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

description: A dataset containing 254115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Malva neglecta Wallr., Matricaria discoidea DC.)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 254115 records from 599 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0159089-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zukm4a
title: Occurrence Download

description: A dataset containing 202 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-116.29318 -24.00610,-13.70682 -24.00610,-13.70682 17.00610,-116.29318 17.00610,-116.29318 -24.00610))",
 "HasCoordinate is true",
 "TaxonKey is Bignonia nocturna (Barb.Rodr.) L.G.Lohmann",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 202 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000013-180920211646547/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5qskuc
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is TRUE",
 "EventDate is less than or equal to 2022-08-10",
 "EventDate is greater than or equal to 2022-08-03",
 "DecimalLatitude is greater than or equal to 35",
 "DecimalLatitude is less than or equal to 72",
 "DecimalLongitude is greater than or equal to -86",
 "DecimalLongitude is less than or equal to -47"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0419740-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m5dsxq
title: Occurrence Download

description: A dataset containing 225002 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Carex pilulifera L."
 ]
}

 The dataset includes 225002 records from 442 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0136746-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u7crg5
title: Occurrence Download

description: A dataset listing the 3380 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-84.78551 10.31895,-84.80018 10.31445,-84.81855 10.30197,-84.80411 10.28696,-84.78101 10.2783,-84.76496 10.28604,-84.76057 10.30475,-84.77246 10.31664,-84.78551 10.31895))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset's 3380 records were derived from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023214-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.erhjqt
title: Occurrence Download

description: A dataset listing the 47 species recorded in GBIF matching the query:
{
 "and" : [
 "Island is Crete",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset's 47 records were derived from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023213-240626123714530/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.26299027
title: <b>Does the “Assistant” Heuristic Really Work? Analyzing the Effect of Perceived Hierarchy of AI through Assigned Job Titles on the Adoption of Conversational AI-based Services</b>

description: Recent marketing trends involve companies using low-status job titles, such as "assistant" (e.g., Google Home Assistant), to label conversational AI agents. This strategy aims to activate the “helper” heuristic and improve people's willingness to use these AI agents. However, this paper — which includes two experiments (N=307, N=300), one partial least squares structural equation modeling analysis (N=309), and one set of interviews (N=15) — reveals the opposite effect. The current research tests four alternative serial mediation pathways involving perceived humanlikeness: perceived warmth, perceived competence, perceived control, and perceived risks. Across the four studies, only the serial mediation pathway through perceived humanlikeness and perceived competence was consistently supported. Our final model indicates that in conversational AI settings, increased perceptions of hierarchical status enhance perceived humanlikeness. This, in turn, boosts perceived competence, which subsequently increases the willingness to use the AI.
DOI: 10.5517/ccdc.csd.cc2j1dss
title: CCDC 2325055: Experimental Crystal Structure Determination

description: Related Article: Biying Zhao, Zhengxuan Yang, Zhigang Duan, Yuanqian Liu, Chunhui Yang, Jingui Duan, Yonggang Zhao, Zhihua Lin|2024|Cryst.Growth Des.|24|5447|doi:10.1021/acs.cgd.4c00068
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jgp3t
title: CSD 2337806: Experimental Crystal Structure Determination

description: Related Article: Andrzej Grzechnik, B. Viliam Hakala, Sophia Kurig, Nicolas Walte, Noriyoshi Tsujino, Sho Kakizawa, Yuji Higo, Dejan Zagorac, Jelena Zagorac, Richard Dronskowski, J. Christian Schön, Karen Friese|2024|Cryst.Growth Des.|24|5582|doi:10.1021/acs.cgd.4c00363
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2j1dpp
title: CCDC 2325052: Experimental Crystal Structure Determination

description: Related Article: Biying Zhao, Zhengxuan Yang, Zhigang Duan, Yuanqian Liu, Chunhui Yang, Jingui Duan, Yonggang Zhao, Zhihua Lin|2024|Cryst.Growth Des.|24|5447|doi:10.1021/acs.cgd.4c00068
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.dsqucf
title: Occurrence Download

description: A dataset containing 24207 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Suidae",
 "Year 2021-2021"
 ]
}

 The dataset includes 24207 records from 483 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023219-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8mqead
title: Occurrence Download

description: A dataset containing 24207 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Suidae",
 "Year 2021-2021"
 ]
}

 The dataset includes 24207 records from 483 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023218-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12738248
title: Navigating Uncertainty in ESG Investing

description:  The widespread confusion among investors regarding Environmental, Social, and Governance (ESG) rankings assigned by rating agencies has underscored a critical issue in sustainable investing. To address this uncertainty, our research has devised methods that not only recognize this ambiguity but also offer tailored investment strategies for different investor profiles. By developing ESG ensemble strategies and integrating ESG scores into a Reinforcement Learning framework, we aim to optimize portfolios that cater to both financial returns and ESG-focused outcomes. Additionally, by proposing a Double Mean-Variance model, we classify three types of investors based on their risk preferences. We also introduce ESG-modified Capital Asset Pricing Models to assess the performance of these optimized portfolios. Ultimately, our comprehensive approach provides investors with tools to navigate the inherent ambiguities of ESG ratings with the goal of facilitating more informed investment decisions.
DOI: 10.5281/zenodo.12738249
title: Navigating Uncertainty in ESG Investing

description:  The widespread confusion among investors regarding Environmental, Social, and Governance (ESG) rankings assigned by rating agencies has underscored a critical issue in sustainable investing. To address this uncertainty, our research has devised methods that not only recognize this ambiguity but also offer tailored investment strategies for different investor profiles. By developing ESG ensemble strategies and integrating ESG scores into a Reinforcement Learning framework, we aim to optimize portfolios that cater to both financial returns and ESG-focused outcomes. Additionally, by proposing a Double Mean-Variance model, we classify three types of investors based on their risk preferences. We also introduce ESG-modified Capital Asset Pricing Models to assess the performance of these optimized portfolios. Ultimately, our comprehensive approach provides investors with tools to navigate the inherent ambiguities of ESG ratings with the goal of facilitating more informed investment decisions.
DOI: 10.25573/serc.23557056
title: Data and R code for "New methods for quantifying the effects of catchment spatial patterns on aquatic responses"

description: This figshare item provides data and R code to reproduce the analysis in the following paper:Weller, DE; ME Baker, and RS King. 2023. New methods for quantifying the effects of catchment spatial patterns on aquatic responses. Landscape Ecology. https://doi.org/10.1007/s10980-023-01706-xThis figshare item provides 14 files: five data files (.csv files), a list of models to be fitted by the R code (Modlist.csv), and seven files of R code (.R files). The file 0SpatialAnalysis.txt provides more information on the spatial analysis we used to generate distance distributions.<br><b>Data files</b>The five data files are· subestPCB.csv· cdist.csv· hdist.csv· ldist.csv· tdist.csvThe file subestPCB.csv provides catchment id numbers, names, and average measured PCB concentrations from fish tissues for 14 study subestuaries. The remaining four files provide the distance distributions for commercial land, high-density residential land, low-density residential land, and all land. Each distance file has four columns, junk, count, catchment id, and distance. Information in the junk column is not used. Count provides land area as the number of 30 by 30 meter (0.09 hectare) pixels. The variable called distance provides the distance to the subestuary shoreline in decameters.<b>R code</b>The R codes reproduce the statistical analysis and most of the tables and figures from the published paper.We ran the codes using Rstudio. We invoked Rstudio’s <i>New Project … &gt; Existing Directory</i> option to establish the directory containing the data files and R codes files as an Rstudio project. Then we ran five R codes in sequence according to the initial numbers in the file names (1ReadData.R, 2FitModels.R, 3Tables.R, 4Figures.R, and 5FigureS3.R). Each program adds to the objects saved in the R workspace within the Rstudio project. Figures and tables are saved in the subdirectory FiguresTables.The five numbered R files also use functions from two other files: DistWeightFunctionsV01.R and AuxillaryFunctionsV01.R.The first R program expects the five data files (subestPCB.csv, cdist.csv, hdist.csv, ldist.csv, and tdist.csv) to reside in the same directory as the program and the Rstudio project.Comments in the R files provide additional information on how each one works.<br>
DOI: 10.25573/serc.23557056.v2
title: Data and R code for "New methods for quantifying the effects of catchment spatial patterns on aquatic responses"

description: This figshare item provides data and R code to reproduce the analysis in the following paper:Weller, DE; ME Baker, and RS King. 2023. New methods for quantifying the effects of catchment spatial patterns on aquatic responses. Landscape Ecology. https://doi.org/10.1007/s10980-023-01706-xThis figshare item provides 14 files: five data files (.csv files), a list of models to be fitted by the R code (Modlist.csv), and seven files of R code (.R files). The file 0SpatialAnalysis.txt provides more information on the spatial analysis we used to generate distance distributions.<br><b>Data files</b>The five data files are· subestPCB.csv· cdist.csv· hdist.csv· ldist.csv· tdist.csvThe file subestPCB.csv provides catchment id numbers, names, and average measured PCB concentrations from fish tissues for 14 study subestuaries. The remaining four files provide the distance distributions for commercial land, high-density residential land, low-density residential land, and all land. Each distance file has four columns, junk, count, catchment id, and distance. Information in the junk column is not used. Count provides land area as the number of 30 by 30 meter (0.09 hectare) pixels. The variable called distance provides the distance to the subestuary shoreline in decameters.<b>R code</b>The R codes reproduce the statistical analysis and most of the tables and figures from the published paper.We ran the codes using Rstudio. We invoked Rstudio’s <i>New Project … &gt; Existing Directory</i> option to establish the directory containing the data files and R codes files as an Rstudio project. Then we ran five R codes in sequence according to the initial numbers in the file names (1ReadData.R, 2FitModels.R, 3Tables.R, 4Figures.R, and 5FigureS3.R). Each program adds to the objects saved in the R workspace within the Rstudio project. Figures and tables are saved in the subdirectory FiguresTables.The five numbered R files also use functions from two other files: DistWeightFunctionsV01.R and AuxillaryFunctionsV01.R.The first R program expects the five data files (subestPCB.csv, cdist.csv, hdist.csv, ldist.csv, and tdist.csv) to reside in the same directory as the program and the Rstudio project.Comments in the R files provide additional information on how each one works.<br>
DOI: 10.25573/serc.23557056.v1
title: Data and R code for "New methods for quantifying the effects of catchment spatial patterns on aquatic responses"

description: This figshare item provides data and R code to reproduce the analysis in the following paper:
Weller, DE; ME Baker, and RS King. 2023. New methods for quantifying the effects of catchment spatial patterns on aquatic responses. Landscape Ecology. https://doi.org/10.1007/s10980-023-01706-x 
This figshare item provides 13 files: five data files (.csv files), a list of models to be fitted by the R code (Modlist.csv), and seven files of R code (.R files).
<strong>Data files</strong>
The five data files are
· subestPCB.csv
· cdist.csv
· hdist.csv
· ldist.csv
· tdist.csv
The file subestPCB.csv provides catchment id numbers, names, and average measured PCB concentrations from fish tissues for 14 study subestuaries.  The remaining four files provide the distance distributions for commercial land, high-density residential land, low-density residential land, and all land.  Each distance file has four columns, junk, count, catchment id, and distance. Information in the junk column is not used. Count provides land area as the number of 30 by 30 meter (0.09 hectare) pixels. The variable called distance provides the distance to the subestuary shoreline in decameters.
<strong>R code</strong>
The R codes reproduce the statistical analysis and most of the tables and figures from the published paper.
We ran the codes using Rstudio. We invoked Rstudio’s <em>New Project … &gt; Existing Directory</em> option to establish the directory containing the data files and R codes files as an Rstudio project. Then we ran five R codes in sequence according to the initial numbers in the file names (1ReadData.R, 2FitModels.R, 3Tables.R, 4Figures.R, and 5FigureS3.R). Each program adds to the objects saved in the R workspace within the Rstudio project. Figures and tables are saved in the subdirectory FiguresTables.
The five numbered R files also use functions from two other files: DistWeightFunctionsV01.R and AuxillaryFunctionsV01.R.
The first R program expects the five data files (subestPCB.csv, cdist.csv, hdist.csv, ldist.csv, and tdist.csv) to reside in the same directory as the program and the Rstudio project.
Comments in the R files provide additional information on how each one works.
DOI: 10.5281/zenodo.10059241
title: Catálogo de imaxes de exemplares nas coleccións de plantas do herbario SANT
title: SANT herbarium vascular plant images catalog
DOI: 10.5281/zenodo.10059242
title: Catálogo de imaxes de exemplares nas coleccións de plantas do herbario SANT
title: SANT herbarium vascular plant images catalog
DOI: 10.26093/cds/vizier.74111013
title: CaII triplet in Sextans dSph galaxy

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' Study of the Sextans dwarf spheroidal galaxy from the DART Ca II triplet survey.' (bibcode: 2011MNRAS.411.1013B)
DOI: 10.26093/cds/vizier.74110102
title: AzTEC/ASTE 1.1mm observations in the ADF-S

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' AzTEC/ASTE 1.1-mm survey of the AKARI Deep Field South: source catalogue and number counts.' (bibcode: 2011MNRAS.411..102H)
DOI: 10.26093/cds/vizier.74111367
title: 12mm line survey towards W28 SNR

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' 12 mm line survey of the dense molecular gas towards the W28 field TeV gamma-ray sources.' (bibcode: 2011MNRAS.411.1367N)
DOI: 10.26093/cds/vizier.74111536
title: Spectroscopy of of IC4499 red giants

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' Radial velocity and metallicity of the globular cluster IC4499 obtained with AAOmega.' (bibcode: 2011MNRAS.411.1536H)
DOI: 10.26093/cds/vizier.74111585
title: CZ Lac multiperiodic Blazhko modulation

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' The multiperiodic Blazhko modulation of CZ Lacertae.' (bibcode: 2011MNRAS.411.1585S)
DOI: 10.26093/cds/vizier.74111597
title: LMC point source classification in SAGE-Spec

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' The SAGE-Spec Spitzer Legacy programme: the life-cycle of dust and gas in the Large Magellanic Cloud. Point source classification I.' (bibcode: 2011MNRAS.411.1597W)
DOI: 10.26093/cds/vizier.74111744
title: Observations of RR Lyr stars in M5

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' Long-term photometric monitoring of Messier 5 variables. I. Period changes of RR Lyrae stars.' (bibcode: 2011MNRAS.411.1744S)
DOI: 10.26093/cds/vizier.74112009
title: H narrow-band imaging of XMMU J2235.3-2557

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' Star formation in the XMMU J2235.3-2557 galaxy cluster at z= 1.39.' (bibcode: 2011MNRAS.411.2009B)
DOI: 10.26093/cds/vizier.74112099
title: Fast-rotating M-dwarf stars in NGC 2547

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' Investigating coronal saturation and supersaturation in fast-rotating M-dwarf stars.' (bibcode: 2011MNRAS.411.2099J)
DOI: 10.26093/cds/vizier.74112439
title: Coma Treasury Survey. Structural parameters

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' The HST/ACS Coma cluster survey. III. Structural parameters of galaxies using single Sersic fits.' (bibcode: 2011MNRAS.411.2439H)
DOI: 10.26093/cds/vizier.74112530
title: UBVRIJHK photometry in NGC1624

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' A multiwavelength census of stellar contents in the young cluster NGC 1624.' (bibcode: 2011MNRAS.411.2530J)
DOI: 10.26093/cds/vizier.74112558
title: Coma cluster galaxies absorption-line indices

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' The stellar populations of bright Coma cluster galaxies.' (bibcode: 2011MNRAS.411.2558P)
DOI: 10.26093/cds/vizier.74112596
title: High Galactic latitude runaway stars

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' Ejection velocities of high Galactic latitude runaway stars.' (bibcode: 2011MNRAS.411.2596S)
DOI: 10.26093/cds/vizier.74110263
title: JHK variable stars in M33

description: VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' The UK Infrared Telescope M 33 monitoring project. I. Variable red giant stars in the central square kiloparsec.' (bibcode: 2011MNRAS.411..263J)