cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.36696
title: Data supporting the study "The Impact of Trait Mindfulness on Early Prosocial Behavior in Adolescents."
title: 支持《特质正念对青少年早期亲社会行为的影响》研究的数据

description: This dataset supports a study on the influence of trait mindfulness on prosocial behavior in early adolescence. The data were derived from an anonymous questionnaire survey conducted in November 2025 among middle school students from a public secondary school in Changzhou City, Jiangsu Province, China. Using a cluster sampling method, data were collected online via the Wenjuanxing platform, yielding 688 valid responses. The dataset includes measurements from several standardized scales: the Chinese version of the Five Facet Mindfulness Questionnaire (FFMQ) was used to assess trait mindfulness; the Prosocial Behavior Scale (PBS) was used to measure prosocial behavior; the Interpersonal Reactivity Index (IRI-C) was used to measure empathy; and the Heartland Forgiveness Scale (HFS) was used to assess forgiveness. Basic demographic information (e.g., gender, age, grade) is also included. The data are stored in a structured table format (.xlsx), containing 688 rows (observations) and 159 columns (variables). Acquired under strict ethical review and informed consent procedures, this dataset is suitable for investigating the relationships between mindfulness, socio-emotional competencies (e.g., empathy, forgiveness), and prosocial behavior in adolescents. It is valuable for researchers in behavioral science, developmental psychology, and education for cross-sectional analysis or as baseline data for secondary research.
description: This dataset supports a study on the influence of trait mindfulness on prosocial behavior in early adolescence. The data were derived from an anonymous questionnaire survey conducted in November 2025 among middle school students from a public secondary school in Changzhou City, Jiangsu Province, China. Using a cluster sampling method, data were collected online via the Wenjuanxing platform, yielding 688 valid responses. The dataset includes measurements from several standardized scales: the Chinese version of the Five Facet Mindfulness Questionnaire (FFMQ) was used to assess trait mindfulness; the Prosocial Behavior Scale (PBS) was used to measure prosocial behavior; the Interpersonal Reactivity Index (IRI-C) was used to measure empathy; and the Heartland Forgiveness Scale (HFS) was used to assess forgiveness. Basic demographic information (e.g., gender, age, grade) is also included. The data are stored in a structured table format (.xlsx), containing 688 rows (observations) and 159 columns (variables). Acquired under strict ethical review and informed consent procedures, this dataset is suitable for investigating the relationships between mindfulness, socio-emotional competencies (e.g., empathy, forgiveness), and prosocial behavior in adolescents. It is valuable for researchers in behavioral science, developmental psychology, and education for cross-sectional analysis or as baseline data for secondary research.
DOI: 10.57760/sciencedb.j00289.00044
title: Dataset on digital dynamic capabilities, innovation activity, and new quality productive forces of SRUI enterprises, 2017-2023
title: 专精特新企业数字化动态能力、创新活跃度与新质生产力2017-2023年数据集

description: From 2017 to 2023, the Ministry of Industry and Information Technology of China successively identified and announced eight batches of national-level manufacturing champion enterprises and five batches of national-level SRUI "little giant" enterprises, totaling 14,345 companies. By the end of 2023, 1,803 of these enterprises were listed on the Shanghai and Shenzhen A-share markets. Data on their digital transformation, innovation activity, new quality productive forces, and other aspects were statistically observed. The identification batches were matched with their corresponding years, and enterprises subjected to ST or *ST treatments, those with missing or abnormal key data, were further excluded. Ultimately, 691 samples were selected for the study. Among them, the sample sizes for the manufacturing champions are as follows: 21 from the first batch, 27 from the second, 21 from the third, 19 from the fourth, 30 from the fifth, 34 from the sixth, 45 from the seventh, and 131 from the eighth. For the "little giant" enterprises, the sample sizes are: 16 from the first batch, 63 from the second, 75 from the third, 113 from the fourth, and 96 from the fifth. The data primarily come from the CSMAR database, Wind database, and annual reports of listed companies.
description: From 2017 to 2023, the Ministry of Industry and Information Technology of China successively identified and announced eight batches of national-level manufacturing champion enterprises and five batches of national-level SRUI "little giant" enterprises, totaling 14,345 companies. By the end of 2023, 1,803 of these enterprises were listed on the Shanghai and Shenzhen A-share markets. Data on their digital transformation, innovation activity, new quality productive forces, and other aspects were statistically observed. The identification batches were matched with their corresponding years, and enterprises subjected to ST or *ST treatments, those with missing or abnormal key data, were further excluded. Ultimately, 691 samples were selected for the study. Among them, the sample sizes for the manufacturing champions are as follows: 21 from the first batch, 27 from the second, 21 from the third, 19 from the fourth, 30 from the fifth, 34 from the sixth, 45 from the seventh, and 131 from the eighth. For the "little giant" enterprises, the sample sizes are: 16 from the first batch, 63 from the second, 75 from the third, 113 from the fourth, and 96 from the fifth. The data primarily come from the CSMAR database, Wind database, and annual reports of listed companies.
DOI: 10.57760/sciencedb.36889
title: A dataset of pictures of the entire bodies and faces of three breeds of sheep from Australia
title: 澳大利亚三个品种绵羊全身和脸部图片数据集

description: The dataset of Australian Merino sheep, unbranded Torquay sheep, Suffolk sheep, and White Suffolk sheep includes 1200 images of their faces and entire bodies.
description: The dataset of Australian Merino sheep, unbranded Torquay sheep, Suffolk sheep, and White Suffolk sheep includes 1200 images of their faces and entire bodies.
DOI: 10.57760/sciencedb.18337
title: Actual production data and equipment data set of a standardized farm with 23,000 fattening pigs located in Liuhe District, Nanjing
title: Actual production data and equipment data set of a standardized farm with 23,000 fattening pigs located in Liuhe District, Nanjing

description: (1) Pollutant emission standard data (including: exhaust gas emission standard, solid waste emission standard including pig manure and biogas residue water pollutant emission standard and noise emission standard). (2) A set of data on the condition of farm buildings and breeding equipment (plant plan) layout and the type and number of electrical and environmental control equipment used on the farm. (3) A set of farm pollutant emission accounting data, including: emission of waste gas pollutants (harmless treatment of dead pig exhaust gas, emission summary of waste gas pollutants, project soot) waste gas and pig pen waste gas production summary, water pollution production, solid pollutant emission summary (dead pig, solid waste) production summary and accounting. (4) The operating expenses of the farm include a summary of energy consumption. (5) Temperature, humidity and carbon dioxide values are collected for one month in spring, summer, autumn and winter, and the specific collection time is January and June in spring and summer, and July and December in autumn and winter.
description: (1) Pollutant emission standard data (including: exhaust gas emission standard, solid waste emission standard including pig manure and biogas residue water pollutant emission standard and noise emission standard). (2) A set of data on the condition of farm buildings and breeding equipment (plant plan) layout and the type and number of electrical and environmental control equipment used on the farm. (3) A set of farm pollutant emission accounting data, including: emission of waste gas pollutants (harmless treatment of dead pig exhaust gas, emission summary of waste gas pollutants, project soot) waste gas and pig pen waste gas production summary, water pollution production, solid pollutant emission summary (dead pig, solid waste) production summary and accounting. (4) The operating expenses of the farm include a summary of energy consumption. (5) Temperature, humidity and carbon dioxide values are collected for one month in spring, summer, autumn and winter, and the specific collection time is January and June in spring and summer, and July and December in autumn and winter.
DOI: 10.57760/sciencedb.08865
title: Images of Pigs at Different Stages From a Group of Shandong Farms
title: Images of Pigs at Different Stages From a Group of Shandong Farms

description: (1)A set of images of pigs in different stages from Shandong, China. After,years of image acquisition, more than one images, videos and video frame extraction images of piglets (including piglets around 50 days old and 70-80 days old), fattening pigs, sows and male breeding pigs were collected.(2) The dataset contains 63 images for piglets around 50 days, 139 images for those between 70 and 80 days, 91 images for fattening pigs at 160 days and 26 images for sows.(3) The dataset contains 1 video for piglets at about 50 days, 3 videos for those at 70-80 days, 5 videos for fattening pigs at 160 days, 4 videos for sows, and 4 videos for male breeding pigs.(4) Pig dataset from pig farms in Shandong for visual feature-based classification.(5) The dataset of multi-class problem is called multi-class problem because it has visual features such as reducing the chance of overfitting, improving accuracy, and reducing the training time of the model.
description: (1)A set of images of pigs in different stages from Shandong, China. After,years of image acquisition, more than one images, videos and video frame extraction images of piglets (including piglets around 50 days old and 70-80 days old), fattening pigs, sows and male breeding pigs were collected.(2) The dataset contains 63 images for piglets around 50 days, 139 images for those between 70 and 80 days, 91 images for fattening pigs at 160 days and 26 images for sows.(3) The dataset contains 1 video for piglets at about 50 days, 3 videos for those at 70-80 days, 5 videos for fattening pigs at 160 days, 4 videos for sows, and 4 videos for male breeding pigs.(4) Pig dataset from pig farms in Shandong for visual feature-based classification.(5) The dataset of multi-class problem is called multi-class problem because it has visual features such as reducing the chance of overfitting, improving accuracy, and reducing the training time of the model.
DOI: 10.57760/sciencedb.08863
title: Pictures of different types of diseased pigs
title: Pictures of different types of diseased pigs

description: Collect and manually classify over 200 pictures of diseased pigs through internet and other means. Classify these images according to disease characterization and severity. Contains classified folders such as abnormal secretion，cenker，disfiguration，hernia ，presence of lumps，prolapses，skin changes.According to the classification of the condition, it includes 5 categories: Mild, Moderate, Severe, Critical,and Deceased.
description: Collect and manually classify over 200 pictures of diseased pigs through internet and other means. Classify these images according to disease characterization and severity. Contains classified folders such as abnormal secretion，cenker，disfiguration，hernia ，presence of lumps，prolapses，skin changes.According to the classification of the condition, it includes 5 categories: Mild, Moderate, Severe, Critical,and Deceased.
DOI: 10.57760/sciencedb.36897
title: Buffalo-Pak: Buffalo Breed Dataset from Pakistan for Visual Features Based Classification
title: 布法罗-帕克：来自巴基斯坦的布法罗品种数据集，用于基于视觉特征的分类

description: Description(1) Buffalo-Pak: A data set of images of 3 different Buffalo species from Pakistan, i.e. Neli Ravi, Khundi, and Mix.(2) Buffalo-Pak data set contains 325 images with 112 (Neli Ravi), 138 (mix), and 75 (Khundi).(3) Buffalo-Pak data set can be used for image classification and segmentation based on their visual features.(4) The data set is referred to for the multiclass problem as it holds several visual features.
description: Description(1) Buffalo-Pak: A data set of images of 3 different Buffalo species from Pakistan, i.e. Neli Ravi, Khundi, and Mix.(2) Buffalo-Pak data set contains 325 images with 112 (Neli Ravi), 138 (mix), and 75 (Khundi).(3) Buffalo-Pak data set can be used for image classification and segmentation based on their visual features.(4) The data set is referred to for the multiclass problem as it holds several visual features.
DOI: 10.15468/4cy8aw
title: Longhorn Beetles of Ireland

description: Spatial distribution of records of longhorn beetles.
DOI: 10.15468/hfs8d4
title: Tallinn Botanic Garden

description: Tallinn Botanic Garden stores more than 95 000 herbarium specimens - plants, bryophytes, fungi and lichens, but also fruits, cones and wood.
DOI: 10.5281/zenodo.18491844
title: Dataset for Exploring Tidal Modulation of Seismicity in Southern California

description: 1. Original_catalog1.1 SCSN_original_catalog.txt: Original HypoDD version of the SCSN seismic catalog, without any declustering or screening processing.1.2 QTM_12dev.txt: Original QTM seismic catalog filtered by a 12x detection threshold, without declustering processing.

2. Decluster_catalog2.1 SCSN_decluster_m1.5.txt: SCSN seismic catalog declustered using the ETAS model, with a completeness magnitude of 1.5.2.2 QTM_decluster_m0.1.txt: QTM seismic catalog declustered using the ETAS model, with a completeness magnitude of 0.1.

3. Fault_geometry_information3.1 CFM5.3_Metadata.xlsx: Original data description document for the CFM5.3 fault model, including model design, parameter definitions, data sources, and other metadata.3.2 CFM5.3_traces.lonLat: Basic geometric information file for the CFM5.3 fault model, containing only fault spatial coordinates and no strike, dip, or rake angles.3.3 CFM5.3_traceslonLat_fault_geometry.txt: CFM5.3 fault geometry information file withstrike, dip, and rake angles. Important: The fault names in `CFM5.3_traces.lonLat` have been replaced with strike, dip, and rake angles, and the order is in one-to-one correspondence with the original file.

4. Tidal stress4.1 Tidal_N_0.txt/Tidal_N_90.txt/Tidal_S_0.txt/Tidal_Vol.txt: Strain data calculated by SPOTL at the point (-117, 35), with the start time of '1981-01-01' and a sampling interval (t_sample) of 6000s.4.2 calc_stress.py: Code that provides the process of converting strain to stress on the fault plane. Note that strike/dip/rake need to be specified, which can be obtained from the Fault_geometry_Information

If you have any questions, please contact:

luweifan001@gmail.com or lu.weifan@univ-grenoble-alpes.fr
DOI: 10.5281/zenodo.18491845
title: Dataset for Exploring Tidal Modulation of Seismicity in Southern California

description: 1. Original_catalog1.1 SCSN_original_catalog.txt: Original HypoDD version of the SCSN seismic catalog, without any declustering or screening processing.1.2 QTM_12dev.txt: Original QTM seismic catalog filtered by a 12x detection threshold, without declustering processing.

2. Decluster_catalog2.1 SCSN_decluster_m1.5.txt: SCSN seismic catalog declustered using the ETAS model, with a completeness magnitude of 1.5.2.2 QTM_decluster_m0.1.txt: QTM seismic catalog declustered using the ETAS model, with a completeness magnitude of 0.1.

3. Fault_geometry_information3.1 CFM5.3_Metadata.xlsx: Original data description document for the CFM5.3 fault model, including model design, parameter definitions, data sources, and other metadata.3.2 CFM5.3_traces.lonLat: Basic geometric information file for the CFM5.3 fault model, containing only fault spatial coordinates and no strike, dip, or rake angles.3.3 CFM5.3_traceslonLat_fault_geometry.txt: CFM5.3 fault geometry information file withstrike, dip, and rake angles. Important: The fault names in `CFM5.3_traces.lonLat` have been replaced with strike, dip, and rake angles, and the order is in one-to-one correspondence with the original file.

4. Tidal stress4.1 Tidal_N_0.txt/Tidal_N_90.txt/Tidal_S_0.txt/Tidal_Vol.txt: Strain data calculated by SPOTL at the point (-117, 35), with the start time of '1981-01-01' and a sampling interval (t_sample) of 6000s.4.2 calc_stress.py: Code that provides the process of converting strain to stress on the fault plane. Note that strike/dip/rake need to be specified, which can be obtained from the Fault_geometry_Information

If you have any questions, please contact:

luweifan001@gmail.com or lu.weifan@univ-grenoble-alpes.fr
DOI: 10.57760/sciencedb.36398
title: Correlated Experimental Dataset for High-sodium Content Magnesium Oxysulfide Cement Papers (2026-2027)
title: 2026-2027 高钠含量硫氧镁水泥论文实验关联数据集

description: The dataset includes: 1. pH and ion concentration data, 2. SEM images, 3. TEM images, 4. weathering images, 5. XRD refinement data, 6. mechanical property data, 7. capillary water absorption test data, 8. cement setting time data, 9. thermogravimetric test data, 10. hydration heat data, 11. mercury intrusion porosimetry data, 12. raw material data
description: The dataset includes: 1. pH and ion concentration data, 2. SEM images, 3. TEM images, 4. weathering images, 5. XRD refinement data, 6. mechanical property data, 7. capillary water absorption test data, 8. cement setting time data, 9. thermogravimetric test data, 10. hydration heat data, 11. mercury intrusion porosimetry data, 12. raw material data
DOI: 10.57724/gdc_20240114_radiant_msn
title: INFLUENCE OF MACROALGAL STRANDINGS ON THE CHARACTERISTICS OF THE INTERSTITIAL PORE WATER AND NUTRIENTS SUPPLY ALONG THE COAST OF FOSTER BAY, DECEPTION ISLAND_YEAR 2024
DOI: 10.57760/sciencedb.36861
title: Raw data for the article "VE-Cadherin dual reporter mouse lines for studying endothelial functions"
title: Raw data for the article "VE-Cadherin dual reporter mouse lines for studying endothelial functions"

description: This zip package includes all the raw data involving in the article "VE-Cadherin dual reporter mouse lines for studying endothelial functions" submitted to the journal Circulation Research
description: This zip package includes all the raw data involving in the article "VE-Cadherin dual reporter mouse lines for studying endothelial functions" submitted to the journal Circulation Research
DOI: 10.57760/sciencedb.36895
title: Long-term Soil Warming Experiment Reveals Five-Year Dynamics of Forest Soil Bacterial Communities (2019–2023)
title: Long-term Soil Warming Experiment Reveals Five-Year Dynamics of Forest Soil Bacterial Communities (2019–2023)

description: This dataset was generated from a five-year (2019–2023) in situ soil warming manipulation experiment conducted in the Qingyuan Forest Ecosystem, Liaoning Province, China. The experiment was designed to systematically characterize the temporal dynamics of forest soil bacterial community structure and diversity under long-term experimental warming. Soil samples were collected during the growing season following a standardized sampling protocol. Data files are stored in widely used formats, including FASTQ files, and can be directly accessed and analyzed using mainstream microbial ecology and bioinformatics software.
description: This dataset was generated from a five-year (2019–2023) in situ soil warming manipulation experiment conducted in the Qingyuan Forest Ecosystem, Liaoning Province, China. The experiment was designed to systematically characterize the temporal dynamics of forest soil bacterial community structure and diversity under long-term experimental warming. Soil samples were collected during the growing season following a standardized sampling protocol. Data files are stored in widely used formats, including FASTQ files, and can be directly accessed and analyzed using mainstream microbial ecology and bioinformatics software.
DOI: 10.57760/sciencedb.36886
title: Data for verlamelin A derivatives
title: Data for verlamelin A derivatives

description: This dateset contains figures and tables for verlamelin A derivatives
description: This dateset contains figures and tables for verlamelin A derivatives
DOI: 10.5281/zenodo.13910634
title: Monthly 30-m Surface Water Dataset of the Tibetan Plateau from 2000 to 2021

description: This repository contains the relevant data for the paper A gap-free monthly 30-m surface water dataset for the Tibetan Plateau (2000–2021) reconstructed from JRC Global Surface Water products using the stepwise gap-filling method.

The reconstructed Tibetan Plateau monthly water history (TP MWH) dataset from 2000 to 2021 is stored in the file “TP2000_2021_MonthlyWaterHistory_afterSGF.zip.” This collection includes 264 GeoTIFF images, each viewable in QGIS software. The images use the EPSG: 4326-WGS84 Coordinate Reference System, with each image comprising a grid of 136,036 × 51,976 pixels at a 30-meter resolution, classifying each pixel as either water or non-water. Validation data obtained via stratified random sampling are provided in the file “TP_validation_samples.csv,” which contains 12,141 random samples. For each sample, the file includes the sample coordinates, Google Earth high-resolution image acquisition date, actual classifications by visual interpretation, and dataset classification results.
DOI: 10.5281/zenodo.13910635
title: Monthly 30-m Surface Water Dataset of the Tibetan Plateau from 2000 to 2021

description: This repository contains the relevant data for the paper A gap-free monthly 30-m surface water dataset for the Tibetan Plateau (2000–2021) reconstructed from JRC Global Surface Water products using the stepwise gap-filling method.

The reconstructed Tibetan Plateau monthly water history (TP MWH) dataset from 2000 to 2021 is stored in the file “TP2000_2021_MonthlyWaterHistory_afterSGF.zip.” This collection includes 264 GeoTIFF images, each viewable in QGIS software. The images use the EPSG: 4326-WGS84 Coordinate Reference System, with each image comprising a grid of 136,036 × 51,976 pixels at a 30-meter resolution, classifying each pixel as either water or non-water. Validation data obtained via stratified random sampling are provided in the file “TP_validation_samples.csv,” which contains 12,141 random samples. For each sample, the file includes the sample coordinates, Google Earth high-resolution image acquisition date, actual classifications by visual interpretation, and dataset classification results.
DOI: 10.5281/zenodo.18491760
title: Multibeam data collected by our team
DOI: 10.5281/zenodo.18491761
title: Multibeam data collected by our team