cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.f8u5c3
title: Occurrence Download

description: A dataset containing 350773 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rhus typhina, Parthenocissus quinquefolia, Spiraea billardii, Prunus laurocerasus, Rosa rugosa, Spiraea alba, Pistia stratiotes, Solidago gigantea, Rubus armeniacus, Robinia pseudoacacia, Acer negundo, Ailanthus altissima, Alternanthera philoxeroides, Aponogeton distachyos, Arundo donax, Asclepias syriaca, Azolla filiculoides, Baccharis halimifolia, Cabomba caroliniana, Carpobrotus, Cotoneaster horizontalis, Egeria densa, Eichhornia crassipes, Elaeagnus angustifolia, Elodea canadensis, Elodea nuttallii, Fallopia baldschuanica, Fraxinus pennsylvanica, Gymnocoronis spilanthoides, Helianthus tuberosus, Helianthus laetiflorus, Heracleum mantegazzianum, Hydrilla verticillata, Hydrocotyle ranunculoides, Impatiens capensis, Impatiens glandulifera, Koenigia polystachya, Laburnum anagyroides, Lagarosiphon major, Lemna minuta, Lemna turionifera, Limnobium laevigatum, Reynoutria japonica, Myriophyllum rubricaule, Prunus serotina, Parthenocissus tricuspidata, Mahonia aquifolium, Parthenocissus inserta, Spiraea tomentosa, Spiraea douglasii, Reynoutria bohemica, Salvinia molesta, Myriophyllum heterophyllum, Rudbeckia laciniata, Ludwigia grandiflora, Reynoutria sachalinensis, Spiraea salicifolia, Solidago canadensis, Myriophyllum aquaticum, Ludwigia peploides, Zizania latifolia)",
 "Country is Belgium",
 "Year is greater than or equal to 2013",
 "HasCoordinate is true",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 350773 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002100-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18535904
title: Pattern Recognition as Singular Intelligence: An Epistemological Bypass of Gardner's Multiple Intelligences Framework
DOI: 10.5281/zenodo.18535905
title: Pattern Recognition as Singular Intelligence: An Epistemological Bypass of Gardner's Multiple Intelligences Framework
DOI: 10.15468/dl.s7c7ep
title: Occurrence Download

description: A dataset containing 9604 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hakea sericea)",
 "OccurrenceStatus is Present",
 "SpeciesKey is not null",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2026",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 },
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation)"
 ]
}

 The dataset includes 9604 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002116-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w6e97q
title: Occurrence Download

description: A dataset containing 1916 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Grus nigricollis",
 "Year 2005-2025"
 ]
}

 The dataset includes 1916 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002117-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tb7g22
title: Occurrence Download

description: A dataset containing 740026 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= 41.89415215079384
 AND decimallatitude &lt;= 46.479780028101516
 AND decimallongitude &gt;= -100.71036946873933
 AND decimallongitude &lt;= -89.91112128543796
 AND GBIF_Within('POLYGON ((-89.911121 41.894152, -89.911121 46.479780, -100.710369 46.479780, -100.710369 41.894152, -89.911121 41.894152))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 740026 records were derived from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002112-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mxhn29
title: Occurrence Download

description: A dataset containing 13052 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Amaranthus viridis"
 ]
}

 The dataset includes 13052 records from 510 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002118-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18252361
title: Supplemantary table for Efficacy of Large Language Models for Screening of Systematic Reviews on Periprosthetic Joint Infection
DOI: 10.5281/zenodo.18252362
title: Supplemantary table for Efficacy of Large Language Models for Screening of Systematic Reviews on Periprosthetic Joint Infection
DOI: 10.5281/zenodo.18536098
title: THE IMPACT OF STRESS AND DEPRESSION ON THE CENTRAL NERVOUS SYSTEM

description: Stress and depression are among the most prevalent neuropsychological conditions affecting modern society. While acute stress may serve adaptive functions, chronic stress and depressive disorders exert profound negative effects on the central nervous system. These conditions are associated with structural, functional, and biochemical alterations in the brain, leading to impaired cognition, emotional dysregulation, and increased vulnerability to neurological and psychiatric disorders. This article provides a comprehensive clinical and neurobiological review of the effects of stress and depression on the central nervous system. Particular attention is given to hypothalamic–pituitary–adrenal HPA axis dysregulation, neurotransmitter imbalance, neuroplasticity impairment, and neuroinflammatory processes. Understanding these mechanisms is essential for improving diagnostic accuracy, therapeutic strategies, and preventive interventions.
DOI: 10.5281/zenodo.18536099
title: THE IMPACT OF STRESS AND DEPRESSION ON THE CENTRAL NERVOUS SYSTEM

description: Stress and depression are among the most prevalent neuropsychological conditions affecting modern society. While acute stress may serve adaptive functions, chronic stress and depressive disorders exert profound negative effects on the central nervous system. These conditions are associated with structural, functional, and biochemical alterations in the brain, leading to impaired cognition, emotional dysregulation, and increased vulnerability to neurological and psychiatric disorders. This article provides a comprehensive clinical and neurobiological review of the effects of stress and depression on the central nervous system. Particular attention is given to hypothalamic–pituitary–adrenal HPA axis dysregulation, neurotransmitter imbalance, neuroplasticity impairment, and neuroinflammatory processes. Understanding these mechanisms is essential for improving diagnostic accuracy, therapeutic strategies, and preventive interventions.
DOI: 10.15468/dl.63qrwm
title: Occurrence Download

description: A dataset containing 174680 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Armadillidium",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 174680 records from 487 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002099-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18536090
title: CONSTRAINT-FIRST EPISTEMOLOGY 2: THE MILLENNIUM AUDIT COLLECTION
DOI: 10.5281/zenodo.18536091
title: CONSTRAINT-FIRST EPISTEMOLOGY 2: THE MILLENNIUM AUDIT COLLECTION
DOI: 10.3929/ethz-c-000783628
title: Dataset on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of Swiss grassland farmers

description: We present a data set on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of 206 farmers managing grassland in Switzerland (CH). The survey data consists of two parts: 1) a choice experiment; and 2) questions about farmers’ and farm characteristics, attitudes, and preferences. In the choice experiment (Part 1) non-monetary attributes included 1) group size (i.e., the number of partners a farmer works in a collective scheme with), 2) monitoring (i.e., execution of the biodiversity monitoring process), and 3) discretion (i.e., responsibility for management decisions at the farm to enhance biodiversity conservation: the farmers or an external consultant). The survey questions (Part 2) collected data on farmers’ personal and farm characteristics, production orientation, attitudes towards collaboration, the environment, trust and self-efficacy, their risk, time, and social network preferences, as well as their income satisfaction. The data has been collected as part of the research project GreeNet – Grassland conservation across European landscapes protecting biodiversity and ecosystem services with ecological networks. GreeNet is funded by Biodiversa+, the European Biodiversity Partnership under the 2021-2022 BiodivProtect joint call for research proposals, co-funded by the European Commission and national funding organizations. All personally identifiable data has been removed for reasons of confidentiality.
DOI: 10.3929/ethz-c-000783632
title: Dataset on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of Irish grassland farmers

description: We present a data set on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of 132 farmers managing grassland in Ireland (IE). The survey data consists of two parts: 1) a choice experiment; and 2) questions about farmers’ and farm characteristics, attitudes, and preferences. In the choice experiment (Part 1) non-monetary attributes included 1) group size (i.e., the number of partners a farmer works in a collective scheme with), 2) monitoring (i.e., execution of the biodiversity monitoring process), and 3) discretion (i.e., responsibility for management decisions at the farm to enhance biodiversity conservation: the farmers or an external consultant). The survey questions (Part 2) collected data on farmers’ personal and farm characteristics, production orientation, attitudes towards collaboration, the environment, trust and self-efficacy, their risk, time, and social network preferences, as well as their income satisfaction. The data has been collected as part of the research project GreeNet – Grassland conservation across European landscapes protecting biodiversity and ecosystem services with ecological networks. GreeNet is funded by Biodiversa+, the European Biodiversity Partnership under the 2021-2022 BiodivProtect joint call for research proposals, co-funded by the European Commission and national funding organizations. All personally identifiable data has been removed for reasons of confidentiality.
DOI: 10.3929/ethz-c-000795455
title: Dataset on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of Norwegian grassland farmers

description: We present a data set on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of 141 farmers managing grassland in Norway (NO). The survey data consists of two parts: 1) a choice experiment; and 2) questions about farmers’ and farm characteristics, attitudes, and preferences. In the choice experiment (Part 1) non-monetary attributes included 1) group size (i.e., the number of partners a farmer works in a collective scheme with), 2) monitoring (i.e., execution of the biodiversity monitoring process), and 3) discretion (i.e., responsibility for management decisions at the farm to enhance biodiversity conservation: the farmers or an external consultant). The survey questions (Part 2) collected data on farmers’ personal and farm characteristics, production orientation, attitudes towards collaboration, the environment, trust and self-efficacy, their risk, time, and social network preferences, as well as their income satisfaction. The data has been collected as part of the research project GreeNet – Grassland conservation across European landscapes protecting biodiversity and ecosystem services with ecological networks. GreeNet is funded by Biodiversa+, the European Biodiversity Partnership under the 2021-2022 BiodivProtect joint call for research proposals, co-funded by the European Commission and national funding organizations. All personally identifiable data has been removed for reasons of confidentiality.
DOI: 10.3929/ethz-c-000783630
title: Dataset on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of Estonian grassland farmers

description: We present a data set on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of 106 farmers managing grassland in Estonia (EE). The survey data consists of two parts: 1) a choice experiment; and 2) questions about farmers’ and farm characteristics, attitudes, and preferences. In the choice experiment (Part 1) non-monetary attributes included 1) group size (i.e., the number of partners a farmer works in a collective scheme with), 2) monitoring (i.e., execution of the biodiversity monitoring process), and 3) discretion (i.e., responsibility for management decisions at the farm to enhance biodiversity conservation: the farmers or an external consultant). The survey questions (Part 2) collected data on farmers’ personal and farm characteristics, production orientation, attitudes towards collaboration, the environment, trust and self-efficacy, their risk, time, and social network preferences, as well as their income satisfaction. The data has been collected as part of the research project GreeNet – Grassland conservation across European landscapes protecting biodiversity and ecosystem services with ecological networks. GreeNet is funded by Biodiversa+, the European Biodiversity Partnership under the 2021-2022 BiodivProtect joint call for research proposals, co-funded by the European Commission and national funding organizations. All personally identifiable data has been removed for reasons of confidentiality.
DOI: 10.3929/ethz-c-000783629
title: Dataset on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of German grassland farmers

description: We present a data set on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of 173 farmers managing grassland in Germany (DE). The survey data consists of two parts: 1) a choice experiment; and 2) questions about farmers’ and farm characteristics, attitudes, and preferences. In the choice experiment (Part 1) non-monetary attributes included 1) group size (i.e., the number of partners a farmer works in a collective scheme with), 2) monitoring (i.e., execution of the biodiversity monitoring process), and 3) discretion (i.e., responsibility for management decisions at the farm to enhance biodiversity conservation: the farmers or an external consultant). The survey questions (Part 2) collected data on farmers’ personal and farm characteristics, production orientation, attitudes towards collaboration, the environment, trust and self-efficacy, their risk, time, and social network preferences, as well as their income satisfaction. The data has been collected as part of the research project GreeNet – Grassland conservation across European landscapes protecting biodiversity and ecosystem services with ecological networks. GreeNet is funded by Biodiversa+, the European Biodiversity Partnership under the 2021-2022 BiodivProtect joint call for research proposals, co-funded by the European Commission and national funding organizations. All personally identifiable data has been removed for reasons of confidentiality.
DOI: 10.3929/ethz-c-000783621
title: Dataset on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of Austrian grassland farmers

description: We present a data set on the stated willingness to accept collective agri-environmental schemes for biodiversity conservation of 195 farmers managing grassland in Austria (AT). The survey data consists of two parts: 1) a choice experiment; and 2) questions about farmers’ and farm characteristics, attitudes, and preferences. In the choice experiment (Part 1) non-monetary attributes included 1) group size (i.e., the number of partners a farmer works in a collective scheme with), 2) monitoring (i.e., execution of the biodiversity monitoring process), and 3) discretion (i.e., responsibility for management decisions at the farm to enhance biodiversity conservation: the farmers or an external consultant). The survey questions (Part 2) collected data on farmers’ personal and farm characteristics, production orientation, attitudes towards collaboration, the environment, trust and self-efficacy, their risk, time, and social network preferences, as well as their income satisfaction. The data has been collected as part of the research project GreeNet – Grassland conservation across European landscapes protecting biodiversity and ecosystem services with ecological networks. GreeNet is funded by Biodiversa+, the European Biodiversity Partnership under the 2021-2022 BiodivProtect joint call for research proposals, co-funded by the European Commission and national funding organizations. All personally identifiable data has been removed for reasons of confidentiality.
DOI: 10.5281/zenodo.18533706
title: SuperDARN data in netCDF format (2021-Mar)

description: 2021-Mar SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7620734
title: SuperDARN data in netCDF format (2021-Mar)

description: 2021-Mar SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.raa27u
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",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Anabasis annua"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002120-260208012135463/datasets/export for details.
DOI: 10.25403/upresearchdata.30752267
title: Physico-chemical changes of Kissra during fermentation, baking and shelf life

description: <b>The different varieties of sorghum flour were fermented using sourdough fermentation to assess the impact of fermentation on the techno-functional, microstructural, macrostructural, and nutritional properties of the sorghum batter and the final product</b><br>
DOI: 10.25403/upresearchdata.30752267.v1
title: Physico-chemical changes of Kissra during fermentation, baking and shelf life

description: <b>The different varieties of sorghum flour were fermented using sourdough fermentation to assess the impact of fermentation on the techno-functional, microstructural, macrostructural, and nutritional properties of the sorghum batter and the final product</b><br>
DOI: 10.5281/zenodo.18536185
title: THE ROLE OF ARTIFICIAL INTELLIGENCE IN PERSONALIZED ENGLISH LANGUAGE LEARNING

description: This article explores the role of artificial intelligence in personalized English language learning. In particular, it analyzes the capabilities of AI-based educational platforms in providing adaptive learning approaches tailored to learners’ individual needs and abilities. The study theoretically examines the significance of artificial intelligence in increasing learner motivation, supporting independent learning, and enhancing the overall effectiveness of the learning process.
DOI: 10.5281/zenodo.18536184
title: THE ROLE OF ARTIFICIAL INTELLIGENCE IN PERSONALIZED ENGLISH LANGUAGE LEARNING

description: This article explores the role of artificial intelligence in personalized English language learning. In particular, it analyzes the capabilities of AI-based educational platforms in providing adaptive learning approaches tailored to learners’ individual needs and abilities. The study theoretically examines the significance of artificial intelligence in increasing learner motivation, supporting independent learning, and enhancing the overall effectiveness of the learning process.