cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18207204
title: Conjunto de notas de leitura de processos relativos à ação "Construcción del Grupo de Viviendas Raimundo Fernández Cuesta, Zamora [1954-1956]"

description: Conjunto de notas de leitura de processos relativos à ação “Construcción del Grupo de Viviendas Raimundo Fernández Cuesta, Zamora [1954-1956]” existentes no Archivo Histórico Provincial de Zamora, em Zamora. 

Registo efetuado em maio de 2024 no âmbito do projeto de investigação ReARQ.IB – Built Environment Knowledge for Resilient, Sustainable Communities: Understanding Everyday Modern Architecture and Urban Design in the Iberian Peninsula (1939-1985) (ERC GA949686) e publicado online em https://arquitecturaaqui.eu/es/actividades/acciones/38714/construccion-del-grupo-de-viviendas-raimundo-fernandez-cuesta-zamora-1954-1956. 

Dados exportados da plataforma Arquitectura Aqui (https://arquitecturaaqui.eu), repositório do referido projeto, em janeiro de 2026.
DOI: 10.15468/dl.56q398
title: Occurrence Download

description: A dataset containing 4269 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Ibervillea Greene"
 ]
}

 The dataset includes 4269 records from 101 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003700-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.naypgp
title: Occurrence Download

description: A dataset containing 101246 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chlidonias niger (Linnaeus, 1758)",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 101246 records from 237 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003698-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2tgdkx
title: Occurrence Download

description: A dataset containing 5213518 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Falco sparverius Linnaeus, 1758"
 ]
}

 The dataset includes 5213518 records from 626 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003682-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m2hfxy
title: Occurrence Download

description: A dataset containing 389 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Idiospermum S.T.Blake"
 ]
}

 The dataset includes 389 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003702-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3zt9dz
title: Occurrence Download

description: A dataset containing 4932 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chloephaga picta (J.F.Gmelin, 1789)",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 4932 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003703-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11851988
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.qh5dgr
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chrysolophus amherstiae (Leadbeater, 1829)",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 154 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003704-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6kq7bp
title: Occurrence Download

description: A dataset containing 424 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Iguanura Blume"
 ]
}

 The dataset includes 424 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003705-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15453427
title: Model weights for RFdiffusion flexible peptide binder design

description: This repository contains model weights (InpaintSeq_ckpt.pt, InpaintSeq_Fold_ckpt.pt) used in the RFdiffusion-based binder design described in our manuscript “Diffusing protein binders to intrinsically disordered proteins”. These files were  hosted on GitHub but are now mirrored here for accessibility and reproducibility.
DOI: 10.5281/zenodo.15453428
title: Model weights for RFdiffusion flexible peptide binder design

description: This repository contains model weights (InpaintSeq_ckpt.pt, InpaintSeq_Fold_ckpt.pt) used in the RFdiffusion-based binder design described in our manuscript “Diffusing protein binders to intrinsically disordered proteins”. These files were  hosted on GitHub but are now mirrored here for accessibility and reproducibility.
DOI: 10.5281/zenodo.14640851
title: Dataset - High-resolution mapping of wood burning heat sources using Energy Performance Certificates: A case study of England and Wales

description: This repository contains open data and code to replicate the analysis in the manuscript "High-resolution mapping of wood burning appliance hotspots using Energy Performance Certificates: A case study of England and Wales". 

To recreate the analysis on your local device, please carry out the following steps:





Clone the GitHub repository (available at: https://github.com/UCL-Wellcome-Trust-Air-Pollution/EPC_mapping_project_code) to your local device, or download the codebase from the 'Code.zip' folder and unzip in your project directory. Please ensure you use the directory with the R Project in it as your root directory.




Download the 'Data.tar' file and unzip the file in the R Project directory. The data should be in a folder called 'Data' in the root directory. All non-EPC data is provided under the UK Open Government License version 3.0. EPC data is provided under licence from the Department for Levelling Up, Housing and Communities: https://epc.opendatacommunities.org/docs/copyright. 




Download the main EPC data to your local device and unzip (see below for detailed instructions on how to do this). For Windows users, the 'Scripts' folder of the repository contains a .bat file which can be used to unzip the data. Note that this file requires the user to have installed 7Zip and added 7Zip to the system path. Otherwise, the .tar file can be unzipped manually.




In an R terminal, run 'renv::restore()'. This should install all the necessary packages to replicate the analysis. On Linux/MacOS operating systems, there may be errors relating to re-installing specific packages from the renv lockfile. If this happens, install the package manually from source (install.packages("package_name", type = "source"), then run 'renv::snapshot()' and 'renv::restore()' again. Some packages (e.g. "sf") also require additional dependencies to be installed on Linux. Please install these dependencies before running 'renv::restore()'.




Once the project library has been successfully loaded, run the 'run.R' file in the 'Scripts' folder of the directory. You may need to change the 'path_data_epc_folders' variable to the path to the unzipped EPC data folders on your local device (see step 3). The full pipeline should now run.




Once you have run the pipeline for the first time, you should see a file called 'data_epc_raw.parquet' in the 'Data/raw/epc_data' folder. If you run the pipeline again, you will be prompted that the raw EPC data .parquet file already exists, and you have the option to skip the merging of raw data files.
DOI: 10.5281/zenodo.14640852
title: Dataset - High-resolution mapping of wood burning appliance hotspots using Energy Performance Certificates: A case study of England and Wales

description: This repository contains open data and code to replicate the analysis in the manuscript "High-resolution mapping of wood burning appliance hotspots using Energy Performance Certificates: A case study of England and Wales". 

To recreate the analysis on your local device, please carry out the following steps:





Clone the GitHub repository (available at: https://github.com/UCL-Wellcome-Trust-Air-Pollution/EPC_mapping_project_code) to your local device, or download the codebase from the 'Code.tar' folder and unzip in your project directory. Please ensure you use the directory with the R Project in it as your root directory.




Download the 'Data.tar' file and unzip the file in the R Project directory. The data should be in a folder called 'Data' in the root directory. All non-EPC data is provided under the UK Open Government License version 3.0. EPC data is provided under licence from DLUHC: https://epc.opendatacommunities.org/docs/copyright.




Download the main EPC data to your local device and unzip (see below for detailed instructions on how to do this). For Windows users, the 'Scripts' folder of the repository contains a .bat file which can be used to unzip the data. Note that this file requires the user to have installed 7Zip and added 7Zip to the system path. Otherwise, the .tar file can be unzipped manually.




Run the 'run.R' file in the 'Scripts' folder of the directory. You may need to change the 'path_data_epc_folders' variable to the path to the unzipped EPC data folders on your local device (see step 3). The full pipeline should now run.




Once you have run the pipeline for the first time, you should see a file called 'data_epc_raw.parquet' in the 'Data/raw/epc_data' folder. Once you have verified this is the case, you can safely delete the original unzipped EPC data folder, since the file is very large (>40Gb). If you run the pipeline again, you will be prompted that the raw EPC data .parquet file already exists, and you have the option to skip the merging of raw data files.
DOI: 10.5281/zenodo.14863663
title: Dataset - High-resolution mapping of wood burning heat sources using Energy Performance Certificates: A case study of England and Wales

description: This repository contains open data and code to replicate the analysis in the manuscript "High-resolution mapping of wood burning appliance hotspots using Energy Performance Certificates: A case study of England and Wales". 

To recreate the analysis on your local device, please carry out the following steps:





Clone the GitHub repository (available at: https://github.com/UCL-Wellcome-Trust-Air-Pollution/EPC_mapping_project_code) to your local device, or download the codebase from the 'Code.zip' folder and unzip in your project directory. Please ensure you use the directory with the R Project in it as your root directory.




Download the 'Data.tar' file and unzip the file in the R Project directory. The data should be in a folder called 'Data' in the root directory. All non-EPC data is provided under the UK Open Government License version 3.0. EPC data is provided under licence from the Department for Levelling Up, Housing and Communities: https://epc.opendatacommunities.org/docs/copyright. 




Download the main EPC data to your local device and unzip (see below for detailed instructions on how to do this). For Windows users, the 'Scripts' folder of the repository contains a .bat file which can be used to unzip the data. Note that this file requires the user to have installed 7Zip and added 7Zip to the system path. Otherwise, the .tar file can be unzipped manually.




Run the 'run.R' file in the 'Scripts' folder of the directory. You may need to change the 'path_data_epc_folders' variable to the path to the unzipped EPC data folders on your local device (see step 3). The full pipeline should now run.




Once you have run the pipeline for the first time, you should see a file called 'data_epc_raw.parquet' in the 'Data/raw/epc_data' folder. If you run the pipeline again, you will be prompted that the raw EPC data .parquet file already exists, and you have the option to skip the merging of raw data files.
DOI: 10.5281/zenodo.14888530
title: Dataset - High-resolution mapping of wood burning heat sources using Energy Performance Certificates: A case study of England and Wales

description: This repository contains open data and code to replicate the analysis in the manuscript "High-resolution mapping of wood burning appliance hotspots using Energy Performance Certificates: A case study of England and Wales". 

To recreate the analysis on your local device, please carry out the following steps:





Clone the GitHub repository (available at: https://github.com/UCL-Wellcome-Trust-Air-Pollution/EPC_mapping_project_code) to your local device, or download the codebase from the 'Code.zip' folder and unzip in your project directory. Please ensure you use the directory with the R Project in it as your root directory.




Download the 'Data.tar' file and unzip the file in the R Project directory. The data should be in a folder called 'Data' in the root directory. All non-EPC data is provided under the UK Open Government License version 3.0. EPC data is provided under licence from the Department for Levelling Up, Housing and Communities: https://epc.opendatacommunities.org/docs/copyright. 




Download the main EPC data to your local device and unzip (see below for detailed instructions on how to do this). For Windows users, the 'Scripts' folder of the repository contains a .bat file which can be used to unzip the data. Note that this file requires the user to have installed 7Zip and added 7Zip to the system path. Otherwise, the .tar file can be unzipped manually.




In an R terminal, run 'renv::restore()'. This should install all the necessary packages to replicate the analysis. On Linux/MacOS operating systems, there may be errors relating to re-installing specific packages from the renv lockfile. If this happens, install the package manually from source (install.packages("package_name", type = "source"), then run 'renv::snapshot()' and 'renv::restore()' again. Some packages (e.g. "sf") also require additional dependencies to be installed on Linux. Please install these dependencies before running 'renv::restore()'.




Once the project library has been successfully loaded, run the 'run.R' file in the 'Scripts' folder of the directory. You may need to change the 'path_data_epc_folders' variable to the path to the unzipped EPC data folders on your local device (see step 3). The full pipeline should now run.




Once you have run the pipeline for the first time, you should see a file called 'data_epc_raw.parquet' in the 'Data/raw/epc_data' folder. If you run the pipeline again, you will be prompted that the raw EPC data .parquet file already exists, and you have the option to skip the merging of raw data files.
DOI: 10.5281/zenodo.15453789
title: Dataset - High-resolution mapping of wood burning heat sources using Energy Performance Certificates: A case study of England and Wales

description: This repository contains open data and code to replicate the analysis in the manuscript "High-resolution mapping of wood burning appliance hotspots using Energy Performance Certificates: A case study of England and Wales". 

To recreate the analysis on your local device, please carry out the following steps:





Clone the GitHub repository (available at: https://github.com/UCL-Wellcome-Trust-Air-Pollution/EPC_mapping_project_code) to your local device, or download the codebase from the 'Code.zip' folder and unzip in your project directory. Please ensure you use the directory with the R Project in it as your root directory.




Download the 'Data.tar' file and unzip the file in the R Project directory. The data should be in a folder called 'Data' in the root directory. All non-EPC data is provided under the UK Open Government License version 3.0. EPC data is provided under licence from the Department for Levelling Up, Housing and Communities: https://epc.opendatacommunities.org/docs/copyright. 




Download the main EPC data to your local device and unzip (see below for detailed instructions on how to do this). For Windows users, the 'Scripts' folder of the repository contains a .bat file which can be used to unzip the data. Note that this file requires the user to have installed 7Zip and added 7Zip to the system path. Otherwise, the .tar file can be unzipped manually.




In an R terminal, run 'renv::restore()'. This should install all the necessary packages to replicate the analysis. On Linux/MacOS operating systems, there may be errors relating to re-installing specific packages from the renv lockfile. If this happens, install the package manually from source (install.packages("package_name", type = "source"), then run 'renv::snapshot()' and 'renv::restore()' again. Some packages (e.g. "sf") also require additional dependencies to be installed on Linux. Please install these dependencies before running 'renv::restore()'.




Once the project library has been successfully loaded, run the 'run.R' file in the 'Scripts' folder of the directory. You may need to change the 'path_data_epc_folders' variable to the path to the unzipped EPC data folders on your local device (see step 3). The full pipeline should now run.




Once you have run the pipeline for the first time, you should see a file called 'data_epc_raw.parquet' in the 'Data/raw/epc_data' folder. If you run the pipeline again, you will be prompted that the raw EPC data .parquet file already exists, and you have the option to skip the merging of raw data files.
DOI: 10.15468/dl.f77mb5
title: Occurrence Download

description: A dataset containing 414 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chrysolophus pictus (Linnaeus, 1758)",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 414 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003706-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qsv8pg
title: Occurrence Download

description: A dataset containing 2745 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Ileostylus Tiegh."
 ]
}

 The dataset includes 2745 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003707-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15429926
title: Datasets of Collaborative Hybrid Approach for the Distributed Vehicle Routing Problem

description: This dataset includes all original Cordeau MDVRP instances, along with additional scenario-based instances derived from both modified and non-modified versions.

The modified instances are constructed by extracting the first 20 customers from the original Cordeau MDVRP datasets. These instances are used to evaluate the performance of the proposed collaborative hybrid approach.
DOI: 10.5281/zenodo.15429927
title: Datasets of Collaborative Hybrid Approach for the Distributed Vehicle Routing Problem

description: This dataset includes all original Cordeau MDVRP instances, along with additional scenario-based instances derived from both modified and non-modified versions.

The modified instances are constructed by extracting the first 20 customers from the original Cordeau MDVRP datasets. These instances are used to evaluate the performance of the proposed collaborative hybrid approach.
DOI: 10.6084/m9.figshare.24130368
title: Effect of Problem-based learning on students' problem-solving ability in learning simple machines in physics.

description: The study aimed to examine the effect of problem-based learning (PBL) on students' problem-solving ability in the teaching and learning of simple machines in physics. The study was guided by socio constructionist theory. Quasi-experiment was conducted with 829 form-2 Physics students from eight (8) selected lower secondary schools in Sheema District, Uganda. Schools were assigned to treatment and control groups using a purposive random sampling technique. Students’ problem-solving ability was measured by conducting a problem-solving ability test in each group before and after studying a chapter on simple machines in physics. The data can be viewed in the Excel sheets provided
DOI: 10.6084/m9.figshare.24130368.v1
title: Effect of Problem-based learning on students' problem-solving ability in learning simple machines in physics.

description: The study aimed to examine the effect of problem-based learning (PBL) on students' problem-solving ability in the teaching and learning of simple machines in physics. The study was guided by socio constructionist theory. Quasi-experiment was conducted with 829 form-2 Physics students from eight (8) selected lower secondary schools in Sheema District, Uganda. Schools were assigned to treatment and control groups using a purposive random sampling technique. Students’ problem-solving ability was measured by conducting a problem-solving ability test in each group before and after studying a chapter on simple machines in physics. The data can be viewed in the Excel sheets provided
DOI: 10.15468/dl.8zrzyr
title: Occurrence Download

description: A dataset containing 32616 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence, Observation)",
 "Country is United States of America",
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((-124.93831 42.13349,-124.51505 37.59308,-122.70658 32.49473,-117.53128 31.4943,-113.87586 32.41778,-114.01053 34.89961,-119.53214 39.09372,-119.58985 42.19121,-124.93831 42.13349))",
 "OccurrenceStatus is Present",
 "TaxonKey is Botaurus lentiginosus (Rackett, 1813)"
 ]
}

 The dataset includes 32616 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003711-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.