cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15409533
title: Supplementary material for: "LiDAR-derived high resolution vegetation structure and selection patterns of the Common Nightingale (Luscinia megarhynchos) in riparian habitats"

description: Abstract

Human-induced alterations in natural water flow have seriously impaired the integrity of riverine ecosystems. Nonetheless, even in human-altered riverine and adjacent terrestrial habitats, there is considerable potential for the protection of rare species if management practices prioritize biodiversity conservation. However, the management of such areas often presents complex challenges. On the one hand, efforts to mitigate natural hazards frequently overshadow biodiversity conservation objectives. On the other hand, high-resolution maps of forest structures are often lacking but could be very useful for spatial prioritization of conservation efforts, especially as vegetation structure can be directly managed through local restoration activities. Here, we used an airborne LiDAR-derived vegetation structure along an 80 km stretch of the Rhône River (Valais, Switzerland) to assess the habitat characteristics that best explain the presence of a flagship species, the Common Nightingale (Luscinia megarhynchos), a species that historically thrived along this river system but has experienced a drastic population decline over the past decades. Nightingales showed a preference for dense vegetation in the lower strata above ground (3–6m), as opposed to an open and sparsely vegetated ground level (0-1m). The preferred habitats were predominantly located within forested regions, as indicated by a preference for taller canopies. These findings align surprisingly well with prior field research on the species, demonstrating the capability of high-resolution LiDAR to upscale locally derived habitat preferences across very large areas. Based on LiDAR outputs, we proposed management recommendations for the whole river. Such spatially detailed information furthers our understanding of local habitat preferences of endangered species, thus facilitating the formulation of conservation recommendations at the scale of entire populations.
DOI: 10.5281/zenodo.15409534
title: Supplementary material for: "LiDAR-derived high resolution vegetation structure and selection patterns of the Common Nightingale (Luscinia megarhynchos) in riparian habitats"

description: Abstract

Human-induced alterations in natural water flow have seriously impaired the integrity of riverine ecosystems. Nonetheless, even in human-altered riverine and adjacent terrestrial habitats, there is considerable potential for the protection of rare species if management practices prioritize biodiversity conservation. However, the management of such areas often presents complex challenges. On the one hand, efforts to mitigate natural hazards frequently overshadow biodiversity conservation objectives. On the other hand, high-resolution maps of forest structures are often lacking but could be very useful for spatial prioritization of conservation efforts, especially as vegetation structure can be directly managed through local restoration activities. Here, we used an airborne LiDAR-derived vegetation structure along an 80 km stretch of the Rhône River (Valais, Switzerland) to assess the habitat characteristics that best explain the presence of a flagship species, the Common Nightingale (Luscinia megarhynchos), a species that historically thrived along this river system but has experienced a drastic population decline over the past decades. Nightingales showed a preference for dense vegetation in the lower strata above ground (3–6m), as opposed to an open and sparsely vegetated ground level (0-1m). The preferred habitats were predominantly located within forested regions, as indicated by a preference for taller canopies. These findings align surprisingly well with prior field research on the species, demonstrating the capability of high-resolution LiDAR to upscale locally derived habitat preferences across very large areas. Based on LiDAR outputs, we proposed management recommendations for the whole river. Such spatially detailed information furthers our understanding of local habitat preferences of endangered species, thus facilitating the formulation of conservation recommendations at the scale of entire populations.
DOI: 10.15468/dl.jbrgpa
title: Occurrence Download

description: A dataset containing 283731 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Pilosella officinarum",
 "Year 1975-2025"
 ]
}

 The dataset includes 283731 records from 1215 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006629-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16992528
title: Identifying factors influencing satisfaction and behavioral intention in Surabaya's urban public transport
DOI: 10.5281/zenodo.16992529
title: Identifying factors influencing satisfaction and behavioral intention in Surabaya's urban public transport
DOI: 10.34740/kaggle/dsv/12903927
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.q4kr7u
title: Occurrence Download

description: A dataset containing 54054 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Trifolium pratense pratense"
 ]
}

 The dataset includes 54054 records from 283 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006619-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sau6he
title: Occurrence Download

description: A dataset containing 425 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Chiloglanis carnosus, Chiloglanis congicus, Chiloglanis deckenii, Chiloglanis devosi, Chiloglanis dialloi, Chiloglanis disneyi, Chiloglanis elisabethianus, Chiloglanis emarginatus, Chiloglanis fasciatus, Chiloglanis harbinger, Chiloglanis kabaensis, Chiloglanis kalambo, Chiloglanis kerioensis, Chiloglanis kolente, Chiloglanis lamottei, Chiloglanis loffabrevum, Chiloglanis longibarbis, Chiloglanis lufirae, Chiloglanis lukugae, Chiloglanis macropterus, Chiloglanis marlieri, Chiloglanis mbozi, Chiloglanis microps, Chiloglanis modjensis, Chiloglanis mongoensis, Chiloglanis neumanni, Chiloglanis niger, Chiloglanis niloticus, Chiloglanis normani, Chiloglanis nzerekore, Chiloglanis occidentalis, Chiloglanis paratus, Chiloglanis pezoldi, Chiloglanis pojeri, Chiloglanis polyodon, Chiloglanis polypogon, Chiloglanis pretoriae, Chiloglanis reticulatus, Chiloglanis rukwaensis, Chiloglanis ruziziensis, Chiloglanis sanagaensis, Chiloglanis sardinhai, Chiloglanis somereni, Chiloglanis swierstrai, Chiloglanis trilobatus, Chiloglanis tweddlei, Chiloglanis voltae, Chiloglanis waterloti, Chilotilapia euchilus, Chilotilapia rhoadesii, Chindongo ater, Chindongo bellicosus, Chindongo cyaneus, Chindongo demasoni, Chindongo flavus, Chindongo heteropictus, Maylandia heteropicta, Chindongo longior, Chindongo minutus, Chindongo saulosi, Chromidotilapia cavalliensis, Chromidotilapia elongata, Chromidotilapia guntheri, Chromidotilapia kingsleyae, Chromidotilapia linkei, Chromidotilapia mamonekenei, Chromidotilapia melaniae, Chromidotilapia mrac, Chromidotilapia nana, Chromidotilapia regani, Chromidotilapia schoutedeni, Chrysichthys aluuensis, Chrysichthys ansorgii, Chrysichthys auratus, Chrysichthys bocagii, Chrysichthys brachynema, Chrysichthys brevibarbis, Chrysichthys cranchii, Chrysichthys dageti, Chrysichthys delhezi, Chrysichthys dendrophorus, Chrysichthys duttoni, Chrysichthys furcatus, Chrysichthys nigrodigitatus, Chrysichthys grandis, Chrysichthys graueri, Chrysichthys habereri, Chrysichthys helicophagus, Chrysichthys hildae, Chrysichthys johnelsi, Chrysichthys laticeps, Chrysichthys levequei, Chrysichthys longibarbis, Chrysichthys longidorsalis, Chrysichthys longipinnis, Chrysichthys mabusi, Chrysichthys macropterus, Chrysichthys maurus, Chrysichthys nyongensis)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((28.1382 2.0599,19.1382 2.0599,19.1382 -6.9401,28.1382 -6.9401,28.1382 2.0599))"
 ]
}

 The dataset includes 425 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006677-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8h53kd
title: Occurrence Download

description: A dataset listing the 81 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-13.87313 28.74608,-13.87443 28.74403,-13.87427 28.74243,-13.87448 28.74331,-13.8749 28.74314,-13.87475 28.74254,-13.87381 28.74198,-13.87185 28.74171,-13.87083 28.74169,-13.87067 28.74305,-13.86935 28.74403,-13.86815 28.74512,-13.87215 28.74578,-13.87313 28.74608))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 81 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006666-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5vh8ea
title: Occurrence Download

description: A dataset containing 18206 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Neophron percnopterus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-6.6 29.26705, -6.6 50.94, 40.81373 50.94, 40.81373 29.26705, -6.6 29.26705))",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Material sample)"
 ]
}

 The dataset includes 18206 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006598-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14740203
title: Near-equilibrium plagioclase dissolution rates and its implications for ocean crust alteration and carbonation dataset and thermodynamic databases

description: Experimental Data for experiments on plagioclase dissolution
DOI: 10.5281/zenodo.14740227
title: Near-equilibrium plagioclase dissolution rates and its implications for ocean crust alteration and carbonation dataset and thermodynamic databases

description: Experimental Data for experiments on plagioclase dissolution
DOI: 10.15131/shef.data.28912367
title: TikTok post IDs used in Social Media + Society paper: 'Identity Roles and Sociality on TikTok: Performance in Hereditary Cancer Content'

description: This research examined how identity in the context of hereditary cancer syndromes (BRCA and Lynch Syndrome) is performed on the social media platform: TikTok.Data was collected using a computational tool developed by The Bright Data Initiative. We collected public posts on TikTok for 15 days across the months of March and April 2023. The posts were retrieved using hashtag based queries (#BRCA and #lynchsyndrome) and were limited to those in the English language. The posts collected reflected what was shown on the respective hashtag page at the time of data collection. Posts displayed on TikTok’s hashtag pages are ordered algorithmically (rather than chronologically). In this way, the data we collected represented content that was deemed ‘top’ by TikTok’s algorithms at the time of collection. The sample of TikTok posts used in this research was taken from a collection conducted on the 2nd March 2023.We conducted a Qualitative Content Analysis on 100 TikTok videos from our sample (50 BRCA posts and 50 Lynch Syndrome posts).We have deposited to ORDA the post IDs of the TikTok videos used as examples in our Social Media + Society paper titled: <b>Identity roles and sociality on TikTok: Performance in Hereditary Cancer Content (#BRCA and #Lynchsyndrome)</b>Due to the sensitive nature of the topics explored in the posts (health, illness, cancer) access to these IDs is restricted but can be requested directly from the authors (contact: hannah.ditchfield@sheffield.ac.uk or s.vicari@sheffield.ac.uk).This data was collected and analysed as part of the Previvorship in the Platform Society project funded by the Leverhulme Trust, PI Dr Stefania Vicari. This research received ethical approval from the University of Sheffield (Application 044795).
DOI: 10.15131/shef.data.28912367.v1
title: TikTok post IDs used in Social Media + Society paper: 'Identity Roles and Sociality on TikTok: Performance in Hereditary Cancer Content'

description: This research examined how identity in the context of hereditary cancer syndromes (BRCA and Lynch Syndrome) is performed on the social media platform: TikTok.Data was collected using a computational tool developed by The Bright Data Initiative. We collected public posts on TikTok for 15 days across the months of March and April 2023. The posts were retrieved using hashtag based queries (#BRCA and #lynchsyndrome) and were limited to those in the English language. The posts collected reflected what was shown on the respective hashtag page at the time of data collection. Posts displayed on TikTok’s hashtag pages are ordered algorithmically (rather than chronologically). In this way, the data we collected represented content that was deemed ‘top’ by TikTok’s algorithms at the time of collection. The sample of TikTok posts used in this research was taken from a collection conducted on the 2nd March 2023.We conducted a Qualitative Content Analysis on 100 TikTok videos from our sample (50 BRCA posts and 50 Lynch Syndrome posts).We have deposited to ORDA the post IDs of the TikTok videos used as examples in our Social Media + Society paper titled: <b>Identity roles and sociality on TikTok: Performance in Hereditary Cancer Content (#BRCA and #Lynchsyndrome)</b>Due to the sensitive nature of the topics explored in the posts (health, illness, cancer) access to these IDs is restricted but can be requested directly from the authors (contact: hannah.ditchfield@sheffield.ac.uk or s.vicari@sheffield.ac.uk).This data was collected and analysed as part of the Previvorship in the Platform Society project funded by the Leverhulme Trust, PI Dr Stefania Vicari. This research received ethical approval from the University of Sheffield (Application 044795).
DOI: 10.15468/dl.z3zfp9
title: Occurrence Download

description: A dataset containing 93529 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Canis lupus Linnaeus, 1758)",
 "Country is one of (Åland Islands, Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Republic of, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland)"
 ]
}

 The dataset includes 93529 records from 337 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021196-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t9uwa9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Zapus hudsonius (Zimmermann, 1780))",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021208-250525065834625/datasets/export for details.
DOI: 10.6084/m9.figshare.29184551
title: Dataset

description: Source data for the figures of the article By Hanen Ben Ameur
DOI: 10.6084/m9.figshare.29184551.v2
title: Dataset

description: Source data for the figures of the article By Hanen Ben Ameur
DOI: 10.6084/m9.figshare.29184551.v3
title: Dataset

description: Source data for the figures of the article By Hanen Ben Ameur
DOI: 10.15468/dl.r47xst
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Zapus hudsonius (Zimmermann, 1780))",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021211-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15596603
title: Survey of determinants of acceptance of mobile banking used by livestock farmers, Antioquia, Colombia.

description: Survey of determinants of acceptance of mobile banking used by livestock farmers
DOI: 10.5281/zenodo.15596604
title: Survey of determinants of acceptance of mobile banking used by livestock farmers, Antioquia, Colombia.

description: Survey of determinants of acceptance of mobile banking used by livestock farmers
DOI: 10.5281/zenodo.15596549
title: Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples (Extended Dataset)

description: Extended Dataset for 'Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples' (NDSS 2025)
DOI: 10.5281/zenodo.15596550
title: Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples (Extended Dataset)

description: Extended Dataset for 'Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples' (NDSS 2025)
DOI: 10.15468/dl.urs22q
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Costa Rica",
 "TaxonKey is Canavalia ensiformis (L.) DC."
 ]
}

 The dataset includes 22 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021212-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uambw9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Zapus hudsonius (Zimmermann, 1780))",
 "Country is one of (Algeria, Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Congo, Democratic Republic of the, Côte d’Ivoire, Djibouti, Egypt, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, French Southern Territories, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mayotte, Morocco, Mozambique, Namibia, Niger, Nigeria, Réunion, Rwanda, Saint Helena, Ascension and Tristan da Cunha, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Tanzania, United Republic of, Togo, Tunisia, Uganda, Western Sahara, Zambia, Zimbabwe)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021213-250525065834625/datasets/export for details.
DOI: 10.15468/dl.7adq5m
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Zapus hudsonius (Zimmermann, 1780))",
 "Country is one of (Åland Islands, Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Republic of, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021215-250525065834625/datasets/export for details.
DOI: 10.15468/dl.qeztkj
title: Occurrence Download

description: A dataset containing 322 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dicentrarchus punctatus (Bloch, 1792)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 322 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021216-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.28w69b
title: Occurrence Download

description: A dataset containing 86 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Costa Rica",
 "TaxonKey is Canavalia oxyphylla Standl. & L.O.Williams"
 ]
}

 The dataset includes 86 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021217-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.