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

description: A dataset containing 20933 species occurrences available in GBIF matching the query:
{
 "and" : [
 "InstitutionCode is Pro Natura",
 "TaxonKey is Plantae",
 "HasCoordinate is TRUE",
 {
 "or" : [
 "Country is Albania",
 "Country is Austria",
 "Country is Bosnia and Herzegovina",
 "Country is Belgium",
 "Country is Bulgaria",
 "Country is Belarus",
 "Country is Switzerland",
 "Country is Cyprus",
 "Country is Czechia",
 "Country is Germany",
 "Country is Denmark",
 "Country is Estonia",
 "Country is Spain",
 "Country is Finland",
 "Country is France",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "Country is Greece",
 "Country is Croatia",
 "Country is Hungary",
 "Country is Ireland",
 "Country is Iceland",
 "Country is Italy",
 "Country is Lithuania",
 "Country is Luxembourg",
 "Country is Latvia",
 "Country is Moldova, Republic of",
 "Country is Montenegro",
 "Country is Malta",
 "Country is North Macedonia",
 "Country is Netherlands",
 "Country is Norway",
 "Country is Poland",
 "Country is Portugal",
 "Country is Romania",
 "Country is Serbia",
 "Country is Russian Federation",
 "Country is Sweden",
 "Country is Slovenia",
 "Country is Slovakia",
 "Country is Ukraine"
 ]
 }
 ]
}

 The dataset includes 20933 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009831-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q7uxwe
title: Occurrence Download

description: A dataset containing 175 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((103.80294799804688 -4.97955322265625,103.96774291992188 -4.97955322265625,103.96774291992188 -4.81475830078125,103.80294799804688 -4.81475830078125,103.80294799804688 -4.97955322265625))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 175 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0316982-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fa58cc
title: Occurrence Download

description: A dataset containing 90 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Limonium humile Mill.",
 "MediaType is Image",
 "BasisOfRecord is Human Observation"
 ]
}

 The dataset includes 90 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001442-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yptm8t
title: Occurrence Download

description: A dataset containing 369 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2024",
 "Geometry POLYGON ((-130.7383528944533 -59.46314024951374, -130.7383528944533 32.71876227804215, -26.26729810228821 32.71876227804215, -26.26729810228821 -59.46314024951374, -130.7383528944533 -59.46314024951374))",
 "OccurrenceStatus is Present",
 "TaxonKey is Glaucidium parkeri Robbins & S.N.G.Howell, 1995"
 ]
}

 The dataset includes 369 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051209-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6pdsxa
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Astragalus muticus Pau"
 ]
}

 The dataset includes 19 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0219036-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hvnsh5
title: Occurrence Download

description: A dataset containing 499 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Elephas maximus indicus Cuvier, 1798"
 ]
}

 The dataset includes 499 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035156-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7710595
title: Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery

description: See GitHub repository for data processing functions and examples: https://github.com/soerenbrandt/sniffing-sensor <strong>Abstract</strong>:<br> Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.
DOI: 10.15468/dl.ceywnr
title: Occurrence Download

description: A dataset containing 50 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-100.06506580504082 26.285772084275017,-100.06506580504082 22.285772084275017,-96.06506580504082 22.285772084275017,-96.06506580504082 26.285772084275017,-100.06506580504082 26.285772084275017))",
 "HasCoordinate is true",
 "TaxonKey is Mantidae",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 50 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009832-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11322925
title: Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery

description: Version 2: Added missing files to sniffing_data.zip


See GitHub repository for data processing functions and examples: https://github.com/soerenbrandt/sniffing-sensor

Abstract:Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.
DOI: 10.15468/dl.3rbyu8
title: Occurrence Download

description: A dataset listing the 9 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dasypus novemcinctus Linnaeus, 1758"
 ]
}

 The dataset's 9 records were derived from 298 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0228466-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9cr92m
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Observation"
 ]
 },
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "Month is October",
 "Month is November",
 "Month is December",
 "Month is January",
 "Month is February",
 "Month is March"
 ]
 },
 "TaxonKey is Amazilia yucatanensis (S.Cabot, 1845)",
 "Year 1971-1980"
 ]
}

 The dataset includes 46 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0292238-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11342878
title: Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery

description: Version 2: Added missing files to sniffing_data.zip


See GitHub repository for data processing functions and examples: https://github.com/soerenbrandt/sniffing-sensor

Abstract:Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.
DOI: 10.5281/zenodo.7710594
title: Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery

description: Version 2: Added missing files to sniffing_data.zip


See GitHub repository for data processing functions and examples: https://github.com/soerenbrandt/sniffing-sensor

Abstract:Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.
DOI: 10.15468/dl.9t5z7d
title: Occurrence Download

description: A dataset containing 2163 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Magicicada Davis, 1925"
 ]
}

 The dataset includes 2163 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0085149-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qwxyw3
title: Occurrence Download

description: A dataset containing 148 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Solidobalanus fallax (Broch, 1927))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 148 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051208-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t83gz9
title: Occurrence Download

description: A dataset containing 42202485 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-168.22796 5.92319,36.79842 5.92319,36.79842 85.76968,-168.22796 85.76968,-168.22796 5.92319))",
 "OccurrenceStatus is Present",
 "TaxonKey is Lepidoptera",
 "Year 1990-2019"
 ]
}

 The dataset includes 42202485 records from 2169 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0258706-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0161584.10fu
title: SH0161584.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0161585.10fu
title: SH0161585.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0161586.10fu
title: SH0161586.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0161587.10fu
title: SH0161587.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.17028/rd.lboro.31035109
title: Supplementary information files for "The understanding of the agriculturally shaped environment: from the theoretical construct to an applied indicator of sustainable development."

description: Supplementary files for article "The understanding of the agriculturally shaped environment: from the theoretical construct to an applied indicator of sustainable development"<br><br>Sustainable development of the resource ‘land’ is increasingly being discussed with a focus on rural areas. Understanding is key to solving land use conflicts. It can lead to joint solutions and can thus enable sustainable development at a practical level. It goes beyond environmental consciousness, which is related to general issues, and instead aims to address concrete challenges in the context of sustainable development at an applicable level. ‘Understanding’ with regard to land use conflicts has not yet been defined in the literature. Based on this motivation, it is the aim of this study to create the construct of ‘understanding’ conceptually, to validate it empirically with structural equation modelling, and to demonstrate that understanding might be an important prerequisite for sustainable development. In this case, the focus is not on a general kind of understanding, but rather on specific aspects of understanding in relation to the agriculturally shaped environment in rural areas. The empirical data for the paper were collected by means of a large-scale population survey in Western Pomerania, Germany, a rural peripheral region characterized by typical land-use conflicts in predominantly rural areas. A tripartite division of the construct into cognitive, emotional, and opinion levels was derived theoretically. The construct is supported empirically and that it can be applied as an SDG indicator. Thus, the refined construct of understanding the agriculturally shaped environment can make a substantial contribution towards closing the knowledge/attitude-behavior gap.<br><br>© The Author(s), CC BY 4.0