cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17611/dp/19457831
title: Global ShakeMovie synthetic for October 06, 2021 MW 5.5 KYUSHU, JAPAN
DOI: 10.17611/dp/19457835
title: Global ShakeMovie synthetic for October 06, 2021 MW 6.0 PAKISTAN
DOI: 10.17611/dp/19461310
title: Global ShakeMovie synthetic for October 07, 2021 MW 6.0 NEAR EAST COAST OF HONSHU, JAPAN
DOI: 10.17611/dp/19461314
title: Global ShakeMovie synthetic for October 07, 2021 MW 5.3 SOUTH SANDWICH ISLANDS REGION
DOI: 10.17611/dp/19461318
title: Global ShakeMovie synthetic for October 07, 2021 MW 5.5 MYANMAR
DOI: 10.17611/dp/19461322
title: Global ShakeMovie synthetic for October 07, 2021 MW 6.0 MACQUARIE ISLAND REGION
DOI: 10.17611/dp/19468159
title: Global ShakeMovie synthetic for October 09, 2021 MW 5.5 SOUTHERN PERU
DOI: 10.17611/dp/19468163
title: Global ShakeMovie synthetic for October 09, 2021 MW 6.9 VANUATU ISLANDS REGION
DOI: 10.17611/dp/19474136
title: Global ShakeMovie synthetic for October 09, 2021 MW 5.6 NEW BRITAIN REGION, P.N.G.
DOI: 10.17611/dp/19474140
title: Global ShakeMovie synthetic for October 10, 2021 MW 5.6 CENTRAL PERU
DOI: 10.17611/dp/19474144
title: Global ShakeMovie synthetic for October 10, 2021 MW 5.9 HAWAII
DOI: 10.17611/dp/19474148
title: Global ShakeMovie synthetic for October 11, 2021 MW 6.9 ALASKA PENINSULA
DOI: 10.17611/dp/19474152
title: Global ShakeMovie synthetic for October 11, 2021 MW 5.8 NORTHERN MOLUCCA SEA
DOI: 10.17611/dp/19480357
title: Global ShakeMovie synthetic for October 11, 2021 MW 5.8 KURIL ISLANDS
DOI: 10.17611/dp/19480361
title: Global ShakeMovie synthetic for October 12, 2021 MW 6.5 CRETE, GREECE
DOI: 10.17611/dp/19483209
title: Global ShakeMovie synthetic for October 14, 2021 MW 5.6 VANUATU ISLANDS
DOI: 10.17611/dp/19483213
title: Global ShakeMovie synthetic for October 14, 2021 MW 5.6 NORTHERN MOLUCCA SEA
DOI: 10.17611/dp/19485126
title: Global ShakeMovie synthetic for October 15, 2021 MW 6.4 SOLOMON ISLANDS
DOI: 10.17611/dp/19487589
title: Global ShakeMovie synthetic for October 18, 2021 MW 6.2 VANUATU ISLANDS
DOI: 10.17611/dp/19488617
title: Global ShakeMovie synthetic for October 19, 2021 MW 5.9 EASTERN MEDITERRANEAN SEA
DOI: 10.17611/dp/19489371
title: Global ShakeMovie synthetic for October 19, 2021 MW 5.5 TONGA ISLANDS
DOI: 10.17611/dp/19490339
title: Global ShakeMovie synthetic for October 21, 2021 MW 5.6 SOUTHEAST OF HONSHU, JAPAN
DOI: 10.17611/dp/19490343
title: Global ShakeMovie synthetic for October 21, 2021 MW 6.1 SOUTH OF FIJI ISLANDS
DOI: 10.17611/dp/19491372
title: Global ShakeMovie synthetic for October 21, 2021 MW 5.6 NORTH ISLAND, NEW ZEALAND
DOI: 10.17611/dp/19497452
title: Global ShakeMovie synthetic for October 24, 2021 MW 6.2 TAIWAN
DOI: 10.17611/dp/19500438
title: Global ShakeMovie synthetic for October 26, 2021 MW 5.9 EAST OF SOUTH SANDWICH ISLANDS
DOI: 10.17611/dp/19505193
title: Global ShakeMovie synthetic for October 30, 2021 MW 5.6 FIJI ISLANDS REGION
DOI: 10.17611/dp/19505197
title: Global ShakeMovie synthetic for October 29, 2021 MW 5.5 KERMADEC ISLANDS REGION
DOI: 10.17611/dp/19505201
title: Global ShakeMovie synthetic for October 30, 2021 MW 5.5 BANDA SEA
DOI: 10.17611/dp/19505205
title: Global ShakeMovie synthetic for October 31, 2021 MW 5.7 SOUTH OF ALASKA
DOI: 10.17611/dp/19505977
title: Global ShakeMovie synthetic for October 30, 2021 MW 5.9 WESTERN INDIAN-ANTARCTIC RIDGE
DOI: 10.17611/dp/19505981
title: Global ShakeMovie synthetic for November 01, 2021 MW 6.0 SOUTH SANDWICH ISLANDS REGION
DOI: 10.17611/dp/19505985
title: Global ShakeMovie synthetic for November 01, 2021 MW 5.9 OFF W COAST OF NORTHERN SUMATRA
DOI: 10.17611/dp/19506993
title: Global ShakeMovie synthetic for November 02, 2021 MW 5.5 SOUTHEAST OF LOYALTY ISLANDS
DOI: 10.17611/dp/19506997
title: Global ShakeMovie synthetic for November 02, 2021 MW 5.9 BANDA SEA
DOI: 10.17611/dp/19507001
title: Global ShakeMovie synthetic for November 02, 2021 MW 5.5 BALLENY ISLANDS REGION
DOI: 10.17611/dp/19507749
title: Global ShakeMovie synthetic for November 03, 2021 MW 5.7 MENDOZA PROVINCE, ARGENTINA
DOI: 10.17611/dp/19507753
title: Global ShakeMovie synthetic for November 03, 2021 MW 5.8 SOUTH OF TONGA ISLANDS
DOI: 10.17611/dp/19508501
title: Global ShakeMovie synthetic for November 02, 2021 MW 5.4 MID-INDIAN RIDGE
DOI: 10.17611/dp/19508505
title: Global ShakeMovie synthetic for November 04, 2021 MW 5.8 SERAM, INDONESIA
DOI: 10.6084/m9.figshare.31236322
title: <b>Mexico GeoJSON — States and Municipalities (2024)</b>

description: This dataset provides Mexico’s 2024 political–administrative boundaries at the state and municipal levels, converted from authoritative CONABIO shapefiles into GeoJSON and delivered in both full-resolution and topology-preserving simplified forms.All processing was performed using GeoPandas to ensure cartographic integrity, metric-based generalization, and compatibility with modern web-mapping and GIS workflows.Two 8K sample maps are included, showing national coverage at the state and municipal levels.Processing MethodOriginal CONABIO shapefiles were loaded into GeoPandas.Data were re-projected from EPSG:4326 to EPSG:3857 to enable meter-based geometric operations.Topology-preserving simplification (Douglas–Peucker) was applied:States: 100 m toleranceMunicipalities: 15 m toleranceLayers were re-projected back to EPSG:4326 (WGS84).All layers were exported to GeoJSON.The preserve_topology=True option ensures that polygon adjacency, shared borders, and spatial validity are maintained, preventing gaps, overlaps, and self-intersections.Included Filesstates_full.geojson — Original state geometries in GeoJSON (no simplification)states_simple.geojson — State boundaries simplified at 100 m, topology preservedmunicipalities_full.geojson — Original municipal geometries in GeoJSONmunicipalities_simple.geojson — Municipal boundaries simplified at 15 m, topology preservedstates.png — 8K reference map of state boundariesmunicipalities.png — 8K reference map of municipal boundariesUse CasesFull layers: spatial analysis, legal or administrative mapping, high-precision cartography<br>Simplified layers: web maps, dashboards, interactive GIS, and high-performance renderingData SourceCONABIO — Comisión Nacional para el Conocimiento y Uso de la Biodiversidad<br>http://www.conabio.gob.mx/informacion/gis/
DOI: 10.6084/m9.figshare.31236322.v1
title: <b>Mexico GeoJSON — States and Municipalities (2024)</b>

description: This dataset provides Mexico’s 2024 political–administrative boundaries at the state and municipal levels, converted from authoritative CONABIO shapefiles into GeoJSON and delivered in both full-resolution and topology-preserving simplified forms.All processing was performed using GeoPandas to ensure cartographic integrity, metric-based generalization, and compatibility with modern web-mapping and GIS workflows.Two 8K sample maps are included, showing national coverage at the state and municipal levels.Processing MethodOriginal CONABIO shapefiles were loaded into GeoPandas.Data were re-projected from EPSG:4326 to EPSG:3857 to enable meter-based geometric operations.Topology-preserving simplification (Douglas–Peucker) was applied:States: 100 m toleranceMunicipalities: 15 m toleranceLayers were re-projected back to EPSG:4326 (WGS84).All layers were exported to GeoJSON.The preserve_topology=True option ensures that polygon adjacency, shared borders, and spatial validity are maintained, preventing gaps, overlaps, and self-intersections.Included Filesstates_full.geojson — Original state geometries in GeoJSON (no simplification)states_simple.geojson — State boundaries simplified at 100 m, topology preservedmunicipalities_full.geojson — Original municipal geometries in GeoJSONmunicipalities_simple.geojson — Municipal boundaries simplified at 15 m, topology preservedstates.png — 8K reference map of state boundariesmunicipalities.png — 8K reference map of municipal boundariesUse CasesFull layers: spatial analysis, legal or administrative mapping, high-precision cartography<br>Simplified layers: web maps, dashboards, interactive GIS, and high-performance renderingData SourceCONABIO — Comisión Nacional para el Conocimiento y Uso de la Biodiversidad<br>http://www.conabio.gob.mx/informacion/gis/
DOI: 10.15468/dl.83esar
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "TaxonKey is Dalbergia lateriflora Benth."
 ]
}

 The dataset includes 123 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014662-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t7v65s
title: Occurrence Download

description: A dataset containing 88446 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Cynoglossum officinale"
 ]
}

 The dataset includes 88446 records from 846 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025490-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k756ub
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "DatasetKey is one of (VectorNet data Luxembourg 2023-2024, Tick collection_OH ALLIES project_Ireland, OH_SURVector_Hungary_Mosquitoes, VectorNet observations, Investigating the Role of Stable Flies (Stomoxys calcitrans) and Biting Midges (Culicoides spp.) of the genus Culicoides as Potential Mechanical Vectors of African Swine Fever Virus in Lithuania, Poland and Romania, Vector collection_OH surveillance project_Sweden, Monitoring competent vectors of Crimea Congo Haemorragic Fever (ticks) , West Nile and Rift Valley Fever in Spain (mosquitos), OH4Surveillance vector collection in Latvia, OH4Surveillance vector collection in Latvia, One Health Surveillance (OH4S) Vector Collection in Poland - ticks collection, One Health Surveillance (OH4S) Vector Collection in Poland - mosquitoes collection, Mosquito data OH4surveillance Denmark 2024, Tick and moquito collection_OH SURVECTOR project_Slovakia, Vector collection_OH surveillance project Estonia, VectorCollection_SIVIZ_project_Portugal, National tick surveillance in Austria for the detection of tick-borne pathogens 2024 - Project OH SURVector, Results of Tick (Hyalomma and Ixodes ricinus) and Mosquito Surveillance in Croatia in 2024, National mosquito surveillance in Austria for the detection of mosquito-borne pathogens 2024 - Project OH SURVector, VectorDistributionStatus)"
 ]
 },
 {
 "and" : [
 "TaxonKey is one of (Culex pipiens [GBIF Backbone Taxonomy])",
 "Country is one of (Moldova, Republic of)"
 ]
 }
 ]
}

 The dataset includes 16 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001638-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34620/dadosipb/cdhffd
title: Comunidade Intermunicipal das Terras de Trás-os-Montes, CIM-TTM (Zeive, Vilarinho de Cova de Lua, São Joanico) / Top-view RGB Images Dataset (Canopy Imagery)

description: This collection of datasets comprises top-view RGB image samples acquired using a LiDAR L1 Zenmuse sensor transported by a MATRICE 300 RTK UAV, and processed with QGIS v3.34.11 software. The RGB images and elevation values were extracted from point clouds through rasterized models with a resolution of 10x10 centimeters per pixel. The datasets were collected from forested areas in Portugal, including regions dominated by Quercus pyrenaica Willd. and Pinus pinaster Ait., during specific periods to capture relevant vegetation characteristics. Each dataset contains top-view RGB images (canopy imagery) with resolutions of 16x16, 32x32, or 64x64 pixels, and each sample is assigned a unique identifier (filename). The PNG files show the initials of their respective datasets, with "SJ" for samples from São Joanico, "V" for Vilarinho de Cova de Lua, and "Z" for Zeive. All datasets have additional descriptions stored in TXT files. Some datasets include labeled values stored in CSV files, which provide information such as average tree height above 1.3 meters, normalized total height, and vegetation coverage density. These datasets are suitable for applications such as tree height analysis, forest inventories, vegetation coverage studies, carbon studies, and other related research. The data collection was conducted as part of the iCarbono (icarbono.ipb.pt) and LIFE SILFORE projects.
DOI: 10.15468/dl.3narpb
title: Occurrence Download

description: A dataset containing 5648 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is DEU.10_1",
 "MediaType is Image",
 "OccurrenceStatus is Present",
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 5648 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001640-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18428369
title: Amplifying soil's self-cleansing of organochlorine pesticides pollution enhances soil and human health and food security
DOI: 10.5281/zenodo.18428370
title: Amplifying soil's self-cleansing of organochlorine pesticides pollution enhances soil and human health and food security
DOI: 10.15468/dl.tz4s9t
title: Occurrence Download

description: A dataset containing 981400 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Labidochromis zebroides, Melanochromis auratus, Melanochromis chipokae, Labidochromis joanjohnsonae, Pseudotropheus johannii, Abactochromis labrosus, Melanochromis melanopterus, Pseudotropheus perspicax, Melanochromis simulans, Melanochromis vermivorus, Petrotilapia genalutea, Petrotilapia nigra, Petrotilapia tridentiger, Chindongo ater, Maylandia aurora, Maylandia barlowi, Pseudotropheus crabro, Chindongo cyaneus, Pseudotropheus elegans, Chindongo elongatus, Maylandia fainzilberi, Chindongo flavus, Pseudotropheus fuscoides, Pseudotropheus fuscus, Maylandia greshakei, Maylandia hajomaylandi, Chindongo heteropictus, Maylandia lanisticola, Pseudotropheus livingstoni, Maylandia lombardoi, Tropheops lucerna, Tropheops macrophthalmus, Tropheops microstoma, Chindongo minutus, Tropheops novemfasciatus, Chindongo socolofi, Tropheops tropheops, Pseudotropheus tursiops, Serranochromis robustus, Protopterus annectens, Polypterus endlicherii, Polypterus palmas, Polypterus retropinnis, Heterotis niloticus, Rubricatochromis, Hemichromis fasciatus, Marcusenius furcidens, Marcusenius senegalensis, Marcusenius ussheri, Mormyrops anguilloides, Mormyrops breviceps, Mormyrus hasselquistii, Mormyrus rume, Petrocephalus bane, Petrocephalus bovei, Petrocephalus pellegrini, Micralestes acutidens, Micralestes comoensis, Micralestes occidentalis, Alestes baremoze, Distichodus rostratus, Nannocharax ansorgii, Nannocharax fasciatus, Enteromius ablabes, Enteromius baudoni, Enteromius bigornei, Enteromius callipterus, Enteromius eburneensis, Clypeobarbus hypsolepis, Enteromius inaequalis, Enteromius leonensis, Enteromius macinensis, Enteromius macrops, Gasterosteus aculeatus, Labeobarbus parawaldroni, Enteromius pobeguini, Enteromius punctitaeniatus, Labeobarbus sacratus, Enteromius stigmatopygus, Enteromius subinensis, Enteromius sublineatus, Enteromius traorei, Enteromius trispilos, Enteromius walkeri, Labeo alluaudi, Labeo coubie, Labeo parvus, Labeo senegalensis, Auchenoglanis occidentalis, Cottus gobio, Chrysichthys nigrodigitatus, Chrysichthys maurus, Chrysichthys teugelsi, Chrysichthys auratus, Chrysichthys johnelsi, Chrysichthys walkeri, Schilbe mandibularis, Schilbe mystus, Amphilius atesuensis, Amphilius platychir)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 981400 records from 839 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001604-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.31204897
title: Supplementary information files for "Self-perceived life course sleep duration trajectories and risk and age at onset of Parkinson’s disease"

description: Supplementary files for article "Self-perceived life course sleep duration trajectories and risk and age at onset of Parkinson’s disease"<br><br>Sleep disturbances are both prodromes and potential risk factors for Parkinson’s disease (PD), yet associations between long-term sleep patterns and PD remain unclear. We analyzed data from two online cohorts—PPMI-Online (n = 15,905) and Fox Insight (n = 1929)—to examine associations between self-reported sleep duration trajectories across life stages and PD risk and age at onset (AAO). Latent class growth analysis identified distinct trajectories, linear and logistic regression examined their associations with PD risk and AAO, adjusting for demographics, lifestyle, and comorbidities. In PPMI-Online, sleep duration was generally stable in early adulthood with divergence in midlife. Compared to stable sleepers (7–8 h/day), individuals with midlife reductions (6–7 to ≤5–6 h/day: OR = 1.90; 7–8 to ≤6–7 h/day: OR = 1.64) and persistent short sleep (≤6 h/day: OR = 1.41) had higher PD risk, independent of comorbidities. Similar patterns were observed in probable prodromal PD. Persistent short sleep and sleep decline were also linked to earlier AAO (up to –4.23 years) across cohorts. These findings suggest that midlife sleep reduction may signal early PD, while chronic short sleep may represent a modifiable risk factor, highlighting the need for prospective studies to explore early detection and prevention potential.<br><br>©The Author(s), CC BY 4.0
DOI: 10.17028/rd.lboro.31204897.v1
title: Supplementary information files for "Self-perceived life course sleep duration trajectories and risk and age at onset of Parkinson’s disease"

description: Supplementary files for article "Self-perceived life course sleep duration trajectories and risk and age at onset of Parkinson’s disease"<br><br>Sleep disturbances are both prodromes and potential risk factors for Parkinson’s disease (PD), yet associations between long-term sleep patterns and PD remain unclear. We analyzed data from two online cohorts—PPMI-Online (n = 15,905) and Fox Insight (n = 1929)—to examine associations between self-reported sleep duration trajectories across life stages and PD risk and age at onset (AAO). Latent class growth analysis identified distinct trajectories, linear and logistic regression examined their associations with PD risk and AAO, adjusting for demographics, lifestyle, and comorbidities. In PPMI-Online, sleep duration was generally stable in early adulthood with divergence in midlife. Compared to stable sleepers (7–8 h/day), individuals with midlife reductions (6–7 to ≤5–6 h/day: OR = 1.90; 7–8 to ≤6–7 h/day: OR = 1.64) and persistent short sleep (≤6 h/day: OR = 1.41) had higher PD risk, independent of comorbidities. Similar patterns were observed in probable prodromal PD. Persistent short sleep and sleep decline were also linked to earlier AAO (up to –4.23 years) across cohorts. These findings suggest that midlife sleep reduction may signal early PD, while chronic short sleep may represent a modifiable risk factor, highlighting the need for prospective studies to explore early detection and prevention potential.<br><br>©The Author(s), CC BY 4.0
DOI: 10.5281/zenodo.10708991
title: The Molecular Landscape of Cellular Metal Ion Biology - supplementary information Cell Systems version

description: Supplementary Data files for the Cell Systems paper 

"The Molecular Landscape of Cellular Metal Ion Biology"  DOI: 10.1016/j.cels.2025.101319

by 

Simran Kaur Aulakh 1,2, Oliver Lemke 3,4, Lukasz Szyrwiel 3 , Stephan Kamrad 1, Yu Chen 5,6 , Johannes Hartl 3,4, Michael Muelleder 3, Jens Nielsen 5 and Markus Ralser 1, 2, 3, 4, 7 # 

1 Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, NW1 1AT, London, United Kingdom

 2 Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, United Kingdom

3 Department of Biochemistry, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany

4 Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Berlin, Germany

5 Department of Biology and Biological Engineering, Chalmers University of Technology, SE41128 Gothenburg, Sweden

6 Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

7 Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany

 

PLEASE USE THIS VERSION TO DOWNLOAD ALL DATA CORRESPONDING TO THE CELL SYSTEMS VERSION OF THIS PAPER. THE PREVIOUS VERSION CORRESPONDS TO THE BIORXIV SUBMISSION.
DOI: 10.5281/zenodo.18428635
title: The Molecular Landscape of Cellular Metal Ion Biology - supplementary information Cell Systems version

description: Supplementary Data files for the Cell Systems paper 

"The Molecular Landscape of Cellular Metal Ion Biology"  DOI: 10.1016/j.cels.2025.101319

by 

Simran Kaur Aulakh 1,2, Oliver Lemke 3,4, Lukasz Szyrwiel 3 , Stephan Kamrad 1, Yu Chen 5,6 , Johannes Hartl 3,4, Michael Muelleder 3, Jens Nielsen 5 and Markus Ralser 1, 2, 3, 4, 7 # 

1 Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, NW1 1AT, London, United Kingdom

 2 Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, United Kingdom

3 Department of Biochemistry, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany

4 Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Berlin, Germany

5 Department of Biology and Biological Engineering, Chalmers University of Technology, SE41128 Gothenburg, Sweden

6 Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

7 Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany

 

PLEASE USE THIS VERSION TO DOWNLOAD ALL DATA CORRESPONDING TO THE CELL SYSTEMS VERSION OF THIS PAPER. THE PREVIOUS VERSION CORRESPONDS TO THE BIORXIV SUBMISSION.
DOI: 10.5281/zenodo.18428753
title: XORAZM MUSIQA SAN'ATI ASARLARI NOMLARINING ONOMASTIK XUSUSIYATLARI: LISONIY VA MADANIY TAHLIL

description: Ushbu maqola Xorazm musiqa san’ati asarlari (kuy va qo‘shiq) nomlarini onomastika nuqtai nazaridan tadqiq etishga bag‘ishlangan. Nomlarning atalish motivlari, lug‘aviy-ma’noviy qatlamlari, genetik xususiyatlari va o‘zbek tilining onomastik tizimidagi o‘rni tahlil qilinadi. Tadqiqot Xorazm vohasining qadimiy madaniy merosini saqlash va yosh avlodni milliy an’analar ruhida tarbiyalash masalalariga xizmat qiladi.
DOI: 10.5281/zenodo.18428754
title: XORAZM MUSIQA SAN'ATI ASARLARI NOMLARINING ONOMASTIK XUSUSIYATLARI: LISONIY VA MADANIY TAHLIL

description: Ushbu maqola Xorazm musiqa san’ati asarlari (kuy va qo‘shiq) nomlarini onomastika nuqtai nazaridan tadqiq etishga bag‘ishlangan. Nomlarning atalish motivlari, lug‘aviy-ma’noviy qatlamlari, genetik xususiyatlari va o‘zbek tilining onomastik tizimidagi o‘rni tahlil qilinadi. Tadqiqot Xorazm vohasining qadimiy madaniy merosini saqlash va yosh avlodni milliy an’analar ruhida tarbiyalash masalalariga xizmat qiladi.
DOI: 10.15468/dl.hhphmc
title: Occurrence Download

description: A dataset containing 13568 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "OccurrenceStatus is Present",
 "TaxonKey is Sus scrofa",
 "Year 2016-2026"
 ]
}

 The dataset includes 13568 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001642-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.