cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1127738.10fu
title: SH1127738.10FU

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/sh1127740.10fu
title: SH1127740.10FU

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/sh1127743.10fu
title: SH1127743.10FU

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/sh1127744.10fu
title: SH1127744.10FU

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/sh1127746.10fu
title: SH1127746.10FU

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.ppkuq4
title: Occurrence Download

description: A dataset containing 59807 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-58.32914 -34.27806,-59.19056 -34.22447,-59.27855 -34.91852,-58.59312 -35.20662,-58.0328 -35.12934,-57.67991 -34.87038,-58.32914 -34.27806))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 59807 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002641-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1127747.10fu
title: SH1127747.10FU

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/sh1127748.10fu
title: SH1127748.10FU

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/sh1127749.10fu
title: SH1127749.10FU

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/sh1127753.10fu
title: SH1127753.10FU

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: 10.71795/7gtn-cd58
title: General Multilingual Environmental Thesaurus
title: GEMET
DOI: 10.71795/vws6-6625
title: Getty Thesaurus of Geographic Names
title: TGN
DOI: 10.15468/dl.9x7hdq
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Abrotanella diemii Cabrera"
 ]
}

 The dataset includes 8 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001813-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dpx6u3
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Abrotanella diemii Cabrera"
 ]
}

 The dataset includes 8 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001812-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18150/51v9pc
title: Somatostatin-expressing interneurons modulate neocortical network through GABAb receptors in a synapse-specific manner

description: Electrophysiological raw files used for the study published in:Kanigowski D, Bogaj K, Barth AL, Urban-Ciecko J. Somatostatin-expressing interneurons modulate neocortical network through GABAb receptors in a synapse-specific manner. Sci Rep. 2023 May 31;13(1):8780. doi: 10.1038/s41598-023-35890-2. PMID: 37258641; PMCID: PMC10232538. Raw data contains electrophysiological records in the following formats:- *.abf (Clampfit - available online),- *.pxp (Igor PRO; free Clampfit software can also be used to access files),- *.txt (Igor PRO exported to txt).For more details see the related paper and readme file in MS Word. All the abbreviations are explained in the related paper.
DOI: 10.15468/dl.8ct86m
title: Occurrence Download

description: A dataset containing 61 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sedum ochroleucum Vill.",
 "Year 1990-2025"
 ]
}

 The dataset includes 61 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001816-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ujtkhy
title: Occurrence Download

description: A dataset containing 26677 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lactarius deterrimus Gröger"
 ]
}

 The dataset includes 26677 records from 230 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001815-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15109623
title: Terrestrial High-rate Centimeter-level Sea Surface Height Monitoring  for About One Month using GNSS-R
DOI: 10.5281/zenodo.15109624
title: Terrestrial High-rate Centimeter-level Sea Surface Height Monitoring  for About One Month using GNSS-R
DOI: 10.6084/m9.figshare.28721387
title: PIFR: pose angle-based fall detection.zip

description: The proposed approach extracts nine critical geometric features, including the center of mass and various body angles. These features are used to train a support vector machine (SVM) model for binary classification, distinguishing between standing and lying with high precision.
DOI: 10.71795/temn-n708
title: Graphikvokabular
DOI: 10.35097/sg9tcyz24sr4cv77
title: Research data to "Adsorption of CO on α-Al2O3(0001): A combined experimental and computational study"

description: Forschungsdaten zur Publikation: XPS, IRRAS, "Periodic DFT calculations", "Embedded cluster calculations".
description: Research raw data to publication: XPS, IRRAS, Periodic DFT calculations, Embedded cluster calculations.
description: Raw data available for experimental results (XPS, polarization-resolved IRRAS) and theoretical calculations (periodic DFT and embedded cluster calculations).
DOI: 10.5281/zenodo.15130066
title: The $0.80 Signal: How One Tweet and a Senate Vote Moved the GSE Market
DOI: 10.5281/zenodo.15130067
title: The $0.80 Signal: How One Tweet and a Senate Vote Moved the GSE Market
DOI: 10.6084/m9.figshare.26425081
title: edgehog_figshare_repository.zip

description: This repository contains all the scripts and datasets (simulations, YGOB, OMA Vertebrata species) used for the benchmarking of EdgeHOG. It all also contains the data and scripts used for downstream analyses, i.e the functional annotation of reconstructed contigs in LECA, the study of the conservation of LECA's adjacencies in extant eukaryotes and the dating of gene adjacencies in extant eukaryotes
DOI: 10.6084/m9.figshare.26425081.v2
title: edgehog_figshare_repository.zip

description: This repository contains all the scripts and datasets (simulations, YGOB, OMA Vertebrata species) used for the benchmarking of EdgeHOG. It all also contains the data and scripts used for downstream analyses, i.e the functional annotation of reconstructed contigs in LECA, the study of the conservation of LECA's adjacencies in extant eukaryotes and the dating of gene adjacencies in extant eukaryotes
DOI: 10.6084/m9.figshare.26425081.v1
title: edgehog_figshare_repository.zip

description: This repository contains all the scripts and datasets (simulations, YGOB, OMA Vertebrata species) used for the benchmarking of EdgeHOG. It all also contains the data and scripts used for downstream analyses, i.e the functional annotation of reconstructed contigs in LECA, the study of the conservation of LECA's adjacencies in extant eukaryotes and the dating of gene adjacencies in extant eukaryotes
DOI: 10.6084/m9.figshare.25262746
title: Data from: Global phylogenetic and functional structure of rodent assemblages

description: Data used in "Global phylogenetic and functional structure of rodent assemblages" by Yoan Fourcade and Bader H. Alhajeri.locations.csv: ID, coordinates, ecoregion/realm/biome membership of all rodent assemblages analysed, sampled using a stratified sampling strategy in order to span all terrestrial biomes.assemblages_current.csv: species composition of these assemblages, using current species range maps.assemblages_historical.csv: species composition of these assemblages, using historical species range maps.traits.csv: 27 species traits describing diet, reproduction, morphology and habitat of rodent species.MamPhy_BDvr_DNAonly_topoFree_NDexp_pruned-RODENTIA_sample100_Modifed_10-Oct-19.nex: random sample of 100 rodent phylogenetic trees from Upham 2019, PlosBiol, containing only species for which DNA data were available.MamPhy_BDvr_Completed_topoCons_NDexp_pruned-RODENTIA_sample100_Modifed_10-Oct-19.nex: random sample of 100 rodent phylogenetic trees from Upham 2019, PlosBiol, imputed for species missing DNA data.
DOI: 10.71795/qdzn-sv61
title: Heritage Crime Thesaurus
DOI: 10.15468/dl.e396y3
title: Occurrence Download

description: A dataset containing 256 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Occurrence evidence"
 ]
 },
 "TaxonKey is Globularia trichosantha Fisch. & C.A.Mey."
 ]
}

 The dataset includes 256 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0375031-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.71795/dqht-8j11
title: Historic Aircraft Types thesaurus
DOI: 10.6084/m9.figshare.26526529
title: Benchmark data and code for evaluating and enhancing spatial cognition abilities of large language models (LLMs)

description: Large Language Models (LLMs) exhibit various capabilities that were previously exclusive to humans. However, existing evidence is insufficient to determine whether LLMs have developed spatial cognition, a core aspect of human cognition that supports logical-mathematical reasoning and various other skills. Previous studies on this topic primarily focused on small-scale perceptions, leaving spatial cognition in the context of GIScience unexplored. This paper adheres to the established framework of spatial cognition research, encompassing three types of spatial knowledge: landmark knowledge, route knowledge, and survey knowledge. We present a benchmark assessing the spatial cognition abilities across seven categories to systematically evaluate how well LLMs process and generate the three types of spatial knowledge. Additionally, we propose Hybrid Mind, a tool-augmented approach that integrates LLMs with deterministic GIS algorithms to enhance their performance in spatial cognitive tasks. The core idea involves implementing a mental map builder that generates a quantitative map based on segmented qualitative constraints, effectively overcoming LLMs' fallacies in synthesizing spatial information. Our experimental results indicated that although LLMs showed potential for spatial cognition, their performance was poor in most spatial cognitive tasks, particularly in building route knowledge and survey knowledge. Larger models performed better, yet even the leading model correctly answered fewer than one-fourth of the questions. An examination of samples revealed that LLMs tended to make simple mistakes and often failed when transitioning to egocentric views, deciding turns, and synthesizing information. The Hybrid Mind system significantly improved performance, correctly solving 70.48% of the questions.<br><br>The materials contain the following:<br><b>questions.db</b>: A SQLite database for the questions in the benchmark<b>results.db</b>: A SQLite database for the evaluations of LLMs and the Hybrid Mind.<b>run_benchmark</b>: Source code of the experiment runner and the Hybrid Mind<b>analyze</b>: Source code to analyze the results and plot charts<br>
DOI: 10.5281/zenodo.15113104
title: Derotated Microphone Array Data for Aeroacoustic Source Mapping of an Axial Fan – Acoular Workshop Supplementary Material

description: This dataset contains pre-processed microphone array time signals from the FAN-01 benchmark derotated using the mode-time domain Virtual Rotating Array (VRA-M) method. The VRA-M method compensates for the fan's rotational motion, enabling spatial separation of individual source contributions using frequency-domain microphone array methods. The resulting derotated time signals are stored in HDF5 format. Details on the measurement setup and evaluation using the Acoular framework is given in the preprint.pdf

How to Use:

The data supplements the presentation and publication An Interactive Tutorial on Advanced Microphone Array Methods for Acoustic Source Mapping, which was held at DAS | DAGA 2025 in Copenhagen. The presentation can be found here.

One can use the Acoular Python framework to load and process the dataset. Example code for loading the data is provided below:



import acoular as ac


vra_time_data = ac.TimeSamples(file='2015-08-12_16-55-10_n1ug_no_grid_1_4_derotated.h5')
mics = ac.MicGeom(file='ring64.xml')
DOI: 10.5281/zenodo.15113105
title: Derotated Microphone Array Data for Aeroacoustic Source Mapping of an Axial Fan – Acoular Workshop Supplementary Material

description: This dataset contains pre-processed microphone array time signals from the FAN-01 benchmark derotated using the mode-time domain Virtual Rotating Array (VRA-M) method. The VRA-M method compensates for the fan's rotational motion, enabling spatial separation of individual source contributions using frequency-domain microphone array methods. The resulting derotated time signals are stored in HDF5 format. Details on the measurement setup and evaluation using the Acoular framework is given in the preprint.pdf

How to Use:

The data supplements the presentation and publication An Interactive Tutorial on Advanced Microphone Array Methods for Acoustic Source Mapping, which was held at DAS | DAGA 2025 in Copenhagen. The presentation can be found here.

One can use the Acoular Python framework to load and process the dataset. Example code for loading the data is provided below:



import acoular as ac


vra_time_data = ac.TimeSamples(file='2015-08-12_16-55-10_n1ug_no_grid_1_4_derotated.h5')
mics = ac.MicGeom(file='ring64.xml')