cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13378751
title: Preliminary bioclimatic range components for 36 European mammal species

description:  This dataset comprises raster files that represent the bioclimatic range components for 36 European mammal species, for which modelled habitat suitability maps for both present (time frame 1981-2010) and future (time frame 2041-2070, scenario SSP3-7.0 ) were available by July 2024. Csv file contains a table with species names and respecitve number of clusters. The results presented here are preliminary and may be updated in future analysis iterations. Species distributions were divided into distinct climatic partitions, which can then be used in climate connectivity or spatial conservation prioritization analyses as “bioclimatic components” for each species. The values in each raster correspond to the cluster IDs assigned to individual points within the species distribution model (SDM).
DOI: 10.15468/dl.xmrtaf
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-37.52941 30.42857,-5.41546 30.42857,-5.41546 46.38481,-37.52941 46.38481,-37.52941 30.42857))",
 "TaxonKey is Eustomias contiguus Gomon & Gibbs, 1985"
 ]
}

 The dataset includes 10 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113838-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6htvc3
title: Occurrence Download

description: A dataset containing 245 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amyema friesiana (Schum.) Danser"
 ]
}

 The dataset includes 245 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113839-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yp7b7v
title: Occurrence Download

description: A dataset containing 489 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Tapirus pinchaque (Roulin, 1829)"
 ]
}

 The dataset includes 489 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113840-240626123714530/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.26894173
title: Posts file (in csv).

description: CSV files resulted from the search for posts about fluoride in English and Portuguese on CrowndTangle.
DOI: 10.6084/m9.figshare.26894173.v1
title: Posts file (in csv).

description: CSV files resulted from the search for posts about fluoride in English and Portuguese on CrowndTangle.
DOI: 10.25403/upresearchdata.26855203
title: Experimental results for solar melting of zinc metal using multi-facet parabolic dish and a cavity receiver

description: The dataset contains the experimental data collected from thermocouples positioned inside a dual cavity solar receiver, used to demonstrate and evaluate the melting of zinc metal using only concentrated solar power as heat input. More information pertaining to the thermocouple positioning and the receiver design can be found in the thesis titled "Testing and development of a solar-dish cavity receiver for the melting of zinc metal".
Experiments were conducted, each with a unique set of environmental conditions:

 Experiment 1 – 26<sup>th</sup> of July 2022 = “Exp 1_26072022”
 Experiment 2 – 04t of August 2022= “Exp 2_04082022”
 Experiment 3 – 16<sup>th</sup> of August 2022= “Exp 3_16082022”
 Experiment 4 – 21<sup>st</sup> of August 2022= “Exp 4_21082022”
 Experiment 5 – 5<sup>th</sup> of September 2022= “Exp 5_05092022”

Also included in the dataset are the original weather data collected on the respective experimental test work days as well as the weather data in the processed form after correcting the weather data to serve as input for the numerical model developed in the Python coding language.
Raw weather data:

 Exp 1_Weather data_Original_26072022
 Exp 2_Weather data_Original_04082022
 Exp 3_Weather data_Original_16082022
 Exp 4_Weather data_Original_21082022
 Exp 5_Weather data_Original_05092022

Processed weather data:

 Exp 1_Weather data_Post-process_26072022
 Exp 2_Weather data_Post-process_04082022
 Exp 3_Weather data_Post-process_16082022
 Exp 4_Weather data_Post-process_21082022
 Exp 5_Weather data_Post-process_05092022

In addition to all the weather data and the experimental results collected on the five experimental runs, the dataset also contains the Python code used to predict the zinc temperature in the cavity receiver. The code was compiled in Jupyter Notebook and the files consist of the heat loss calculations and zinc temperature prediction for each experimental run. The code contained has been validated against the experimental data and has been demonstrated to have a mean absolute percentage error (MAPE) of 2.7%. The code can thus be used to within 2.7% accuracy predict the zinc temperature inside a cavity receiver, by making use of actual weather data as input.
Python code for each experiment, with heat transfer factor validated using experimental data mentioned above:

 Experiment 1.ipynb
 Experiment 2.ipynb
 Experiment 3.ipynb
 Experiment 4.ipynb
 Experiment 5.ipynb
DOI: 10.25403/upresearchdata.26855203.v1
title: Experimental results for solar melting of zinc metal using multi-facet parabolic dish and a cavity receiver

description: The dataset contains the experimental data collected from thermocouples positioned inside a dual cavity solar receiver, used to demonstrate and evaluate the melting of zinc metal using only concentrated solar power as heat input. More information pertaining to the thermocouple positioning and the receiver design can be found in the thesis titled "Testing and development of a solar-dish cavity receiver for the melting of zinc metal".
Experiments were conducted, each with a unique set of environmental conditions:

 Experiment 1 – 26<sup>th</sup> of July 2022 = “Exp 1_26072022”
 Experiment 2 – 04t of August 2022= “Exp 2_04082022”
 Experiment 3 – 16<sup>th</sup> of August 2022= “Exp 3_16082022”
 Experiment 4 – 21<sup>st</sup> of August 2022= “Exp 4_21082022”
 Experiment 5 – 5<sup>th</sup> of September 2022= “Exp 5_05092022”

Also included in the dataset are the original weather data collected on the respective experimental test work days as well as the weather data in the processed form after correcting the weather data to serve as input for the numerical model developed in the Python coding language.
Raw weather data:

 Exp 1_Weather data_Original_26072022
 Exp 2_Weather data_Original_04082022
 Exp 3_Weather data_Original_16082022
 Exp 4_Weather data_Original_21082022
 Exp 5_Weather data_Original_05092022

Processed weather data:

 Exp 1_Weather data_Post-process_26072022
 Exp 2_Weather data_Post-process_04082022
 Exp 3_Weather data_Post-process_16082022
 Exp 4_Weather data_Post-process_21082022
 Exp 5_Weather data_Post-process_05092022

In addition to all the weather data and the experimental results collected on the five experimental runs, the dataset also contains the Python code used to predict the zinc temperature in the cavity receiver. The code was compiled in Jupyter Notebook and the files consist of the heat loss calculations and zinc temperature prediction for each experimental run. The code contained has been validated against the experimental data and has been demonstrated to have a mean absolute percentage error (MAPE) of 2.7%. The code can thus be used to within 2.7% accuracy predict the zinc temperature inside a cavity receiver, by making use of actual weather data as input.
Python code for each experiment, with heat transfer factor validated using experimental data mentioned above:

 Experiment 1.ipynb
 Experiment 2.ipynb
 Experiment 3.ipynb
 Experiment 4.ipynb
 Experiment 5.ipynb
DOI: 10.5281/zenodo.13629074
title: RAW DATA: FullThrOTTLE-trIR: Time resolved IR Spectroscopy of electrochemically generated species using a Full Throughput Optically Transparent Thin Layer Electrochemical Cell

description: Data behind the figures in the Manuscript: FullThrOTTLE-trIR: Time resolved IR Spectroscopy of electrochemically generated species using a Full Throughput Optically Transparent Thin Layer Electrochemical Cell submitted to J. Phys. Chem.
DOI: 10.5281/zenodo.13629075
title: RAW DATA: FullThrOTTLE-trIR: Time resolved IR Spectroscopy of electrochemically generated species using a Full Throughput Optically Transparent Thin Layer Electrochemical Cell

description: Data behind the figures in the Manuscript: FullThrOTTLE-trIR: Time resolved IR Spectroscopy of electrochemically generated species using a Full Throughput Optically Transparent Thin Layer Electrochemical Cell submitted to J. Phys. Chem.
DOI: 10.15468/dl.afkc6u
title: Occurrence Download

description: A dataset containing 8028 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acanthaster planci (Linnaeus, 1758)"
 ]
}

 The dataset includes 8028 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113841-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4w8v6s
title: Occurrence Download

description: A dataset containing 1995 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Issue is one of (Geodetic datum assumed WGS84, Coordinate rounded, Coordinate reprojected)",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Helianthemum ledifolium (L.) Mill., Helianthemum ledifolium Sieber ex C.Presl, Helianthemum ledifolium Sieber, Helianthemum ledifolium Spach, Helianthemum ledifolium subsp. ledifolium, Helianthemum ledifolium subsp. apertum (Pomel) Greuter & Burdet, Helianthemum ledifolium subsp. campylopus (Rech.fil.) Greuter & Burdet, Helianthemum ledifolium subsp. lasiocarpum (Jacques & Hérincq) Nyman, Helianthemum ledifolium var. spicatum Willk., Helianthemum ledifolium var. microcarpum (Boiss.) Willk.)"
 ]
}

 The dataset includes 1995 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113842-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dbcgbe
title: Occurrence Download

description: A dataset containing 640979 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.21.15_1",
 "Year is 2021"
 ]
}

 The dataset includes 640979 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113814-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13628880
title: Physical parameters for the Orion fibers

description: Table with the physical properties determined for the 152 velocity-coherent fibers identified in the EMERGE Early ALMA Survey.

The latter is a sample of 7 star-forming regions in Orion homogeneously surveyed with ALMA in N2H+ (1-0) at 4.5".

The raw data was reduced with a uniform analysis using a combinaiton of custom spectral fitting routines, the identification algorithm in the PPV plane HIFIVe (Hacar et al. 2018) and the radial profile fitting routine FilChap (Suri et al. 2019).

The header of the table includes the following parameters:



ID: fiber identification number within the region assigned by HIFIVe during the identification process;

x0, y0: average x-y coordinates of the fiber with respect to the reference pixel in the datacube [unit: ''];

dV, sd_dV: median velocity dispersion (as FWHM of the spectral line) and corresponding standard deviation across the structure [unit: km s-1];

sNT, sd_sNT: median non-thermal dispersion (in units of the sound speed) and corresponding standard deviation across the structure;

Vlsr, sd_Vlsr: median centroid velocity and corresponding standard deviation across the structure [unit: km s-1];

Mass: total mass of the strcuture [unit: Msun];

points: number of fields associated to the structure;

Tk: median kinetic temperature of the structure [unit: K];

L: length of the structure [unit: pc];

AR: aspect ratio (computed as L / FWHM);

gradVx: median centroid velocity gradient along the axis knots [unit: km s-1 pc-1];

ML: line mass of the fiber (computed as M / L) [unit: Msun pc-1];

lgrad: mean velocity gradient across the whole axis length [unit: km s-1 pc-1];

P: number of protostars associated to the structure;

source: region in which the fiber was identified;

FWHM: fiber width as derived from the fitting of its column density radial profile [unit: pc];

N0: fiber peak column density as derived from the fitting of its column density radial profile [unit: cm-2];

gradT: temperature gradient determined around the fiber axis [unit: K pc-1].
DOI: 10.5281/zenodo.13628881
title: Physical parameters for the Orion fibers

description: Table with the physical properties determined for the 152 velocity-coherent fibers identified in the EMERGE Early ALMA Survey.

The latter is a sample of 7 star-forming regions in Orion homogeneously surveyed with ALMA in N2H+ (1-0) at 4.5".

The raw data was reduced with a uniform analysis using a combinaiton of custom spectral fitting routines, the identification algorithm in the PPV plane HIFIVe (Hacar et al. 2018) and the radial profile fitting routine FilChap (Suri et al. 2019).

The header of the table includes the following parameters:



ID: fiber identification number within the region assigned by HIFIVe during the identification process;

x0, y0: average x-y coordinates of the fiber with respect to the reference pixel in the datacube [unit: ''];

dV, sd_dV: median velocity dispersion (as FWHM of the spectral line) and corresponding standard deviation across the structure [unit: km s-1];

sNT, sd_sNT: median non-thermal dispersion (in units of the sound speed) and corresponding standard deviation across the structure;

Vlsr, sd_Vlsr: median centroid velocity and corresponding standard deviation across the structure [unit: km s-1];

Mass: total mass of the strcuture [unit: Msun];

points: number of fields associated to the structure;

Tk: median kinetic temperature of the structure [unit: K];

L: length of the structure [unit: pc];

AR: aspect ratio (computed as L / FWHM);

gradVx: median centroid velocity gradient along the axis knots [unit: km s-1 pc-1];

ML: line mass of the fiber (computed as M / L) [unit: Msun pc-1];

lgrad: mean velocity gradient across the whole axis length [unit: km s-1 pc-1];

P: number of protostars associated to the structure;

source: region in which the fiber was identified;

FWHM: fiber width as derived from the fitting of its column density radial profile [unit: pc];

N0: fiber peak column density as derived from the fitting of its column density radial profile [unit: cm-2];

gradT: temperature gradient determined around the fiber axis [unit: K pc-1].
DOI: 10.15468/dl.pstucv
title: Occurrence Download

description: A dataset listing the 97 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-6.55743 41.73196,-6.55835 41.70048,-6.46888 41.70137,-6.46827 41.73687,-6.55743 41.73687,-6.55743 41.73196))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 97 records were derived from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113816-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13627184
title: Data repository for manuscript "A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi"

description: Dataset to accompany the publication “A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi” by Margherita Molaro, Sakshi Mohan, Bingling She, Martin Chalkley, Tim Colbourn, Joseph H. Collins, Emilia Connolly, Matthew M. Graham, Eva Janoušková, Ines Li Lin, Gerald Manthalu, Emmanuel Mnjowe, Dominic Nkhoma, Pakwanja D. Twea, Andrew N. Phillips, Paul Revill, Asif U. Tamuri, Joseph Mfutso-Bengo, Tara Mangal, and Timothy B. Hallett.

The Thanzi La Onse (TLO) model used to produce this data is open source and available for review and usage at https://github.com/UCL/TLOmodel. In particular, the outputs analysed in this study can be reproduced from model tag "Molaro_et_al_2024_HBP_design" (accessible at https://github.com/UCL/TLOmodel/tags) using the scenario file src/scripts/healthsystem/impact_of_policy/scenario_impact_of_policy.py. All analysis scripts used to generate the plots in the manuscript are located in the same directory and have filenames beginning with "analysis_impact_of_policy_".

This repository contains post-processed simulation outputs, which were generated using the script src/scripts/healthsystem/impact_of_policy/analysis_extract_data.py (available from the same tag). The data included have the following structure:

"Draw": Represents a specific prioritisation-policy, identified by the acronyms listed in Table 1 of the publication.

"Run": Represents a single simulation instance of a draw. Each draw was simulated 10 times, each with independent random sampling, resulting in 10 "runs" per draw.

The data files included in this repository are:

DALYS_by_cause_with_time.csv: DALYs (as defined in the publication) incurred on a given year due to each of the causes of DALYs considered.

HSIs_requested_by_type_and_facility_level_with_time.csv: total number of requested HSIs on a given year, broken down by HSI type and the facility level at which they were requested.

HSIs_delivered_by_type_and_facility_level_with_time.csv:total number of HSIs delivered on a given year broken down by HSI type and the facility level at which they were delivered.

Population_with_time.csv:total population size on a given year.
DOI: 10.5281/zenodo.13627185
title: Data repository for manuscript "A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi"

description: Dataset to accompany the publication “A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi” by Margherita Molaro, Sakshi Mohan, Bingling She, Martin Chalkley, Tim Colbourn, Joseph H. Collins, Emilia Connolly, Matthew M. Graham, Eva Janoušková, Ines Li Lin, Gerald Manthalu, Emmanuel Mnjowe, Dominic Nkhoma, Pakwanja D. Twea, Andrew N. Phillips, Paul Revill, Asif U. Tamuri, Joseph Mfutso-Bengo, Tara Mangal, and Timothy B. Hallett.

The Thanzi La Onse (TLO) model used to produce this data is open source and available for review and usage at https://github.com/UCL/TLOmodel. In particular, the outputs analysed in this study can be reproduced from model tag "Molaro_et_al_2024_HBP_design" (accessible at https://github.com/UCL/TLOmodel/tags) using the scenario file src/scripts/healthsystem/impact_of_policy/scenario_impact_of_policy.py. All analysis scripts used to generate the plots in the manuscript are located in the same directory and have filenames beginning with "analysis_impact_of_policy_".

This repository contains post-processed simulation outputs, which were generated using the script src/scripts/healthsystem/impact_of_policy/analysis_extract_data.py (available from the same tag). The data included have the following structure:

"Draw": Represents a specific prioritisation-policy, identified by the acronyms listed in Table 1 of the publication.

"Run": Represents a single simulation instance of a draw. Each draw was simulated 10 times, each with independent random sampling, resulting in 10 "runs" per draw.

The data files included in this repository are:

DALYS_by_cause_with_time.csv: DALYs (as defined in the publication) incurred on a given year due to each of the causes of DALYs considered.

HSIs_requested_by_type_and_facility_level_with_time.csv: total number of requested HSIs on a given year, broken down by HSI type and the facility level at which they were requested.

HSIs_delivered_by_type_and_facility_level_with_time.csv:total number of HSIs delivered on a given year broken down by HSI type and the facility level at which they were delivered.

Population_with_time.csv:total population size on a given year.
DOI: 10.15468/dl.shxza9
title: Occurrence Download

description: A dataset containing 472458 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.33.32_1",
 "Year is 2021"
 ]
}

 The dataset includes 472458 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113823-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4snpv8
title: Occurrence Download

description: A dataset containing 29071244 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is South Africa",
 "Year 2000-2024"
 ]
}

 The dataset includes 29071244 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113810-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wf2xjq
title: Occurrence Download

description: A dataset listing the 42 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-6.54854 41.72588,-6.55439 41.72239,-6.55527 41.7178,-6.54268 41.70271,-6.47475 41.70162,-6.47534 41.72654,-6.54854 41.72588))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 42 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113809-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xhj5qg
title: Occurrence Download

description: A dataset containing 502795 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.33.24_1",
 "Year is 2022"
 ]
}

 The dataset includes 502795 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113813-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rxcjpv
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rosa micrantha Sumnev.",
 "Year 1950-2025"
 ]
}

 The dataset includes 20 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022390-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9tfrae
title: Occurrence Download

description: A dataset containing 3070 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is one of (Spain, Portugal)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rosa pouzinii Tratt.",
 "Year 1950-2025"
 ]
}

 The dataset includes 3070 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022391-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.