cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18710/qhuaz2/ovwg8k
title: 2021-02-16-00_00_2021-02-16-23_59_DEM_COPERNICUS_30_GRAYSCALE.png
DOI: 10.18710/qhuaz2/rjrzdc
title: MTP_summer_TURBAN.nc
DOI: 10.18710/qhuaz2/smkj2g
title: air quality data Winter.xlsx
DOI: 10.18710/qhuaz2/tmlg7m
title: DEM_PALM.py
DOI: 10.18710/qhuaz2/wvuuky
title: winter episode.jpg
DOI: 10.5281/zenodo.17924175
title: Modelo 3D en Blender (para renders) – Máquina de ensayo de tracción ASTM D638 - v1.0
title: Blender Assembly (for renders) – Tensile Testing Machine ASTM D638 - v1.0

description: Resumen

Proyecto completo de Blender utilizado para generar los renders y visualizaciones de la máquina de ensayo de tracción ASTM D638. Incluye archivo .blend, materiales, texturas y configuraciones de render.

Material complementario al diseño CAD publicado en GitHub:https://github.com/diegura386/tensile-testing-machine-astm-d638Licencia: CERN-OHL-S v2.

El proyecto de origen de este registo, junto con sus anexos, documentación técnica y archivos digitales complementarios, ha sido depositado y preservado en el repositorio Zenodo:

DOI: https://doi.org/10.5281/zenodo.17943716
description: Complete Blender project used to generate renders and visualizations of the ASTM D638 tensile testing machine. It includes the .blend file, materials, textures and render configurations.

This package serves as complementary material to the CAD design published on GitHub:https://github.com/diegura386/tensile-testing-machine-astm-d638

License: CERN-OHL-S v2.

The original project to which this record belongs, together with its annexes, technical documentation and complementary digital files, has been deposited and preserved in the Zenodo repository:

DOI: https://doi.org/10.5281/zenodo.17943716
DOI: 10.18710/7puepj/5uxf74
title: 20220216_NYA_REDOBS_gal.txt
DOI: 10.18710/7puepj/lpaice
title: 20220104_NYA_REDOBS_gal.txt
DOI: 10.18710/7puepj/zm4ydb
title: 20220205_NYA_REDOBS_gps.txt
DOI: 10.18710/emjbeo/2qgmh7
title: 20220326_HOP_REDOBS_glo.txt
DOI: 10.18710/emjbeo/6wp5bb
title: 20220412_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/hkoqhs
title: 20220518_HOP_REDOBS_glo.txt
DOI: 10.18710/emjbeo/zwc3ei
title: 20220521_HOP_REDOBS_gps.txt
DOI: 10.18710/mwvqfd/7x073k
title: 20220122_BJN_REDOBS_gps.txt
DOI: 10.18710/mwvqfd/a0jvyx
title: 20220525_BJN_REDOBS_gps.txt
DOI: 10.18710/mwvqfd/lgdrsh
title: 20220105_BJN_REDOBS_gal.txt
DOI: 10.18710/mwvqfd/myewfy
title: 20220216_BJN_REDOBS_gal.txt
DOI: 10.18710/mwvqfd/o7xaex
title: 20220321_BJN_REDOBS_gal.txt
DOI: 10.18710/mwvqfd/x8cnbd
title: 20220303_BJN_REDOBS_glo.txt
DOI: 10.18710/wgrd0d/bietyw
title: 20220303_NYA_REDTEC_glo.txt
DOI: 10.18710/wgrd0d/bzo5pf
title: 20220328_NYA_REDTEC_gal.txt
DOI: 10.18710/wgrd0d/kk0h9q
title: 20220124_NYA_REDTEC_glo.txt
DOI: 10.18710/7puepj/8bnjio
title: 20220214_NYA_REDOBS_gal.txt
DOI: 10.18710/7puepj/itfs1x
title: 20220315_NYA_REDOBS_gal.txt
DOI: 10.18710/7puepj/yvgcyh
title: 20220328_NYA_REDOBS_gal.txt
DOI: 10.18710/emjbeo/l0g1tz
title: 20220110_HOP_REDOBS_gps.txt
DOI: 10.18710/emjbeo/porsny
title: 20220320_HOP_REDOBS_gps.txt
DOI: 10.18710/emjbeo/wjegiv
title: 20220425_HOP_REDOBS_glo.txt
DOI: 10.18710/kmpo6n/a63fnu
title: 20220419_KHO_REDOBS_gal.txt
DOI: 10.18710/kmpo6n/bjywax
title: 20220507_KHO_REDOBS_glo.txt
DOI: 10.18710/kmpo6n/cvuaaw
title: 20220408_KHO_REDOBS_gal.txt
DOI: 10.18710/kmpo6n/dzebp0
title: 20220530_KHO_REDOBS_glo.txt
DOI: 10.18710/kmpo6n/rjcezq
title: 20220424_KHO_REDOBS_glo.txt
DOI: 10.18710/kmpo6n/rwmgc8
title: 20220525_KHO_REDOBS_gal.txt
DOI: 10.18710/mwvqfd/cnzqt8
title: 20220128_BJN_REDOBS_glo.txt
DOI: 10.18710/q2zvbc/7ga0kz
title: CAGE-17_2_EM302_raw_23.zip
DOI: 10.18710/qhuaz2/g7r2zj
title: landmarks.xlsx
DOI: 10.18710/wgrd0d/lc2kwa
title: 20220129_NYA_REDTEC_gal.txt
DOI: 10.18710/wgrd0d/qzzuim
title: 20220117_NYA_REDTEC_glo.txt
DOI: 10.18710/wgrd0d/r4wkpl
title: 20220317_NYA_REDTEC_glo.txt
DOI: 10.18710/wgrd0d/udxpgo
title: 20220417_NYA_REDTEC_gal.txt
DOI: 10.18710/wgrd0d/vab0t5
title: 20220106_NYA_REDTEC_glo.txt
DOI: 10.18710/wgrd0d/xzldie
title: 20220201_NYA_REDTEC_glo.txt
DOI: 10.18710/emjbeo/1a8nt1
title: 20220207_HOP_REDOBS_gps.txt
DOI: 10.18710/emjbeo/6ucxyk
title: 20220206_HOP_REDOBS_glo.txt
DOI: 10.18710/emjbeo/7vxxtd
title: 20220214_HOP_REDOBS_glo.txt
DOI: 10.18710/emjbeo/ia88tx
title: 20220209_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/kx0ylm
title: 20220322_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/plhvsp
title: 20220327_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/rlm4ov
title: 20220313_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/stnlrb
title: 20220228_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/t8oxbz
title: 20220116_HOP_REDOBS_gal.txt
DOI: 10.18710/kmpo6n/66idrt
title: 20220505_KHO_REDOBS_gal.txt
DOI: 10.18710/kmpo6n/6gaqpg
title: 20220610_KHO_REDOBS_glo.txt
DOI: 10.18710/kmpo6n/9cnno6
title: 20221011_KHO_REDOBS_gal.txt
DOI: 10.18710/kmpo6n/ae7y7r
title: 20220423_KHO_REDOBS_gal.txt
DOI: 10.18710/kmpo6n/gzvyhx
title: 20220609_KHO_REDOBS_gal.txt
DOI: 10.18710/kmpo6n/jziixn
title: 20220513_KHO_REDOBS_gps.txt
DOI: 10.18710/kmpo6n/l1opao
title: 20220526_KHO_REDOBS_gps.txt
DOI: 10.18710/kmpo6n/ljz8qw
title: 20220516_KHO_REDOBS_glo.txt
DOI: 10.18710/kmpo6n/vqk2pk
title: 20220428_KHO_REDOBS_gps.txt
DOI: 10.18710/kmpo6n/wxhaqq
title: 20220426_KHO_REDOBS_gal.txt
DOI: 10.18710/mwvqfd/aooafm
title: 20220118_BJN_REDOBS_glo.txt
DOI: 10.18710/o2zapj/taoucq
title: :unav

description: :unav
DOI: 10.18710/qhuaz2/pse0jt
title: Luftm_linger_for_ju_351695a.pdf
DOI: 10.18710/5nix4n
title: Russian pora ‘time’ vs. vremja ‘time’

description: In order to shed light on the distribution of the Russian nouns pora and vremja, both of which mean ‘time’, I created a database of examples of both nouns from the Russian National Corpus (RNC, syntactic subcorpus). The database comprises all examples where pora or vremja function as a grammatical subject, a grammatical object, or as an adverbial realized as a bare noun phrase or a preposition phrase. All search results were exported and then merged into one spreadsheet. Irrelevant examples (e.g., where pora means ‘pore in the skin’) and doublets were manually removed. In addition, lists of collocations from the web corpus Araneum Russicum Russicum Maius were created. Finally, for both nouns I created lists of semantic neighbors based on semantic vectors.
description: Excel
description: Word Embedding for Semantic Similarity of Words
DOI: 10.18710/7puepj/1rfthw
title: 20220210_NYA_REDOBS_glo.txt
DOI: 10.18710/7puepj/mjdyrp
title: 20220324_NYA_REDOBS_gal.txt
DOI: 10.18710/emjbeo/9dbaan
title: 20220217_HOP_REDOBS_gps.txt
DOI: 10.18710/emjbeo/9eqzgp
title: 20220318_HOP_REDOBS_glo.txt
DOI: 10.18710/emjbeo/d5vbhh
title: 20220122_HOP_REDOBS_gps.txt
DOI: 10.18710/emjbeo/gqohph
title: 20220213_HOP_REDOBS_glo.txt
DOI: 10.18710/emjbeo/i3vwkc
title: 20220325_HOP_REDOBS_gps.txt
DOI: 10.18710/emjbeo/ia84xd
title: 20220215_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/iemmxq
title: 20220220_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/jbaotd
title: 20220315_HOP_REDOBS_glo.txt
DOI: 10.18710/emjbeo/jmkhmz
title: 20220124_HOP_REDOBS_glo.txt
DOI: 10.18710/emjbeo/jozo4l
title: 20220205_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/kvdarv
title: 20220127_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/ner6zy
title: 20220113_HOP_REDOBS_glo.txt
DOI: 10.18710/emjbeo/ooz922
title: 20220216_HOP_REDOBS_gps.txt
DOI: 10.18710/emjbeo/opsbh5
title: 20220615_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/osgfhf
title: 20220219_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/qcrklq
title: 20220101_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/tyzfme
title: 20220103_HOP_REDOBS_gps.txt
DOI: 10.18710/emjbeo/wykju2
title: 20220106_HOP_REDOBS_gal.txt
DOI: 10.18710/emjbeo/z3cfga
title: 20220509_HOP_REDOBS_gps.txt
DOI: 10.18710/kmpo6n
title: GNSS Scintillation Data (60 s) at Longyearbyen in 2022

description: &lt;p&gt;This data set contains phase and amplitude scintillation data at 60 seconds time resolution at Kjell Henriksen Observatory (KHO) at Longyearbyen, Svalbard.

The measurements were collected by the University of Bergen using a NovAtel GPStation-6 global navigation satellite system receiver. The measurements include signals from GPS, GLONASS, and GALILEO at different frequencies. These data are used for research on space weather disturbances in the polar ionosphere.&lt;/p&gt;

&lt;p&gt;A detailed description of the data structure and format is gathered in the documentation data set: &lt;i&gt;Oksavik, Kjellmar, 2020, "Documentation of GNSS Total Electron Content and Scintillation Data (60 s) at Svalbard", DataverseNO, &lt;A HREF="https://doi.org/10.18710/EA5BYX"&gt;https://doi.org/10.18710/EA5BYX&lt;/A&gt;&lt;/i&gt;
&lt;/p&gt;
&lt;p&gt;&lt;b&gt;This data set is part of a larger collection: &lt;i&gt;Oksavik, Kjellmar, 2020. "The University of Bergen Global Navigation Satellite System Data Collection". DataverseNO. &lt;A HREF="https://doi.org/10.18710/AJ4S-X394"&gt;https://doi.org/10.18710/AJ4S-X394&lt;/A&gt;.&lt;/i&gt;&lt;/b&gt;
&lt;/p&gt;
description: Longyearbyen OBS 60 s
description: Matlab, R2018a
DOI: 10.15468/dl.c6j9vw
title: Occurrence Download

description: A dataset containing 131 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ulex europaeus L.",
 {
 "or" : [
 "Country is United States",
 "Country is Canada",
 "Continent is North America"
 ]
 }
 ]
}

 The dataset includes 131 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0125438-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.897uy2
title: Occurrence Download

description: A dataset containing 437891 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Vulpes vulpes (Linnaeus, 1758)"
 ]
}

 The dataset includes 437891 records from 916 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0132308-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.11rvu2
title: Occurrence Download

description: A dataset containing 3416 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Literature Occurrence",
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Unknown evidence"
 ]
 },
 "Geometry POLYGON((-75.3511 6.20063,-75.44174 6.15694,-75.34561 5.99853,-75.13687 6.14329,-75.30167 6.29073,-75.3511 6.20063))",
 "TaxonKey is Chordata",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3416 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001606-190307172214381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.taukbm
title: Occurrence Download

description: A dataset containing 153 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Biston panterinaria (Bremer & Grey, 1853), Oraesia excavata Butler, 1878, Locastra muscosalis Walker, 1865, Spilarctia alba (Bremer & Grey, 1853), Parum colligata Walker, 1856, Fentonia ocypete (Bremer, 1861), Notonagemia analis, Arctornis submarginata (Walker, 1855), Quadricalcarifera subgeneris Strand, 1915, Euthrix laeta (Walker, 1855), Parallelia arctotaenia (Guenée, 1852), Spirama retorta Clerck, 1764, Acosmeryx castanea Rothschild & Jordan, 1903, Harpyia milhauseri (Fabricius, 1775), Lagoptera juno Dalman, 1823, Uropyia meticulodina (Oberthür, 1884), Theretra tibetiana Vaglia & Haxaire, 2010, Pingasa lariaria (Walker, 1860), Phyllosphingia dissimilis Bremer, 1861, Epodonta lineata (Oberthür, 1880), Artena dotata (Fabricius, 1794), Acosmeryx pseudomissa Mell, 1922, Ampelophaga rubiginosa Bremer & Grey, 1853, Harpyia umbrosa (Staudinger, 1892), Catocala patala Felder & Rogenhofer, 1874, Creatonotos transiens (Walker, 1855), Dendrolimus punctata (Walker, 1855), Acherontia lachesis Fabricius, 1798, Rhagastis mongoliana Butler, Macroglossum fritzei Rothschild & Jordan, 1903, Theretra japonica Orza, 1869, Marumba gaschkewitschii Bremer & Grey, 1853, Ercheia dubia Butler, 1874, Hypomecis subdetractaria (Prout, 1923), Stauropus basalis Moore, 1877, Macrauzata maxima Inoue, 1960, Arcte coerula (Guenée, 1852), Spirama helicina Hübner, 1827, Deilephila elpenor Linnaeus, 1758, Arctornis semihyalina (Swinhoe, 1904), Lymantria sinica, Pindara illibata (Fabricius, 1775), Jankowskia athleta Oberthür, 1884, Theretra latreillii MacLeay, Eterusia aedea (Clerck, 1759), Ericeia subcinerea Snellen, 1880, Aloa lactinea (Cramer, 1777), Adris tyrannus (Guenée, 1852), Cechetra minor (Butler, 1875), Oreta rosea Walker, 1855, Gastropacha quercifolia (Linnaeus, 1758), Callambulyx rubricosa Walker, 1856, Thosea sinensis (Walker, 1855), Metopta rectifasciata Ménétriés, 1863, Cleora repulsaria (Walker, 1860), Phalera flavescens (Bremer & Grey, 1853), Bastilla joviana (Stoll, 1782), Episparis costistriga (Walker, 1864), Psilalcis galsworthyi Sato, 1996, Erebus ephesperis (Hübner, 1827), Ambulyx tobii Inoue, 1976, Agrius convolvuli Linnaeus, 1758, Amraica ponderata, Drymonia dodonaea Hübner, 1800, Darapsa choerilus Cramer, 1779, Disparia obliquiplaga Moore, 1879, Pachyodes ornataria Moore, 1888, Hypopyra vespertilio Fabricius, 1787, Tanaorhinus rafflesii (Moore), Spilosoma subcarnea Walker, 1855, Theretra oldenlandiae Fabricius, 1775)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "Country is one of (China)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 153 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0201366-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.66zhce
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Conophytum minutum var. pearsonii (N.E.Br.) Boom"
 ]
}

 The dataset includes 16 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0201378-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w8ka7s
title: Occurrence Download

description: A dataset containing 1698 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Portugal",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Grus grus (Linnaeus, 1758)"
 ]
}

 The dataset includes 1698 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0132311-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q0wduq
title: Occurrence Download

description: A dataset containing 6756 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Diversité floristique des espèces menacées prioritaires du Bénin (Afrique de l'Ouest)."
 ]
}

 The dataset includes 6756 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001606-190320150433242/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n5hhey
title: Occurrence Download

description: A dataset containing 2749 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-90.3176392999999962 38.6347633000000030, -90.3176392999999962 38.6365265000000022, -90.3176392999999962 38.6387934799999968, -90.3176392999999962 38.6404307399999993, -90.3176392999999962 38.6424458199999989, -90.3176392999999962 38.6447128000000006, -90.3176392999999962 38.6473575999999994, -90.3149906799999940 38.6473575999999994, -90.3129725400000041 38.6473575999999994, -90.3099453300000050 38.6473575999999994, -90.3066658500000017 38.6473575999999994, -90.3031342999999964 38.6473575999999994, -90.3031342999999964 38.6457203399999969, -90.3031342999999964 38.6439571399999977, -90.3031342999999964 38.6413123399999989, -90.3031342999999964 38.6392972499999985, -90.3031342999999964 38.6374081000000018, -90.3031342999999964 38.6347633000000030, -90.3065397200000035 38.6347633000000030, -90.3104498699999994 38.6347633000000030, -90.3128464100000059 38.6347633000000030, -90.3149906799999940 38.6347633000000030, -90.3176392999999962 38.6347633000000030))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 2749 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0092582-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.favk8p
title: Occurrence Download

description: A dataset containing 52 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Terminalia sericea Burch. ex DC.",
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Material sample"
 ]
 },
 "HasCoordinate is true",
 "DecimalLatitude is greater than or equal to -12",
 "DecimalLatitude is less than or equal to 0.25",
 "DecimalLongitude is greater than or equal to 28",
 "DecimalLongitude is less than or equal to 41.25",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1985",
 "Year is less than or equal to 2020"
 ]
}

 The dataset includes 52 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024931-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zqregs
title: Occurrence Download

description: A dataset containing 285 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mimon bennettii (Gray, 1838)"
 ]
}

 The dataset includes 285 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0350556-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6zf44e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-88.9727175000000017 39.8348230000000001, -88.9727175000000017 39.8356359599999976, -88.9727175000000017 39.8361685799999989, -88.9727175000000017 39.8367012100000011, -88.9727175000000017 39.8372618699999990, -88.9727175000000017 39.8376262999999966, -88.9724091700000059 39.8376262999999966, -88.9717925000000065 39.8376262999999966, -88.9713440200000036 39.8376262999999966, -88.9707834200000036 39.8376262999999966, -88.9699144799999999 39.8376262999999966, -88.9694659999999971 39.8376262999999966, -88.9694659999999971 39.8373739999999970, -88.9694659999999971 39.8368974400000013, -88.9694659999999971 39.8365049800000008, -88.9694659999999971 39.8360003900000024, -88.9694659999999971 39.8355798899999982, -88.9694659999999971 39.8348230000000001, -88.9702508499999993 39.8348230000000001, -88.9707834200000036 39.8348230000000001, -88.9711758400000008 39.8348230000000001, -88.9720728000000065 39.8348230000000001, -88.9727175000000017 39.8348230000000001))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097480-210914110416597/datasets/export for details.
DOI: 10.15468/dl.jrgt5h
title: Occurrence Download

description: A dataset containing 324 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Turdoides hindei (Sharpe, 1900)"
 ]
}

 The dataset includes 324 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024933-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14262068
title: Resultant force on grains of a real sand dune

description: This repository contains the dataaset used to train a convolutional neural network (CNN) designed to measure the resultant forces acting on a barchan dune. The model has been trained using numerical data to predict force distributions based on the morphological features of dunes.

 

In our research, we developed a groundbreaking method that leverages deep learning to estimate the forces acting on real barchan dunes at grain scale. This method combines image data from subaqueous experiments with numerical simulations, offering a novel approach to granular mechanics measurement. The trained CNN model is capable of predicting force distributions on dunes from their morphological features, even when applied to dune configurations not seen during training.
DOI: 10.5281/zenodo.14262069
title: Resultant force on grains of a real sand dune

description: This repository contains the dataaset used to train a convolutional neural network (CNN) designed to measure the resultant forces acting on a barchan dune. The model has been trained using numerical data to predict force distributions based on the morphological features of dunes.

 

In our research, we developed a groundbreaking method that leverages deep learning to estimate the forces acting on real barchan dunes at grain scale. This method combines image data from subaqueous experiments with numerical simulations, offering a novel approach to granular mechanics measurement. The trained CNN model is capable of predicting force distributions on dunes from their morphological features, even when applied to dune configurations not seen during training.
DOI: 10.5281/zenodo.14624367
title: Datasets - Lethal effects and sex-specific tolerance of copper and cadmium in the buff-tailed bumble bee

description: Exposure to environmental pollutants, including trace metals, is a major driver of bee decline worldwide. While pesticides undergo standardised risk assessments (i.e., LD50) and the implementation of online databases, no such approaches exist for trace metals. Here, using acute oral exposure, we determined the LC50, LD50, and mass- standardised LD50 of copper and cadmium, essential and non-essential metals, respectively, in workers and males of the buff-tailed bumble bee. We also evaluated gut damage and sucrose consumption in workers post- exposure. Cadmium was more toxic than copper for workers at same doses, although both metals induced se- vere gut melanisation and reduced sucrose consumption at high concentrations. Males displayed higher tolerance to cadmium, but it was correlated to their higher body mass, emphasising the necessity for reporting mass- standardised LD50 for genuine sex comparisons in risk assessments. Our findings advocate for the establish- ment of databases focusing on metal-induced lethal effects on model bee species.