cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14266690
title: ROS2 bagfiles associated to the European project Robs4Crops

description: This project contains multiple datasets in the form of ROS2 bagfiles, recorded during various agricultural robotics experiments in diverse environments: vineyards and apple orchards. Each dataset includes sensor data collected from agricultural vehicles (two retrofitted tractors and a differential robot called Carob) equipped with state-of-the-art perception, localization, and navigation systems.  

The datasets include:  - Camera data: RGB images, depth information, and camera calibration details.- LiDAR data: 3D point clouds from the environment.- IMU data: acceleration and angular rates.  - GNSS data: Global positioning for outdoor localization in format WGS84.- Transformations: Static and dynamic transformations between sensor frames and vehicle base frames.

Each experiment contains a readme.docx file that documents the sensor configurations, frame conventions, and trajectories followed during the experiment and other relevant information to understand how to use the data in the proper dataset.
DOI: 10.5281/zenodo.14266691
title: ROS2 bagfiles associated to the European project Robs4Crops

description: This project contains multiple datasets in the form of ROS2 bagfiles, recorded during various agricultural robotics experiments in diverse environments: vineyards and apple orchards. Each dataset includes sensor data collected from agricultural vehicles (two retrofitted tractors and a differential robot called Carob) equipped with state-of-the-art perception, localization, and navigation systems.  

The datasets include:  - Camera data: RGB images, depth information, and camera calibration details.- LiDAR data: 3D point clouds from the environment.- IMU data: acceleration and angular rates.  - GNSS data: Global positioning for outdoor localization in format WGS84.- Transformations: Static and dynamic transformations between sensor frames and vehicle base frames.

Each experiment contains a readme.docx file that documents the sensor configurations, frame conventions, and trajectories followed during the experiment and other relevant information to understand how to use the data in the proper dataset.
DOI: 10.15468/dl.b2pjgk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Poa annua L.Sp.Pl"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043590-241126133413365/datasets/export for details.
DOI: 10.18150/d4luhw
title: Complete X-ray source

description: The collection contains illustrations, measurement results and images obtained during the characterization of a miniature MEMS X-ray source.
DOI: 10.5281/zenodo.14501403
title: Spatiotemporal Clustering of Functional Ultrasound Signals at the Single-Voxel Level

description: Micro-Doppler movies used in the research paper : "Spatiotemporal Clustering of Functional Ultrasound Signals at the Single-Voxel Level" by Lambert T., Niknejad H.R., Kil D, Montaldo G., Nuttin B., Brunner C. and Urban A.

Functional ultrasound (fUS) imaging is a well-established neuroimaging technology that offers high spatiotemporal resolution and a large field of view. Typical strategies for analysing fUS data comprise either region-based averaging, typically based on reference atlases, or correlation with experimental events. Nevertheless, these methodologies possess several inherent limitations, including a restricted utilisation of the spatial dimension and a pronounced bias influenced by preconceived notions about the recorded activity. In this study, we put forth single-voxel clustering as a third method to address these issues. A comparison was conducted between the three strategies on a typical dataset comprising visually evoked activity in the superior colliculus. The application of single-voxel clustering yielded the generation of detailed activity maps, which revealed a consistent layout of activity and a clear separation between haemodynamic responses. This method is best considered as a complement to region-based averaging and correlation. It has direct applicability to challenging contexts, such as paradigm-free analysis on behaving subjects and brain decoding. 
description: The application of spatiotemporal clustering at single-voxel resolution for functional ultrasound (fUS) signal analysis significantly enhances sensitivity in comparison to conventional methods, such as region-based averaging or event correlation. Conventional approaches frequently rely on predefined atlases or specific experimental conditions, which inherently restrict spatiotemporal resolution. In contrast, single-voxel clustering optimises the potential of fUS, facilitating the detection of intricate activity patterns throughout the brain without the necessity for prior assumptions. This approach enables more precise differentiation of hemodynamic responses and more reliable activity mapping. It is particularly advantageous in complex or paradigm-free studies, offering a high-resolution alternative to standard techniques.
DOI: 10.5281/zenodo.14501404
title: Spatiotemporal Clustering of Functional Ultrasound Signals at the Single-Voxel Level

description: Micro-Doppler movies used in the research paper : "Spatiotemporal Clustering of Functional Ultrasound Signals at the Single-Voxel Level" by Lambert T., Niknejad H.R., Kil D, Montaldo G., Nuttin B., Brunner C. and Urban A.

Functional ultrasound (fUS) imaging is a well-established neuroimaging technology that offers high spatiotemporal resolution and a large field of view. Typical strategies for analysing fUS data comprise either region-based averaging, typically based on reference atlases, or correlation with experimental events. Nevertheless, these methodologies possess several inherent limitations, including a restricted utilisation of the spatial dimension and a pronounced bias influenced by preconceived notions about the recorded activity. In this study, we put forth single-voxel clustering as a third method to address these issues. A comparison was conducted between the three strategies on a typical dataset comprising visually evoked activity in the superior colliculus. The application of single-voxel clustering yielded the generation of detailed activity maps, which revealed a consistent layout of activity and a clear separation between haemodynamic responses. This method is best considered as a complement to region-based averaging and correlation. It has direct applicability to challenging contexts, such as paradigm-free analysis on behaving subjects and brain decoding. 
description: The application of spatiotemporal clustering at single-voxel resolution for functional ultrasound (fUS) signal analysis significantly enhances sensitivity in comparison to conventional methods, such as region-based averaging or event correlation. Conventional approaches frequently rely on predefined atlases or specific experimental conditions, which inherently restrict spatiotemporal resolution. In contrast, single-voxel clustering optimises the potential of fUS, facilitating the detection of intricate activity patterns throughout the brain without the necessity for prior assumptions. This approach enables more precise differentiation of hemodynamic responses and more reliable activity mapping. It is particularly advantageous in complex or paradigm-free studies, offering a high-resolution alternative to standard techniques.
DOI: 10.15468/dl.pujg5g
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cercospora leucaenae-leucocephalae Raghu Ram & Mallaiah"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043591-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cz9vve
title: Occurrence Download

description: A dataset containing 699 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trachurus mediterraneus (Steindachner, 1868)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900",
 "CoordinateUncertaintyInMeters is less than or equal to 10000"
 ]
}

 The dataset includes 699 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043592-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2tjcxw
title: Occurrence Download

description: A dataset containing 25836797 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is one of (Agelaius phoeniceus (Linnaeus, 1766), Molothrus aeneus (Wagler, 1829), Molothrus ater (Boddaert, 1783))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 25836797 records from 195 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043570-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nar6z5
title: Occurrence Download

description: A dataset containing 1183 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Miscanthus sacchariflorus (Maxim.) Benth., 1887"
 ]
}

 The dataset includes 1183 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043593-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3ey6zu
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Poa annua Schltdl. & Cham."
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043594-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4bqugg
title: Occurrence Download

description: A dataset containing 154408 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eucalyptus globulus Labill."
 ]
}

 The dataset includes 154408 records from 519 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043579-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nrcbyv
title: Occurrence Download

description: A dataset containing 329 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trachurus picturatus (Bowdich, 1825)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900",
 "CoordinateUncertaintyInMeters is less than or equal to 10000"
 ]
}

 The dataset includes 329 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043596-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u5qwk2
title: Occurrence Download

description: A dataset containing 820 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Miscanthus sacchariflorus (Maxim.) Hack."
 ]
}

 The dataset includes 820 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043597-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.syg5s8
title: Occurrence Download

description: A dataset containing 41953 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Centaurea stoebe L."
 ]
}

 The dataset includes 41953 records from 575 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043595-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1162670.10fu
title: SH1162670.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1162671.10fu
title: SH1162671.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1162674.10fu
title: SH1162674.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1162675.10fu
title: SH1162675.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1162676.10fu
title: SH1162676.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1162677.10fu
title: SH1162677.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1162678.10fu
title: SH1162678.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1162679.10fu
title: SH1162679.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1162681.10fu
title: SH1162681.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1162685.10fu
title: SH1162685.10FU

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