cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29849534
title: Genome-wide association analysis to identify candidate genes for growth rate traits in Chinese endemic geese

description: China has abundant resources of local goose breeds with significant differences in growth rates among breeds. These differences provide natural genetic materials for exploring key functional genes and molecular markers of growth traits. The genetic loci related to growth rate are important economic traits that affect slaughter weight and feeding costs. In view of this, we used genome-wide association analysis (GWAS) method to determine the genes related to growth rate. From hatching to 10 weeks of age, the weight data of 255 China local geese were measured every 2 weeks. Based on the growth curve, we evaluated the relative and absolute growth rates (RGR and AGR) during the early rapid growth stages of 0-2, 2-4, 4-6, 6-8, and 8-10 weeks of age. GWAS analysis of AGR identified 32 SNPs and annotated 17 associated genes, including STX11, PNDC1, MRPL18, and others. There are 38 SNPs related to RGR, and 19 important candidate genes have been annotated, including SLC24A4, CPSF2, MAP2, and other genes. Five genes are shared in AGR and RGR, namely FAM210B, GULP1, MAS1, MRPL18, and PNLDC1. In addition, through further enrichment analysis, pathways related to growth and development were identified, and candidate genes such as GULP1, STX11, METAP2 were further screened.This study identified 70 SNPs associated with growth rate and annotated 36 genes. Further analysis suggests that GULP1, STX11, METAP2, PTH1R, SMAD7, GK, CTBP2, and DEF6 are important candidate genes. These findings offer valuable data resources for the molecular breeding of indigenous Chinese geese and provide a robust theoretical framework to inform future breeding programs aimed at developing fast-growing Chinese goose breeds.
DOI: 10.6084/m9.figshare.29849534.v1
title: Genome-wide association analysis to identify candidate genes for growth rate traits in Chinese endemic geese

description: China has abundant resources of local goose breeds with significant differences in growth rates among breeds. These differences provide natural genetic materials for exploring key functional genes and molecular markers of growth traits. The genetic loci related to growth rate are important economic traits that affect slaughter weight and feeding costs. In view of this, we used genome-wide association analysis (GWAS) method to determine the genes related to growth rate. From hatching to 10 weeks of age, the weight data of 255 China local geese were measured every 2 weeks. Based on the growth curve, we evaluated the relative and absolute growth rates (RGR and AGR) during the early rapid growth stages of 0-2, 2-4, 4-6, 6-8, and 8-10 weeks of age. GWAS analysis of AGR identified 32 SNPs and annotated 17 associated genes, including STX11, PNDC1, MRPL18, and others. There are 38 SNPs related to RGR, and 19 important candidate genes have been annotated, including SLC24A4, CPSF2, MAP2, and other genes. Five genes are shared in AGR and RGR, namely FAM210B, GULP1, MAS1, MRPL18, and PNLDC1. In addition, through further enrichment analysis, pathways related to growth and development were identified, and candidate genes such as GULP1, STX11, METAP2 were further screened.This study identified 70 SNPs associated with growth rate and annotated 36 genes. Further analysis suggests that GULP1, STX11, METAP2, PTH1R, SMAD7, GK, CTBP2, and DEF6 are important candidate genes. These findings offer valuable data resources for the molecular breeding of indigenous Chinese geese and provide a robust theoretical framework to inform future breeding programs aimed at developing fast-growing Chinese goose breeds.
DOI: 10.15468/dl.mh3gk6
title: Occurrence Download

description: A dataset containing 442 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erinaceus amurensis",
 "Country is China",
 "HasCoordinate is true"
 ]
}

 The dataset includes 442 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024443-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16758769
title: Data of "Year-long real-time measurements of fine particles in the northeastern Tibetan Plateau: insights into seasonal variation and chemical processes"

description: Aerosol composition, elemental ratios and meteorological parameters measured at Waliguan Baseline Observation from June 2023 to May 2024.
DOI: 10.5281/zenodo.16758768
title: Data of "Year-long real-time measurements of fine particles in the northeastern Tibetan Plateau: insights into seasonal variation and chemical processes"

description: Aerosol composition, elemental ratios and meteorological parameters measured at Waliguan Baseline Observation from June 2023 to May 2024.
DOI: 10.5281/zenodo.16758999
title: The Dialectical Evolution of Trade Governance and Financial Decentralization

description: This archive contains all anonymized datasets, Python/R analysis scripts,NVivo export instructions, and smart-contract simulations used in our mixed-methods study of blockchain-enabled arbitration versus WTO/ICJ dispute resolution.
DOI: 10.5281/zenodo.15395534
title: JL1-CD

description: JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework.
DOI: 10.5281/zenodo.15395535
title: JL1-CD

description: JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework.
DOI: 10.15468/dl.h3bu6c
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sicyopus jonklaasi)",
 "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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024444-250802193616735/datasets/export for details.
DOI: 10.34740/kaggle/dsv/12698680
title: OBELiX
DOI: 10.15468/dl.r88q8t
title: Occurrence Download

description: A dataset containing 1067 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tibouchina aspera Aubl.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1067 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009958-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/5576
title: Nanozymes
DOI: 10.15468/dl.gkcw9d
title: Occurrence Download

description: A dataset containing 130 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is Italy",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sesleria nitida Ten."
 ]
}

 The dataset includes 130 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009959-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/nddcvkpy6h.1
title: Supplementary data for "Development and validation of untargeted lipidomic approaches to determine the source and fate of organic matter in coastal sediments"

description: The dataset contains bulk and molecular parameters of sediment samples for the study "Development and validation of untargeted lipidomic approaches to determine the source and fate of organic matter in coastal sediments".
DOI: 10.17632/fpbsk3rn6k
title: Operational (Oven) data from a commercial bakery in Jordan

description: The dataset consists of over 80 recorded failure events and includes the following eight features:
- Date: Timestamp of the incident.
- Machine: A categorical variable indicating the workstation.
- Failure Type: Categorical failure class (Mechanical, Electrical, Overheating, etc.).
- Downtime (hrs): Continuous target variable for post-failure impact.
- Temperature (°C), Vibration (g), Speed (units/hr): Continuous condition-monitoring features.
- Sensor Error: Boolean indicator of faulty sensor readings.
DOI: 10.17632/fpbsk3rn6k.1
title: Operational (Oven) data from a commercial bakery in Jordan

description: The dataset consists of over 80 recorded failure events and includes the following eight features:
- Date: Timestamp of the incident.
- Machine: A categorical variable indicating the workstation.
- Failure Type: Categorical failure class (Mechanical, Electrical, Overheating, etc.).
- Downtime (hrs): Continuous target variable for post-failure impact.
- Temperature (°C), Vibration (g), Speed (units/hr): Continuous condition-monitoring features.
- Sensor Error: Boolean indicator of faulty sensor readings.
DOI: 10.5281/zenodo.14834224
title: Structural basis of lateral gate dynamics at the outer membrane lipopolysaccharide holo-translocon

description: Cα Root Mean Square Fluctuation (RMSF) analysis of protein complexes. RMSF values were calculated for the Cα atoms of each residue across all simulated complexes to assess residue-level flexibility. Line graphs represent the average RMSF across three independent simulation repeats, with standard deviation shown at each residue position. Regions of elevated RMSF indicate increased structural mobility, while lower values reflect more rigid or stable regions. Simulations are shown for the five contracted (a, c, e, g, and i) and extended (b, d, f, h, and j) states for (a, b) LptDE, (c, d) LptDEM, (e, f,) LptDEY, (g, h) LptDEMY, and (l, j) LptDEMY with bound KLA, in simulations where both (A) LptD and (B) LptE are present. Meaurements of (C) LptM RMSFs are shown for (a, b) LptDEM, (c, d) LptDEMY, and (e, f) LptDEMY. While calculations of (D) LptY RMSFs are shown for (a, b) LptDEY, (c, d) LptDEMY, and (e, f) LptDEMY.
DOI: 10.5281/zenodo.16643358
title: Structural basis of lateral gate dynamics at the outer membrane lipopolysaccharide holo-translocon

description: Cα Root Mean Square Fluctuation (RMSF) analysis of protein complexes. RMSF values were calculated for the Cα atoms of each residue across all simulated complexes to assess residue-level flexibility. Line graphs represent the average RMSF across three independent simulation repeats, with standard deviation shown at each residue position. Regions of elevated RMSF indicate increased structural mobility, while lower values reflect more rigid or stable regions. Simulations are shown for the five contracted (a, c, e, g, and i) and extended (b, d, f, h, and j) states for (a, b) LptDE, (c, d) LptDEM, (e, f,) LptDEY, (g, h) LptDEMY, and (l, j) LptDEMY with bound KLA, in simulations where both (A) LptD and (B) LptE are present. Meaurements of (C) LptM RMSFs are shown for (a, b) LptDEM, (c, d) LptDEMY, and (e, f) LptDEMY. While calculations of (D) LptY RMSFs are shown for (a, b) LptDEY, (c, d) LptDEMY, and (e, f) LptDEMY.
DOI: 10.17632/77jmzhcssr.2
title: Replication Folder for "Pension savings, health insurance, and pension insurance: evidence from China"

description: This replication folder contains the necessary codes to replicate the quantitative results of the paper.
DOI: 10.17632/pfv7rcfprr
title: Science and Technology Finance Policy and Urban Energy Efficiency

description: The file contains all the data I have studied the relationship between STFP and UEE.
DOI: 10.17632/pfv7rcfprr.1
title: Science and Technology Finance Policy and Urban Energy Efficiency

description: The file contains all the data I have studied the relationship between STFP and UEE.
DOI: 10.17632/z45bptxptf.1
title: Indonesian Pharmaceutical Dataset

description: Indonesian Pharmaceutical Dataset is a dataset containing drug and disease information relevant to Indonesia, written entirely in Bahasa Indonesia. It includes structured data on drug names, descriptions, usage rules, dosage, side effects, associated diseases, and trademarks. For diseases, it covers names, descriptions, causes, and symptoms. The dataset has been cleaned and formatted for ease of use and is available in CSV format.
DOI: 10.17632/9zjy644z4s
title: Calming the Chaos: Exploring the Association of Past Positive Time Perception with Alexithymia and Attachment Dimensions among Young Adults

description: • The study explored how recalling positive past experiences (PPTP) can support young adults’ emotional resilience, particularly in the context of attachment styles and alexithymia.
• PPTP was found to be negatively associated with avoidant attachment, indicating that reminiscing about positive experiences may help avoidant individuals become more emotionally open.
• Alexithymia was positively correlated with both anxious and avoidant attachment styles.
• Anxiously attached individuals may struggle to express emotions due to hyper-activation, while avoidant individuals tend to suppress emotions, reinforcing emotional detachment.
DOI: 10.17632/9zjy644z4s.1
title: Calming the Chaos: Exploring the Association of Past Positive Time Perception with Alexithymia and Attachment Dimensions among Young Adults

description: • The study explored how recalling positive past experiences (PPTP) can support young adults’ emotional resilience, particularly in the context of attachment styles and alexithymia.
• PPTP was found to be negatively associated with avoidant attachment, indicating that reminiscing about positive experiences may help avoidant individuals become more emotionally open.
• Alexithymia was positively correlated with both anxious and avoidant attachment styles.
• Anxiously attached individuals may struggle to express emotions due to hyper-activation, while avoidant individuals tend to suppress emotions, reinforcing emotional detachment.
DOI: 10.22033/esgf/cmip6.8081
title: NCC NorESM2-LM model output prepared for CMIP6 OMIP omip1

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.OMIP.NCC.NorESM2-LM.omip1' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The NorESM2-LM (low atmosphere-medium ocean resolution, GHG concentration driven) climate model, released in 2017, includes the following components:
 aerosol: OsloAero, atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb), atmosChem: OsloChemSimp, land: CLM, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC, seaIce: CICE. 
The model was run by the NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway (NCC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.22033/esgf/cmip6.7982
title: NCC NorESM2-LM model output prepared for CMIP6 LUMIP hist-noLu

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.LUMIP.NCC.NorESM2-LM.hist-noLu' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The NorESM2-LM (low atmosphere-medium ocean resolution, GHG concentration driven) climate model, released in 2017, includes the following components:
 aerosol: OsloAero, atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb), atmosChem: OsloChemSimp, land: CLM, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC, seaIce: CICE. 
The model was run by the NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway (NCC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.15468/dl.35hc43
title: Occurrence Download

description: A dataset containing 22591 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myrceugenia O.Berg"
 ]
}

 The dataset includes 22591 records from 284 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004614-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22033/esgf/cmip6.7984
title: NCC NorESM2-LM model output prepared for CMIP6 AerChemMIP hist-piAer

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.AerChemMIP.NCC.NorESM2-LM.hist-piAer' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The NorESM2-LM (low atmosphere-medium ocean resolution, GHG concentration driven) climate model, released in 2017, includes the following components:
 aerosol: OsloAero, atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb), atmosChem: OsloChemSimp, land: CLM, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC, seaIce: CICE. 
The model was run by the NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway (NCC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.15468/dl.ch9ocz
title: Occurrence Download

description: A dataset containing 1031 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leopardus wiedii (Schinz, 1821)"
 ]
}

 The dataset includes 1031 records from 109 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013459-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.