | Trichomes | 2 | 45 | 1 | 1 |</code></pre>
DOI: 10.5281/zenodo.3891000
title: Uncovering dryland woody dynamics using optical, microwave, and field data: Prolonged above-average rainfall paradoxically contributes to woody plant die-off in the western Sahel

description: Data accompanying the paper: Bernardino, P., Brandt, M., De Keersmaecker, W., Horion, S., Fensholt, R., Storms, I., Wigneron, JP., Verbesselt, J. and Somers, B. 2020. Uncovering dryland woody dynamics using optical, microwave, and field data: Prolonged above-average rainfall paradoxically contributes to woody plant die-off in the western Sahel. <em>Remote Sensing</em>, 12(14), 2332.
DOI: 10.5281/zenodo.3473213
title: Uncovering dryland woody dynamics using optical, microwave, and field data: Prolonged above-average rainfall paradoxically contributes to woody plant die-off in the western Sahel

description: Data accompanying the paper: Bernardino, P., Brandt, M., De Keersmaecker, W., Horion, S., Fensholt, R., Storms, I., Wigneron, JP., Verbesselt, J. and Somers, B. 2020. Uncovering dryland woody dynamics using optical, microwave, and field data: Prolonged above-average rainfall paradoxically contributes to woody plant die-off in the western Sahel. <em>Remote Sensing</em>, 12(14), 2332.
DOI: 10.5281/zenodo.3591395
title: TAASRAD19 Radar Scans 2017-2019

description: TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps.<br> The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration.<br> Software methods for data pre-processing, model training and inference, and a pre-trained model are<br> publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility.
description: This dataset contains the radar scans for the years 2017 - 2019. The radar scans for years 2010 - 2016 are available here: https://doi.org/10.5281/zenodo.3577451 The precipitation sequences in HDF5 format, extracted from the full scan dataset are available here: https://doi.org/10.5281/zenodo.3591404
DOI: 10.5281/zenodo.3866204
title: TAASRAD19 Radar Sequences 2010-2019 NetCDF

description: TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps.<br> The dataset includes 894,916 time steps of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration.<br> Software methods for data pre-processing, model training and inference, and a pre-trained model are<br> publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility.
description: This dataset contains the ready-to-use precipitation sequences in NetCDF format, extracted from the scan full dataset. An HDF5 version of the same dataset is available here: https://doi.org/10.5281/zenodo.3865889 The full archive of all radar scans is available at the following links: https://doi.org/10.5281/zenodo.3577451 (years 2010 - 2016) https://doi.org/10.5281/zenodo.3591396 (years 2017 - 2019)
DOI: 10.5281/zenodo.3866203
title: TAASRAD19 Radar Sequences 2010-2019 NetCDF

description: TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps.<br> The dataset includes 894,916 time steps of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration.<br> Software methods for data pre-processing, model training and inference, and a pre-trained model are<br> publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility.
description: This dataset contains the ready-to-use precipitation sequences in NetCDF format, extracted from the scan full dataset. An HDF5 version of the same dataset is available here: https://doi.org/10.5281/zenodo.3865889 The full archive of all radar scans is available at the following links: https://doi.org/10.5281/zenodo.3577451 (years 2010 - 2016) https://doi.org/10.5281/zenodo.3591396 (years 2017 - 2019)
DOI: 10.5281/zenodo.3577450
title: TAASRAD19 Radar Scans 2010-2016

description: TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps.<br> The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration.<br> Software methods for data pre-processing, model training and inference, and a pre-trained model are<br> publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility.
description: This dataset contains the radar scans for the years 2010 - 2016. The radar scans for years 2017 - 2019 are available here: https://doi.org/10.5281/zenodo.3591396 The precipitation sequences in HDF5 format, extracted from the full scan dataset are available here: https://doi.org/10.5281/zenodo.3591404
DOI: 10.5281/zenodo.3865889
title: TAASRAD19 Radar Sequences 2010-2019 HDF5

description: TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps.<br> The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration.<br> Software methods for data pre-processing, model training and inference, and a pre-trained model are<br> publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility.
description: This dataset contains the ready-to-use precipitation sequences in HDF5 format, extracted from the scan full dataset. A NetCDF version of the same dataset is available here: https://doi.org/10.5281/zenodo.3866204 The full archive of all radar scans is available at the following links: https://doi.org/10.5281/zenodo.3577451 (years 2010 - 2016) https://doi.org/10.5281/zenodo.3591396 (years 2017 - 2019)
DOI: 10.5281/zenodo.3591403
title: TAASRAD19 Radar Sequences 2010-2019 HDF5

description: TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps.<br> The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration.<br> Software methods for data pre-processing, model training and inference, and a pre-trained model are<br> publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility.
description: This dataset contains the ready-to-use precipitation sequences in HDF5 format, extracted from the scan full dataset. A NetCDF version of the same dataset is available here: https://doi.org/10.5281/zenodo.3866204 The full archive of all radar scans is available at the following links: https://doi.org/10.5281/zenodo.3577451 (years 2010 - 2016) https://doi.org/10.5281/zenodo.3591396 (years 2017 - 2019)
DOI: 10.15156/bio/sh2663731.08fu
title: SH2663731.08FU

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/sh2663732.08fu
title: SH2663732.08FU

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/sh1973268.08fu
title: SH1973268.08FU

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/sh1973269.08fu
title: SH1973269.08FU

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/sh2663733.08fu
title: SH2663733.08FU

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/sh2663734.08fu
title: SH2663734.08FU

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/sh2663735.08fu
title: SH2663735.08FU

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/sh1973270.08fu
title: SH1973270.08FU

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.