cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.iga.00571
title: Heilongjiang Province 1:3 million Electric Power Industry Distribution Dataset (1976)
title: 黑龙江省1：300万电力工业分布数据集(1976年)

description: a. Data content (data file/table name, including observation index content)

Data file name: Heilongjiang Province 1:3 million Electric Power Industry Distribution Dataset (1976)

Main content: Distribution point of power industry_ Data entity.shp

b. Construction purpose

Understanding the distribution and supply of electricity in Heilongjiang Province provides a prerequisite for China's economic development.

c. Service object

Students and researchers engaged in agricultural ecological research and environmental pollution.

d. Time range of data

The data is from 1976.

e. The spatial range and projection method of data

Space scope: Heilongjiang Province.
description: a. Data content (data file/table name, including observation index content)

Data file name: Heilongjiang Province 1:3 million Electric Power Industry Distribution Dataset (1976)

Main content: Distribution point of power industry_ Data entity.shp

b. Construction purpose

Understanding the distribution and supply of electricity in Heilongjiang Province provides a prerequisite for China's economic development.

c. Service object

Students and researchers engaged in agricultural ecological research and environmental pollution.

d. Time range of data

The data is from 1976.

e. The spatial range and projection method of data

Space scope: Heilongjiang Province.
DOI: 10.57760/sciencedb.iga.00572
title: Heilongjiang Province 1:3 Million Coal Industry Distribution Dataset (1976)
title: 黑龙江省1:300万煤炭工业分布数据集(1976年)

description: a. Data content (data file/table name, including observation index content)

Data File Name: Heilongjiang Province 1:3 Million Coal Industry Distribution Dataset (1976)

Main content:

This dataset reflects the distribution of coal industry in Heilongjiang Province in 1976, including one data entity: Heilongjiang Province 1:3 million coal industry distribution dataset (1976) - data entity. shp

b. Construction purpose

Understand the distribution and supply of coal energy in Heilongjiang Province, and provide data support for energy distribution, environmental pollution, soil pollution, etc. in China.

c. Service object

Students and researchers engaged in disciplines such as mineralogy, energy science, and environment.

d. Time range of data

The data is from 1976.

e. The spatial range and projection method of data

Space scope: Heilongjiang Province
description: a. Data content (data file/table name, including observation index content)

Data File Name: Heilongjiang Province 1:3 Million Coal Industry Distribution Dataset (1976)

Main content:

This dataset reflects the distribution of coal industry in Heilongjiang Province in 1976, including one data entity: Heilongjiang Province 1:3 million coal industry distribution dataset (1976) - data entity. shp

b. Construction purpose

Understand the distribution and supply of coal energy in Heilongjiang Province, and provide data support for energy distribution, environmental pollution, soil pollution, etc. in China.

c. Service object

Students and researchers engaged in disciplines such as mineralogy, energy science, and environment.

d. Time range of data

The data is from 1976.

e. The spatial range and projection method of data

Space scope: Heilongjiang Province
DOI: 10.57760/sciencedb.j00186.00287
title: Data Support of Generalization Ability of a CNN γ-ray Localization Model for Radiation Imaging
title: Data Support of Generalization Ability of a CNN γ-ray Localization Model for Radiation Imaging

description: In γ-ray imaging, localization of the γ-ray interaction in the scintillator is critical. Convolutional neural network (CNN) techniques are highly promising for improving γ-ray localization. Our study evaluated the generalization capabilities of a CNN localization model with respect to the γ-ray energy and thickness of the crystal. The model maintained a high positional linearity (PL) and spatial resolution (SR) for ray energies between 59–1460 keV. The PL at the incident surface of the detector was 0.99, and the resolution of the central incident point source ranged between 0.52–1.19 mm. In modified uniform redundant array (MURA) imaging systems using a thick crystal, the CNN γ-ray localization model significantly improved the useful field-of-view (UFOV) from 60.32% to 93.44% compared to the classical centroid localization methods. Additionally, the signal-to-noise ratio (SNR) of the reconstructed images increased from 0.95 to 5.63.
description: In γ-ray imaging, localization of the γ-ray interaction in the scintillator is critical. Convolutional neural network (CNN) techniques are highly promising for improving γ-ray localization. Our study evaluated the generalization capabilities of a CNN localization model with respect to the γ-ray energy and thickness of the crystal. The model maintained a high positional linearity (PL) and spatial resolution (SR) for ray energies between 59–1460 keV. The PL at the incident surface of the detector was 0.99, and the resolution of the central incident point source ranged between 0.52–1.19 mm. In modified uniform redundant array (MURA) imaging systems using a thick crystal, the CNN γ-ray localization model significantly improved the useful field-of-view (UFOV) from 60.32% to 93.44% compared to the classical centroid localization methods. Additionally, the signal-to-noise ratio (SNR) of the reconstructed images increased from 0.95 to 5.63.
DOI: 10.57760/sciencedb.space.01122
title: Velocity data observed by moorings at 140°E, 4.7°N and 142°E, 4.7°N
title: Velocity data observed by moorings at 140°E, 4.7°N and 142°E, 4.7°N

description: This dataset contains the velocity data observed by moorings at 140°E, 4.7°N and 142°E, 4.7°N . It is supplementary of the paper "A New North Intermediate Countercurrent Long-Termly Observed in the Western Pacific" submitted to the Geophysical Research Letters. If you need any more information or want to utilize the data for research purpose, please contact the author first (Dr. Jianing Wang; E-mail: wjn@qdio.ac.cn).
description: This dataset contains the velocity data observed by moorings at 140°E, 4.7°N and 142°E, 4.7°N . It is supplementary of the paper "A New North Intermediate Countercurrent Long-Termly Observed in the Western Pacific" submitted to the Geophysical Research Letters. If you need any more information or want to utilize the data for research purpose, please contact the author first (Dr. Jianing Wang; E-mail: wjn@qdio.ac.cn).
DOI: 10.57760/sciencedb.12137
title: Hispanic political behavior
title: Hispanic political behavior

description: Survey data collected from Texas in 2018
description: Survey data collected from Texas in 2018
DOI: 10.57760/sciencedb.12139
title: The signal decomposition feature dataset of WD patients and healthy individuals
title: The signal decomposition feature dataset of WD patients and healthy individuals

description: The three files features_DMFCC.pkl, features_HCC.pkl, and features_MFCC.pkl are the improved Mel-frequency Cepstrum Coefficient using signal decomposition feature file, Hilbert Cepstrum Coefficient feature file, and Mel-frequency Cepstrum Coefficient feature file, respectively. The feature data file contains 120 native Mandarin-speaking participants, including 60 WD patients and 60 sex-matched healthy individuals. The first five columns and the last column of data for all the feature data files are ID number, label (0 for healthy individuals and 1 for WD patients), age, sex one-hot code (gender_0=1 for females and gender_1=1 for males), and Frenchay dysarthria scale scores, respectively. The features_DMFCC.pkl and features_HCC.pkl files have VMD, CEEMDAN, and EMD feature columns representing complete ensemble EMD with adaptive noise features, variational mode decomposition features, and empirical mode decomposition features, respectively. The features_MFCC.pkl file has MFCC feature columns representing Mel-frequency cepstrum coefficients features.
description: The three files features_DMFCC.pkl, features_HCC.pkl, and features_MFCC.pkl are the improved Mel-frequency Cepstrum Coefficient using signal decomposition feature file, Hilbert Cepstrum Coefficient feature file, and Mel-frequency Cepstrum Coefficient feature file, respectively. The feature data file contains 120 native Mandarin-speaking participants, including 60 WD patients and 60 sex-matched healthy individuals. The first five columns and the last column of data for all the feature data files are ID number, label (0 for healthy individuals and 1 for WD patients), age, sex one-hot code (gender_0=1 for females and gender_1=1 for males), and Frenchay dysarthria scale scores, respectively. The features_DMFCC.pkl and features_HCC.pkl files have VMD, CEEMDAN, and EMD feature columns representing complete ensemble EMD with adaptive noise features, variational mode decomposition features, and empirical mode decomposition features, respectively. The features_MFCC.pkl file has MFCC feature columns representing Mel-frequency cepstrum coefficients features.
DOI: 10.57760/sciencedb.12141
title: The test-retest speech feature data
title: The test-retest speech feature data

description: This study uses intra-class correlation coefficients to evaluate the test-retest reliability of speech features of commonly used speech tasks in a healthy population.The feature data file contains 40 participants whose native language is Mandarin, including 25 males and 15 females. The Excel file shows the raw data of participants' speech features, where the id is the participant's number, the session is the participant's measurement of two tests, and subsequent columns represent the names of speech features.Based on the extracted speech feature data, calculate the intra-class correlation values of each feature to evaluate the retest reliability of each speech feature.
description: This study uses intra-class correlation coefficients to evaluate the test-retest reliability of speech features of commonly used speech tasks in a healthy population.The feature data file contains 40 participants whose native language is Mandarin, including 25 males and 15 females. The Excel file shows the raw data of participants' speech features, where the id is the participant's number, the session is the participant's measurement of two tests, and subsequent columns represent the names of speech features.Based on the extracted speech feature data, calculate the intra-class correlation values of each feature to evaluate the retest reliability of each speech feature.
DOI: 10.57760/sciencedb.12142
title: Language Feature Dataset for Detecting Alzheimer's Disease
title: Language Feature Dataset for Detecting Alzheimer's Disease

description: This study explores the effectiveness of Automatic Speech Recognition (ASR) in building end-to-end automatic speech diagnosis and prediction models. We implemented three publicly available ASR engines including Xunfei, Tencent, and Aliyun, and compared the classifiability using the ADReSS-IS2020 public dataset (https://dementia.talkbank.org/). The dataset is a balanced subset selected from the Pitt corpus in the DementiaBank database with the effects of gender and age bias removed. The provided feature file name is composed of the ASR engine name and the data collection category. Our feature data file contains 157 native English-speaking participants, including 78 AD patients and 78 healthy individuals. The test set division for classification was officially provided, where the training set contained 108 participants and the test set contained 48 testers. The data columns contain the sex and label of the participants and the names of the extracted acoustic and textual features. Here we have used only textual features for all the experiments.
description: This study explores the effectiveness of Automatic Speech Recognition (ASR) in building end-to-end automatic speech diagnosis and prediction models. We implemented three publicly available ASR engines including Xunfei, Tencent, and Aliyun, and compared the classifiability using the ADReSS-IS2020 public dataset (https://dementia.talkbank.org/). The dataset is a balanced subset selected from the Pitt corpus in the DementiaBank database with the effects of gender and age bias removed. The provided feature file name is composed of the ASR engine name and the data collection category. Our feature data file contains 157 native English-speaking participants, including 78 AD patients and 78 healthy individuals. The test set division for classification was officially provided, where the training set contained 108 participants and the test set contained 48 testers. The data columns contain the sex and label of the participants and the names of the extracted acoustic and textual features. Here we have used only textual features for all the experiments.
DOI: 10.15468/dl.vk4ypk
title: Occurrence Download

description: A dataset containing 2812162374 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2812162374 records from 48977 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000717-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d8tuku
title: Occurrence Download

description: A dataset containing 25352 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chenopodium ambrosioides L."
 ]
}

 The dataset includes 25352 records from 658 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013271-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ydf8dd
title: Occurrence Download

description: A dataset containing 22323 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Buceros bicornis)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 22323 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084231-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f6v9f2
title: Occurrence Download

description: A dataset containing 18392 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 (Spilopelia senegalensis)",
 "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 18392 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084232-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2fuhyt
title: Occurrence Download

description: A dataset containing 1854 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Buceros hydrocorax)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 1854 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084233-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26180/28845839
title: Automatic helpdesk response generation

description: This research investigates corpus-based methods for the automation of help-desk e-mail responses.
DOI: 10.26180/28845839.v1
title: Automatic helpdesk response generation

description: This research investigates corpus-based methods for the automation of help-desk e-mail responses.
DOI: 10.5281/zenodo.18237163
title: Adiantum pseudotinctum Hieron. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:30
DOI: 10.5281/zenodo.18237162
title: Adiantum pseudotinctum Hieron. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:30
DOI: 10.15468/dl.hn94cn
title: Occurrence Download

description: A dataset containing 366 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyclopia subternata Vogel"
 ]
}

 The dataset includes 366 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077628-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d2d3wu
title: Occurrence Download

description: A dataset containing 1140 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((18.83504 -34.31464,26.04546 -34.31464,26.04546 -31.94882,18.83504 -31.94882,18.83504 -34.31464))",
 "HasGeospatialIssue is false",
 "TaxonKey is Pinus L."
 ]
}

 The dataset includes 1140 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077626-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jag9bk
title: Occurrence Download

description: A dataset containing 2679 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhynchophorus ferrugineus (A.G.Olivier, 1791)",
 "Year 1970-2024"
 ]
}

 The dataset includes 2679 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077625-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nar3jn
title: Occurrence Download

description: A dataset containing 1419 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((18.6927 -34.28435,25.60379 -34.28435,25.60379 -31.88575,18.6927 -31.88575,18.6927 -34.28435))",
 "HasGeospatialIssue is false",
 "TaxonKey is Hakea Schrad."
 ]
}

 The dataset includes 1419 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077627-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v9hfcy
title: Occurrence Download

description: A dataset containing 11218983 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is China"
 ]
}

 The dataset includes 11218983 records from 5170 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077506-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p9ujfxa5
title: CE-QUAL-W2 models for select U.S. Army Corps of Engineers reservoirs in the Willamette Valley Project and an inter-reservoir reach of the Middle Fork Willamette River, northwestern Oregon, 2011, 2015, and 2016 (ver. 1.1, May 2025)

description: The Willamette Valley Project (WVP) is a system of revetments, fish hatcheries, and 13 dams in the Willamette Basin of northwestern Oregon that is operated by the U.S. Army Corps of Engineers to provide flood risk management, irrigation, power generation, water quality improvement, and recreational opportunities, among other authorized purposes. By reducing available habitat and altering the natural hydrologic and thermal regimes in the Willamette Basin, the WVP has negatively influenced native populations of anadromous fish, including spring-run Chinook salmon (Oncorhynchus tshawytscha) and winter-run steelhead (O. mykiss), which were designated as threatened under the Endangered Species Act of 1973 (Public Law 93–205, 87 Stat. 884, as amended) in 1999. CE-QUAL-W2, a two-dimensional, hydrodynamic water quality model, has been used to simulate and analyze the temperature of water released from key Willamette Valley Project dams. and the effect on river reaches downstream that might result from a variety of theoretical management scenarios. This data release includes input, output, and calibration files for CE-QUAL-W2 models of Hills Creek Lake and Hills Creek Dam, the Middle Fork Willamette River between Hills Creek Dam and Lookout Point Lake, Lookout Point Lake and Lookout Point Dam, Dexter Lake and Dexter Dam, Cougar Reservoir and Cougar Dam, Green Peter Lake and Green Peter Dam, Foster Lake and Foster Dam, Detroit Lake and Detroit Dam, and Big Cliff Lake and Big Cliff Dam. The models, built by other researchers in the early to-mid-2000s to simulate a disparate range of time periods, were upgraded to CE-QUAL-W2 version 4.2 with additional USGS modifications. Models are set up to run from January through December of 2011, 2015, and 2016 except where truncated for the purposes of model stability. CE-QUAL-W2 models in this data release can be combined with CE-QUAL-W2 river models of the Fall Creek and the Coast Fork Willamette, Middle Fork Willamette, Row, South Fork McKenzie, McKenzie, South Santiam, North Santiam, Santiam, and Willamette Rivers, documented in OFR 2022-1017 (see External Sources), to run model scenarios for an "integrated, basin-wide" model of the Willamette Valley Project.
 
First release: 2023
Revised: May 2025 (ver. 1.1)