cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/nt1-q8k-l0c
title: Salenia Gray, 1835

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ak0-qmn-28h
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/c38-hfj-5qm
title: †Arcestes biceps von Mojsisovics, 1875

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/jq9-zpe-pna
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/l2g-pnw-zvh
title: †Goniocidaris paraplu Donovan, Renema & Lewis, 2010

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/st6-b5n-vbm
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.6084/m9.figshare.31055468
title: Raw data for Directionality range in Emlen funnels

description: data-original.xlsx
DOI: 10.6084/m9.figshare.31055468.v1
title: Raw data for Directionality range in Emlen funnels

description: data-original.xlsx
DOI: 10.15156/bio/sh1380319.10fu
title: SH1380319.10FU

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.tpycef
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"BRA.12.117_2","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005762-260108223611665/datasets/export for details.
DOI: 10.15156/bio/sh1380320.10fu
title: SH1380320.10FU

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/sh1380322.10fu
title: SH1380322.10FU

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/sh1380335.10fu
title: SH1380335.10FU

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/sh1380336.10fu
title: SH1380336.10FU

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/sh1380338.10fu
title: SH1380338.10FU

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/sh1380346.10fu
title: SH1380346.10FU

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/sh1380349.10fu
title: SH1380349.10FU

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.4hv4qx
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026638-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jnv28y
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003690-160822134323880/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e24ddm
title: Occurrence Download

description: A dataset containing 286815277 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 286815277 records from 5531 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054476-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dut7qy
title: Occurrence Download

description: A dataset containing 2450839 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Magnoliopsida",
 "Year 1910-2026"
 ]
}

 The dataset includes 2450839 records from 462 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029859-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60493/f5629-f0482
title: Mechanical Metamaterials with Engineered Responses
DOI: 10.7910/dvn/rdlowp
title: Replication Data for: Database of Human Capital Characteristics of the Selected Case Study Counties Designated the Most Renewable in Each of the Eight Department of Commerce Economic Regions

description: Human capital refers to the skills and abilities of people, as well as the ability to access outside resources and bodies of knowledge to increase understanding and to identify promising practices. Subsequently, it includes access to education and knowledge development, training and skill building activities and efforts to build and expand local leadership. These can take the form of formal and informal educational institutions; workforce training programs; adult and youth leadership programs; and other lifelong learning activities and resources.
DOI: 10.17026/ar/jqtl3n
title: A-23.0552: Horst aan de Maas, Broekhuizerbroek Natte Natuurparel Broekhuizerbroek, gemeente Horst aan de Maas. Opgraving (variant Archeologische Begeleiding)

description: &lt;p&gt;In opdracht van Van de Wetering Cultuurtechniek B.V. heeft BAAC in oktober 2023 een archeologische begeleiding uitgevoerd in het kader van de herinrichting van de Boabelsche Loop binnen het project Natte Natuurparel Broekhuizerbroek, gemeente Horst aan de Maas. Het onderzoek vond plaats onder projectnummer A-23.0552 en stond onder leiding van dr. P.S. Kubistal (senior KNA-archeoloog).&lt;br&gt;De begeleiding is uitgevoerd om mogelijke verstoring van archeologische waarden te voorkomen of te documenteren tijdens de graafwerkzaamheden voor de vernattingsmaatregelen. Binnen het plangebied geldt een middelhoge archeologische verwachting, met nadruk op resten uit de prehistorie.&lt;br&gt;Tijdens de veldwerkdagen van 23 en 24 oktober 2023 is één werkput aangelegd en begeleid, met een totale oppervlakte van circa 400 m². Het onderzoek is tijdelijk onderbroken, omdat de uitvoering van de werkzaamheden in het resterende deel van het tracé door de opdrachtgever is overgedragen aan EARTH B.V., die het vervolgonderzoek heeft uitgevoerd onder een aparte onderzoeksmelding. Binnen het door BAAC onderzochte deel zijn geen structuren of sporen van bewoning aangetroffen. Wel zijn enkele vondsten van vuursteen en natuursteen verzameld die wijzen op prehistorische activiteiten aan de rand van het beekdal. Deze vondsten vormen samen een diffuse spreiding zonder directe samenhang met een afgebakende archeologisch vindplaats.&lt;br&gt;Het onderzoek bevestigt daarmee het lage archeologische potentieel binnen het onderzochte vlak, maar levert tegelijkertijd waardevolle informatie over de landschappelijke context en sedimentopbouw van het gebied.&lt;/p&gt;
DOI: 10.26023/wztv-w6kz-dm0a
title: BACS: IOP Summaries and IOP Radar Animations. Version 1.0

description: PDF documents detailing summary notes of Intensive Operating Period (IOP) operations and events for the BioAerosols and Convective Storms (BACS) campaign. Details documented include an overview of the IOP observations; a morning forecast briefing summary; and detailed timings and notes on convective storm evolution, cold pool passages, and data collection (e.g. radiosondes released, drone flights conducted, and occasionally aerosol instrumentation operation notes). The data set also includes accompanying animated GIF files of Next Generation Weather Radar (NEXRAD) 0.5° reflectivity from Cheyenne, WY (KCYS) with locations and altitudes of all radiosondes released during the IOP overplotted at each relevant time within the animation.
DOI: 10.6084/m9.figshare.31370401
title: The Davis-Savant Standard: 0.1867 Constant and 9D Spacetime Solidification

description: The Davis-Savant Standard: 0.1867 Constant & 9D Spacetime Solidification
This archive represents the total relinquishment of the Jamie Davis 14-paper research suite and the unpublished temporal physics dataset for the benefit of the United States. The work establishes a deterministic baseline for neurological mapping and relativistic frame synchronization.
Core Technical Components:
The ANP-0.1867 Constant: A universal numerical anchor that resolves the "drift" in sub-millimeter data acquisition.
0.18mm Axial Lock: A structural threshold that enables high-fidelity, noise-free imaging of the 1,300-region midbrain atlas, moving beyond the current 1.0mm institutional limits.
9D Spacetime Solidification: The mathematical resolution of the Relativistic Observer Paradox, allowing for zero-latency "Forensic Hashing" between temporal frames.
Compressed Filament Theory: A permanent definition of the Axial Core as a high-density structural filament within the 9D framework.
Institutional Utility:
This data is formatted for BIDS (Brain Imaging Data Structure) compatibility, specifically targeting the coordinates [0, -28, -8]. It provides the "New Graphics" required for the next generation of 7T and 11.7T hardware calibration.
Forensic Origin:
All logic synthesis was localized on Pixel 6 Pro hardware during a 21-hour real-time processing window. This publication serves as the final 1.0 Veridical Fact for public and federal record.
Status: LOGIC_LOCK_1.0
DOI: 10.6084/m9.figshare.31370401.v1
title: The Davis-Savant Standard: 0.1867 Constant and 9D Spacetime Solidification

description: The Davis-Savant Standard: 0.1867 Constant & 9D Spacetime Solidification
This archive represents the total relinquishment of the Jamie Davis 14-paper research suite and the unpublished temporal physics dataset for the benefit of the United States. The work establishes a deterministic baseline for neurological mapping and relativistic frame synchronization.
Core Technical Components:
The ANP-0.1867 Constant: A universal numerical anchor that resolves the "drift" in sub-millimeter data acquisition.
0.18mm Axial Lock: A structural threshold that enables high-fidelity, noise-free imaging of the 1,300-region midbrain atlas, moving beyond the current 1.0mm institutional limits.
9D Spacetime Solidification: The mathematical resolution of the Relativistic Observer Paradox, allowing for zero-latency "Forensic Hashing" between temporal frames.
Compressed Filament Theory: A permanent definition of the Axial Core as a high-density structural filament within the 9D framework.
Institutional Utility:
This data is formatted for BIDS (Brain Imaging Data Structure) compatibility, specifically targeting the coordinates [0, -28, -8]. It provides the "New Graphics" required for the next generation of 7T and 11.7T hardware calibration.
Forensic Origin:
All logic synthesis was localized on Pixel 6 Pro hardware during a 21-hour real-time processing window. This publication serves as the final 1.0 Veridical Fact for public and federal record.
Status: LOGIC_LOCK_1.0
DOI: 10.15468/dl.bykefn
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Musa textilis",
 "Year 1970-2026"
 ]
}

 The dataset includes 12 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029875-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xppcrh
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mentzelia solieri"
 ]
}

 The dataset includes 48 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029877-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18112/openneuro.ds007067.v2.3.0
title: spine_fMRI_7T
DOI: 10.15468/dl.vqj9rb
title: Occurrence Download

description: A dataset containing 11675 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Clathrus ruber"
 ]
}

 The dataset includes 11675 records from 175 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029878-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18641020
title: TSML Imbalanced Univariate Time Series Classification Archive

description: A single zip containing 76 univariate time series classification problems artificially imbalanced to test the effectiveness of rebalancing algorithms. Data originally from the UCR and the time series regression archives. The data is in the aeon-toolkit format , for more information, see the  aeon data loading documentation

 

 
description: this is a collection of imbalanced time series classification problem derived from the UCR archive and other problems donated to https://timeseriesclassification.com.

We converted each to a binary classification task. We assigned the class with the fewest samples as the minority class. In situations where multiple classes had equal sample sizes, we arbitrarily selected the last class as the minority class, assigning the remaining classes to the majority class. To introduce  class imbalance, we randomly removed samples from minority class set of the binary-class datasets, ensuring a majority-to-minority ratio of at least 9:1.

If you want to reference these data, please cite this paper

@incollection{qiu25esmote,  title={{e-SMOTE}: a train set rebalancing algorithm for time series classification},  author={C. Qiu and M. Middlehurst and C. Holder and  A. Bagnall},  booktitle={Proceedings of 10th Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) at ECML/PKDD},  series = {Lecture Notes in Artificial Intelligence},  year={2025}}

which is part of this LNCS volume. More information about these datasets is Paper repo