cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/8v8nkfncty
title: Non-destructive thickness testing of TBCs using synthetic aperture imaging with pulsed terahertz waves

description: This dataset is used for the non-destructive evaluation of thermal barrier coatings (TBCs). It contains all the original data used in the associated publication, including:
 • 3D terahertz (THz) reconstruction imaging scripts,
 • Two-stage interface extraction algorithms,
 • Deep learning-based interface extraction program,
 • Thickness measurement program,

First, use Image.m to read TBC, curved, or flat samples and generate sliced THz images.
Then, use main.py to extract the interfaces.
Finally, use 3D_detect to perform thickness extraction.
When using the two-stage method for extracting target interfaces, please ensure to adjust the jida_num parameter located in the dataprocess module.
DOI: 10.15468/dl.5zknfp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Anorrhinus galeritus)",
 "Country is one of (Åland Islands, Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Republic of, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020609-250802193616735/datasets/export for details.
DOI: 10.17632/8v8nkfncty.1
title: Non-destructive thickness testing of TBCs using synthetic aperture imaging with pulsed terahertz waves

description: This dataset is used for the non-destructive evaluation of thermal barrier coatings (TBCs). It contains all the original data used in the associated publication, including:
 • 3D terahertz (THz) reconstruction imaging scripts,
 • Two-stage interface extraction algorithms,
 • Deep learning-based interface extraction program,
 • Thickness measurement program,

First, use Image.m to read TBC, curved, or flat samples and generate sliced THz images.
Then, use main.py to extract the interfaces.
Finally, use 3D_detect to perform thickness extraction.
When using the two-stage method for extracting target interfaces, please ensure to adjust the jida_num parameter located in the dataprocess module.
DOI: 10.5281/zenodo.16748581
title: Replication package for: "Expectations and Adaptation to Environmental Threats"

description: Husnain Fateh, Matthew Gibson, Fatiq Nadeem, Sanval Nasim, and Arman Rezaee (Forthcoming). "Expectations and Adaptation to Environmental Threats," The Economic Journal.
DOI: 10.5281/zenodo.16748582
title: Replication package for: "Expectations and Adaptation to Environmental Threats"

description: Husnain Fateh, Matthew Gibson, Fatiq Nadeem, Sanval Nasim, and Arman Rezaee (Forthcoming). "Expectations and Adaptation to Environmental Threats," The Economic Journal.
DOI: 10.15468/dl.sscaak
title: Occurrence Download

description: A dataset containing 78 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Scaphosepalum breve"
 ]
}

 The dataset includes 78 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020612-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ggbkm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Anorrhinus galeritus)",
 "Country is one of (Algeria, Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Congo, Democratic Republic of the, Côte d’Ivoire, Djibouti, Egypt, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, French Southern Territories, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mayotte, Morocco, Mozambique, Namibia, Niger, Nigeria, Réunion, Rwanda, Saint Helena, Ascension and Tristan da Cunha, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Tanzania, United Republic of, Togo, Tunisia, Uganda, Western Sahara, Zambia, Zimbabwe)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020611-250802193616735/datasets/export for details.
DOI: 10.5281/zenodo.16539384
title: Data for "Compression of Mercury's Dayside Magnetopause to the Surface: a Three-Dimensional Model of Magnetospheric Structure and Dynamics" by Glebe et al.

description: Accompanying data products for manuscript entitled "Compression of Mercury's Dayside Magnetopause to the Surface: a Three-Dimensional Model of Magnetospheric Structure and Dynamics".

Data includes model outputs that are depicted in this work.

Information regarding the organization and file structure can be found in readme.txt.
DOI: 10.60635/c3sc98
title: Monthly summary_Culebra Station Tower

description: Punta Culebra Nature Center meteorological station. Established in 2002.
 Location: 8.910718°N, -79.528919°
 Parameters: air temperature, relative humidity, wind speed and direction, precipitation, solar radiation (pyranometer)
 Historical Datasets can be located here: https://smithsonian.figshare.com/articles/dataset/Monthly_summary_Culebra_Station_Tower/10059470
DOI: 10.15468/dl.86ksfh
title: Occurrence Download

description: A dataset containing 5412 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Phyllomyias sclateri)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 5412 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020613-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j7zuje
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Anorrhinus galeritus)",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020610-250802193616735/datasets/export for details.
DOI: 10.17632/bx7wjt95w5
title: DCLLdata

description: Table 1: Description of general data can be obtained from the DCL data (general data).
Tables 2, 3, and 4: The scores for each scale, correlation analysis, and univariate and multivariate regression analyses can be obtained from the DCLL data.
Table 5: The structural equation code can be obtained from the DCLL structural equation code.
Table 6: Confirmatory factor analysis of each scale has been conducted, and relevant information can be obtained from the document "S1 Confirmatory Factor Analysis of Each Scale".
DOI: 10.17632/bx7wjt95w5.1
title: DCLLdata

description: Table 1: Description of general data can be obtained from the DCL data (general data).
Tables 2, 3, and 4: The scores for each scale, correlation analysis, and univariate and multivariate regression analyses can be obtained from the DCLL data.
Table 5: The structural equation code can be obtained from the DCLL structural equation code.
Table 6: Confirmatory factor analysis of each scale has been conducted, and relevant information can be obtained from the document "S1 Confirmatory Factor Analysis of Each Scale".
DOI: 10.15468/dl.r2y9w5
title: Occurrence Download

description: A dataset containing 4117 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Phyllomyias uropygialis)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 4117 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020614-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/926hgzprx7.1
title: EPM Model Dataset for Bitcoin Pricing

description: This dataset supports the Equilibrium Pricing Model (EPM) for Bitcoin, as described in "What Determines The Price Of Bitcoin? The Bitcoin Equilibrium Pricing Model" (https://ssrn.com/abstract=5363189). It includes monthly M2 data for top 29 economies, daily Bitcoin OHLCV and market cap data, aggregated Bitcoin block statistics, and processed monthly data for the EPM model. Suitable for research in Economics, Finance, Cryptocurrency, Blockchain, and Data Science.
DOI: 10.60635/c3np60
title: Monthly summary_Agua Salud, Celestino Tower

description: Agua Salud Celestino TowerYearly, monthly and daily summary Excel spreadsheet. Contains interval average/maximum/minimum/variance statistics plus summary graphsLocation 9.21142N, 79.72656WParameters: air temperature, relative humidity, wind speed and direction, precipitation, solar radiation (pyranometer, photosynthetically active radiation), soil moisture, net radiation, air pressure, evapotranspiration.Established in 2015 and is located at the eastern edge of the Soberania Nation Park, in an area of pastures and forest patches. The tower is 40ft (12.2m) tall.Historical versions of this data package can be found at https://smithsonian.figshare.com/articles/dataset/Monthly_summary_Agua_Salud_Celestino_Tower/10059467
DOI: 10.5281/zenodo.16754511
title: Disentangling the Impact of Quasiparticles and Two-Level Systems on the Statistics of Superconducting Qubit Lifetime
DOI: 10.5281/zenodo.16754512
title: Disentangling the Impact of Quasiparticles and Two-Level Systems on the Statistics of Superconducting Qubit Lifetime
DOI: 10.17632/ntxzd8krv4
title: Synthetic Time-Series Dataset for Machine Learning-Based Early Detection of Grid Collapse

description: This dataset is a synthetic time-series dataset designed to simulate power grid operations with the goal of training machine learning models for early detection of grid collapses. It captures multi-dimensional features that influence grid stability over time.

Key Characteristics:
Size: 527,040 records (likely representing 1-minute intervals over a full year)

Type: Synthetic data mimicking real-world grid behavior patterns

Purpose: Train ML models to predict grid collapse events

Features:
Temporal Marker:

timestamp: Date and time (minute-level precision)

Grid Operational Metrics:

frequency_hz: Grid frequency (nominally 50Hz)

load_MW: Total power demand (in Megawatts)

gen_gas_MW: Gas-powered generation output

gen_hydro_MW: Hydroelectric generation output

voltage_pu: Voltage in per-unit values (1.0 = nominal)

Environmental Factor:

weather_index: Numeric indicator of weather conditions (negative values suggest severe weather)

Event Flags (Binary):

line_trip: Transmission line failure (0/1)

gen_outage_collapse: Generator outage leading to collapse (target variable)

Observed Patterns:
Shows gradual load fluctuations with corresponding generation adjustments

Frequency deviations from 50Hz suggest grid stress

Voltage variations (0.94-1.06 pu range visible) may indicate instability

Weather index correlates with some operational changes

Machine Learning Relevance:
Enables supervised learning for binary classification (collapse prediction) and regression prediction.

Time-series nature allows for sequence modeling (RNNs, Transformers)

Feature correlations can reveal precursor patterns to collapse

Synthetic nature ensures availability of rare event data (collapses)
DOI: 10.17632/ntxzd8krv4.1
title: Synthetic Time-Series Dataset for Machine Learning-Based Early Detection of Grid Collapse

description: This dataset is a synthetic time-series dataset designed to simulate power grid operations with the goal of training machine learning models for early detection of grid collapses. It captures multi-dimensional features that influence grid stability over time.

Key Characteristics:
Size: 527,040 records (likely representing 1-minute intervals over a full year)

Type: Synthetic data mimicking real-world grid behavior patterns

Purpose: Train ML models to predict grid collapse events

Features:
Temporal Marker:

timestamp: Date and time (minute-level precision)

Grid Operational Metrics:

frequency_hz: Grid frequency (nominally 50Hz)

load_MW: Total power demand (in Megawatts)

gen_gas_MW: Gas-powered generation output

gen_hydro_MW: Hydroelectric generation output

voltage_pu: Voltage in per-unit values (1.0 = nominal)

Environmental Factor:

weather_index: Numeric indicator of weather conditions (negative values suggest severe weather)

Event Flags (Binary):

line_trip: Transmission line failure (0/1)

gen_outage_collapse: Generator outage leading to collapse (target variable)

Observed Patterns:
Shows gradual load fluctuations with corresponding generation adjustments

Frequency deviations from 50Hz suggest grid stress

Voltage variations (0.94-1.06 pu range visible) may indicate instability

Weather index correlates with some operational changes

Machine Learning Relevance:
Enables supervised learning for binary classification (collapse prediction) and regression prediction.

Time-series nature allows for sequence modeling (RNNs, Transformers)

Feature correlations can reveal precursor patterns to collapse

Synthetic nature ensures availability of rare event data (collapses)
DOI: 10.17632/kk49s4zjgz
title: Betting against the odds? The effect of mental health on problem gambling behavior

description: This study investigates mental health as a risk factor for problem gambling using longitudinal data from Australia. We hypothesize that poor mental health increases the likelihood of engaging in problem gambling. Consistent with this hypothesis, we find that individuals experiencing poor mental health are more likely to report problem gambling behavior. These findings highlight the importance of prioritizing mental health alongside, or even above, changes to gambling regulations.

The analysis is based on unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The views expressed in this paper are those of the authors and do not reflect those of the Australian Government Department of Social Services (DSS) or the Melbourne Institute.

Under the terms of the user agreement, HILDA data must not be matched, linked, shared, integrated, or merged with any other dataset without explicit permission from DSS. As such, the dataset cannot be shared. Researchers may apply for access to HILDA data via the Australian Data Archive Dataverse platform. To facilitate replication, the authors provide STATA do-files, including: (1) a file for cleaning the data and constructing the variables used in the analysis, and (2) a file to reproduce the tables reported in the paper. Please follow "Steps to reproduce" below.

For users without access to the HILDA data, a synthetic dataset is provided to enable preliminary analysis. However, regression estimates derived from the synthetic data may differ from those reported in the paper.
DOI: 10.17632/kk49s4zjgz.5
title: Betting against the odds? The effect of mental health on problem gambling behavior

description: This study investigates mental health as a risk factor for problem gambling using longitudinal data from Australia. We hypothesize that poor mental health increases the likelihood of engaging in problem gambling. Consistent with this hypothesis, we find that individuals experiencing poor mental health are more likely to report problem gambling behavior. These findings highlight the importance of prioritizing mental health alongside, or even above, changes to gambling regulations.

The analysis is based on unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The views expressed in this paper are those of the authors and do not reflect those of the Australian Government Department of Social Services (DSS) or the Melbourne Institute.

Under the terms of the user agreement, HILDA data must not be matched, linked, shared, integrated, or merged with any other dataset without explicit permission from DSS. As such, the dataset cannot be shared. Researchers may apply for access to HILDA data via the Australian Data Archive Dataverse platform. To facilitate replication, the authors provide STATA do-files, including: (1) a file for cleaning the data and constructing the variables used in the analysis, and (2) a file to reproduce the tables reported in the paper. Please follow "Steps to reproduce" below.

For users without access to the HILDA data, a synthetic dataset is provided to enable preliminary analysis. However, regression estimates derived from the synthetic data may differ from those reported in the paper.
DOI: 10.15468/dl.nmr89e
title: Occurrence Download

description: A dataset containing 75 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Boiga multomaculata",
 "Country is China",
 "HasCoordinate is true"
 ]
}

 The dataset includes 75 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020617-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/wv5vvd99sv
title: Daily precipitation data from 13 stations in the department of Tacna, Peru

description: This file contains filtered and sorted daily precipitation data from the raw meteorological data, which only appears on the website, of SENAMHI (National Meteorological and Hydrological Service of Peru), for 13 stations (1: 'Jorge Basadre', 2: 'Calana', 3: 'Sama Grande', 4: 'Toquela', 5: 'Palca', 6: 'Paucarani', 7: 'Locumba', 8: 'Chuapalca', 9: 'Tarata', 10: 'Talabaya', 11: 'Sitajara', 12: 'Susapaya', 13: 'Challapalca'), with their respective UTM coordinates and geographic elevation; between the years 1963 and 2015. In column A we find the station ID number; in column B the date in day, month, year format; in column C the precipitation data in mm; in column D the east coordinate of the station; in column E the north coordinate of the station; and finally in column F we have the altitude at which the station is located in meters above sea level. Remember that Tacna is located in time zone 19S. This document was used to characterize, using clusters, the spatiotemporal dependence of extreme precipitation in the arid coastal region of Tacna. If you need filtered historical precipitation information for this region, this file can serve as a reference.
DOI: 10.17632/wv5vvd99sv.1
title: Daily precipitation data from 13 stations in the department of Tacna, Peru

description: This file contains filtered and sorted daily precipitation data from the raw meteorological data, which only appears on the website, of SENAMHI (National Meteorological and Hydrological Service of Peru), for 13 stations (1: 'Jorge Basadre', 2: 'Calana', 3: 'Sama Grande', 4: 'Toquela', 5: 'Palca', 6: 'Paucarani', 7: 'Locumba', 8: 'Chuapalca', 9: 'Tarata', 10: 'Talabaya', 11: 'Sitajara', 12: 'Susapaya', 13: 'Challapalca'), with their respective UTM coordinates and geographic elevation; between the years 1963 and 2015. In column A we find the station ID number; in column B the date in day, month, year format; in column C the precipitation data in mm; in column D the east coordinate of the station; in column E the north coordinate of the station; and finally in column F we have the altitude at which the station is located in meters above sea level. Remember that Tacna is located in time zone 19S. This document was used to characterize, using clusters, the spatiotemporal dependence of extreme precipitation in the arid coastal region of Tacna. If you need filtered historical precipitation information for this region, this file can serve as a reference.
DOI: 10.60635/c3hw42
title: Monthly summary_Bocas Station

description: Bocas del Toro Research StationData Summaries, Excel spread sheet. Provides daily, monthly and yearly summaries. Contains interval averages, maximum, minimum and variability statistics, plus graphsLocation: 9.351553°, -82.256565°Parameters: air temperature, relative humidity, wind speed and direction, precipitation, sea surface temperature, solar radiation (pyranometer), air pressure, dissolved oxygen, turbidity, salinity, pHSensors located on a platform located approximately 60m from the end of the station’s dock. Platform includes a meteorological tower, Sea Surface Temperature probe and several sea level sensors. The current platform was built in 2012 to replace the original platform.Historical datasets can be located here: https://smithsonian.figshare.com/articles/dataset/Monthly_summary_Bocas_Station/10059464
DOI: 10.15468/dl.s735j6
title: Occurrence Download

description: A dataset containing 4488 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Phyllomyias virescens)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 4488 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020615-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h87vgd
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Parotocinclus seridoensis)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020616-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rg64vk8tsz
title: Predicting Macrolide Resistance in Pediatric Mycoplasma pneumoniae Pneumonia: A Machine Learning Modeling Study

description: This is the first externally validated machine learning model to accurately predict macrolide resistance in children with Mycoplasma pneumoniae pneumonia, establishing a data-driven approach to guide personalized antibiotic therapy.