cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29474690
title: Multi-level Supportive Policy Dataset for China's Resource-Based Cities (2003 to 2023)

description: In China’s hierarchical governance system, the central government formulates overarching strategies and targets, while provincial, municipal, and county authorities implement them after making appropriate adjustments based on local resource endowments, administrative capabilities, and development priorities. However, existing research often uses proxy variables for policy factors, and current policy intensity indices predominantly focus on policy objectives and instruments, lacking direct, comprehensive quantification methods. To bridge this gap, this study expands the policy intensity concept to incorporate policy levels, objectives, and instruments, quantified through word frequency analysis. Using 3788 government work reports (2003-2023) across national, provincial, and prefectural levels, we construct a multidimensional supportive policy intensity index. The dataset, available in .dta and .xlsx formats, includes individual policy report inventory and intensity, and aggregated policy intensity across four administrative levels, four objectives, and five policy instruments. When integrated with micro- and macro-level data on green transformation performance and environmental pollution, this dataset offers substantial potential for interdisciplinary research.
DOI: 10.6084/m9.figshare.29474690.v1
title: Multi-level Supportive Policy Dataset for China's Resource-Based Cities (2003 to 2023)

description: In China’s hierarchical governance system, the central government formulates overarching strategies and targets, while provincial, municipal, and county authorities implement them after making appropriate adjustments based on local resource endowments, administrative capabilities, and development priorities. However, existing research often uses proxy variables for policy factors, and current policy intensity indices predominantly focus on policy objectives and instruments, lacking direct, comprehensive quantification methods. To bridge this gap, this study expands the policy intensity concept to incorporate policy levels, objectives, and instruments, quantified through word frequency analysis. Using 3788 government work reports (2003-2023) across national, provincial, and prefectural levels, we construct a multidimensional supportive policy intensity index. The dataset, available in .dta and .xlsx formats, includes individual policy report inventory and intensity, and aggregated policy intensity across four administrative levels, four objectives, and five policy instruments. When integrated with micro- and macro-level data on green transformation performance and environmental pollution, this dataset offers substantial potential for interdisciplinary research.
DOI: 10.5281/zenodo.17499692
title: The Influence of Millennials' Views on Electric Car Adoption: An Analysis of Inhibiting and Driving Factors in the Level of Technology Readiness and Trust in AI Use in Electric Cars
DOI: 10.5281/zenodo.17499693
title: The Influence of Millennials' Views on Electric Car Adoption: An Analysis of Inhibiting and Driving Factors in the Level of Technology Readiness and Trust in AI Use in Electric Cars
DOI: 10.5281/zenodo.17499682
title: Replication package study LLMs pre-/post-conditions

description: Containing a replication package of our study on LLMs' ability to extract pre- and post-conditions of programs.
DOI: 10.5281/zenodo.17499683
title: Replication package study LLMs pre-/post-conditions

description: Containing a replication package of our study on LLMs' ability to extract pre- and post-conditions of programs.
DOI: 10.34740/kaggle/dsv/13574725
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.48512/xcv8517579
title: rim5a series L

description: This excel file provides proveniences for rims for type 5a for observations in Series L.
DOI: 10.48512/xcv8517568
title: rim6d series B

description: This files provides provenience information for rims of type 6d for which observations were made in Series B.
DOI: 10.48512/xcv8517567
title: nonrim6p series B

description: This files provides provenience information for nonrims of type 6p for which observations were made in Series B.
DOI: 10.48512/xcv8517574
title: rim37b series G

description: This file provides provenience information for rims of type 37b for which observations were made in Series G.
DOI: 10.48512/xcv8517558
title: rim3b series A

description: This excel file provides proveniences for type 3b rim observations in subgroup Series A.
DOI: 10.48512/xcv8517566
title: nonrim6d series B

description: This files provides provenience information for nonrims of type 6d for which observations were made in Series B.
DOI: 10.48512/xcv8517555
title: nonrim42b series A

description: This excel file provides proveniences for type 42b observations in subgroup Series A.
DOI: 10.48512/xcv8517573
title: rim37a series F

description: This file provides provenience information for rims of type 37a for which observations were made in Series F.
DOI: 10.48512/xcv8517578
title: rim6m series K

description: This excel file provides proveniences for rims for type 6m for observations in Series K.
DOI: 10.15468/dl.ahxu7t
title: Occurrence Download

description: A dataset containing 15254 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((50.09052 35.27642,87.88177 35.27642,87.88177 54.25002,50.09052 54.25002,50.09052 35.27642))",
 "TaxonKey is Sturnus vulgaris"
 ]
}

 The dataset includes 15254 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010617-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48512/xcv8517576
title: rim60b series I

description: This excel file provides proveniences for rims for type 60b for observations in Series I.
DOI: 10.48512/xcv8517560
title: rim16c series A

description: This file provides proveniences for type 16c rim observations in series A.
DOI: 10.15468/dl.hnpej2
title: Occurrence Download

description: A dataset containing 21051 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((50.09052 35.27642,87.88177 35.27642,87.88177 54.25002,50.09052 54.25002,50.09052 35.27642))",
 "TaxonKey is Acridotheres tristis"
 ]
}

 The dataset includes 21051 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010618-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3y7yeb
title: Occurrence Download

description: A dataset containing 11733 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Junonia lemonias"
 ]
}

 The dataset includes 11733 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010616-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48512/xcv8517569
title: rim6p series B

description: This files provides provenience information for rims of type 6p for which observations were made in Series B.
DOI: 10.48512/xcv8517564
title: rim42b series A

description: This file provides proveniences for type 42b rim observations in series A.
DOI: 10.48512/xcv8517570
title: rim2c series C

description: This file provides provenience information for rims of type 2c for which observations were made in Series C.
DOI: 10.48512/xcv8517552
title: nonrim17b series A

description: This excel file provides proveniences for type 17b observations in subgroup Series A.
DOI: 10.48512/xcv8517565
title: rim42n series A

description: This file provides proveniences for type 42n rim observations in series A.
DOI: 10.48512/xcv8517580
title: nonrim5a series L

description: This excel file provides proveniences for nonrims for type 5a for observations in Series L.
DOI: 10.48512/xcv8517551
title: nonrim16c series A

description: This excel file gives provenience for type 16c nonrims with Series A observations.
DOI: 10.48512/xcv8517572
title: rim29 series E

description: This file provides provenience information for rims of type 29 for which observations were made in Series E.
DOI: 10.48512/xcv8517553
title: nonrim30b series A

description: This excel file provides proveniences for type 30b observations in subgroup Series A.
DOI: 10.48512/xcv8517575
title: rim55b series H

description: This file provides provenience information for rims of type 55b for which observations were made in Series H.
DOI: 10.48512/xcv8517563
title: rim30n series A

description: This file provides proveniences for type 30n rim observations in series A.
DOI: 10.48512/xcv8517557
title: nonrim42n series A

description: This excel file provides proveniences for type 42n observations in subgroup Series A.
DOI: 10.48512/xcv8517562
title: rim30b series A

description: This file provides proveniences for type 30b rim observations in series A.
DOI: 10.48512/xcv8517561
title: rim17b series A

description: This file provides proveniences for type 17b rim observations in series A.
DOI: 10.48512/xcv8517554
title: nonrim30n series A

description: This excel file provides proveniences for type 30n observations in subgroup Series A.
DOI: 10.48512/xcv8517559
title: rim5d series A

description: This file provides proveniences for type 5d rim observations in series A.
DOI: 10.48512/xcv8517577
title: rim6a series J

description: This excel file provides proveniences for rims for type 6a for observations in Series J.
DOI: 10.48512/xcv8517571
title: rim25 series D

description: This file provides provenience information for rims of type 25 for which observations were made in Series D.
DOI: 10.5281/zenodo.14847838
title: National Scale Above-Ground Biomass Density (AGBD) Map in Nepal's Forests at 30m Resolution (Circa 2015)

description: AGBD mapping in Nepal's forests circa 2015 at 30m resolution using Landsat-8, ALOS-PALSAR, and field data within Google Earth Engine. 

Out of 1,010 field-measured AGBD samples, 965 AGBD plots were used to train and validate the machine learning model.

The CART model achieved 88% accuracy (R² = 0.88), with an RMSE of 40.93 Mg ha⁻¹ and an MAE of 27.24 Mg ha⁻¹. 

Nepal's total Forest AGBD is estimated at 1.335 × 1010 Mg (13.35 Pg), with a mean of 173.13 Mg ha⁻¹ and a standard deviation of 112.48 Mg ha⁻¹. 

There is high uncertainty in mountainous areas and low uncertainty in dense forests, which supports conservation efforts. The study promotes REDD+ initiatives and sustainable forest management with open-access AGBD data.
DOI: 10.5281/zenodo.17493215
title: National Scale Above-Ground Biomass Density (AGBD) Map in Nepal's Forests at 30m Resolution (Circa 2015)

description: AGBD mapping in Nepal's forests circa 2015 at 30m resolution using Landsat-8, ALOS-PALSAR, and field data within Google Earth Engine. 

Out of 1,010 field-measured AGBD samples, 965 AGBD plots were used to train and validate the machine learning model.

The CART model achieved 88% accuracy (R² = 0.88), with an RMSE of 40.93 Mg ha⁻¹ and an MAE of 27.24 Mg ha⁻¹. 

Nepal's total Forest AGBD is estimated at 1.335 × 1010 Mg (13.35 Pg), with a mean of 173.13 Mg ha⁻¹ and a standard deviation of 112.48 Mg ha⁻¹. 

There is high uncertainty in mountainous areas and low uncertainty in dense forests, which supports conservation efforts. The study promotes REDD+ initiatives and sustainable forest management with open-access AGBD data.
DOI: 10.6084/m9.figshare.30507293
title: <b>High-Quality SARS-CoV-2 Historical Genomes from China (2020 - 2022): A Curated QC-Passed Dataset (n=157)</b>

description: This dataset comprises <b>157</b> high-quality, near-full-length SARS-CoV-2 genomes from China (Historical Data), rigorously filtered for completeness (≥29,000 nt) and low ambiguity (≤1% N). Sequences are present as individual FASTA files with city-prefixed names (e.g., Wuhan_EPI_ISL_123456.fasta), accompanied by comprehensive metadata. From an initial 9 GB global dataset, only 157 high-quality Chinese genomes met stringent QC criteria, suggesting either limited deposition of complete sequences from China or a high proportion of fragmented, low-coverage submissions in public repositories. This curated set highlights a critical gap in representative genomic surveillance during the pandemic’s early years.Licensed under CC BY-NC 4.0, it permits academic reuse with attribution while prohibiting commercial use, supporting transparent, equitable research on early pandemic viral diversity. Available at: https://doi.org/10.6084/m9.figshare.30507293
DOI: 10.6084/m9.figshare.30507293.v1
title: <b>High-Quality SARS-CoV-2 Historical Genomes from China (2020 - 2022): A Curated QC-Passed Dataset (n=157)</b>

description: This dataset comprises <b>157</b> high-quality, near-full-length SARS-CoV-2 genomes from China (Historical Data), rigorously filtered for completeness (≥29,000 nt) and low ambiguity (≤1% N). Sequences are present as individual FASTA files with city-prefixed names (e.g., Wuhan_EPI_ISL_123456.fasta), accompanied by comprehensive metadata. From an initial 9 GB global dataset, only 157 high-quality Chinese genomes met stringent QC criteria, suggesting either limited deposition of complete sequences from China or a high proportion of fragmented, low-coverage submissions in public repositories. This curated set highlights a critical gap in representative genomic surveillance during the pandemic’s early years.Licensed under CC BY-NC 4.0, it permits academic reuse with attribution while prohibiting commercial use, supporting transparent, equitable research on early pandemic viral diversity. Available at: https://doi.org/10.6084/m9.figshare.30507293
DOI: 10.5281/zenodo.17499857
title: Exploring the Effect of Artificial Intelligence AI Adoption in Higher Education Using UTAUT Questionnaire
DOI: 10.5281/zenodo.17499856
title: Exploring the Effect of Artificial Intelligence AI Adoption in Higher Education Using UTAUT Questionnaire
DOI: 10.5281/zenodo.14600269
title: Reconstructed Chandrayaan-3's ILSA dataset

description: Here, the reconstructed Chandrayaan-3's accelerometer dataset is stored.

Under the leading directory (i.e., Full_reconst_data), CSV data can be found in each date directory (yyyymmdd).

In each CSV file, you can find seven columns, including (a) time in UTC, (b) ILSA sensor temperature data in degrees Celsius, and (c) acceleration data in the XYZ components in nm/s^2.
DOI: 10.5281/zenodo.14600270
title: Reconstructed Chandrayaan-3's ILSA dataset

description: Here, the reconstructed Chandrayaan-3's accelerometer dataset is stored.

Under the leading directory (i.e., Full_reconst_data), CSV data can be found in each date directory (yyyymmdd).

In each CSV file, you can find seven columns, including (a) time in UTC, (b) ILSA sensor temperature data in degrees Celsius, and (c) acceleration data in the XYZ components in nm/s^2.
DOI: 10.15468/dl.e8k2b9
title: Occurrence Download

description: A dataset containing 213228 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "OccurrenceStatus is Present",
 "TaxonKey is Poaceae"
 ]
}

 The dataset includes 213228 records from 216 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010614-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17430460
title: Rain drop size distribution (DSD) measurement dataset for different sites in Norway and in Greenland, 2016-2025

description: This dataset contains the measurement data from two disdrometers of type Parsivel2 (Ott GmbH) that were operated by the Geophysical institute, University of Bergen, Norway in the time period August 2016 to August 2025. The measurement dataset has been prepared for upload to the DISDRODB disdrometer database.

Throughout the majority of the measurement period from 08 August 2016 to 13 August 2025, the disdrometer was installed at the rooftop measurement platform of the Geophysical institute, University of Bergen, Norway (60.3837ºN, 5.3319ºE, 45 m a.s.l.).

During the time period covered by the entire dataset, two identical instruments with different serial numbers were used. Before 08 May 2020, a Parsivel2 disdrometer with serial number 369157 was used. After 08 May 2020, a Parsivel2 disdrometer with serial number 450790 was used. The archived data files at site Bergen-Florida is contains two files, one per instrument used at the site.

For several field measurement campaigns, the disdrometer was taken to other locations for a limited time period. These field installation periods were: 



EastGRIP, Greenland (18.05.2017 to 12.08.2017, 75ºN, 20ºW, 2708 m a.s.l.)

Finse, Norway (12.03.2020 to 08.05.2020, 60.593536ºN, 7.524266ºE, 1222 m a.s.l.)

Oksebasen, Andøya, Norway (16.03.2021 to 30.03.2021, 69.2954ºN, 16.0337ºE, 20m a.s.l.)

Andenes, Andøya, Norway (17.03.2022 to 07.04.2022, 69.2986ºN, 16.0480ºE, 9m a.s.l.)


Separate files are provided for each deployment site.
DOI: 10.5281/zenodo.17430461
title: Rain drop size distribution (DSD) measurement dataset for different sites in Norway and in Greenland, 2016-2025

description: This dataset contains the measurement data from two disdrometers of type Parsivel2 (Ott GmbH) that were operated by the Geophysical institute, University of Bergen, Norway in the time period August 2016 to August 2025. The measurement dataset has been prepared for upload to the DISDRODB disdrometer database.

Throughout the majority of the measurement period from 08 August 2016 to 13 August 2025, the disdrometer was installed at the rooftop measurement platform of the Geophysical institute, University of Bergen, Norway (60.3837ºN, 5.3319ºE, 45 m a.s.l.).

During the time period covered by the entire dataset, two identical instruments with different serial numbers were used. Before 08 May 2020, a Parsivel2 disdrometer with serial number 369157 was used. After 08 May 2020, a Parsivel2 disdrometer with serial number 450790 was used. The archived data files at site Bergen-Florida is contains two files, one per instrument used at the site.

For several field measurement campaigns, the disdrometer was taken to other locations for a limited time period. These field installation periods were: 



EastGRIP, Greenland (18.05.2017 to 12.08.2017, 75ºN, 20ºW, 2708 m a.s.l.)

Finse, Norway (12.03.2020 to 08.05.2020, 60.593536ºN, 7.524266ºE, 1222 m a.s.l.)

Oksebasen, Andøya, Norway (16.03.2021 to 30.03.2021, 69.2954ºN, 16.0337ºE, 20m a.s.l.)

Andenes, Andøya, Norway (17.03.2022 to 07.04.2022, 69.2986ºN, 16.0480ºE, 9m a.s.l.)


Separate files are provided for each deployment site.
DOI: 10.15468/dl.4mwqk9
title: Occurrence Download

description: A dataset containing 3026726 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Italy",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 3026726 records from 499 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010615-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8f69mg
title: Occurrence Download

description: A dataset containing 3500092557 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3500092557 records from 66350 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010580-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/87zg-zh52
title: "Abdominal Aortic Aneurysm Datasets"

description: "abdominal aortic aneurysm"
DOI: 10.6084/m9.figshare.30507296
title: <i>KDM4B/DHX9</i> promotes chemoresistance in small cell lung cancer through <i>MYCN</i>-Driven signaling pathway

description: Small cell lung cancer (SCLC) is prone to developing chemoresistance, which is associated with epigenetic reprogramming. While the involvement of <i>LSD1</i>-mediated histone demethylation has been reported, a systematic investigation into the role of the histone demethylase gene family in SCLC chemoresistance is lacking. In this study, we established nine chemoresistant cell lines from parental cells by gradually increasing drug concentrations and conducted RNA sequencing on these lines. Then, we performed linear regression and random effects meta-analysis on the RNA sequencing data to identify genes associated with chemoresistance among 23 demethylated proteins. Subsequently, we validated our findings using two neuroendocrine cell lines and their corresponding chemoresistant counterparts.
DOI: 10.6084/m9.figshare.30507296.v1
title: <i>KDM4B/DHX9</i> promotes chemoresistance in small cell lung cancer through <i>MYCN</i>-Driven signaling pathway

description: Small cell lung cancer (SCLC) is prone to developing chemoresistance, which is associated with epigenetic reprogramming. While the involvement of <i>LSD1</i>-mediated histone demethylation has been reported, a systematic investigation into the role of the histone demethylase gene family in SCLC chemoresistance is lacking. In this study, we established nine chemoresistant cell lines from parental cells by gradually increasing drug concentrations and conducted RNA sequencing on these lines. Then, we performed linear regression and random effects meta-analysis on the RNA sequencing data to identify genes associated with chemoresistance among 23 demethylated proteins. Subsequently, we validated our findings using two neuroendocrine cell lines and their corresponding chemoresistant counterparts.
DOI: 10.34740/kaggle/dsv/13573507
title: Filtered SQLi 2
DOI: 10.34740/kaggle/dsv/13574894
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.17491203
title: LiDAR Accumulated Snow Dataset (Subset)

description: This dataset contains several LiDAR point cloud samples collected in partially snow-covered environments.Each point cloud includes x, y, z coordinates, intensity, reflectivity, and a binary snow field (1 = snow, 0 = non-snow) for each point.
DOI: 10.5281/zenodo.17491204
title: LiDAR Accumulated Snow Dataset (Subset)

description: This dataset contains several LiDAR point cloud samples collected in partially snow-covered environments.Each point cloud includes x, y, z coordinates, intensity, reflectivity, and a binary snow field (1 = snow, 0 = non-snow) for each point.