cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e195462
title: ECIN Replication Package for "Social Learning about Climate Risks"

description: Replication code and data for Social Learning about Climate Risk.<br><br>Abstract: This paper
examines whether social learning facilitates climate risk perception updates to
inform climate adaptation. We estimate a network difference-in-differences
(NDID) model with a social network adjacency matrix constructed from the
Facebook Social Connectedness Index (SCI). We find that regional flooding
brought by Hurricanes Harvey and Irma increased flood insurance policies nationwide
to the extent of each county’s social network proximity to the flooded areas. Such
social learning induced 250,000 more policies in flooded counties and 81,000
more policies in unflooded counties in three years. We find evidence of the salience
effect but no support for adverse selection or over-insurance.
DOI: 10.3886/e195462v3
title: ECIN Replication Package for "Social Learning about Climate Risks"

description: Replication code and data for Social Learning about Climate Risk.<br><br>Abstract: This paper
examines whether social learning facilitates climate risk perception updates to
inform climate adaptation. We estimate a network difference-in-differences
(NDID) model with a social network adjacency matrix constructed from the
Facebook Social Connectedness Index (SCI). We find that regional flooding
brought by Hurricanes Harvey and Irma increased flood insurance policies nationwide
to the extent of each county’s social network proximity to the flooded areas. Such
social learning induced 250,000 more policies in flooded counties and 81,000
more policies in unflooded counties in three years. We find evidence of the salience
effect but no support for adverse selection or over-insurance.
DOI: 10.5281/zenodo.7987473
title: Data for manuscript: Functional Protein Dynamics in a Crystal

description: The data is provided as a part of the manuscript "<strong>Functional Protein Dynamics in a Crystal</strong>". This repository includes an archive with folders:<br> <br> <strong>md_data</strong> contains various simulation systems (crystal supercell, apo and ligand-bound solution) built from the crystal structure of the PDZ domain (PDB ID: 5E11) and carried out using three force fields: Amber ff14SB, CHARMM36m, Amber ff94. <em>The details of the simulations are provided in the Methods and Supplementary methods sections of the manuscript. </em> <strong>fig_data</strong> contains the data sets underlying Figures 1-5 of the manuscript's main text.
DOI: 10.5281/zenodo.10563084
title: Data for manuscript: Functional Protein Dynamics in a Crystal

description: The data is provided as a part of the manuscript "Functional Protein Dynamics in a Crystal".  This repository includes an archive with folders:

md_data



	
contains various simulation systems (crystal supercell, apo and ligand-bound solution) built from the crystal structure of the PDZ domain (PDB ID: 5E11) and carried out using three force fields: Amber ff14SB, CHARMM36m, Amber ff94. The details of the simulations are provided in the Methods and Supplementary methods sections of the manuscript. 



fig_data



	
contains the data sets underlying Figures 1-5 of the manuscript's main text.
DOI: 10.5281/zenodo.7987472
title: Data for manuscript: Functional Protein Dynamics in a Crystal

description: The data is provided as a part of the manuscript "Functional Protein Dynamics in a Crystal".  This repository includes an archive with folders:

md_data



	
contains various simulation systems (crystal supercell, apo and ligand-bound solution) built from the crystal structure of the PDZ domain (PDB ID: 5E11) and carried out using three force fields: Amber ff14SB, CHARMM36m, Amber ff94. The details of the simulations are provided in the Methods and Supplementary methods sections of the manuscript. 



fig_data



	
contains the data sets underlying Figures 1-5 of the manuscript's main text.
DOI: 10.5281/zenodo.10562949
title: Data and Code of A3SbI3

description: We have used the Quantum Espresso software in Linux operating system for running all the code.1. By making a psedo file with the pseudopotential of Ba/Sr/Ca, Sb, and I.

2. Then we have coded the codes in appropriate manner.

3. Then we have run the scf file by using the command: pw.x<Ba3SbI3.scf.in/Sr3SbI3.scf.in/Ca3SbI3.scf.in>BaSbI3.scf.out/Sr3SbI3.scf.out/Ca3SbI3.scf.out4. Then we have run the nscf file by using the command: pw.x<Ba3SbI3.nscf.in/Sr3SbI3.nscf.in/Ca3SbI3.nscf.in>BaSbI3.nscf.out/Sr3SbI3.nscf.out/Ca3SbI3.nscf.out5. Then we have run the bands file by using the command: bands.x<Ba3SbI3.bands.in/Sr3SbI3.bnads.in/Ca3SbI3.bands.in>BaSbI3.bands.out/Sr3SbI3.bands.out/Ca3SbI3.bands.out and using plotband.x plot the bands fig in xmgrace.6. Then we have run the pdos file by using the command: projwfc.x<Ba3SbI3.pdos.in/Sr3SbI3.pdos.in/Ca3SbI3.pdos.in>BaSbI3.pdos.out/Sr3SbI3.pdos.out/Ca3SbI3.pdos.out7. Then we have run the eps file by using the command: epsilon.x<Ba3SbI3.eps.in/Sr3SbI3.eps.in/Ca3SbI3.eps.in>BaSbI3.eps.out/Sr3SbI3.eps.out/Ca3SbI3.eps.out

After that we have get the values from SCAPS-1D software and plot the given the data on this file using Origin Pro 2018 software.
DOI: 10.5281/zenodo.10562950
title: Data and Code of A3SbI3

description: We have used the Quantum Espresso software in Linux operating system for running all the code.1. By making a psedo file with the pseudopotential of Ba/Sr/Ca, Sb, and I.

2. Then we have coded the codes in appropriate manner.

3. Then we have run the scf file by using the command: pw.x<Ba3SbI3.scf.in/Sr3SbI3.scf.in/Ca3SbI3.scf.in>BaSbI3.scf.out/Sr3SbI3.scf.out/Ca3SbI3.scf.out4. Then we have run the nscf file by using the command: pw.x<Ba3SbI3.nscf.in/Sr3SbI3.nscf.in/Ca3SbI3.nscf.in>BaSbI3.nscf.out/Sr3SbI3.nscf.out/Ca3SbI3.nscf.out5. Then we have run the bands file by using the command: bands.x<Ba3SbI3.bands.in/Sr3SbI3.bnads.in/Ca3SbI3.bands.in>BaSbI3.bands.out/Sr3SbI3.bands.out/Ca3SbI3.bands.out and using plotband.x plot the bands fig in xmgrace.6. Then we have run the pdos file by using the command: projwfc.x<Ba3SbI3.pdos.in/Sr3SbI3.pdos.in/Ca3SbI3.pdos.in>BaSbI3.pdos.out/Sr3SbI3.pdos.out/Ca3SbI3.pdos.out7. Then we have run the eps file by using the command: epsilon.x<Ba3SbI3.eps.in/Sr3SbI3.eps.in/Ca3SbI3.eps.in>BaSbI3.eps.out/Sr3SbI3.eps.out/Ca3SbI3.eps.out

After that we have get the values from SCAPS-1D software and plot the given the data on this file using Origin Pro 2018 software.
DOI: 10.17028/rd.lboro.23904399
title: Supplementary Information: Using machine learning to expound energy poverty in the global south: Understanding and predicting access to cooking with clean energy

description: <b>Article abstract:</b>Efforts towards achieving high access to cooking with clean energy have not been transformative due to a limited understanding of the clean-energy drivers and a lack of evidence-based clean-energy policy recommendations. This study addresses this gap by building a high-performing machine learning model to predict and understand the mechanisms driving energy poverty - specifically access to cooking with clean energy. In a first-of-a-kind, the estimated cost of US14.5 to enable universal access to cooking with clean energy encompasses all the intermediate inputs required to build self-sufficient ecosystems by creating value-addition sectors. Unlike previous studies, the data-driven clean-cooking transition pathways provide foundations for shaping policy that can transform the energy and cooking landscape. Developing these pathways is necessary to increase people's financial resilience to tackle energy poverty. The findings also show the absence of a linear relationship between electricity access and clean cooking - evidencing the need for a rapid paradigm shift to address energy poverty. A new fundamental approach that focuses on improving and sustaining the financial capacity of households through a systems approach is required so that they can afford electricity or fuels for cooking.
DOI: 10.17028/rd.lboro.23904399.v1
title: Supplementary Information: Using machine learning to expound energy poverty in the global south: Understanding and predicting access to cooking with clean energy

description: <b>Article abstract:</b>Efforts towards achieving high access to cooking with clean energy have not been transformative due to a limited understanding of the clean-energy drivers and a lack of evidence-based clean-energy policy recommendations. This study addresses this gap by building a high-performing machine learning model to predict and understand the mechanisms driving energy poverty - specifically access to cooking with clean energy. In a first-of-a-kind, the estimated cost of US14.5 to enable universal access to cooking with clean energy encompasses all the intermediate inputs required to build self-sufficient ecosystems by creating value-addition sectors. Unlike previous studies, the data-driven clean-cooking transition pathways provide foundations for shaping policy that can transform the energy and cooking landscape. Developing these pathways is necessary to increase people's financial resilience to tackle energy poverty. The findings also show the absence of a linear relationship between electricity access and clean cooking - evidencing the need for a rapid paradigm shift to address energy poverty. A new fundamental approach that focuses on improving and sustaining the financial capacity of households through a systems approach is required so that they can afford electricity or fuels for cooking.
DOI: 10.5281/zenodo.10562975
title: Dataset for paper "Experimental characterization of RF-SQUIDs based Josephson Traveling Wave Parametric Amplifier exploiting Resonant Phase Matching scheme"

description: Dataset for the realization of figures published on paper "Experimental characterization of RF-SQUIDs based Josephson Traveling Wave Parametric Amplifier exploiting Resonant Phase Matching scheme". Each file name is related to the corresponding published figure number.
DOI: 10.5281/zenodo.10562976
title: Dataset for paper "Experimental characterization of RF-SQUIDs based Josephson Traveling Wave Parametric Amplifier exploiting Resonant Phase Matching scheme"

description: Dataset for the realization of figures published on paper "Experimental characterization of RF-SQUIDs based Josephson Traveling Wave Parametric Amplifier exploiting Resonant Phase Matching scheme". Each file name is related to the corresponding published figure number.
DOI: 10.5281/zenodo.10562999
title: Data for "Variation in the Pedersen Conductance near Jupiter's Main Emission Aurora: Comparison of Hubble Space Telescope and Galileo Measurements"

description: This data archive contains the intermediate data products for the work "Variation in the Pedersen Conductance near Jupiter's Main Emission Aurora: Comparison of Hubble Space Telescope and Galileo Measurements" by M. J. Rutala et al.

This data archive consists of two datasets: one derived from Hubble Space Telescope (HST) observations of Jupiter's aurorae (HSTAuroralData_Rutala_etal_2024.csv) and one derived from Galileo/PLS measurements of Jupiter's magnetospheric plasma (GalileoPLSData_Rutala_etal_2024.csv).

The format for HSTAuroralData_Rutala_etal_2024.csv is as follows:




Column Name
Column Units
Column Description


DateTime
%d-%b-%Y %H:%M:%S.%f
(Python datetime format)


Subsolar_Lon
Degrees
in System III (SIII) longitude


Subsolar_Lat
Degrees
 


Subearth_Lon
Degrees
SIII longitude


Subearth_Lat
Degrees
 


MLT
Decimal Hours
Magnetic Local Time, out of 24 hours


R
Jupiter Radius
Mapped radial distance of main emission sample


R_Width
Jupiter Radius
Approximate mapped radial width of main emission sample


Lon
Degrees
(SIII) Longitude on planet of sample


Lat
Degrees
Latitude on planet of sample


I
kiloRayleighs
Main emission auroral brightness of sample




The format for GalileoPLSData_Rutala_etal_2024.csv is as follows:




Column Name
Column Units
Column Description


DateTime
%Y-%jT%H:%M:%S.%f
(Python datetime format)


Subsolar_Lon
Degrees
in System III (SIII) longitude


Subsolar_Lat
Degrees
 


Local_Time
Decimal Hours
(Magnetic) Local Time, out of 24 hours


R
Jupiter Radius
Spherical radial distance of spacecraft


Theta
Radians
Spherical polar angle of spacecraft


Phi
Radians
Spherical azimuth angle of spacecraft


Density
cm^-3
Ion density


Density_sigma
cm^-3
Std. Dev. in ion density


FlowSpeed
km/s
Ion velocity


FlowSpeed_sigma
km/s
Std. Dev. in ion velocity


Temperature
K
Ion temperature


Temperature_sigma
K
Std. Dev. in ion temperature
DOI: 10.5281/zenodo.10563000
title: Data for "Variation in the Pedersen Conductance near Jupiter's Main Emission Aurora: Comparison of Hubble Space Telescope and Galileo Measurements"

description: This data archive contains the intermediate data products for the work "Variation in the Pedersen Conductance near Jupiter's Main Emission Aurora: Comparison of Hubble Space Telescope and Galileo Measurements" by M. J. Rutala et al.

This data archive consists of two datasets: one derived from Hubble Space Telescope (HST) observations of Jupiter's aurorae (HSTAuroralData_Rutala_etal_2024.csv) and one derived from Galileo/PLS measurements of Jupiter's magnetospheric plasma (GalileoPLSData_Rutala_etal_2024.csv).

The format for HSTAuroralData_Rutala_etal_2024.csv is as follows:




Column Name
Column Units
Column Description


DateTime
%d-%b-%Y %H:%M:%S.%f
(Python datetime format)


Subsolar_Lon
Degrees
in System III (SIII) longitude


Subsolar_Lat
Degrees
 


Subearth_Lon
Degrees
SIII longitude


Subearth_Lat
Degrees
 


MLT
Decimal Hours
Magnetic Local Time, out of 24 hours


R
Jupiter Radius
Mapped radial distance of main emission sample


R_Width
Jupiter Radius
Approximate mapped radial width of main emission sample


Lon
Degrees
(SIII) Longitude on planet of sample


Lat
Degrees
Latitude on planet of sample


I
kiloRayleighs
Main emission auroral brightness of sample




The format for GalileoPLSData_Rutala_etal_2024.csv is as follows:




Column Name
Column Units
Column Description


DateTime
%Y-%jT%H:%M:%S.%f
(Python datetime format)


Subsolar_Lon
Degrees
in System III (SIII) longitude


Subsolar_Lat
Degrees
 


Local_Time
Decimal Hours
(Magnetic) Local Time, out of 24 hours


R
Jupiter Radius
Spherical radial distance of spacecraft


Theta
Radians
Spherical polar angle of spacecraft


Phi
Radians
Spherical azimuth angle of spacecraft


Density
cm^-3
Ion density


Density_sigma
cm^-3
Std. Dev. in ion density


FlowSpeed
km/s
Ion velocity


FlowSpeed_sigma
km/s
Std. Dev. in ion velocity


Temperature
K
Ion temperature


Temperature_sigma
K
Std. Dev. in ion temperature
DOI: 10.34740/kaggle/dsv/7471993
title: sdloralora
DOI: 10.6084/m9.figshare.24994973.v4
title: Pre-invasion assessment of potential invasive wood-borers on North American tree species in Chinese sentinel gardens

description: Table S1 To maximize transparency and utility of the data, here we include the full record of wood-borers and the colonized plant hosts. Vitality corresponds to tree scores evaluated by standards in Table 2. The 28S and COI represent the partial sequences of large subunit ribosomal RNA gene and the partial sequences of mitochondrial cytochrome c oxidase subunit I gene. Tissues correspond to the specific tissue colonized by the sampled wood borers. The IncidenceID corresponds to the supplementary folder names of the insect's images and sequences.Data for the manuscript titled "Pre-invasion assessment of potential invasive wood borers on North American tree species in Chinese sentinel gardens"<br>
DOI: 10.17028/rd.lboro.21666218
title: Computational Research Data for: "Expedient Synthesis and Luminescence Sensing of the Inositol Pyrophosphate Cellular Messenger 5-PP-InsP5"

description: Computational Research Data underlying the manuscript: "Expedient Synthesis and Luminescence Sensing of the Inositol Pyrophosphate Cellular Messenger 5-PP-InsP5" by  Megan L. Shipton, Afra Jamion, Simon Wheeler, Andrew M. Riley, Felix Plasser, Barry V. L. Potter and Stephen J. Butler.
Content (names of folders and files are given in <strong>bold face</strong>)
Computations are present for the following complexes:

 <strong>EuL_H2O</strong> - [Eu.3]<sup>+</sup> with bound H<sub>2</sub>O molecule
 <strong>EuL_H2O_carbox</strong> - [Eu.3]<sup>+</sup> with bound H<sub>2</sub>O molecule. Binding of carboxylic acid instead of indole.
 <strong>Insp5_bidentate</strong> - Bidentate binding mode of 5-PP-InsP5.
 <strong>Insp5_monodentate</strong> - Monodentate (axial) binding mode of 5-PP-InsP5.
 <strong>Insp6</strong> - Monodentate (axial) binding mode of InsP6.

Each folder contains

 <strong>final.xyz</strong> - converged geometry
 <strong>qchem.[in/out]</strong> - Q-Chem input/output files for optimisation
 <strong>SP_T.orca-gCP/orca.[inp/out]</strong> - gCP singlepoints in orca
 <strong>VEX.more/MOLCAS.input</strong> - input for OpenMolcas SO-RASSI job
 <strong>VEX.more/molcas.log</strong> - output of OpenMolcas SO-RASSI job
 <strong>VEX.more/summ.txt</strong> - energies and oscillator strengths of the <sup>7</sup>F states as used for plotting

<br>
DOI: 10.17028/rd.lboro.21666218.v1
title: Computational Research Data for: "Expedient Synthesis and Luminescence Sensing of the Inositol Pyrophosphate Cellular Messenger 5-PP-InsP5"

description: Computational Research Data underlying the manuscript: "Expedient Synthesis and Luminescence Sensing of the Inositol Pyrophosphate Cellular Messenger 5-PP-InsP5" by  Megan L. Shipton, Afra Jamion, Simon Wheeler, Andrew M. Riley, Felix Plasser, Barry V. L. Potter and Stephen J. Butler.
Content (names of folders and files are given in <strong>bold face</strong>)
Computations are present for the following complexes:

 <strong>EuL_H2O</strong> - [Eu.3]<sup>+</sup> with bound H<sub>2</sub>O molecule
 <strong>EuL_H2O_carbox</strong> - [Eu.3]<sup>+</sup> with bound H<sub>2</sub>O molecule. Binding of carboxylic acid instead of indole.
 <strong>Insp5_bidentate</strong> - Bidentate binding mode of 5-PP-InsP5.
 <strong>Insp5_monodentate</strong> - Monodentate (axial) binding mode of 5-PP-InsP5.
 <strong>Insp6</strong> - Monodentate (axial) binding mode of InsP6.

Each folder contains

 <strong>final.xyz</strong> - converged geometry
 <strong>qchem.[in/out]</strong> - Q-Chem input/output files for optimisation
 <strong>SP_T.orca-gCP/orca.[inp/out]</strong> - gCP singlepoints in orca
 <strong>VEX.more/MOLCAS.input</strong> - input for OpenMolcas SO-RASSI job
 <strong>VEX.more/molcas.log</strong> - output of OpenMolcas SO-RASSI job
 <strong>VEX.more/summ.txt</strong> - energies and oscillator strengths of the <sup>7</sup>F states as used for plotting

<br>
DOI: 10.6084/m9.figshare.23908086
title: Pesticide detections and concentrations in biofilm samples

description: Detections and concentrations of pesticides in freeze-dried biofilm samples collected from 21 stormwater ponds in Brampton, Ontario. Analyses conducted by AFL in Guelph, Ontario.
DOI: 10.6084/m9.figshare.23908086.v1
title: Pesticide detections and concentrations in biofilm samples

description: Detections and concentrations of pesticides in freeze-dried biofilm samples collected from 21 stormwater ponds in Brampton, Ontario. Analyses conducted by AFL in Guelph, Ontario.
DOI: 10.6084/m9.figshare.23907972
title: Stormwater pond locations

description: Locations of 21 stormwater ponds in Brampton, Ontario, selected for surveying in Summer 2022.
DOI: 10.6084/m9.figshare.23907972.v1
title: Stormwater pond locations

description: Locations of 21 stormwater ponds in Brampton, Ontario, selected for surveying in Summer 2022.
DOI: 10.6084/m9.figshare.16625071
title: TrainingDML-AI JSON Encoding

description: Prototype of TrainingDML-AI JSON schema and examples in the paper "Towards an interoperable training data markup language for artificial intelligence in earth observation"<br><br>
DOI: 10.6084/m9.figshare.16625071.v1
title: TrainingDML-AI JSON Encoding

description: Prototype of TrainingDML-AI JSON schema and examples in the paper "Towards an interoperable training data markup language for artificial intelligence in earth observation"<br><br>
DOI: 10.5281/zenodo.6601056
title: Grupos de Investigación UDFJC (Resultados Convocatoria 894 MinCiencias)

description: Relación de los grupos de investigación institucionalizados por la Universidad Distrital Francisco José de Caldas que se encuentran activos a mayo de 2022 y su resultado en la categorización obtenida en el marco de la convocatoria 894 de 2021
DOI: 10.5281/zenodo.6601055
title: Grupos de Investigación UDFJC (Resultados Convocatoria 894 MinCiencias)

description: Relación de los grupos de investigación institucionalizados por la Universidad Distrital Francisco José de Caldas que se encuentran activos a mayo de 2022 y su resultado en la categorización obtenida en el marco de la convocatoria 894 de 2021
DOI: 10.5281/zenodo.6601064
title: Docentes Investigadores UDFJC (Resultados Convocatoria 894 MinCiencias)

description: Relación de los docentes de planta y vinculación especial la Universidad Distrital Francisco José de Caldas que se encuentran vinculados a mayo de 2022 y su resultado en la categorización obtenida en el marco de la convocatoria 894 de 2021
DOI: 10.5281/zenodo.6601063
title: Docentes Investigadores UDFJC (Resultados Convocatoria 894 MinCiencias)

description: Relación de los docentes de planta y vinculación especial la Universidad Distrital Francisco José de Caldas que se encuentran vinculados a mayo de 2022 y su resultado en la categorización obtenida en el marco de la convocatoria 894 de 2021