cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.59m9sj
title: Occurrence Download

description: A dataset containing 584060 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Peru",
 "Month is one of (August, September, October, November, December)"
 ]
}

 The dataset includes 584060 records from 294 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069126-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.33y2ux
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Schwetschkea sinica Broth. & Paris",
 "HasCoordinate is true",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002717-230918134249559/datasets/export for details.
DOI: 10.25549/pcra-c14-15020
title: Druzheskoe slovo = Palavra do amigo = [A friendly word], vol. 8, no. 2-3 (February-March 1935)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.rtjwf5
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Indostomus spinosus Britz & Kottelat, 1999"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0325329-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-14767
title: Druzheskoe slovo = Palavra do amigo = [A friendly word], vol. 6, no. 3-4 (March-April 1933)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.97575d
title: Occurrence Download

description: A dataset containing 11283 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "Geometry POLYGON((-76.64789 5.08136,-74.01412 5.08136,-74.01412 8.64245,-76.64789 8.64245,-77.03832 6.97903,-76.64789 5.08136))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "StateProvince is Antioquia",
 "TaxonKey is Mammalia",
 "Year 2019-2023"
 ]
}

 The dataset includes 11283 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018939-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-15270
title: Druzheskoe slovo = Palavra do amigo = [A friendly word], vol. 9, no. 4 (April 1936)

description: [description english/roman)] Monthly
DOI: 10.15468/dl.pjqhvs
title: Occurrence Download

description: A dataset containing 531 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bryconamericus emperador (Eigenmann & Ogle, 1907)"
 ]
}

 The dataset includes 531 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0325382-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jux999
title: Occurrence Download

description: A dataset containing 3624 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Occurrence evidence)",
 "Country is Mexico",
 "TaxonKey is Pteronotus mexicanus (Miller, 1902)"
 ]
}

 The dataset includes 3624 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001043-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-13468
title: Put' = Voie = [The way], no. 45 (October-December 1934)

description: [description english/roman)] Irregular frequency
DOI: 10.25549/pcra-c14-13491
title: Put' = Voie = [The way], no. 45 (October-December 1934)

description: [description english/roman)] Irregular frequency
DOI: 10.15468/dl.fk5t7r
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calisto occulta Núñez, 2012"
 ]
}

 The dataset includes 10 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005082-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2ywnph
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "MediaType is Image",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Indigofera torulosa E.Mey., Indigofera torulosa var. torulosa, Indigofera tinctoria var. torulosa Baker f.)"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018942-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-13788
title: Put' = Voie = [The way], no. 49 (October-December 1935)

description: [description english/roman)] Irregular frequency
DOI: 10.15468/dl.sdesau
title: Occurrence Download

description: A dataset containing 620 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Proliferodiscus pulveraceus (Alb. & Schwein.) Baral, Propolydesmus helveticus (Verhoeff, 1894))",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 620 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0325319-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8jm6x9
title: Occurrence Download

description: A dataset containing 347 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is TZA",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Adansonia digitata L."
 ]
}

 The dataset includes 347 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001069-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-13758
title: Put' = Voie = [The way], no. 49 (October-December 1935)

description: [description english/roman)] Irregular frequency
DOI: 10.5281/zenodo.34927
title: Bioimages Release 2015-12-06

description: Updated new highres SAP links to Morphbank, added 2005 plantings from Vanderbilt Arboretum
DOI: 10.5281/zenodo.46853
title: Bioimages Release 2016-03-02

description: Updated Standard Views (stdview.rdf) to version 0.9.4 (SKOS-based)
DOI: 10.25549/pcra-c14-13818
title: Put' = Voie = [The way], no. 49 (October-December 1935)

description: [description english/roman)] Irregular frequency
DOI: 10.15468/dl.agfgy4
title: Occurrence Download

description: A dataset containing 36515 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Taxon occurrence data for the FungalRoot database"
 ]
}

 The dataset includes 36515 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001063-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.46893
title: Bioimages Release 2016-03-03

description: Updated dsw.owl file in RDF zip to Darwin-SW version 1.0. Added triples to RDF data so that all preferred inverse properties specified by DSW 1.0 were represented in the data.
DOI: 10.5281/zenodo.806034
title: Bioimages Release 2017-06-11

description: Data updated for publication to https://sparql.vanderbilt.edu
DOI: 10.25549/pcra-c14-14568
title: Druzheskoe slovo = Palavra do amigo = [A friendly word], vol. 4, no. 12 (December 1931)

description: [description english/roman)] Monthly
DOI: 10.5281/zenodo.31194
title: Bioimages: Bioimages Release 2015-09-19

description: Minor update: fixed datatype problem in organism.rdf
DOI: 10.15468/dl.gfdrcn
title: Occurrence Download

description: A dataset containing 975 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Zaluzianskya divaricata (Thunb.) Walp."
 ]
}

 The dataset includes 975 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014433-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.1050457
title: Bioimages Release 2017-11-15

description: Changed Baskauf licenses from CC BY-NC-SA to CC BY and minor Vanderbilt Arboretum cut tree updates
DOI: 10.25549/pcra-c14-14612
title: Druzheskoe slovo = Palavra do amigo = [A friendly word], vol. 5, no. 3 (March 1932)

description: [description english/roman)] Monthly
DOI: 10.5281/zenodo.51121
title: Bioimages Release 2016-05-07

description: This release includes Malu Jorge's camera trap data from the Brazilian Atlantic forest and Patrick Phoebus' trees from the MTSU arboretum
DOI: 10.5281/zenodo.35468
title: Bioimages Release 2015-12-17

description: Added old growth pines from Hartwick Pines State Park
DOI: 10.15468/dl.6ytwvq
title: Occurrence Download

description: A dataset containing 50 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Philydor novaesi Teixeira & Gonzaga, 1983"
 ]
}

 The dataset includes 50 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0260516-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3g9xhc
title: Occurrence Download

description: A dataset containing 3040 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Xanthosoma sagittifolium (L.) Schott"
 ]
}

 The dataset includes 3040 records from 233 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014424-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.22683
title: Bioimages: Bioimages Release 2015-08-03

description: Fixed several errors discovered using the SPARQL endpoint. Added Woodbridge weaselsnout images Finished Thomas quality control. Added Kirchoff test images.
DOI: 10.25549/pcra-c14-15254
title: Druzheskoe slovo = Palavra do amigo = [A friendly word], vol. 9, no. 3 (March 1936)

description: [description english/roman)] Monthly
DOI: 10.5281/zenodo.556219
title: Bioimages Release 2017-04-19

description: Includes SSMV StreamTeam data.
DOI: 10.15468/dl.abbhdn
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Endothiella gyrosa Sacc.",
 {
 "not" : {
 "DatasetKey" : [
 "is Global database of alien macrofungi"
 ]
 }
 },
 {
 "or" : [
 "Country is United States of America",
 "Country is United States",
 "Country is United States of America",
 "Country is Canada",
 "Continent is North America"
 ]
 }
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0133414-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-14500
title: Druzheskoe slovo = Palavra do amigo = [A friendly word], vol. 4, no. 6 (June 1931)

description: [description english/roman)] Monthly
DOI: 10.5281/zenodo.11075516
title: baskaufs/Bioimages: Bioimages Release 2024-04-26

description: Update trees on Vanderbilt campus to reflect recent deaths.
DOI: 10.15468/dl.z5rv8y
title: Occurrence Download

description: A dataset listing the 3 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Apis mellifera Linnaeus, 1758",
 "Year 2022-2022"
 ]
}

 The dataset's 3 records were derived from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001045-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.31130
title: Bioimages: Bioimages Release 2015-09-17

description: Added some Alaska images. Corrections of minor errors.
DOI: 10.25549/pcra-c14-15232
title: Druzheskoe slovo = Palavra do amigo = [A friendly word], vol. 9, no. 2 (February 1936)

description: [description english/roman)] Monthly
DOI: 10.5281/zenodo.45124
title: Bioimages Release 2016-01-24

description: Added Gleditsia triacanthos images from Bluffton University Nature Preserve
DOI: 10.15468/dl.7yxtmc
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters is less than or equal to 2000.0",
 "Country is Greece",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ruspolia nitidula (Scopoli, 1786)"
 ]
}

 The dataset includes 8 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0260503-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.46574
title: Bioimages Release 2016-02-25

description: Included missing thomas.rdf file in the RDF zip archive
DOI: 10.15468/dl.hxusad
title: Occurrence Download

description: A dataset containing 21370 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lycorma delicatula (White, 1845)"
 ]
}

 The dataset includes 21370 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016974-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j6ygjf
title: Occurrence Download

description: A dataset containing 2899667 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Circus aeruginosus (Linnaeus, 1758)"
 ]
}

 The dataset includes 2899667 records from 2308 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008525-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.20366/unimib/unidata/sn225-1.0
title: Validation and psychometric evaluation of the Italian version of the Fear of COVID-19 scale (2020)
title: Validazione e valutazione psicometrica della versione italiana della scala di paura da COVID-19 (2020)

description: The advent of COVID-19 worldwide has led to consequences for people's health, both physical and psychological, such as fear and anxiety. This is the case in Italy, one of the countries most affected by the pandemic. Given the heightened fear concerning COVID-19 in Italy., the study proposed and validated the Italian version of the Fear of COVID-19 Scale (FCV-19S; see Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., & Pakpour, A. H. (2020). The Fear of COVID-19 Scale: Development and Initial Validation. International journal of mental health and addiction, 1–9. Advance online publication. https://doi.org/10.1007/s11469-020-00270-8) and analyzed its psychometric properties. The sample comprised around 250 Italian participants who were administered Italian versions of the FCV-19S, the Hospital Anxiety and Depression Scale (HADS), and the Severity Measure for Specific Phobia-Adult (SMSP-A). The dataset contains, in addition to socio-demographic information, the values collected in the sample on the seven items of the FCV-19S, translated into Italian.<br>
description: Analysis Unit: individual<br>Universe: Italian-speaking citizens aged 18 and over<br>Sample Procedure: 249 individuals. Convenience sampling<br>Weight: No weight used<br>Collection Mode: web-based self-administered questionnaire (CAWI)
DOI: 10.25377/sussex.29414015
title: Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks

description: The data in this repository accompanies the paper 'Efficient Event-based Delay Learning in Spiking Neural Networks'The data relates to 4 benchmarks:Spiking Heidelberg Digits (SHD).Spiking Speech Commands, derived from Google Speech Commands.Yin-Yang dataset.Braille letter reading dataset.The data was generated and analysed with the code available on GitHub at https://github.com/mbalazs98/deventprop/results.py contains all test accuracies shown in figures 4-7. The other 3 files contain the trained models that achieved the corresponding accuracies. Each zip file contains directories for each architecture:for YY hidden layers of sizes, 5, 10, 15, 20, 25, 30 feedforward architecures with and without delays<br>for SHD, SSC and Braille: hidden layers of sizes 64, 128, 256, 512 and 1024, feedforward and recurrent architectures with and without delaysEach architecture directory contains 8 subdirectories for the 8 random seeds. For each connection in a given architecture the synaptic weights are in the files ending in -g.npy and if relevant, delays are provided in the architectures ending in -d.npyTo test the networks run test.py from the GitHub repository provided above, with "arguments" set to the appropriate value. To load the networks through mlGeNN, the package needs to be installed (a description of how to do this is provided at https://github.com/genn-team/ml_genn). Also see attached mlGeNN_readme.txtAbstract:Spiking Neural Networks compute using sparse communication and are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks. While standard Artificial Neural Networks are stateless, spiking neurons are stateful and hence intrinsically recurrent, making them well-suited for spatio-temporal tasks. However, the duration of this intrinsic memory is limited by synaptic and membrane time constants. Delays are a powerful additional mechanism and, in this paper, we propose an event-based training method for Spiking Neural Networks with delays, grounded in the EventProp formalism, which enables the calculation of exact gradients with respect to weights and delays. Our method supports multiple spikes per neuron and introduces a delay learning algorithm that can, in contrast to previous methods, also be applied to recurrent Spiking Neural Networks. We evaluate our method on a simple sequence detection task, as well as the Yin-Yang, Spiking Heidelberg Digits, Spiking Speech Commands and Braille letter reading datasets, demonstrating that our algorithm can optimise delays from suboptimal initial conditions and enhance classification accuracy compared to architectures without delays. We also find that recurrent delays are particularly beneficial in small networks. Finally, we show that our approach uses less than half the memory of the current state-of-the-art delay-learning method and is up to 26x faster.
DOI: 10.25377/sussex.29414015.v1
title: Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks

description: The data in this repository accompanies the paper 'Efficient Event-based Delay Learning in Spiking Neural Networks'The data relates to 4 benchmarks:Spiking Heidelberg Digits (SHD).Spiking Speech Commands, derived from Google Speech Commands.Yin-Yang dataset.Braille letter reading dataset.The data was generated and analysed with the code available on GitHub at https://github.com/mbalazs98/deventprop/results.py contains all test accuracies shown in figures 4-7. The other 3 files contain the trained models that achieved the corresponding accuracies. Each zip file contains directories for each architecture:for YY hidden layers of sizes, 5, 10, 15, 20, 25, 30 feedforward architecures with and without delays<br>for SHD, SSC and Braille: hidden layers of sizes 64, 128, 256, 512 and 1024, feedforward and recurrent architectures with and without delaysEach architecture directory contains 8 subdirectories for the 8 random seeds. For each connection in a given architecture the synaptic weights are in the files ending in -g.npy and if relevant, delays are provided in the architectures ending in -d.npyTo test the networks run test.py from the GitHub repository provided above, with "arguments" set to the appropriate value. To load the networks through mlGeNN, the package needs to be installed (a description of how to do this is provided at https://github.com/genn-team/ml_genn). Also see attached mlGeNN_readme.txtAbstract:Spiking Neural Networks compute using sparse communication and are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks. While standard Artificial Neural Networks are stateless, spiking neurons are stateful and hence intrinsically recurrent, making them well-suited for spatio-temporal tasks. However, the duration of this intrinsic memory is limited by synaptic and membrane time constants. Delays are a powerful additional mechanism and, in this paper, we propose an event-based training method for Spiking Neural Networks with delays, grounded in the EventProp formalism, which enables the calculation of exact gradients with respect to weights and delays. Our method supports multiple spikes per neuron and introduces a delay learning algorithm that can, in contrast to previous methods, also be applied to recurrent Spiking Neural Networks. We evaluate our method on a simple sequence detection task, as well as the Yin-Yang, Spiking Heidelberg Digits, Spiking Speech Commands and Braille letter reading datasets, demonstrating that our algorithm can optimise delays from suboptimal initial conditions and enhance classification accuracy compared to architectures without delays. We also find that recurrent delays are particularly beneficial in small networks. Finally, we show that our approach uses less than half the memory of the current state-of-the-art delay-learning method and is up to 26x faster.
DOI: 10.34740/kaggle/dsv/13477880
title: Predicting Road Accident Risk | Vault