cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.h772sp
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_0_GID","value":"ARG","matchCase":false},{"type":"equals","key":"GADM_LEVEL_1_GID","value":"ARG.22_1","matchCase":false},{"type":"equals","key":"GADM_LEVEL_2_GID","value":"ARG.22.11_1","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 6 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060317-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8zp4b8
title: Occurrence Download

description: A dataset containing 166 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Greenland",
 "Month is April",
 "OccurrenceStatus is Present",
 "PublishingOrg is 2e7df380-8356-4533-bcb3-5459e23c794e",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 166 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060319-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ajb728
title: Occurrence Download

description: A dataset containing 4253 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Orchis mascula speciosa"
 ]
}

 The dataset includes 4253 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060318-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17977269
title: Künstliche Intelligenz in Nichtregierungsorganisationen - quantitativer Datensatz

description: Künstliche Intelligenz (KI) stellt ein wichtiges Thema der Digitalisierung dar und hat das Potenzial viele Arbeitsfelder grundlegend zu verändern. Mit dem Einsatz von KI wird die Hoffnung verbunden durch Automatisierung Zeit und Geld zu sparen. Dies könnte besonders interessant für Organisationen sein, die wenig Ressourcen zur Verfügung haben, da sie nicht gewinnorientiert oder ehrenamtlich arbeiten. Dazu zählen Nichtregierungsorganisationen (NRO), die einen wichtigen Bestandteil der Zivilgesellschaft darstellen. Aufgrund der komplexer werdenden Arbeit von NROs scheint KI Möglichkeiten zur Bewältigung aktueller und zukünftiger Herausforderungen zu bieten. Über den Einsatz von KI in NROs ist derzeit jedoch wenig bekannt. Es wurde eine quantitative Querschnittstudie deutscher Nichtregierungsorganisationen (NRO) zum Thema KI durchgeführt. Der Web-Survey baut auf den Erkenntnissen des Scoping Reviews und der qualitativen Interviews auf und vertieft den Einblick in die Bereiche aktuelle Nutzung von KI, Wissenstand in den Organisationen sowie vorhandene und noch benötigte Ressourcen. Dafür wurden 343 NROs verschiedener Größen und Handlungsfelder befragt und die Ergebnisse quantitativ analysiert.
DOI: 10.5281/zenodo.17977270
title: Künstliche Intelligenz in Nichtregierungsorganisationen - quantitativer Datensatz

description: Künstliche Intelligenz (KI) stellt ein wichtiges Thema der Digitalisierung dar und hat das Potenzial viele Arbeitsfelder grundlegend zu verändern. Mit dem Einsatz von KI wird die Hoffnung verbunden durch Automatisierung Zeit und Geld zu sparen. Dies könnte besonders interessant für Organisationen sein, die wenig Ressourcen zur Verfügung haben, da sie nicht gewinnorientiert oder ehrenamtlich arbeiten. Dazu zählen Nichtregierungsorganisationen (NRO), die einen wichtigen Bestandteil der Zivilgesellschaft darstellen. Aufgrund der komplexer werdenden Arbeit von NROs scheint KI Möglichkeiten zur Bewältigung aktueller und zukünftiger Herausforderungen zu bieten. Über den Einsatz von KI in NROs ist derzeit jedoch wenig bekannt. Es wurde eine quantitative Querschnittstudie deutscher Nichtregierungsorganisationen (NRO) zum Thema KI durchgeführt. Der Web-Survey baut auf den Erkenntnissen des Scoping Reviews und der qualitativen Interviews auf und vertieft den Einblick in die Bereiche aktuelle Nutzung von KI, Wissenstand in den Organisationen sowie vorhandene und noch benötigte Ressourcen. Dafür wurden 343 NROs verschiedener Größen und Handlungsfelder befragt und die Ergebnisse quantitativ analysiert.
DOI: 10.5281/zenodo.17986830
title: Emergence of mutants in bacterial populations: a simulation-based approach for parameter inference in extended Luria & Delbrück scenarios

description: Code and data for our article "Emergence of mutants in bacterial populations: a simulation-based approach for parameter inference in extended Luria & Delbrück scenarios", 2025-12-19 version (biorxiv)
DOI: 10.5281/zenodo.17986829
title: Emergence of mutants in bacterial populations: a simulation-based approach for parameter inference in extended Luria & Delbrück scenarios

description: Code and data for our article "Emergence of mutants in bacterial populations: a simulation-based approach for parameter inference in extended Luria & Delbrück scenarios", 2025-12-19 version (biorxiv)
DOI: 10.15468/dl.6typ56
title: Occurrence Download

description: A dataset containing 1080 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abies guatemalensis"
 ]
}

 The dataset includes 1080 records from 79 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060320-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/omf0jv
title: PAN-00176207 - seal with connecting strip without pin

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00176207&lt;/p&gt;
description: Modified: 2025-11-19T09:32:24+01:00
DOI: 10.15468/dl.pn5u29
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_0_GID","value":"ARG","matchCase":false},{"type":"equals","key":"GADM_LEVEL_1_GID","value":"ARG.22_1","matchCase":false},{"type":"equals","key":"GADM_LEVEL_2_GID","value":"ARG.22.12_1","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 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060322-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fb3u5t
title: Occurrence Download

description: A dataset containing 25122 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "GadmGid is one of (ESP.2_1, ESP.2.2_1, ESP.2.3_1, ESP.2.1_1)",
 "Geometry POLYGON((-2.07113 41.09753,-1.9758 41.10573,-1.84576 41.05046,-1.69342 40.93173,-1.68315 40.85054,-1.61312 40.79567,-1.61629 40.64257,-1.69906 40.64353,-1.74686 40.61703,-1.76051 40.51808,-1.86824 40.4291,-1.85767 40.35693,-1.78768 40.32018,-1.7888 40.25713,-1.75403 40.22973,-1.68352 40.22895,-1.55617 40.13734,-1.50903 40.14575,-1.50962 40.11873,-1.46331 40.0911,-1.29914 40.08881,-1.19434 40.06019,-1.23043 40.02471,-1.23159 39.97969,-1.19765 39.93412,-1.16304 39.91556,-0.97568 39.92144,-0.97779 39.84941,-0.92074 39.80336,-0.85098 39.79307,-0.75615 39.83632,-0.73071 39.89889,-0.77601 39.94481,-0.69232 39.99723,-0.57432 40.02184,-0.49814 40.18237,-0.3321 40.2237,-0.28154 40.3126,-0.22165 40.33819,-0.21869 40.41018,-0.2415 40.42874,-0.20506 40.45489,-0.20319 40.49988,-0.23559 40.57272,-0.23262 40.64471,-0.1728 40.66127,-0.17051 40.71526,0.06725 40.64062,0.10152 40.66851,0.20749 40.68013,0.34469 40.80046,0.31701 40.8989,0.3623 40.95399,0.36086 40.98998,0.34682 41.0437,0.28555 41.08731,0.44717 41.2171,0.4563 41.28935,0.43138 41.31582,0.4412 41.37008,0.4166 41.38754,0.41445 41.44153,0.46159 41.4606,0.51865 41.53389,0.49087 41.63236,0.41819 41.64876,0.40511 41.67548,0.45169 41.71255,0.49973 41.71361,0.53369 41.76838,0.59346 41.77865,0.67502 41.85238,0.67175 41.94238,0.73042 41.98858,0.78511 42.15175,0.77175 42.18752,0.83945 42.3329,0.78587 42.47597,0.82078 42.52166,0.82889 42.63889,0.67862 42.744,0.32542 42.71828,0.32349 42.76326,0.23723 42.77922,0.06673 42.76597,0.0558 42.73868,0 42.71922,0 42.6922,-0.04573 42.69339,-0.15176 42.77712,-0.15052 42.80411,-0.19979 42.79632,-0.2711 42.84306,-0.35667 42.84507,-0.38264 42.80965,-0.43189 42.80176,-0.51633 42.83064,-0.52999 42.79492,-0.55442 42.79544,-0.66099 42.88772,-0.72217 42.88895,-0.71985 42.95194,-0.74369 42.97042,-0.86716 42.94577,-0.91858 42.87468,-0.9207 42.81169,-0.99607 42.75,-1.05714 42.75105,-1.10763 42.69786,-1.18084 42.69905,-1.24338 42.64599,-1.24414 42.61899,-1.30557 42.6019,-1.35573 42.5486,-1.35738 42.48559,-1.40695 42.45027,-1.42159 42.35141,-1.45866 42.32489,-1.4602 42.26187,-1.48487 42.24419,-1.46413 42.09982,-1.40503 42.04497,-1.44303 41.97344,-1.55182 41.96586,-1.61117 42.01163,-1.69568 42.01262,-1.71929 42.0399,-1.81559 42.05895,-1.87601 42.05956,-1.91272 42.03289,-1.92722 41.88892,-1.89184 41.84355,-1.91729 41.76274,-1.86966 41.73526,-1.87013 41.70824,-1.95513 41.655,-2.05146 41.63782,-2.06451 41.56587,-2.04079 41.54766,-2.04172 41.48462,-2.13742 41.49439,-2.23484 41.35996,-2.21331 41.16165,-2.07113 41.09753),(-0.73855 40.01615,-0.7387 40.01167,-0.73853 40.01678,-0.73855 40.01615))"
 ]
 },
 "EventDate 2025-07-01-2025-12-19"
 ]
}

 The dataset includes 25122 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060309-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.26417.85607
title: ecological field experiments
DOI: 10.5281/zenodo.17986890
title: Replication material for paper "Bernauer, Nguyen (2015): Free Trade and/or Environmental Protection? Global Environmental Politics. DOI: 10.1162/GLEP_a_00327"

description: Replication material for paper "Bernauer, Nguyen (2015): Free Trade and/or Environmental Protection? Global Environmental Politics. DOI: 10.1162/GLEP_a_00327"
DOI: 10.5281/zenodo.17986889
title: Replication material for paper "Bernauer, Nguyen (2015): Free Trade and/or Environmental Protection? Global Environmental Politics. DOI: 10.1162/GLEP_a_00327"

description: Replication material for paper "Bernauer, Nguyen (2015): Free Trade and/or Environmental Protection? Global Environmental Politics. DOI: 10.1162/GLEP_a_00327"
DOI: 10.15468/u5p5mk
title: Arctic aphids - Acyrthosiphon svalbardicum, Macrosiphum calvulum, and Pemphigus populiglobuli

description: The dataset was created as part of research published in the article "Macro- and microecological responses of Arctic aphids to climate change: linking distributional shifts and microscale environmental drivers" by Karina Wieczorek, Agnieszka Bugaj-Nawrocka, Stephen J. Coulson, Anders Endrestøl, Carla Suñol-Escribano, and Rúna Magnússon. It contains information on the occurrence of three aphid species in the Svalbard archipelago – Acyrthosiphon svalbardicum, Macrosiphum calvulum, and Pemphigus populiglobuli. Genetic data are also available for some of them.
DOI: 10.5281/zenodo.17969536
title: Data for paper "Stabilizing Fractional Chern States in Twisted MoTe2: Multi-band Correlations via Non-perturbative Renormalization Group"
DOI: 10.5281/zenodo.17969537
title: Data for paper "Stabilizing Fractional Chern States in Twisted MoTe2: Multi-band Correlations via Non-perturbative Renormalization Group"
DOI: 10.17026/ar/bibtu6
title: PAN-00147495 - terra sigillata bowl type Drag. 27

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00147495&lt;/p&gt;
description: Modified: 2025-11-17T12:01:49+01:00
DOI: 10.15468/dl.5tvswt
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_0_GID","value":"ARG","matchCase":false},{"type":"equals","key":"GADM_LEVEL_1_GID","value":"ARG.22_1","matchCase":false},{"type":"equals","key":"GADM_LEVEL_2_GID","value":"ARG.22.13_1","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 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060323-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30919691
title: <b>Data of red deer and moose space use in a confined nature area</b>

description: This data set and R script were used to analyze (1) step length and (2) habitat selection by red deer and moose in a fenced nature area in Denmark.To analyze variation in hourly step length (log-transformed, response variable), we used generalized additive models with a Gamma distribution and log-link of the R package ‘mgcv’, separately for red deer and moose and for the 4 seasons. We included the hour of the day and the number of daily visitors as smooth terms, daily mean temperature and sex as parametric effects, and individual ID as random effect to account for varying numbers of GPS locations and individual variation. Moreover, we included the interaction between daily visitor numbers and time of day to quantify if red deer and moose adjust diel movement conditional on daily visitor numbers. To analyze habitat selection (1 = actual step versus 0 = random step), we used generalized mixed-effects models of R package ‘lme4’ with a binomial family and logit link, including step ID nested within individual ID as a random effect to make sure the model compared each actual step to the generated random steps that belonged to it. We conducted separate analyses for the two species, two behavioral states (inactive versus active) and the 4 seasons to avoid fourth-order interactions. For each of the 16 analyses, we compared 5 candidate models: (1) a basic model including sex, average daily temperature, land cover, the distance to roads, trails, the boundary fence, and the interaction of sex with land cover and of temperature with land cover; (2) a model quantifying if habitat selection was conditional on the time of the day as proxy for visitor intensity, by including visitor intensity (see categorization below) as two-way interaction terms with the above-mentioned habitat variables; (3) a model quantifying if habitat selection was conditional on daily visitor numbers (independent of time of day), including the number of daily visitors as two-way interaction terms with the habitat variables; (4) a combined model including both two-way interactions of the habitat variables with daily visitor numbers and visitor intensity (based on time of the day); and (5) a model quantifying if habitat selection was conditional on daily visitor numbers as well as time of day, including the three-way interaction of the daily visitor numbers, visitor intensity, and the habitat variables. We categorized visitor intensity based on the time of the day: (1) midday, from 10:00 to 15:00, representing high visitor numbers; (2) mornings and evenings, from 6:00 to 9:00 and 16:00 to 20:00, representing low to intermediate visitor numbers; and (3) night, from 21:00 to 5:00, no visitors present.
DOI: 10.6084/m9.figshare.30919691.v1
title: <b>Data of red deer and moose space use in a confined nature area</b>

description: This data set and R script were used to analyze (1) step length and (2) habitat selection by red deer and moose in a fenced nature area in Denmark.To analyze variation in hourly step length (log-transformed, response variable), we used generalized additive models with a Gamma distribution and log-link of the R package ‘mgcv’, separately for red deer and moose and for the 4 seasons. We included the hour of the day and the number of daily visitors as smooth terms, daily mean temperature and sex as parametric effects, and individual ID as random effect to account for varying numbers of GPS locations and individual variation. Moreover, we included the interaction between daily visitor numbers and time of day to quantify if red deer and moose adjust diel movement conditional on daily visitor numbers. To analyze habitat selection (1 = actual step versus 0 = random step), we used generalized mixed-effects models of R package ‘lme4’ with a binomial family and logit link, including step ID nested within individual ID as a random effect to make sure the model compared each actual step to the generated random steps that belonged to it. We conducted separate analyses for the two species, two behavioral states (inactive versus active) and the 4 seasons to avoid fourth-order interactions. For each of the 16 analyses, we compared 5 candidate models: (1) a basic model including sex, average daily temperature, land cover, the distance to roads, trails, the boundary fence, and the interaction of sex with land cover and of temperature with land cover; (2) a model quantifying if habitat selection was conditional on the time of the day as proxy for visitor intensity, by including visitor intensity (see categorization below) as two-way interaction terms with the above-mentioned habitat variables; (3) a model quantifying if habitat selection was conditional on daily visitor numbers (independent of time of day), including the number of daily visitors as two-way interaction terms with the habitat variables; (4) a combined model including both two-way interactions of the habitat variables with daily visitor numbers and visitor intensity (based on time of the day); and (5) a model quantifying if habitat selection was conditional on daily visitor numbers as well as time of day, including the three-way interaction of the daily visitor numbers, visitor intensity, and the habitat variables. We categorized visitor intensity based on the time of the day: (1) midday, from 10:00 to 15:00, representing high visitor numbers; (2) mornings and evenings, from 6:00 to 9:00 and 16:00 to 20:00, representing low to intermediate visitor numbers; and (3) night, from 21:00 to 5:00, no visitors present.
DOI: 10.5281/zenodo.17985995
title: PTM Immunopeptidomics
DOI: 10.5281/zenodo.17985996
title: PTM Immunopeptidomics
DOI: 10.15468/dl.z5b7tk
title: Occurrence Download

description: A dataset containing 4240 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "TaxonKey is Aloe vera"
 ]
}

 The dataset includes 4240 records from 311 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060324-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pgw74y
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_0_GID","value":"ARG","matchCase":false},{"type":"equals","key":"GADM_LEVEL_1_GID","value":"ARG.22_1","matchCase":false},{"type":"equals","key":"GADM_LEVEL_2_GID","value":"ARG.22.14_1","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 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060325-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17986657
title: PODIUM-Dataset-UC5-2025

description: UC5 focused on enhancing safety and cooperative awareness in tunnel environments, where satellite positioning is unavailable. Implemented on the Brenner Motorway, the use case monitored traffic, assessed real-time risks, and provided timely warnings to incoming Connected and Automated Vehicles (CAVs) and Connected Vehicles (CVs). Two main operational scenarios were considered: traffic jams and roadworks warnings. Sub-scenarios evaluated CAV and CV interactions in mixed traffic, including the presence of non-connected vehicles that interrupted shared perception. Roadside sensors counted and classified vehicles, while a digital twin processed this data to generate actionable warnings, enabling vehicles to adjust speed and change lanes as needed. Onboard perception and fused shared data supported cooperative awareness, safe maneuvering, and adaptive behavior. Communication strategies were implemented to ensured efficient data sharing among vehicles avoiding duplications. Positioning inside the tunnel is maintained through a combination of trilateration, dead reckoning, and onboard sensor data fusion, ensuring uninterrupted cooperative services. Trials demonstrated that vehicles could respond effectively to changing traffic conditions, maintain cooperative awareness, and execute safe maneuvers even in mixed traffic scenarios. Key challenges included integrating onboard data fusion with shared perception, managing timing and synchronization issues, and ensuring system resilience under signal degradation. Significant technical effort was required to achieve these capabilities. Operational constraints were also significant, as tunnel environments are high-traffic, difficult to access, and require robust remote support systems to maintain continuous service. Overall, UC5 confirms the feasibility of cooperative tunnel services, demonstrating enhanced traffic safety, reliable and uninterrupted vehicle positioning, and effective cooperative maneuvering. The results provide valuable insights for the future deployment of connected mobility solutions in challenging infrastructure environments