cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.yhpp9z
title: Occurrence Download

description: A dataset containing 70610529 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "VerbatimScientificName is Melospiza lincolnii",
 "VerbatimScientificName is Ammodramus savannarum",
 "VerbatimScientificName is Peucaea cassinii",
 "VerbatimScientificName is Ammodramus bairdii",
 "VerbatimScientificName is Actitis macularius",
 "VerbatimScientificName is Gallinago delicata",
 "VerbatimScientificName is Chordeiles minor",
 "VerbatimScientificName is Tringa semipalmata",
 "VerbatimScientificName is Recurvirostra novaehollandiae",
 "VerbatimScientificName is Biziura lobata",
 "VerbatimScientificName is Falco eleonorae",
 "VerbatimScientificName is Chroicocephalus ridibundus",
 "VerbatimScientificName is Rynchops niger",
 "VerbatimScientificName is Larus delawarensis",
 "VerbatimScientificName is Ichthyaetus audouinii",
 "VerbatimScientificName is Larus dominicanus",
 "VerbatimScientificName is Antigone rubicunda",
 "VerbatimScientificName is Falco peregrinus",
 "VerbatimScientificName is Bubo virginianus",
 "VerbatimScientificName is Geococcyx californianus",
 "VerbatimScientificName is Aquila audax",
 "VerbatimScientificName is Accipiter cooperii",
 "VerbatimScientificName is Buteo lineatus",
 "VerbatimScientificName is Circus cyaneus",
 "VerbatimScientificName is Buteo jamaicensis",
 "VerbatimScientificName is Buteo swainsoni",
 "VerbatimScientificName is Ptilinopus perousii",
 "VerbatimScientificName is Nucifraga columbiana",
 "VerbatimScientificName is Hemiphaga novaeseelandiae",
 "VerbatimScientificName is Podargus strigoides",
 "VerbatimScientificName is Buteo platypterus",
 "VerbatimScientificName is Ardeola ralloides",
 "VerbatimScientificName is Corvus ossifragus",
 "VerbatimScientificName is Plegadis chihi",
 "VerbatimScientificName is Corvus brachyrhynchos",
 "VerbatimScientificName is Falco berigora",
 "VerbatimScientificName is Egretta thula",
 "VerbatimScientificName is Nycticorax caledonicus",
 "VerbatimScientificName is Botaurus lentiginosus",
 "VerbatimScientificName is Nycticorax nycticorax",
 "VerbatimScientificName is Anhinga anhinga",
 "VerbatimScientificName is Threskiornis moluccus",
 "VerbatimScientificName is Platalea regia",
 "VerbatimScientificName is Corvus corax",
 "VerbatimScientificName is Buteo regalis",
 "VerbatimScientificName is Pandion haliaetus",
 "VerbatimScientificName is Ciconia ciconia",
 "VerbatimScientificName is Haliaeetus leucogaster",
 "VerbatimScientificName is Icterus galbula",
 "VerbatimScientificName is Catharus guttatus"
 ]
 }
 ]
}

 The dataset includes 70610529 records from 849 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0343694-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.692ba4
title: Occurrence Download

description: A dataset containing 23992 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carterocephalus silvicola (Meigen, 1829)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 23992 records from 79 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063340-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dctlh2
title: Occurrence Download

description: A dataset containing 2496564 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is True"
 ]
}

 The dataset includes 2496564 records from 1089 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017231-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/rgnqkkfv
title: Countries List
DOI: 10.6084/m9.figshare.28750586
title: Possible futures for working environments

description: In thinking about working environments, the most effective approach is to ask individuals how they work best. This enables them to thrive in the environment most suited to themselves and the particular activity they are undertaking. More importantly, staff should be given the freedom to experiment with different settings, without others imposing judgments based on their own limited perspectives. This openness fosters a supportive and adaptable workplace, enabling everyone to find the spaces that best suit their work and wellbeing.
DOI: 10.6084/m9.figshare.28750586.v1
title: Possible futures for working environments

description: In thinking about working environments, the most effective approach is to ask individuals how they work best. This enables them to thrive in the environment most suited to themselves and the particular activity they are undertaking. More importantly, staff should be given the freedom to experiment with different settings, without others imposing judgments based on their own limited perspectives. This openness fosters a supportive and adaptable workplace, enabling everyone to find the spaces that best suit their work and wellbeing.
DOI: 10.15468/dl.ygrusd
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Conringia orientalis (L.) Dumort.",
 "Geometry POLYGON((-4.57907617500001 39.884719509, -4.57907617500001 41.165844755, -3.05298331400002 41.165844755, -3.05298331400002 39.884719509, -4.57907617500001 39.884719509))"
 ]
}

 The dataset includes 42 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005887-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j5jwf9
title: Occurrence Download

description: A dataset containing 3244 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Atriplex nummularia Lindl.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 3244 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005888-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zvagd7
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Delphinium pubescens DC.",
 "Geometry POLYGON((-4.57907617500001 39.884719509, -4.57907617500001 41.165844755, -3.05298331400002 41.165844755, -3.05298331400002 39.884719509, -4.57907617500001 39.884719509))"
 ]
}

 The dataset includes 9 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005879-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18150/srk93e
title: Data for publication: Superior ionic conductivity of Zr–doped LiTa2PO8 ceramics

description: This dataset contains experimental results obtained through a comprehensive set of complementary techniques to assess the impact of Zr doping on the structural, microstructural, and electrical properties of LTPO ceramics. The following characterization methods were employed:X-ray Diffraction (XRD): Used to determine the phase composition of the synthesized materials.Magic Angle Spinning Nuclear Magnetic Resonance (MAS NMR): Applied to analyze the local environment of 6Li / 7Li and 31P nuclei, providing insight into chemical composition changes within grains and grain boundary phases.Raman Spectroscopy: Used to detect vibrational modes, offering additional information on chemical interactions and phase transformations, particularly in the presence of dopants.Thermomechanical Analysis (TMA): Conducted to determine the optimal sintering conditions for ceramic processing.Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy (SEM/EDX): Performed to examine the microstructure, grain size, porosity, and elemental composition, including mapping of individual element distributions.Density Measurements (Archimedes Method): Used to assess the degree of densification in the ceramic samples.Impedance Spectroscopy (IS): Applied to evaluate the effect of Zr doping on grain and grain boundary ionic conductivity, with temperature-dependent measurements conducted in the range of –100C to 100C to analyze lithium-ion transport mechanisms.Constant Current Tests (Hebb-Wagner Method): Performed to determine the electronic and ionic transference numbers, providing insight into charge transport characteristics.This dataset provides a detailed analysis of Zr-doped LTPO ceramics, supporting investigations into their electrical performance, structural integrity, and potential applications. This dataset includes a readme.txt file. Please refer to it for additional details and instructions on data usage.
DOI: 10.15468/dl.kvevnp
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Convallaria majalis L.",
 "Geometry POLYGON((-4.57907617500001 39.884719509, -4.57907617500001 41.165844755, -3.05298331400002 41.165844755, -3.05298331400002 39.884719509, -4.57907617500001 39.884719509))"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005889-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.67wm7x
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lolium perenne L.",
 "Year 1980-2025"
 ]
}

 The dataset includes 43 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005881-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jcqa5n
title: Occurrence Download

description: A dataset containing 1988 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Atriplex oblongifolia Waldst. & Kit.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 1988 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005882-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.khafzp
title: Occurrence Download

description: A dataset containing 15917 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Datos de ocurrencia de triatominos presentes en Argentina del Laboratorio de Triatominos del CEPAVE (CONICET-UNLP)"
 ]
}

 The dataset includes 15917 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005883-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13943906
title: Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact - PEVA

description: SummaryThis artifact accompanies the PEVA submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes". It contains the implementation (switss-multi) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the experimental results presented in Section 6. Additionally, it also contains the original raw experimental data presented in Section 6 and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation switss-multi and describe how to use our tool via its command-line and programmatically via its Python interface.

Relation to paperThis artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is:- The toy examples presented in Example 10 and Example 12- Table 3 in Section 6- Table 4 in Section 6- Table 5 in Section 6- Table 8 in Section 6- Table 9 in Section 6- Figure 5 in Section 6- Figure 6 in Section 6

StructureThis artifact consists of the following files and folders:- data: Contains the PRISM models, PRISM properties (queries) and original raw experimental data presented in Section 6. Additionally, the log files and scripts for summarizing the raw experimental data are provided.- switss-multi: The source code of the implementation of our presented techniques.- switss-multi-docs: A documentation of the Python API of switss-multi.- peva-docker-image.tar.gz: The compressed Docker image, with the installed implementation (switss-multi), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the data folder, in case you want to run the analysis scripts on the original data.- docker-results: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image.- LICENSE: The license of this artifact (MIT license).- GUROBI-EULA: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/).- GPL-3.0: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it.
DOI: 10.5281/zenodo.14008498
title: Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact - PEVA

description: SummaryThis artifact accompanies the PEVA submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes". It contains the implementation (switss-multi) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the experimental results presented in Section 6. Additionally, it also contains the original raw experimental data presented in Section 6 and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation switss-multi and describe how to use our tool via its command-line and programmatically via its Python interface.

Relation to paperThis artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is:- The toy examples presented in Example 10 and Example 12- Table 3 in Section 6- Table 4 in Section 6- Table 5 in Section 6- Table 8 in Section 6- Table 9 in Section 6- Figure 5 in Section 6- Figure 6 in Section 6

StructureThis artifact consists of the following files and folders:- data: Contains the PRISM models, PRISM properties (queries) and original raw experimental data presented in Section 6. Additionally, the log files and scripts for summarizing the raw experimental data are provided.- switss-multi: The source code of the implementation of our presented techniques.- switss-multi-docs: A documentation of the Python API of switss-multi.- peva-docker-image.tar.gz: The compressed Docker image, with the installed implementation (switss-multi), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the data folder, in case you want to run the analysis scripts on the original data.- docker-results: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image.- LICENSE: The license of this artifact (MIT license).- GUROBI-EULA: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/).- GPL-3.0: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it.
DOI: 10.5281/zenodo.14012421
title: Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact - PEVA

description: SummaryThis artifact accompanies the PEVA submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes". It contains the implementation (switss-multi) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the experimental results presented in Section 6. Additionally, it also contains the original raw experimental data presented in Section 6 and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation switss-multi and describe how to use our tool via its command-line and programmatically via its Python interface.

Relation to paperThis artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is:- The toy examples presented in Example 10 and Example 12- Table 3 in Section 6- Table 4 in Section 6- Table 5 in Section 6- Table 8 in Section 6- Table 9 in Section 6- Figure 5 in Section 6- Figure 6 in Section 6

StructureThis artifact consists of the following files and folders:- data: Contains the PRISM models, PRISM properties (queries) and original raw experimental data presented in Section 6. Additionally, the log files and scripts for summarizing the raw experimental data are provided.- switss-multi: The source code of the implementation of our presented techniques.- switss-multi-docs: A documentation of the Python API of switss-multi.- peva-docker-image.tar.gz: The compressed Docker image, with the installed implementation (switss-multi), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the data folder, in case you want to run the analysis scripts on the original data.- docker-results: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image.- LICENSE: The license of this artifact (MIT license).- GUROBI-EULA: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/).- GPL-3.0: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it.
DOI: 10.5281/zenodo.14970955
title: "고백"
DOI: 10.5281/zenodo.14970956
title: "고백"
DOI: 10.5281/zenodo.13943905
title: Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact - PEVA

description: SummaryThis artifact accompanies the PEVA submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes". It contains the implementation (switss-multi) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the experimental results presented in Section 6. Additionally, it also contains the original raw experimental data presented in Section 6 and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation switss-multi and describe how to use our tool via its command-line and programmatically via its Python interface.

Relation to paperThis artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is:- The toy examples presented in Example 12, Example 14, Example 22 and Example 34- Table 3 in Section 6- Table 4 in Section 6- Table 5 in Section 6- Table 8 in Section 6- Figure 9 in Section 6- Figure 10 in Section 6- Figure 11 in Section 6

StructureThis artifact consists of the following files and folders:- data: Contains original raw experimental data presented in Section 6. Additionally, the log files and scripts for summarizing the raw experimental data are provided.- switss-multi/experiments: Contains the PRISM models, PRISM properties (queries) and scripts for running the experiments.- switss-multi: The source code of the implementation of our presented techniques.- switss-multi-docs: A documentation of the Python API of switss-multi.- peva-docker-image.tar.gz: The compressed Docker image, with the installed implementation (switss-multi), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the data folder, in case you want to run the analysis scripts on the original data.- docker-results: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image.- LICENSE: The license of this artifact (MIT license).- GUROBI-EULA: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/).- GPL-3.0: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it.