cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.qnacs2
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eberlanzia gravida",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 20 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021588-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t2ddfb
title: Occurrence Download

description: A dataset containing 1310 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (North America, South America)",
 "TaxonKey is Cleome serrulata"
 ]
}

 The dataset includes 1310 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021589-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10548444
title: Battery technologies mass weighted SDP

description: Mass weighted supply disruption probability (SDP) calculated based on the methods defined by Terlouw et al. (2019)

 

Metadata descriptor: https://github.com/OpenEnergyPlatform/oemetadata/blob/develop/oemetadata/v1/v160/metadata_key_description.md
DOI: 10.5281/zenodo.11349385
title: Battery technologies mass weighted SDP

description: Mass weighted supply disruption probability (SDP) calculated based on the methods defined by Terlouw et al. (2019)

 

Metadata descriptor: https://github.com/OpenEnergyPlatform/oemetadata/blob/develop/oemetadata/v1/v160/metadata_key_description.md
DOI: 10.15468/dl.6z9254
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((-10.05382 20.48961,120.05382 20.48961,120.05382 72.51039,-10.05382 72.51039,-10.05382 20.48961))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sibiraea altaiensis"
 ]
}

 The dataset includes 10 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021591-260208012135463/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.31345786
title: Thomas & Hairol 2026 - ANON

description: Datasets on CCVD and related issues
DOI: 10.6084/m9.figshare.31345786.v1
title: Thomas & Hairol 2026 - ANON

description: Datasets on CCVD and related issues
DOI: 10.15468/dl.mc8rhd
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ebracteola wilmaniae",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021592-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18655836
title: Pharmaceutics 2025
DOI: 10.5281/zenodo.18655835
title: Pharmaceutics 2025
DOI: 10.15468/mf47s3
title: Porvoo Museum / Collection of Butterflies and Moths by Eino J. Seppänen

description: Collection of butterflies and moths with about 800 species and 400 samples of caterpillars
DOI: 10.15468/3jcf8z
title: Porvoo Museum / Algae Collections

description: Algae Collections in Porvoo Museum
DOI: 10.15468/dl.pvzcp7
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Switzerland",
 "DatasetKey is Schlern - (Bozen)",
 "OccurrenceStatus is Present",
 "TaxonKey is Leontopodium alpinum"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021595-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.95ax9x
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erepsia lacera",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 23 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021596-260208012135463/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.29149898
title: <b>Human-AI Collaboration</b>

description: The widespread application of medical artificial intelligence (AI) raises a critical question: Who should be held accountable when AI-assisted diagnostic decisions result in errors? Based on the Stimulus-Organism-Response (S-O-R) framework, this study employs a scenario-based survey experiment to examine how three responsibility attribution models (AI-primary, shared, and physician-primary responsibility) influence physicians' AI adoption intention and verification behavior through risk perception, internal moral attribution, and trust, while exploring the moderating role of AI literacy. Analysis of data from 487 clinical physicians reveals three key findings. First, physician-primary responsibility significantly reduced internal moral attribution (β=-0.804) and risk perception (β=-0.436), while enhancing benevolence trust (β=0.257). Second, internal moral attribution emerged as the strongest mediator affecting both adoption intention (β=-0.258) and verification behavior (β=0.298), surpassing risk perception. Third, MGA results also show group differences. AI literacy moderated the effects of ability trust and risk perception on adoption intention, with stronger effects observed in the high literacy group. The study contributes by introducing responsibility attribution as an institutional stimulus into medical AI acceptance research, revealing the "responsibility attribution→tool-ification cognition→behavioral change" mechanism. Findings suggest that physician-primary responsibility models coupled with AI literacy enhancement programs can optimize human-AI collaborative decision-making in healthcare.
DOI: 10.6084/m9.figshare.29149898.v2
title: <b>Human-AI Collaboration</b>

description: The widespread application of medical artificial intelligence (AI) raises a critical question: Who should be held accountable when AI-assisted diagnostic decisions result in errors? Based on the Stimulus-Organism-Response (S-O-R) framework, this study employs a scenario-based survey experiment to examine how three responsibility attribution models (AI-primary, shared, and physician-primary responsibility) influence physicians' AI adoption intention and verification behavior through risk perception, internal moral attribution, and trust, while exploring the moderating role of AI literacy. Analysis of data from 487 clinical physicians reveals three key findings. First, physician-primary responsibility significantly reduced internal moral attribution (β=-0.804) and risk perception (β=-0.436), while enhancing benevolence trust (β=0.257). Second, internal moral attribution emerged as the strongest mediator affecting both adoption intention (β=-0.258) and verification behavior (β=0.298), surpassing risk perception. Third, MGA results also show group differences. AI literacy moderated the effects of ability trust and risk perception on adoption intention, with stronger effects observed in the high literacy group. The study contributes by introducing responsibility attribution as an institutional stimulus into medical AI acceptance research, revealing the "responsibility attribution→tool-ification cognition→behavioral change" mechanism. Findings suggest that physician-primary responsibility models coupled with AI literacy enhancement programs can optimize human-AI collaborative decision-making in healthcare.
DOI: 10.15468/dl.99wpbp
title: Occurrence Download

description: A dataset containing 51 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 (GBIF_WITHIN('POLYGON((-6.05759 36.73779,1.99953 36.73779,1.99953 39.96396,-6.05759 39.96396,-6.05759 36.73779))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE OR GBIF_WITHIN('POLYGON((-3.2857 37.25478,-0.03238 37.25478,-0.03238 39.23877,-3.2857 39.23877,-3.2857 37.25478))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE)
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND GBIF_STRINGARRAYCONTAINS(occurrence.classifications['d7dddbf4-2cf0-4f39-9b2a-bb099caae36c'], '2495093', TRUE)
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.classificationdetails['d7dddbf4-2cf0-4f39-9b2a-bb099caae36c']['specieskey'] &lt;&gt; '' AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 51 records were derived from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021543-260208012135463/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.29149898.v1
title: Path Analysis ofMerchants' UseCentral Bank Digital Currency: Evidence from theDigital RMB

description: Central Bank Digital Currency (CBDC) has emerged as a key vehicle to disrupt the existing payment monopolies and foster deeper integration between the digital and the real economies, However, the promotion and widespread adoption of CBDCs still face multiple obstacles. Taking China as an example, this research explores the factors influencing of the willingness merchants’ willnes to adopt the digital RMB(E-CNY). From the perspective of merchants, the adoption of digital currency not only provide new payment options for consumers, but also contributes to building a more competitive and diversified payment ecosystem.Based on network effects and the Technology-Organization -Environment (TOE)framework, this studyconstructs a comprehensive model of E-CNY adoption factors across three dimensions:technological adaptations, organizational supportsand environmental pushes, Utilizing Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA), the study explores the complex interplay among multiple antecedent conditions and merchants' usage behavior.
DOI: 10.15468/dl.wwxqgs
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((51.35277 35.74421,87.64723 35.74421,87.64723 50.25579,51.35277 50.25579,51.35277 35.74421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Spiraeanthus schrenkianus"
 ]
}

 The dataset includes 35 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021599-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zvupk6
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sphagnum teres"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021597-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pfqxsx
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((50.35277 37.24421,86.64723 37.24421,86.64723 51.75579,50.35277 51.75579,50.35277 37.24421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Silene jaxartica"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021598-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4sc3mq
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aedes pampangensis"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021600-260208012135463/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.30043549
title: Impact of policy design and elicitation method on carbon tax acceptability

description: Carbon taxes are among the most effective instruments for reducing greenhouse gas emissions, yet their adoption remains politically sensitive. Public acceptability is crucial for the feasibility of such taxes, but evidence on what drives acceptance has been inconsistent. This inconsistency stems not only from differences in national context but also from the way researchers <b>measure acceptability</b> and how they <b>design policy scenarios</b>.This study addresses these challenges by systematically comparing multiple elicitation methods and policy designs within a single setting. We conducted a <b>within-subject survey with 1,415 respondents in Austria</b>, deliberately holding constant contextual factors such as sample, timing, and national context. Respondents evaluated fuel tax increases through a range of instruments, including:Binary yes/no approval questionsLikert-scale ratingsWillingness-to-pay formats (annual and per liter)An Adaptive Choice-Based Conjoint experiment<b>Key findings:</b><b>Method matters.</b> Explicit formats (e.g., binary yes/no questions) consistently produce lower acceptance rates, while implicit, model-based approaches yield higher estimates.<b>Preferences vs. tolerance.</b> The average <i>preferred</i> tax level is €0.25 per liter, but the <i>maximum acceptable</i> level is much higher at €0.66, suggesting greater scope for ambitious policies.<b>Revenue recycling is essential.</b> Acceptability drops sharply when revenue use is unspecified but increases significantly when revenues are earmarked for rebates or public goods such as public transport.<b>Implications:</b>For <b>policymakers</b>, careful policy design, especially transparent and targeted revenue recycling, can substantially increase public support.For <b>researchers</b>, methodological choices are not neutral. The elicitation format itself can shape acceptability outcomes, complicating cross-study comparisons and meta-analyses. Multi-method designs provide a more robust picture.By clarifying how survey methods and policy features interact, this research helps bridge the gap between academic evidence and political practice. It offers both scholars and decision-makers a more reliable basis for evaluating the political feasibility of carbon taxation and contributes to designing climate policies that are both effective and publicly acceptable.
DOI: 10.6084/m9.figshare.30043549.v1
title: Impact of policy design and elicitation method on carbon tax acceptability

description: Carbon taxes are among the most effective instruments for reducing greenhouse gas emissions, yet their adoption remains politically sensitive. Public acceptability is crucial for the feasibility of such taxes, but evidence on what drives acceptance has been inconsistent. This inconsistency stems not only from differences in national context but also from the way researchers <b>measure acceptability</b> and how they <b>design policy scenarios</b>.This study addresses these challenges by systematically comparing multiple elicitation methods and policy designs within a single setting. We conducted a <b>within-subject survey with 1,415 respondents in Austria</b>, deliberately holding constant contextual factors such as sample, timing, and national context. Respondents evaluated fuel tax increases through a range of instruments, including:Binary yes/no approval questionsLikert-scale ratingsWillingness-to-pay formats (annual and per liter)An Adaptive Choice-Based Conjoint experiment<b>Key findings:</b><b>Method matters.</b> Explicit formats (e.g., binary yes/no questions) consistently produce lower acceptance rates, while implicit, model-based approaches yield higher estimates.<b>Preferences vs. tolerance.</b> The average <i>preferred</i> tax level is €0.25 per liter, but the <i>maximum acceptable</i> level is much higher at €0.66, suggesting greater scope for ambitious policies.<b>Revenue recycling is essential.</b> Acceptability drops sharply when revenue use is unspecified but increases significantly when revenues are earmarked for rebates or public goods such as public transport.<b>Implications:</b>For <b>policymakers</b>, careful policy design, especially transparent and targeted revenue recycling, can substantially increase public support.For <b>researchers</b>, methodological choices are not neutral. The elicitation format itself can shape acceptability outcomes, complicating cross-study comparisons and meta-analyses. Multi-method designs provide a more robust picture.By clarifying how survey methods and policy features interact, this research helps bridge the gap between academic evidence and political practice. It offers both scholars and decision-makers a more reliable basis for evaluating the political feasibility of carbon taxation and contributes to designing climate policies that are both effective and publicly acceptable.
DOI: 10.15468/dl.j8973y
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Esterhuysenia alpina",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 25 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021601-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18656422
title: INTERACT-Questionnaires

description: INTERACT-Questionnaires and replies from demos
DOI: 10.5281/zenodo.18656423
title: INTERACT-Questionnaires

description: INTERACT-Questionnaires and replies from demos
DOI: 10.4121/e5ac7f75-50ee-439d-a2ad-2da341560969
title: Data supporting the publication: Dynamic Multidisciplinary Design Analysis and Optimization Workflows: Concept and Implementation

description: This dataset contains results from Multidisciplinary Design Analysis and Optimization Workflows (MDAO) workflows generated using Optimus 2021.1SP2. The use case is the single-objective Variable Size Design Space (VSDS) Goldstein problem as described in Pelamatti et al. 2021 and the multi-objective variant as described in Valencia-Ibáñez 2023 (see references). The optimization problems have been solved using a newly developed methodology for dynamic MDAO workflows described in Bruggeman et al. (2026) (see References). The data available in this repository was generated to verify and validate the correct working of the novel dynamic MDAO workflows and to quantify their performances compared to the current state-of-the-art, where a static MDAO workflow is formulated for each subproblem.
DOI: 10.4121/e5ac7f75-50ee-439d-a2ad-2da341560969.v1
title: Data supporting the publication: Dynamic Multidisciplinary Design Analysis and Optimization Workflows: Concept and Implementation

description: This dataset contains results from Multidisciplinary Design Analysis and Optimization Workflows (MDAO) workflows generated using Optimus 2021.1SP2. The use case is the single-objective Variable Size Design Space (VSDS) Goldstein problem as described in Pelamatti et al. 2021 and the multi-objective variant as described in Valencia-Ibáñez 2023 (see references). The optimization problems have been solved using a newly developed methodology for dynamic MDAO workflows described in Bruggeman et al. (2026) (see References). The data available in this repository was generated to verify and validate the correct working of the novel dynamic MDAO workflows and to quantify their performances compared to the current state-of-the-art, where a static MDAO workflow is formulated for each subproblem.
DOI: 10.5281/zenodo.18656415
title: {JEPEX HYBRID v7.3–v7.4:}\\ A Bankable 1~GW Firm Clean Power System\\ (Geothermal EGS + Gas CCS Hybrid Architecture
DOI: 10.5281/zenodo.18656416
title: {JEPEX HYBRID v7.3–v7.4:}\\ A Bankable 1~GW Firm Clean Power System\\ (Geothermal EGS + Gas CCS Hybrid Architecture
DOI: 10.15468/dl.5jvd3n
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Faucaria bosscheana",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021602-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.32enz6
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((50.35277 33.74421,86.64723 33.74421,86.64723 48.25579,50.35277 48.25579,50.35277 33.74421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Stipa karataviensis"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021603-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d3ky3q
title: Occurrence Download

description: A dataset containing 417 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acerodon jubatus"
 ]
}

 The dataset includes 417 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021604-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.