cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/kvds2gmw5h
title: GLP- 1 receptor Agonists in patients with cancer are associated with reduced all-cause mortality and hospitalization. eTable 1.

description: Supplemental Table for Manuscript "GLP- 1 Receptor Agonists in Patients with Cancer are Associated with Reduced All-Cause Mortality and Hospitalization". DOI:10.1210/clinem/dgaf703
DOI: 10.17632/kvds2gmw5h.1
title: GLP- 1 receptor Agonists in patients with cancer are associated with reduced all-cause mortality and hospitalization. eTable 1.

description: Supplemental Table for Manuscript "GLP- 1 Receptor Agonists in Patients with Cancer are Associated with Reduced All-Cause Mortality and Hospitalization". DOI:10.1210/clinem/dgaf703
DOI: 10.6084/m9.figshare.31149136
title: <b>Recent Sharp Increase in Greenhouse Gas Emissions from Chinese Reservoirs exacerbated by </b><b>Climate Change</b>

description: Reservoirs are increasingly recognized as significant sources of greenhouse gas (GHG) emissions, yet their long-term emission trajectories remain poorly constrained, particularly at national scales. Here we compile a comprehensive dataset comprising 5,054 measurements of CO<sub>2</sub>, 2,679 of CH<sub>4</sub>, and 1,184 of N<sub>2</sub>O concentrations to quantify monthly GHG emissions from reservoirs across China during 2000–2020. By integrating machine learning based upscaling with monthly reservoir surface area dynamics, we show that total reservoir GHG emissions increased continuously over the past two decades, at a mean rate of 0.76 ± 0.07 Tg CO<sub>2</sub> eq yr⁻¹. Emission growth accelerated markedly after 2010, culminating in peak emissions of 52.6 ± 11.2 Tg CO<sub>2</sub> eq in 2020. Spatially, southeastern China emerged as a hotspot of emission intensification, driven primarily by rising air temperatures and declining precipitation. Given projected climate warming and hydroclimatic shifts, GHG emissions from reservoirs are likely to further intensify. Our findings highlight an increasingly important and previously overlooked role of reservoirs in the regional and global carbon cycle
DOI: 10.6084/m9.figshare.31149136.v1
title: <b>Recent Sharp Increase in Greenhouse Gas Emissions from Chinese Reservoirs exacerbated by </b><b>Climate Change</b>

description: Reservoirs are increasingly recognized as significant sources of greenhouse gas (GHG) emissions, yet their long-term emission trajectories remain poorly constrained, particularly at national scales. Here we compile a comprehensive dataset comprising 5,054 measurements of CO<sub>2</sub>, 2,679 of CH<sub>4</sub>, and 1,184 of N<sub>2</sub>O concentrations to quantify monthly GHG emissions from reservoirs across China during 2000–2020. By integrating machine learning based upscaling with monthly reservoir surface area dynamics, we show that total reservoir GHG emissions increased continuously over the past two decades, at a mean rate of 0.76 ± 0.07 Tg CO<sub>2</sub> eq yr⁻¹. Emission growth accelerated markedly after 2010, culminating in peak emissions of 52.6 ± 11.2 Tg CO<sub>2</sub> eq in 2020. Spatially, southeastern China emerged as a hotspot of emission intensification, driven primarily by rising air temperatures and declining precipitation. Given projected climate warming and hydroclimatic shifts, GHG emissions from reservoirs are likely to further intensify. Our findings highlight an increasingly important and previously overlooked role of reservoirs in the regional and global carbon cycle
DOI: 10.17632/vr59sp8f6z
title: Replication data for: The International Transmission of Asset Market Shocks in Liquidity Traps

description: This dataset contains the full replication package for the paper “The International Transmission of Asset Market Shocks in Liquidity Traps”, including all MATLAB and Dynare code required to reproduce the figures and simulations reported in the paper and its appendices.
DOI: 10.17632/vr59sp8f6z.1
title: Replication data for: The International Transmission of Asset Market Shocks in Liquidity Traps

description: This dataset contains the full replication package for the paper “The International Transmission of Asset Market Shocks in Liquidity Traps”, including all MATLAB and Dynare code required to reproduce the figures and simulations reported in the paper and its appendices.
DOI: 10.17632/wxnmgt7khb
title: Supplementary Table I: Postmarketing Safety of Bimekizumab for Hidradenitis Suppurativa: An Early Pharmacovigilance Analysis Using the FDA Adverse Event Reporting System

description: Clinical Characteristics of Adverse Event (AE) Reports Associated with Bimekizumab in the treatment of Hidradenitis Suppurativa (n=239 AE reports total). AE, Adverse Event.
DOI: 10.17632/wxnmgt7khb.1
title: Supplementary Table I: Postmarketing Safety of Bimekizumab for Hidradenitis Suppurativa: An Early Pharmacovigilance Analysis Using the FDA Adverse Event Reporting System

description: Clinical Characteristics of Adverse Event (AE) Reports Associated with Bimekizumab in the treatment of Hidradenitis Suppurativa (n=239 AE reports total). AE, Adverse Event.
DOI: 10.17632/f68vnm7t7w
title: DeepfakeWatch

description: Deepfake technology is a threat to information integrity across social media platforms. Understanding how the public views and talks about these manipulations produced by AI is critical in the development of methods for countering them. However, manually analysing thousands of comments is labour-intensive and requires special knowledge. This dataset allows one to analyze the public stance and discourse patterns around deepfake content.
I have extracted 2351 English comments from 41 YouTube videos that are about deepfakes between July 2025 and January 2026. The classification of each comment was done using BART-large-mnli for the detection of the stance and identification of the claims. Comments are assigned to four different types of stance: believesreal (where the user had concerns about threats from deepfake use), believesfake (where the user is skeptical), uncertain (where the user is doubtful), and metadiscussion (the user's general commentary about the situation). Six types of claims were identified: detectionliteracy (spotting techniques), voicefraud (audio cloning), warpropaganda (military context), celebrity (impersonation), scamfinance (financial fraud), and electionpolitics (political manipulation).
Topic modeling using NMF identified 8 discussion themes (0.53 coherence), much better than LDA alternatives. We were able to track temporal changes over seven months using the divergence measure called Jensen-Shannon divergence, and we found empirical evidence of significant change points for discourse patterns. Privacy is ensured with SHA-256 hashing of any and all identifiers.
This dataset serves these research areas of computational social science, misinformation detection, and content moderation. Researchers can use it for training models for the detection of stance or analyzing the evolution of discourse or the public awareness of AI-generated content.


1. Primary Dataset:
Records: 2,351 comments
Format: CSV
Columns: 18 (video_id, comment_id_hash, month, like_count, spam_score, stance_label, stance_conf, claim_type, claim_conf, topic_id_nmf, topic_p0-p7_nmf)

2. Stance Distribution:
meta_discussion: 1,264 (53.8%)
uncertain: 484 (20.6%)
believes_fake: 360 (15.3%)
believes_real: 243 (10.3%)

3. Claim Type Distribution:
detection_literacy: 748 (31.8%)
voice_fraud: 565 (24.0%)
war_propaganda: 416 (17.7%)
celebrity: 373 (15.9%)
scam_finance: 137 (5.8%)
election_politics: 112 (4.8%)

4. Topic Model:
Algorithm: NMF (k=8)
Coherence: 0.5310
Features: TF-IDF vectors
Topics: AI-generated content, fake news debates, war propaganda, military authenticity, social discourse, Ukraine narrative, bot accusations, South Asian discussions

5. Quality Metrics:
Duplicates: 0
Missing values: 0
Spam flagged: 9.45% (retained with scores)
Video coverage: 32.8% (41/125 videos)

6. Documentation:
README.md: Complete methodology and usage guide
CODEBOOK.md: All variable definitions and distributions
DOI: 10.17632/f68vnm7t7w.1
title: DeepfakeWatch

description: Deepfake technology is a threat to information integrity across social media platforms. Understanding how the public views and talks about these manipulations produced by AI is critical in the development of methods for countering them. However, manually analysing thousands of comments is labour-intensive and requires special knowledge. This dataset allows one to analyze the public stance and discourse patterns around deepfake content.
I have extracted 2351 English comments from 41 YouTube videos that are about deepfakes between July 2025 and January 2026. The classification of each comment was done using BART-large-mnli for the detection of the stance and identification of the claims. Comments are assigned to four different types of stance: believesreal (where the user had concerns about threats from deepfake use), believesfake (where the user is skeptical), uncertain (where the user is doubtful), and metadiscussion (the user's general commentary about the situation). Six types of claims were identified: detectionliteracy (spotting techniques), voicefraud (audio cloning), warpropaganda (military context), celebrity (impersonation), scamfinance (financial fraud), and electionpolitics (political manipulation).
Topic modeling using NMF identified 8 discussion themes (0.53 coherence), much better than LDA alternatives. We were able to track temporal changes over seven months using the divergence measure called Jensen-Shannon divergence, and we found empirical evidence of significant change points for discourse patterns. Privacy is ensured with SHA-256 hashing of any and all identifiers.
This dataset serves these research areas of computational social science, misinformation detection, and content moderation. Researchers can use it for training models for the detection of stance or analyzing the evolution of discourse or the public awareness of AI-generated content.


1. Primary Dataset:
Records: 2,351 comments
Format: CSV
Columns: 18 (video_id, comment_id_hash, month, like_count, spam_score, stance_label, stance_conf, claim_type, claim_conf, topic_id_nmf, topic_p0-p7_nmf)

2. Stance Distribution:
meta_discussion: 1,264 (53.8%)
uncertain: 484 (20.6%)
believes_fake: 360 (15.3%)
believes_real: 243 (10.3%)

3. Claim Type Distribution:
detection_literacy: 748 (31.8%)
voice_fraud: 565 (24.0%)
war_propaganda: 416 (17.7%)
celebrity: 373 (15.9%)
scam_finance: 137 (5.8%)
election_politics: 112 (4.8%)

4. Topic Model:
Algorithm: NMF (k=8)
Coherence: 0.5310
Features: TF-IDF vectors
Topics: AI-generated content, fake news debates, war propaganda, military authenticity, social discourse, Ukraine narrative, bot accusations, South Asian discussions

5. Quality Metrics:
Duplicates: 0
Missing values: 0
Spam flagged: 9.45% (retained with scores)
Video coverage: 32.8% (41/125 videos)

6. Documentation:
README.md: Complete methodology and usage guide
CODEBOOK.md: All variable definitions and distributions
DOI: 10.17632/9cx9psssds.1
title: QTNano - Theoretical Insights into Methane Activation on Transition-Metal Single-Atom Catalysts Supported on the CeO2(111) Surface, The Journal of Physical Chemistry C, 127, 33, 16357-16366 (2023)

description: Raw data of the published paper - https://doi.org/10.1021/acs.jpcc.3c02653 Cite this: J. Phys. Chem. C 2023, 127, 33, 16357–16366
DOI: 10.17632/skf2mxd5h8
title: Knee OA Dataset

description: De-identified individual patient data of RCT
DOI: 10.17632/skf2mxd5h8.1
title: Knee OA Dataset

description: De-identified individual patient data of RCT
DOI: 10.17632/24nfj2vk3s
title: Potential Toxic Elements (PTEs) Dataset of Surface Sediments in the Sanjiangyuan Region

description: 1. Dataset Title
Potential Toxic Elements (PTEs) Dataset of Surface Sediments in the Sanjiangyuan Region
2. Authors & Affiliation
Dongdong Chen, Qi Li, Yukun Zhang, Li Zhang, Fuquan He, Quanbang Li, Liang Zhao (Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China)
Xin Chen (Xingzhi College, Zhejiang Normal University, Jinhua 321000, China) 
Zongjian Zhao (Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China; Graduate University of Chinese Academy of Sciences, Beijing 100049, China) 
3. Contact Information
Corresponding Author: Liang Zhao
E-mail: lzhao@nwipb.cas.cn
Affiliation: Northwest Institute of Plateau Biology, Chinese Academy of Sciences
Address: Xining 810008, Qinghai Province, China
4. Geographic Coverage
4.1 Study Area
Sanjiangyuan Region, located in the hinterland of the Qinghai-Tibet Plateau (31°39′-36°16′ N, 89°24′-102°23′ E), southern Qinghai Province, China. As the source area of the Yellow River, Yangtze River, and Lancang River (Mekong River), it is known as "China's Water Tower". It plays a critical role in the national ecological security barrier.
4.2 Sampling Site Distribution
Sampling sites cover the main streams and tributaries of three major river systems, with a total of 48 sampling points (Figure 1 in the associated manuscript):
5. Data Content and Structure
The dataset is stored as an Excel file with 4 worksheets, integrating raw concentration data, pollution assessment indices, and source apportionment results. All data are complete (no missing values) and consistent with the analytical results in the associated manuscript.
6. Data Collection and Processing Methods
6.1 Sample Collection
Medium: Surface sediments collected via the multi-point mixing method.
Preservation: Samples sealed in acid-washed polyethylene bags, stored at 4°C, air-dried, and sieved through a 0.15 mm mesh in the laboratory.
6.2 Laboratory Analysis
Instrumentation: Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES) for Cr, Ni, Cu, Zn; atomic fluorescence spectrometry for Hg, As, Pb, Cd (compliant with Chinese national standards GB/T 17141—1997, GB/T 22105.1-2008, GB/T 22105.2—2008).
Quality Control: Method Detection Limits (MDLs): Hg=0.002 mg kg-1, As=0.01 mg kg-1, Pb=17.50 mg kg-1, Cr=7.500 mg kg-1, Cd=0.050 mg kg-1, Ni=1.750 mg kg-1, Cu=1.500 mg kg-1g, Zn=1.000 mg kg-1; recovery rate: 85%–115%.
6.3 Data Calculation
Enrichment Factor (EF): Normalized by Ni (reference element) with background values from Chinese fluvial sediments.
Geo-accumulation Index (Igeo): Igeo = log₂(Cₙ/(1.5×Bₙ)), where Cₙ=measured concentration, Bₙ=background value.
PMF Model: EPA-PMF 5.0 with 5 factors (Qrobust/Qtrue=60.3, residuals within [-3, 3]).
7. Data Format
File Type: Microsoft Excel (.xlsx)
Compression: Uncompressed (single file)
Encoding: UTF-8

8. Acknowledgment
This work was supported by the Chief Scientist Program of Qinghai Province (2024-SF-102).
DOI: 10.17632/24nfj2vk3s.1
title: Potential Toxic Elements (PTEs) Dataset of Surface Sediments in the Sanjiangyuan Region

description: 1. Dataset Title
Potential Toxic Elements (PTEs) Dataset of Surface Sediments in the Sanjiangyuan Region
2. Authors & Affiliation
Dongdong Chen, Qi Li, Yukun Zhang, Li Zhang, Fuquan He, Quanbang Li, Liang Zhao (Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China)
Xin Chen (Xingzhi College, Zhejiang Normal University, Jinhua 321000, China) 
Zongjian Zhao (Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China; Graduate University of Chinese Academy of Sciences, Beijing 100049, China) 
3. Contact Information
Corresponding Author: Liang Zhao
E-mail: lzhao@nwipb.cas.cn
Affiliation: Northwest Institute of Plateau Biology, Chinese Academy of Sciences
Address: Xining 810008, Qinghai Province, China
4. Geographic Coverage
4.1 Study Area
Sanjiangyuan Region, located in the hinterland of the Qinghai-Tibet Plateau (31°39′-36°16′ N, 89°24′-102°23′ E), southern Qinghai Province, China. As the source area of the Yellow River, Yangtze River, and Lancang River (Mekong River), it is known as "China's Water Tower". It plays a critical role in the national ecological security barrier.
4.2 Sampling Site Distribution
Sampling sites cover the main streams and tributaries of three major river systems, with a total of 48 sampling points (Figure 1 in the associated manuscript):
5. Data Content and Structure
The dataset is stored as an Excel file with 4 worksheets, integrating raw concentration data, pollution assessment indices, and source apportionment results. All data are complete (no missing values) and consistent with the analytical results in the associated manuscript.
6. Data Collection and Processing Methods
6.1 Sample Collection
Medium: Surface sediments collected via the multi-point mixing method.
Preservation: Samples sealed in acid-washed polyethylene bags, stored at 4°C, air-dried, and sieved through a 0.15 mm mesh in the laboratory.
6.2 Laboratory Analysis
Instrumentation: Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES) for Cr, Ni, Cu, Zn; atomic fluorescence spectrometry for Hg, As, Pb, Cd (compliant with Chinese national standards GB/T 17141—1997, GB/T 22105.1-2008, GB/T 22105.2—2008).
Quality Control: Method Detection Limits (MDLs): Hg=0.002 mg kg-1, As=0.01 mg kg-1, Pb=17.50 mg kg-1, Cr=7.500 mg kg-1, Cd=0.050 mg kg-1, Ni=1.750 mg kg-1, Cu=1.500 mg kg-1g, Zn=1.000 mg kg-1; recovery rate: 85%–115%.
6.3 Data Calculation
Enrichment Factor (EF): Normalized by Ni (reference element) with background values from Chinese fluvial sediments.
Geo-accumulation Index (Igeo): Igeo = log₂(Cₙ/(1.5×Bₙ)), where Cₙ=measured concentration, Bₙ=background value.
PMF Model: EPA-PMF 5.0 with 5 factors (Qrobust/Qtrue=60.3, residuals within [-3, 3]).
7. Data Format
File Type: Microsoft Excel (.xlsx)
Compression: Uncompressed (single file)
Encoding: UTF-8

8. Acknowledgment
This work was supported by the Chief Scientist Program of Qinghai Province (2024-SF-102).
DOI: 10.17632/f7gfj8zmgd
title: A New Family of 2D Tungsten–Group-13 Monolayers Exhibiting Antiferromagnetic and Ferromagnetic

description: This dataset contains the complete set of VASP 6.3.0 input and output files supporting the prediction of the novel two-dimensional tungsten–group-13 monolayers (WX3, where X = Ga, In, Tl) presented in the associated paper. It encompasses the computational workflow including geometry relaxations, self-consistent field calculations, and non-self-consistent band structure and density of states computations, all performed using the GGA-PBE+U functional with spin–orbit coupling to accurately model the electronic and magnetic properties. The data directly demonstrates the stable structures, intrinsic ferromagnetic and antiferromagnetic order, and the key finding of giant magnetic anisotropy energy (MAE), calculated from total energy differences for specific magnetization axes. The provided results also include the estimated magnetic critical temperatures (Curie/Néel temperatures), which were determined by parameterizing a Heisenberg spin model with our DFT-calculated exchange constants and solving it using Monte Carlo simulations. This dataset is structured by material and calculation type to enable full verification, reproduction, and further exploration of these 2D magnetic systems.
DOI: 10.17632/f7gfj8zmgd.1
title: A New Family of 2D Tungsten–Group-13 Monolayers Exhibiting Antiferromagnetic and Ferromagnetic

description: This dataset contains the complete set of VASP 6.3.0 input and output files supporting the prediction of the novel two-dimensional tungsten–group-13 monolayers (WX3, where X = Ga, In, Tl) presented in the associated paper. It encompasses the computational workflow including geometry relaxations, self-consistent field calculations, and non-self-consistent band structure and density of states computations, all performed using the GGA-PBE+U functional with spin–orbit coupling to accurately model the electronic and magnetic properties. The data directly demonstrates the stable structures, intrinsic ferromagnetic and antiferromagnetic order, and the key finding of giant magnetic anisotropy energy (MAE), calculated from total energy differences for specific magnetization axes. The provided results also include the estimated magnetic critical temperatures (Curie/Néel temperatures), which were determined by parameterizing a Heisenberg spin model with our DFT-calculated exchange constants and solving it using Monte Carlo simulations. This dataset is structured by material and calculation type to enable full verification, reproduction, and further exploration of these 2D magnetic systems.
DOI: 10.17632/vgt9vjj99m
title: Supplementary File: Efficacy and safety of lebrikizumab in adolescents with atopic dermatitis over 24 weeks: An analysis from the BIOREP registry

description: Figure S1. Response to lebrikizumab treatment in adolescents naive to targeted therapy and in
those with prior targeted-therapy failure at 16 and 24 weeks
DOI: 10.17632/vgt9vjj99m.1
title: Supplementary File: Efficacy and safety of lebrikizumab in adolescents with atopic dermatitis over 24 weeks: An analysis from the BIOREP registry

description: Figure S1. Response to lebrikizumab treatment in adolescents naive to targeted therapy and in
those with prior targeted-therapy failure at 16 and 24 weeks
DOI: 10.17632/6jckkzt5kc
title: Maize leaf sheath significantly influences stalk bending strength

description: Data that accompanies a journal paper of the same name.
DOI: 10.17632/6jckkzt5kc.1
title: Maize leaf sheath significantly influences stalk bending strength

description: Data that accompanies a journal paper of the same name.
DOI: 10.17632/29z869rwhb
title: PuSPh
DOI: 10.17632/29z869rwhb.1
title: PuSPh
DOI: 10.17632/gdm7gkwhyr
title: SabiM/QTNano - Unveiling the Interaction Between Fragments of ABX3 Halide Perovskite and Ti3C2F2 MXene Monolayer, rsc Journal of Materials Chemistry A, Accepted (2026)

description: Raw data of the published paper - DOI: 10.1039/D5TA08314A
DOI: 10.17632/gdm7gkwhyr.1
title: SabiM/QTNano - Unveiling the Interaction Between Fragments of ABX3 Halide Perovskite and Ti3C2F2 MXene Monolayer, rsc Journal of Materials Chemistry A, Accepted (2026)

description: Raw data of the published paper - DOI: 10.1039/D5TA08314A