cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7117724
title: Diurnal variation in Uchikomi fitness test performance: Influence of warm-up protocols

description: group time total scores uft(a+b) heart rate NWU morning 31 11 183.83 NWU morning 42 14 178.17 NWU morning 27 9 175.17 NWU morning 42 14 179.33 NWU morning 42 14 179.67 NWU morning 42 15 182.50 NWU morning 42 14 181.67 NWU morning 42 15 182.50 NWU morning 42 14 178.17 NWU morning 42 14 179.67 NWU evening 46 16 185.17 NWU evening 43 15 186.67 NWU evening 49 17 177.33 NWU evening 41 14 182.67 NWU evening 49 17 179.17 NWU evening 48 16 187.00 NWU evening 41 14 158.50 NWU evening 48 16 187.00 NWU evening 43 15 186.67 NWU evening 49 17 179.17 FWU morning 38 14 183.83 FWU morning 53 22 187.00 FWU morning 33 12 175.17 FWU morning 52 19 188.67 FWU morning 41 14 173.00 FWU morning 68 30 176.67 FWU morning 49 18 166.67 FWU morning 68 30 176.67 FWU morning 53 22 187.00 FWU morning 41 14 173.00 FWU evening 44 16 183.83 FWU evening 55 20 188.50 FWU evening 51 16 172.50 FWU evening 62 27 185.17 FWU evening 63 27 173.83 FWU evening 68 25 185.83 FWU evening 52 21 156.83 FWU evening 68 25 185.83 FWU evening 55 20 188.50 FWU evening 63 27 173.83 SWU morning 55 19 169.50 SWU morning 62 23 173.50 SWU morning 55 19 169.50 SWU morning 56 19 179.33 SWU morning 57 20 168.33 SWU morning 68 24 176.33 SWU morning 50 18 158.67 SWU morning 68 24 176.33 SWU morning 62 23 173.50 SWU morning 57 20 168.33 SWU evening 61 21 171.67 SWU evening 66 24 174.50 SWU evening 60 20 171.00 SWU evening 61 22 180.33 SWU evening 57 20 167.83 SWU evening 68 23 176.33 SWU evening 54 18 160.83 SWU evening 70 24 177.00 SWU evening 62 23 173.50 SWU evening 57 20 168.33
DOI: 10.5281/zenodo.7117723
title: Diurnal variation in Uchikomi fitness test performance: Influence of warm-up protocols

description: group time total scores uft(a+b) heart rate NWU morning 31 11 183.83 NWU morning 42 14 178.17 NWU morning 27 9 175.17 NWU morning 42 14 179.33 NWU morning 42 14 179.67 NWU morning 42 15 182.50 NWU morning 42 14 181.67 NWU morning 42 15 182.50 NWU morning 42 14 178.17 NWU morning 42 14 179.67 NWU evening 46 16 185.17 NWU evening 43 15 186.67 NWU evening 49 17 177.33 NWU evening 41 14 182.67 NWU evening 49 17 179.17 NWU evening 48 16 187.00 NWU evening 41 14 158.50 NWU evening 48 16 187.00 NWU evening 43 15 186.67 NWU evening 49 17 179.17 FWU morning 38 14 183.83 FWU morning 53 22 187.00 FWU morning 33 12 175.17 FWU morning 52 19 188.67 FWU morning 41 14 173.00 FWU morning 68 30 176.67 FWU morning 49 18 166.67 FWU morning 68 30 176.67 FWU morning 53 22 187.00 FWU morning 41 14 173.00 FWU evening 44 16 183.83 FWU evening 55 20 188.50 FWU evening 51 16 172.50 FWU evening 62 27 185.17 FWU evening 63 27 173.83 FWU evening 68 25 185.83 FWU evening 52 21 156.83 FWU evening 68 25 185.83 FWU evening 55 20 188.50 FWU evening 63 27 173.83 SWU morning 55 19 169.50 SWU morning 62 23 173.50 SWU morning 55 19 169.50 SWU morning 56 19 179.33 SWU morning 57 20 168.33 SWU morning 68 24 176.33 SWU morning 50 18 158.67 SWU morning 68 24 176.33 SWU morning 62 23 173.50 SWU morning 57 20 168.33 SWU evening 61 21 171.67 SWU evening 66 24 174.50 SWU evening 60 20 171.00 SWU evening 61 22 180.33 SWU evening 57 20 167.83 SWU evening 68 23 176.33 SWU evening 54 18 160.83 SWU evening 70 24 177.00 SWU evening 62 23 173.50 SWU evening 57 20 168.33
DOI: 10.34740/kaggle/dsv/4271013
title: lncRNA of pediatric SARS-CoV-2
DOI: 10.25919/vs2s-ka28
title: Parkes observations for project P737 semester 2022APRS_12

description: The aim of this proposal is to carry out tests of the new backend instruments which cannot be carried out during standard maintenance periods. The scheduled time is used to measure beam shapes, determine polarisation calibration solutions and commission the National Facility observing modes for the new wide-band receiver.
DOI: 10.34740/kaggle/dsv/4271014
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.25919/zfef-mv25
title: Parkes observations for project P885 semester 2022APRS_20

description: We request a continued reduced monitoring cadence for the magnetars 1E 1547.0-5408 and Swift J1818.0-1607 at once every 10 days, to track their rotation, flux density, and polarisation. Swift J1818.0-1607 remains very dynamic, and the unique UWL capabilities promise new opportunities to probe still mysterious radio emission mechanisms in magnetars. Our timing results indicate that 1E 1547.0-5408 is currently a more stable rotator, supporting the diminished cadence. We will observe XTE J1810-197 and PSR J1622-4950 less frequently, to complement monitoring elsewhere. The overall request is 18 hours.
DOI: 10.6084/m9.figshare.21256611.v1
title: RAW_data_for_Nature manuscript 2022-05-07139A-Z

description: RAW_data_for_Nature manuscript 2022-05-07139A-Z
DOI: 10.6084/m9.figshare.21256611.v2
title: RAW_data_for_Nature manuscript 2022-05-07139B

description: RAW_data_for_Nature manuscript 2022-05-07139B
DOI: 10.6084/m9.figshare.21257547
title: Raw Data for Nature manuscript 2022-05-07139B

description: <strong>Raw Data for Nature manuscript 2022-05-07139B</strong>
DOI: 10.6084/m9.figshare.21065908
title: Raw data

description: The raw data of cellular senescence.
DOI: 10.6084/m9.figshare.21065908.v1
title: Raw data

description: The raw data of cellular senescence.
DOI: 10.6084/m9.figshare.21256611
title: RAW_data_for_Nature manuscript 2022-05-07139B

description: RAW_data_for_Nature manuscript 2022-05-07139B
DOI: 10.6084/m9.figshare.21256611.v4
title: RAW_data_for_Nature manuscript 2022-05-07139B

description: RAW_data_for_Nature manuscript 2022-05-07139B
DOI: 10.6084/m9.figshare.21256611.v3
title: RAW_data_for_Nature manuscript 2022-05-07139B

description: RAW_data_for_Nature manuscript 2022-05-07139B
DOI: 10.34740/kaggle/dsv/4271109
title: 2021 World Population (updated daily)
DOI: 10.26024/vvd1-j623
title: CESM2.2:CAM-chem as Boundary Conditions

description: We have saved CAM-chem simulation output that is intended to be used as boundary conditions for regional modeling. We used CAM-chem at 0.9x1.25 horizontal resolution simulation with 32 vertical levels. Temperatures, and winds are nudged to MERRA2 reanalysis fields with by nudging with 12h relaxation (10%) to MERRA2 reanalysis. Data is saved as instantaneous fields every 6 hours. See a list of saved species. Chemistry is the MOZART-T1 mechanism, described in Emmons et al. (2019). You can find a description of all simulated species online. We use the MAM4-VBS scheme to model aerosols. Files are collected as daily information (4 time-slices per file). Anthropogenic emissions are from CAMS V5.1. Biogenic emissions are from online MEGAN2.1, fire emissions are from QFED CO2 x FINN emission ratios. Online ocean DMS emissions are used.
DOI: 10.5281/zenodo.5833928
title: Data for "Computational Design of Alloy Nanostructures for Optical Sensing of Hydrogen"

description: This record contains data pertaining to the publication "Computational Design of Alloy Nanostructures for Optical Sensing of Hydrogen".
DOI: 10.5281/zenodo.5833929
title: Data for "Computational Design of Alloy Nanostructures for Optical Sensing of Hydrogen"

description: This record contains data pertaining to the publication "Computational Design of Alloy Nanostructures for Optical Sensing of Hydrogen".
DOI: 10.34740/kaggle/dsv/4271166
title: Currency Data | Business Analysis
DOI: 10.5281/zenodo.5076169
title: Data and code for "Hydrogen-driven Surface Segregation in Pd-alloys from Atomic Scale Simulations"

description: This record contains data and scripts pertaining to the publication "Hydrogen-driven Surface Segregation in Pd-alloys from Atomic Scale Simulations".
DOI: 10.5281/zenodo.5076168
title: Data and code for "Hydrogen-driven Surface Segregation in Pd-alloys from Atomic Scale Simulations"

description: This record contains data and scripts pertaining to the publication "Hydrogen-driven Surface Segregation in Pd-alloys from Atomic Scale Simulations".
DOI: 10.34740/kaggle/dsv/4271239
title: Elon Musk Tweets
DOI: 10.5281/zenodo.7133189
title: glutinoglossum_americanum_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7133188
title: glutinoglossum_americanum_zanfona_1.fasta.gz

description: .
DOI: 10.34740/kaggle/dsv/4271264
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.6636796
title: RefHiC

description: reference panel for RefHiC
DOI: 10.5281/zenodo.7007962
title: RefHiC

description: reference panel for RefHiC
DOI: 10.5281/zenodo.7011277
title: RefHiC reproducibility

description: scripts (i.e. .R and .ipynb scripts) and data to <strong>reproduce our analysis</strong> in RefHiC manuscript
DOI: 10.5281/zenodo.6636795
title: RefHiC reproducibility

description: scripts (i.e. .R and .ipynb scripts) and data to <strong>reproduce our analysis</strong> in RefHiC manuscript: 1. refhic_reproducibility.zip: scripts to reproduce most of our analysis 2. responseToReviewR1Codes.zip: scripts to reproduce additional experiments during the first round of the review process. (most scripts are for fig.6)
DOI: 10.5281/zenodo.7133194
title: RefHiC reproducibility

description: scripts (i.e. .R and .ipynb scripts) and data to <strong>reproduce our analysis</strong> in RefHiC manuscript: 1. refhic_reproducibility.zip: scripts to reproduce most of our analysis 2. responseToReviewR1Codes.zip: scripts to reproduce additional experiments during the first round of the review process. (most scripts are for fig.6)
DOI: 10.5517/ccdc.csd.cc2b54ym
title: CCDC 2149910: Experimental Crystal Structure Determination

description: Related Article: Anupam Karmakar, Po-Cheng Yu, Femil J. Shajan, Vijay K. Chatare, William A. Sabbers, Eduardo M. Sproviero, Rodrigo B. Andrade|2022|Org.Lett.|24|6548|doi:10.1021/acs.orglett.2c02513
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b54zn
title: CCDC 2149911: Experimental Crystal Structure Determination

description: Related Article: Anupam Karmakar, Po-Cheng Yu, Femil J. Shajan, Vijay K. Chatare, William A. Sabbers, Eduardo M. Sproviero, Rodrigo B. Andrade|2022|Org.Lett.|24|6548|doi:10.1021/acs.orglett.2c02513
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b550q
title: CCDC 2149912: Experimental Crystal Structure Determination

description: Related Article: Anupam Karmakar, Po-Cheng Yu, Femil J. Shajan, Vijay K. Chatare, William A. Sabbers, Eduardo M. Sproviero, Rodrigo B. Andrade|2022|Org.Lett.|24|6548|doi:10.1021/acs.orglett.2c02513
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b551r
title: CCDC 2149913: Experimental Crystal Structure Determination

description: Related Article: Anupam Karmakar, Po-Cheng Yu, Femil J. Shajan, Vijay K. Chatare, William A. Sabbers, Eduardo M. Sproviero, Rodrigo B. Andrade|2022|Org.Lett.|24|6548|doi:10.1021/acs.orglett.2c02513
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.21210353
title: Data supplement for 'A single life-history variable predicts birth rate'

description: Supplementary data set for "A single life-history variable predicts birth rate," a zip file, "AmongCountries.zip," of the folder structure that contains R script, input data, and output. Home folder of project: https://figshare.com/projects/A_single_life-history_variable_predicts_birth_rate/149708
DOI: 10.6084/m9.figshare.21210353.v4
title: Data supplement for 'A single life-history variable predicts birth rate'

description: Supplementary data set for "A single life-history variable predicts birth rate," a zip file, "AmongCountries.zip," of the folder structure that contains R script, input data, and output. Home folder of project: https://figshare.com/projects/A_single_life-history_variable_predicts_birth_rate/149708
DOI: 10.6084/m9.figshare.21210353.v3
title: Data supplement for 'A single life-history variable commands birth rate'

description: Supplementary data set for "A single life-history variable commands birth rate," a zip file, "AmongCountries.zip," of the folder structure that contains R script, input data, and output.
DOI: 10.5281/zenodo.5776219
title: Traffic images captured from UAVs for use in training Machine Vision Algorithms for traffic management

description: <strong>If you use this dataset please cite this paper: Bemposta Rosende, S.; Ghisler, S.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management. Data 2022, 7, 53. https://doi.org/10.3390/data7050053</strong> A dataset of road traffic images taken from unmanned aerial vehicles (UAV) with the purpose of being used to train artificial vision algorithms, among which those based on convolutional neural networks stand out. Dataset is available and accessible in order to improve the performance of road traffic vision and management systems due to the lack of resources in this specific domain. The full description of the characteristics of the dataset, as well as its components and format, can be found here: https://www.mdpi.com/2306-5729/7/5/53 The dataset format is YOLO. Relevant data about the dataset: <strong>Scenes</strong> <strong>Frames</strong> <strong>Targets</strong> <strong>Cars</strong> <strong>Motorbikes</strong> Regional road 4.500 24.858 14.577 10.281 Urban intersection 2.462 10.759 10.759 0 Rural road 1.292 746 746 0 Split roundabout 2.297 3.107 3.107 0 Roundabout (Far) 1.814 71.819 64.844 6.975 Roundabout (Near) 3.997 4.4039 43.569 470 <strong>Total</strong> <strong>15.070</strong> <strong>155.328</strong> <strong>137.602</strong> <strong>17.726</strong>
description: Funding: This publication is part of the I+D+i projects with reference PID2019-104793RB-C32, PIDC2021-121517-C33, funded by MCIN/AEI/10.13039/501100011033/, S2018/EMT-4362/"SEGVAUTO4.0-CM" funded by Regional Government of Madrid and "ESF and ERDF A way of making Europe".
DOI: 10.5281/zenodo.5776218
title: Traffic images captured from UAVs for use in training Machine Vision Algorithms for traffic management

description: <strong>If you use this dataset please cite this paper: Bemposta Rosende, S.; Ghisler, S.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management. Data 2022, 7, 53. https://doi.org/10.3390/data7050053</strong> A dataset of road traffic images taken from unmanned aerial vehicles (UAV) with the purpose of being used to train artificial vision algorithms, among which those based on convolutional neural networks stand out. Dataset is available and accessible in order to improve the performance of road traffic vision and management systems due to the lack of resources in this specific domain. The full description of the characteristics of the dataset, as well as its components and format, can be found here: https://www.mdpi.com/2306-5729/7/5/53 The dataset format is YOLO. Relevant data about the dataset: <strong>Scenes</strong> <strong>Frames</strong> <strong>Targets</strong> <strong>Cars</strong> <strong>Motorbikes</strong> Regional road 4.500 24.858 14.577 10.281 Urban intersection 2.462 10.759 10.759 0 Rural road 1.292 746 746 0 Split roundabout 2.297 3.107 3.107 0 Roundabout (Far) 1.814 71.819 64.844 6.975 Roundabout (Near) 3.997 4.4039 43.569 470 <strong>Total</strong> <strong>15.070</strong> <strong>155.328</strong> <strong>137.602</strong> <strong>17.726</strong>
description: Funding: This publication is part of the I+D+i projects with reference PID2019-104793RB-C32, PIDC2021-121517-C33, funded by MCIN/AEI/10.13039/501100011033/, S2018/EMT-4362/"SEGVAUTO4.0-CM" funded by Regional Government of Madrid and "ESF and ERDF A way of making Europe".
DOI: 10.5281/zenodo.7133199
title: evanradith/datasetEPB7: Dataset Survei Analisa Screen Time dan Pengeluaran Belanja Online

description: Dataset dari hasil survei yang dilakukan untuk menyusun paper berjudul "Analisis Hubungan Screen Time pada Aplikasi Belanja Online di Ponsel dan Bidang Studi dengan Jumlah Pengeluaran pada Masa New Normal Setelah Pandemi (Studi Kasus: Mahasiswa ITS)". Responden dataset merupakan mahasiswa ITS yang telah mengisi kuesioner yang telah disebarkan.
DOI: 10.5281/zenodo.7133200
title: evanradith/datasetEPB7: Dataset Survei Analisa Screen Time dan Pengeluaran Belanja Online

description: Dataset dari hasil survei yang dilakukan untuk menyusun paper berjudul "Analisis Hubungan Screen Time pada Aplikasi Belanja Online di Ponsel dan Bidang Studi dengan Jumlah Pengeluaran pada Masa New Normal Setelah Pandemi (Studi Kasus: Mahasiswa ITS)". Responden dataset merupakan mahasiswa ITS yang telah mengisi kuesioner yang telah disebarkan.
DOI: 10.5281/zenodo.7133245
title: evanradith/datasetEPB7: Dataset Survei Analisa Screen Time dan Pengeluaran Belanja Online

description: Dataset dari hasil survei yang dilakukan untuk menyusun paper berjudul "Analisis Hubungan Screen Time pada Aplikasi Belanja Online di Ponsel dan Bidang Studi dengan Jumlah Pengeluaran pada Masa New Normal Setelah Pandemi (Studi Kasus: Mahasiswa ITS)". Responden dataset merupakan mahasiswa ITS yang telah mengisi kuesioner yang telah disebarkan.
DOI: 10.6084/m9.figshare.21256005
title: Tabular file of essential variables

description: This tabular file is provided so that skeptical reviewers can apply correlations (filterable by year and region) between juvemort (deaths aged 0-24 in proportion to concurrent deaths) and CBR (crude birth rate). Compare such correlations to those between CBR and other variables such as under-5 child mortality, GDP per capita, and years of schooling. The CSV file is compatible with any statistical program including Excel. Variables include: UN: United Nations identifier for a country or area Name ISO: Alpha-3 identifier for country or area Year Region Juvemort: mortality of those aged 0-24 in proportion to total deaths within the year CBR: Crude birth rate per 1,000 population TFR: Total fertility rate, the projected number of children per woman Mort0to4perthou: Under-5 mortality of children per 1,000 live births GDPpc: log(gross domestic product per capita) * 100 Survivors: Number of survivors remaining at age 25 per 100,000 live births of the cohort Urbaniz: UN-defined urbanization YrsSchool: Years of schooling per adult Home folder of project: https://figshare.com/projects/A_single_life-history_variable_predicts_birth_rate/149708
DOI: 10.6084/m9.figshare.20749156
title: Modelling the distribution of the European field cricket in North Rhine-Westphalia.

description: We modelled the distribution of the European field cricket in North Rhine-Westphalia.
DOI: 10.6084/m9.figshare.20749156.v3
title: Modelling the distribution of the European field cricket in North Rhine-Westphalia.

description: We modelled the distribution of the European field cricket in North Rhine-Westphalia.
DOI: 10.6084/m9.figshare.20749156.v2
title: Modelling the distribution of the European field cricket in North Rhine-Westphalia.

description: We modelled the distribution of the European field cricket in North Rhine-Westphalia.
DOI: 10.5281/zenodo.7133255
title: Sample 3D image data from RIMS method for image analysis code demo

description: Sample 3D image data from RIMS method applied to mechanical test on hydrogel sphere packings, to be used in image analysis code demo as demonstrated in the ALERT Geomechanics doctoral school 2022. The data is a small subset from a larger set of data as found on Dryad via 10.5061/dryad.6djh9w0x8 and is separated here on Zenodo to make the subset more machine-readable.
DOI: 10.5281/zenodo.7133254
title: Sample 3D image data from RIMS method for image analysis code demo

description: Sample 3D image data from RIMS method applied to mechanical test on hydrogel sphere packings, to be used in image analysis code demo as demonstrated in the ALERT Geomechanics doctoral school 2022. The data is a small subset from a larger set of data as found on Dryad via 10.5061/dryad.6djh9w0x8 and is separated here on Zenodo to make the subset more machine-readable.
DOI: 10.34740/kaggle/dsv/4271569
title: Tales from the Pizza Guy
DOI: 10.5519/qd.zusont74
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1695 records)
DOI: 10.17909/3pf0-8b20
title: NIRCam Data of CEERS June Observations

description: Data for Onoue et al. (2022) and Ding et al. (2022)
DOI: 10.21227/cfy3-wb59
title: TIE-22-1436

description: Relevant trained model and experimental data for TIE-22-1436
DOI: 10.5281/zenodo.7133305
title: Engr

description: This is a dataset acquired through an IoT weather station, using an embedded system.
DOI: 10.5281/zenodo.7133304
title: Engr

description: This is a dataset acquired through an IoT weather station, using an embedded system.
DOI: 10.34740/kaggle/dsv/4271664
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.7133319
title: catotricha_subobsoleta_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7133318
title: catotricha_subobsoleta_zanfona_1.fasta.gz

description: .
DOI: 10.15468/1d9txo
title: Global Register of Introduced and Invasive Species - El Hierro, Islas Canarias, Spain

description: The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level. Phase 1 of the project focused on developing validated and verified checklists of countries that are Party to the Convention on Biological Diversity (CBD). Phase 2 which is on-going, aims to achieve global coverage including non-party countries and all overseas territories of countries e.g. Netherlands, France and United Kingdom. Species belonging to all Kingdoms are covered as well as occurring in all Environment/systems. Country/ Territory/ Island checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records as well as those under review are presented on the online GRIIS website (www.griis.org). Individual species records are flagged with a ‘yes’ for verification. Only verified checklists/ species records are presented on the GBIF Portal. This annotated checklist is of introduced (alien) and invasive alien species known to occur on the island of El Hierro, Islas Canarias an Autonomous community of Spain
DOI: 10.5281/zenodo.7046063
title: A rapidly evolving high-amplitude δ Scuti star crossing the Hertzsprung Gap

description: The datasets includes the work folder which we used in the paper to construct the evolutionary models based on MESA (r15140).