cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23895906
title: Enablers, barriers, and future considerations for living lab effectiveness in environmental and agricultural sustainability transitions: a review of studies evaluating living labs

description: Living labs are promoted as an effective open innovation approach that accelerates the adoption of innovations. However, there remain knowledge gaps about factors that influence their effectiveness, success, and application to sustainability transitions. Through a scoping review on the evaluation of living labs, we identified 43 enablers and 37 barriers to effectiveness and success of living labs organised around the themes of governance, processes, features of living labs, characteristics of participants, adaptability, social dimensions, training and research, technology, and elements beyond the living lab (e.g. conditions for transition to the real world). Key enablers included strong collaborative and iterative processes with networks and partnerships, while key barriers included issues with supporting technology, the time and cost of collaboration, and challenges ensuring the longevity of living labs. We also reviewed study objectives, knowledge gaps, and future considerations to identify priorities for future research about living lab effectiveness and provide recommendations for their implementation. We recommend the development of frameworks for measuring and monitoring the success of living labs, and explore other considerations to promote their effectiveness based on the enablers and barriers identified. Lastly, we discuss how our findings on living lab effectiveness and success related to this special issue. This paper contributes to the body of research by our team (Beaudoin et al. 2022; Bronson, Devkota, and Nguyen 2021) that aims to explore living labs in the context of conservation, environmental, and agricultural sustainability to facilitate transformative social-ecological change.
DOI: 10.6084/m9.figshare.23895906.v1
title: Enablers, barriers, and future considerations for living lab effectiveness in environmental and agricultural sustainability transitions: a review of studies evaluating living labs

description: Living labs are promoted as an effective open innovation approach that accelerates the adoption of innovations. However, there remain knowledge gaps about factors that influence their effectiveness, success, and application to sustainability transitions. Through a scoping review on the evaluation of living labs, we identified 43 enablers and 37 barriers to effectiveness and success of living labs organised around the themes of governance, processes, features of living labs, characteristics of participants, adaptability, social dimensions, training and research, technology, and elements beyond the living lab (e.g. conditions for transition to the real world). Key enablers included strong collaborative and iterative processes with networks and partnerships, while key barriers included issues with supporting technology, the time and cost of collaboration, and challenges ensuring the longevity of living labs. We also reviewed study objectives, knowledge gaps, and future considerations to identify priorities for future research about living lab effectiveness and provide recommendations for their implementation. We recommend the development of frameworks for measuring and monitoring the success of living labs, and explore other considerations to promote their effectiveness based on the enablers and barriers identified. Lastly, we discuss how our findings on living lab effectiveness and success related to this special issue. This paper contributes to the body of research by our team (Beaudoin et al. 2022; Bronson, Devkota, and Nguyen 2021) that aims to explore living labs in the context of conservation, environmental, and agricultural sustainability to facilitate transformative social-ecological change.
DOI: 10.6084/m9.figshare.28448166
title: The local context of energy assistance: exploring spatial associations between community characteristics and Low Income Home Energy Assistance Program facilities

description: Social policy literature reveals proximity to social service offices and programme enrolment is linked. Individuals’ inability to walk to social service offices are consistently cited obstacles for applicants. Research, however, has yet to investigate spatial accessibility in the context of U.S. energy assistance. To fill this gap, we explore spatial associations between the location of energy assistance facilities (i.e. offices that facilitate applications), local socioeconomic indicators, and geographic contexts. Employing descriptive, spatial, and empirical analyses, we find spatial accessibility to energy assistance facilities is limited in the U.S. Approximately 6 percent of the population lives within close walking distance – within 1 mile – of its nearest facility, 21.1 percent live between 1 and 3 miles from a facility, and 18.2 percent of the population lives outside of any facility’s service area. Linear regression estimates confirm facilities are located farther from residential communities. Additionally, empirical models find local context is important to how select spatial associations manifest. While regression estimates suggest facilities are located closer to disadvantaged populations, including Black, Hispanic, and renters, despite geographic context, others vary. On average, energy assistance facilities are located farther from populations with higher energy burdens; however, this outcome only holds in non-urban areas whereas communities with higher energy burdens in urbanised areas are located closer to facilities. Our results will help administrators identify gaps in energy assistance accessibility and coverage, informing future facility siting determinations and outreach efforts.
DOI: 10.6084/m9.figshare.28448166.v1
title: The local context of energy assistance: exploring spatial associations between community characteristics and Low Income Home Energy Assistance Program facilities

description: Social policy literature reveals proximity to social service offices and programme enrolment is linked. Individuals’ inability to walk to social service offices are consistently cited obstacles for applicants. Research, however, has yet to investigate spatial accessibility in the context of U.S. energy assistance. To fill this gap, we explore spatial associations between the location of energy assistance facilities (i.e. offices that facilitate applications), local socioeconomic indicators, and geographic contexts. Employing descriptive, spatial, and empirical analyses, we find spatial accessibility to energy assistance facilities is limited in the U.S. Approximately 6 percent of the population lives within close walking distance – within 1 mile – of its nearest facility, 21.1 percent live between 1 and 3 miles from a facility, and 18.2 percent of the population lives outside of any facility’s service area. Linear regression estimates confirm facilities are located farther from residential communities. Additionally, empirical models find local context is important to how select spatial associations manifest. While regression estimates suggest facilities are located closer to disadvantaged populations, including Black, Hispanic, and renters, despite geographic context, others vary. On average, energy assistance facilities are located farther from populations with higher energy burdens; however, this outcome only holds in non-urban areas whereas communities with higher energy burdens in urbanised areas are located closer to facilities. Our results will help administrators identify gaps in energy assistance accessibility and coverage, informing future facility siting determinations and outreach efforts.
DOI: 10.15468/dl.cyqzre
title: Occurrence Download

description: A dataset containing 273 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((117.781 5.785142, 117.71038 5.779571, 117.641504 5.762997, 117.576069 5.735827, 117.515691 5.698733, 117.461858 5.652629, 117.415895 5.598653, 117.378934 5.538136, 117.351883 5.472569, 117.335405 5.403569, 117.329903 5.332835, 117.335507 5.262108, 117.352077 5.193131, 117.379201 5.1276, 117.416209 5.067128, 117.462188 5.013202, 117.516005 4.967149, 117.576336 4.9301, 117.641698 4.902966, 117.710482 4.886414, 117.781 4.880851, 117.851518 4.886414, 117.920302 4.902966, 117.985664 4.9301, 118.045995 4.967149, 118.099812 5.013202, 118.145791 5.067128, 118.182799 5.1276, 118.209923 5.193131, 118.226493 5.262108, 118.232097 5.332835, 118.226595 5.403569, 118.210117 5.472569, 118.183066 5.538136, 118.146105 5.598653, 118.100142 5.652629, 118.046309 5.698733, 117.985931 5.735827, 117.920496 5.762997, 117.85162 5.779571, 117.781 5.785142))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 273 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002153-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17084350
title: Structure-based prediction of SARS-CoV-2 variant properties using machine learning on mutational neighborhoods

description: This dataset provides a comprehensive analysis of protein mutations, detailing structural and energetic metrics such as RMSD, TM-score, SASA, and binding energies. It includes sequence identifiers, mutation sites, and various quantitative measures critical for understanding protein stability, folding, and interactions, particularly with the ACE-2 receptor. The data is structured to support FAIR principles, promoting interoperability and machine actionability.
DOI: 10.5281/zenodo.17084351
title: Structure-based prediction of SARS-CoV-2 variant properties using machine learning on mutational neighborhoods

description: This dataset provides a comprehensive analysis of protein mutations, detailing structural and energetic metrics such as RMSD, TM-score, SASA, and binding energies. It includes sequence identifiers, mutation sites, and various quantitative measures critical for understanding protein stability, folding, and interactions, particularly with the ACE-2 receptor. The data is structured to support FAIR principles, promoting interoperability and machine actionability.
DOI: 10.6084/m9.figshare.31109440
title: Unforeseen potential of afforestation to support soil life and carbon stocks worldwide

description: Unforeseen potential of afforestation to support soil life and carbon stocks worldwide
DOI: 10.6084/m9.figshare.31109440.v1
title: Unforeseen potential of afforestation to support soil life and carbon stocks worldwide

description: Unforeseen potential of afforestation to support soil life and carbon stocks worldwide
DOI: 10.6084/m9.figshare.31109440.v2
title: Unforeseen potential of afforestation to support soil life and carbon stocks worldwide

description: Unforeseen potential of afforestation to support soil life and carbon stocks worldwide
DOI: 10.15468/dl.huhtdu
title: Occurrence Download

description: A dataset containing 70804 records in GBIF matching the query:
SELECT
 '9' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey 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 decimalLatitude &gt;= -41.89415215079384
 AND decimalLatitude &lt;= -37.42857426825602
 AND decimalLongitude &gt;= 138.1538923057312
 AND decimalLongitude &lt;= 149.85186880906326
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 70804 records were derived from 183 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002139-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cfjpge
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((121.36 1.478183, 121.289714 1.472615, 121.221159 1.456048, 121.156025 1.42889, 121.095916 1.391811, 121.042312 1.345724, 120.996533 1.291764, 120.959707 1.23126, 120.932738 1.165702, 120.916292 1.096705, 120.910771 1.025968, 120.916311 0.955233, 120.932775 0.88624, 120.959758 0.820689, 120.996593 0.760194, 121.042375 0.706244, 121.095976 0.660166, 121.156076 0.623096, 121.221196 0.5959449999999999, 121.289733 0.579383, 121.36 0.573816, 121.430267 0.579383, 121.498804 0.5959449999999999, 121.563924 0.623096, 121.624024 0.660166, 121.677625 0.706244, 121.723407 0.760194, 121.760242 0.820689, 121.787225 0.88624, 121.803689 0.955233, 121.809229 1.025968, 121.803708 1.096705, 121.787262 1.165702, 121.760293 1.23126, 121.723467 1.291764, 121.677688 1.345724, 121.624084 1.391811, 121.563975 1.42889, 121.498841 1.456048, 121.430286 1.472615, 121.36 1.478183))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002155-260120142942310/datasets/export for details.
DOI: 10.15468/dl.zvzr26
title: Occurrence Download

description: A dataset containing 368 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-1.34175 41.82371,-1.30193 41.82278,-1.3014 41.8594,-1.34068 41.86034,-1.34175 41.82371))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2015-2026"
 ]
}

 The dataset includes 368 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002156-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18324503
title: TCRvdb-v2 without CIN, LLW, and LTD epitopes

description: Validating, non-validating, and ambiguous labels for the TTD, GIL, YLQ, NLV, and GLC epitopes. Labels for the CIN, LLW, LTD epitopes TCRvdb will be deposited after the upcoming IMMREP competition, as these epitopes will be part of the competition. After publication and the IMMREP competition, dataset including CIN, LLW, and LTD epitopes will be available via a github repository.
DOI: 10.5281/zenodo.18324502
title: TCRvdb-v2 without CIN, LLW, and LTD epitopes

description: Validating, non-validating, and ambiguous labels for the TTD, GIL, YLQ, NLV, and GLC epitopes. Labels for the CIN, LLW, LTD epitopes TCRvdb will be deposited after the upcoming IMMREP competition, as these epitopes will be part of the competition. After publication and the IMMREP competition, dataset including CIN, LLW, and LTD epitopes will be available via a github repository.
DOI: 10.17632/nywdrv695j
title: Banco Marcilei

description: Banco da dissertação
DOI: 10.17632/nywdrv695j.1
title: Banco Marcilei

description: Banco da dissertação
DOI: 10.15468/dl.zt4ca8
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((116.363 5.786142, 116.29238 5.780571, 116.223503 5.763997, 116.158069 5.736827, 116.097691 5.699733, 116.043857 5.653629, 115.997894 5.599653, 115.960933 5.539136, 115.933882 5.473569, 115.917405 5.404569, 115.911902 5.333835, 115.917507 5.263108, 115.934077 5.194131, 115.961201 5.1286, 115.998208 5.068128, 116.044187 5.014202, 116.098005 4.968149, 116.158336 4.9311, 116.223697 4.903966, 116.292482 4.887414, 116.363 4.881851, 116.433518 4.887414, 116.502303 4.903966, 116.567664 4.9311, 116.627995 4.968149, 116.681813 5.014202, 116.727792 5.068128, 116.764799 5.1286, 116.791923 5.194131, 116.808493 5.263108, 116.814098 5.333835, 116.808595 5.404569, 116.792118 5.473569, 116.765067 5.539136, 116.728106 5.599653, 116.682143 5.653629, 116.628309 5.699733, 116.567931 5.736827, 116.502497 5.763997, 116.43362 5.780571, 116.363 5.786142))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 45 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002157-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/vt6z-de12
title: "\u5e7f\u4e1c\u6280\u672f\u5e08\u8303\u5927\u5b66\u8f74\u627f\u5b9e\u9a8c\u6570\u636e\u96c6"

description: "The GPNU dataset encompassed three types of individual fault modes, namely inner race fault, outer race fault, and rolling element fault, and two types of normal operating condition signals. In addition, this dataset included six types of compound faults. As detailed in Table I, the sampling frequency of the GPNU dataset was 10 kHz, and each fault category contained 400 samples."
DOI: 10.6084/m9.figshare.31109600
title: Who’s a part(y) of this family? Only noneconomic niche parties are still responsive to partisans

description: Foundational manuscripts examined whether niche parties and mainstream parties differed in their party-voter linkages. Specifically, they examine “mean electorate” and “partisan electorate” forms of party responsiveness using data from the twentieth century. Importantly, these early studies followed a party family approach and defined all nationalist, communist, and ecological parties as “niche parties”. Using data covering 75 European parties in the period 2006-2019, we show that this foundational difference between niche and mainstream parties is still valid, though not on the same set of party families. Economically-focused mainstream parties continue to respond to the mean electorate rather than their partisans, but so do communist parties, which have previously been classified as niche. We propose an alternative operationalization of niche party families, which include ethno-regional and rural-interest parties and in turn exclude communist parties. With this distinction, we do indeed find that niche parties respond to their partisans on both the general left-right dimension as well as on two issue dimensions. These results also serve to caution future users of the niche party concept to focus on parties whose <i>raison d'etre</i> are noneconomic issues when using a party family approach.
DOI: 10.6084/m9.figshare.31109600.v1
title: Who’s a part(y) of this family? Only noneconomic niche parties are still responsive to partisans

description: Foundational manuscripts examined whether niche parties and mainstream parties differed in their party-voter linkages. Specifically, they examine “mean electorate” and “partisan electorate” forms of party responsiveness using data from the twentieth century. Importantly, these early studies followed a party family approach and defined all nationalist, communist, and ecological parties as “niche parties”. Using data covering 75 European parties in the period 2006-2019, we show that this foundational difference between niche and mainstream parties is still valid, though not on the same set of party families. Economically-focused mainstream parties continue to respond to the mean electorate rather than their partisans, but so do communist parties, which have previously been classified as niche. We propose an alternative operationalization of niche party families, which include ethno-regional and rural-interest parties and in turn exclude communist parties. With this distinction, we do indeed find that niche parties respond to their partisans on both the general left-right dimension as well as on two issue dimensions. These results also serve to caution future users of the niche party concept to focus on parties whose <i>raison d'etre</i> are noneconomic issues when using a party family approach.