cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0969910.10fu
title: SH0969910.10FU

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: 10.18738/t8/z7tcae/7cintw
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw430.tif
DOI: 10.18738/t8/z7tcae/buzu2j
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw429.tif
DOI: 10.18738/t8/z7tcae/nze7qf
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw43.tif
DOI: 10.15156/bio/sh0969911.10fu
title: SH0969911.10FU

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: 10.18738/t8/z7tcae/7wzczq
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw431.tif
DOI: 10.18738/t8/z7tcae/cyyyxk
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw433.tif
DOI: 10.18738/t8/z7tcae/mu6iy7
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw434.tif
DOI: 10.18738/t8/z7tcae/nty7xb
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw432.tif
DOI: 10.15156/bio/sh0969912.10fu
title: SH0969912.10FU

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: 10.18738/t8/z7tcae/7ti8i0
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw435.tif
DOI: 10.18738/t8/z7tcae/w3vaxv
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw437.tif
DOI: 10.18738/t8/z7tcae/yihpvm
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw436.tif
DOI: 10.15156/bio/sh0969913.10fu
title: SH0969913.10FU

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: 10.18738/t8/z7tcae/drkhzi
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw438.tif
DOI: 10.18738/t8/z7tcae/nctzya
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw439.tif
DOI: 10.15156/bio/sh0969914.10fu
title: SH0969914.10FU

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: 10.18738/t8/z7tcae/bgre8d
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw440.tif
DOI: 10.18738/t8/z7tcae/uhrftq
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw44.tif
DOI: 10.18738/t8/z7tcae/uz0pme
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw441.tif
DOI: 10.15156/bio/sh0969915.10fu
title: SH0969915.10FU

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: 10.18738/t8/z7tcae/9syhnz
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw442.tif
DOI: 10.18738/t8/z7tcae/i8tpgd
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw443.tif
DOI: 10.18738/t8/z7tcae/oy5fqo
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw444.tif
DOI: 10.15156/bio/sh0969916.10fu
title: SH0969916.10FU

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: 10.18738/t8/z7tcae/41xv5s
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw445.tif
DOI: 10.18738/t8/z7tcae/dzjmsi
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw447.tif
DOI: 10.18738/t8/z7tcae/gkt2bn
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw446.tif
DOI: 10.15156/bio/sh0969917.10fu
title: SH0969917.10FU

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: 10.18738/t8/z7tcae/66cqhk
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw448.tif
DOI: 10.18738/t8/z7tcae/cujwpg
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw449.tif
DOI: 10.18738/t8/z7tcae/e0kwvz
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw45.tif
DOI: 10.15156/bio/sh0969918.10fu
title: SH0969918.10FU

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: 10.18738/t8/z7tcae/6kpaxv
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw452.tif
DOI: 10.18738/t8/z7tcae/mubeog
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw451.tif
DOI: 10.18738/t8/z7tcae/vjzd9i
title: LSK062_30.75um_8bu_seg3phases_550x550x1180.raw450.tif
DOI: 10.15156/bio/sh0969919.10fu
title: SH0969919.10FU

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: 10.15468/dl.ju5d77
title: Occurrence Download

description: A dataset containing 98964 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-180 -90, 180 -90, 180 90, -180 90, -180 -90))",
 "Depth is greater than or equal to 0m",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "TaxonKey is Cheloniidae",
 "CoordinateUncertaintyInMeters is less than 1e+05"
 ]
}

 The dataset includes 98964 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0109360-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10986756
title: Dallmin Adler Apotheke

description: Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963[Funktion: Im Findbuch anzeigen]Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht veröffentlicht werden dürfen).
DOI: 10.5281/zenodo.10908627
title: Companinon Dataset for PASTIS : VHR satellite images (SPOT 6-7)

description: To enhance the spatial resolution and utility of PASTIS-R dataset, we introduce PASTIS-HD, which integrates contemporaneous VHR satellite images (SPOT 6-7), resampled to a 1m resolution and converted to 8 bits. This enhancement significantly improves the dataset's spatial content, providing more granular information for agricultural parcel segmentation.

This folder can be added to the PASTIS-R dataset to get the PASTIS-HD version.The SPOT images are opendata thanks to the Dataterra Dinamis initiative in the case of the "Couverture France DINAMIS" program.

If you use PASTIS please cite the related paper:



@article{garnot2021panoptic,  title={Panoptic Segmentation of Satellite Image Time Serieswith Convolutional Temporal Attention Networks},  author={Sainte Fare Garnot, Vivien  and Landrieu, Loic },  journal={ICCV},  year={2021}}


For the PASTIS-R optical-radar fusion dataset, please also cite this paper:



@article{garnot2021mmfusion,
  title    = {Multi-modal temporal attention models for crop mapping from satellite time series},
  journal  = {ISPRS Journal of Photogrammetry and Remote Sensing},
  year     = {2022},
  doi      = {https://doi.org/10.1016/j.isprsjprs.2022.03.012},
  author   = {Vivien {Sainte Fare Garnot} and Loic Landrieu and Nesrine Chehata},
}


For the PASTIS-HD with the 3 modality optical-radar time series plus VHR images dataset, please also cite this paper:



@article{astruc2024omnisat,  title={Omni{S}at: {S}elf-Supervised Modality Fusion for {E}arth Observation},  author={Astruc, Guillaume and Gonthier, Nicolas and Mallet, Clement and Landrieu, Loic},  journal={arXiv preprint arXiv:2404.08351},  year={2024}}
DOI: 10.5281/zenodo.10986759
title: Cleaned EPC for residential building of Lombardia Region - Cened1.2+
DOI: 10.15468/dl.86ctfk
title: Occurrence Download

description: A dataset containing 1074 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Scutellaria scordiifolia Fisch. ex Schrank"
 ]
}

 The dataset includes 1074 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0138036-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ebpfqk
title: Occurrence Download

description: A dataset containing 35739 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Netta rufina (Pallas, 1773)"
 ]
}

 The dataset includes 35739 records from 198 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004455-170714134226665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10908628
title: Companinon Dataset for PASTIS : VHR satellite images (SPOT 6-7)

description: To enhance the spatial resolution and utility of PASTIS-R dataset, we introduce PASTIS-HD, which integrates contemporaneous VHR satellite images (SPOT 6-7), resampled to a 1m resolution and converted to 8 bits. This enhancement significantly improves the dataset's spatial content, providing more granular information for agricultural parcel segmentation.

This folder can be added to the PASTIS-R dataset to get the PASTIS-HD version.The SPOT images are opendata thanks to the Dataterra Dinamis initiative in the case of the "Couverture France DINAMIS" program.

If you use PASTIS please cite the related paper:



@article{garnot2021panoptic,  title={Panoptic Segmentation of Satellite Image Time Serieswith Convolutional Temporal Attention Networks},  author={Sainte Fare Garnot, Vivien  and Landrieu, Loic },  journal={ICCV},  year={2021}}


For the PASTIS-R optical-radar fusion dataset, please also cite this paper:



@article{garnot2021mmfusion,
  title    = {Multi-modal temporal attention models for crop mapping from satellite time series},
  journal  = {ISPRS Journal of Photogrammetry and Remote Sensing},
  year     = {2022},
  doi      = {https://doi.org/10.1016/j.isprsjprs.2022.03.012},
  author   = {Vivien {Sainte Fare Garnot} and Loic Landrieu and Nesrine Chehata},
}


For the PASTIS-HD with the 3 modality optical-radar time series plus VHR images dataset, please also cite this paper:



@article{astruc2024omnisat,  title={Omni{S}at: {S}elf-Supervised Modality Fusion for {E}arth Observation},  author={Astruc, Guillaume and Gonthier, Nicolas and Mallet, Clement and Landrieu, Loic},  journal={arXiv preprint arXiv:2404.08351},  year={2024}}
DOI: 10.5281/zenodo.10986518
title: Shear wave velocities and associated CPT data for S-PCPT testing in the North Sea

description: This dataset combines shear wave velocity measurements and associated CPT for offshore wind farm sites in The Netherlands and Germany. The data has been collected by RVO in The Netherlands and BSH in Germany and is available in the public domain. The author has processed the data as part of his PhD research at Vrije Universiteit Brussel.
DOI: 10.5281/zenodo.10986519
title: Shear wave velocities and associated CPT data for S-PCPT testing in the North Sea

description: This dataset combines shear wave velocity measurements and associated CPT for offshore wind farm sites in The Netherlands and Germany. The data has been collected by RVO in The Netherlands and BSH in Germany and is available in the public domain. The author has processed the data as part of his PhD research at Vrije Universiteit Brussel.
DOI: 10.5281/zenodo.10986567
title: Data and code for the publication 'Fully Non-Linear Neuromorphic Computing with Linear Wave Scattering'

description: This repository contains the source code for the paper https://arxiv.org/abs/2308.16181 on nonlinear neuromorphic computing via linear wave scattering as well as the source data for the figures in the paper.

The idea behind this work is to send optical waves through a linear scattering system like an array of waveguides and optical resonators. These optical resonators or other elements may have tuneable parameters. These tuneable parameters now serve two functions in trying to use the system to solve a machine-learning task: Some of the parameters can be used to inject the input (e.g. images to be classified). Other parameters are trainable and will be slowly updated during training. The code given here simulates physical scattering setups, observes the scattering response for many different training samples, and updates the trainable parameters via gradient descent to minimize the deviation from the desired target output for the training samples. Evaluation of the scattering response as well as calculation of the gradients is done using jax, and training updates are implemented via jax or optax.

See the two subdirectories for the code used in handwritten-digit recognition (a scaled-down version of MNIST) and for fashion-MNIST (with many more neurons and trainable parameters). This code can be run directly to reproduce the results shown in the figures (although a GPU is advisable). To run the code, you need to install jax and optax (and tensorflow for importing data sets).
DOI: 10.5281/zenodo.10986758
title: Cleaned EPC for residential building of Lombardia Region - Cened1.2+