cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.26369
title: Autonomous vehicle testing
title: 自动驾驶车辆测试

description: 自动驾驶车辆测试数据简介 自动驾驶车辆测试数据是验证其技术安全性与可靠性的核心依据，主要围绕车辆运行、环境感知、决策控制等维度展开，具体内容如下： 1. 数据核心范畴 - 传感器数据：包含摄像头拍摄的道路图像、激光雷达生成的三维点云、毫米波雷达探测的障碍物距离与速度，以及高精度定位系统的实时坐标与姿态信息，用于还原车辆周边动态环境（如行人、车辆、交通信号、道路标线等）。- 车辆运行数据：记录车速、加速度、转向角度、制动状态、动力系统输出等基础参数，以及算法决策日志（如场景识别结果、路径规划轨迹、加减速/转向控制指令），反映车辆实时运行状态与系统响应逻辑。- 环境与交互数据：涵盖天气（晴、雨、雾）、光照、道路材质（干燥、湿滑）等外部环境信息，以及人类驾驶员接管次数、接管响应时间、风险场景等级等交互数据，用于评估系统在复杂场景下的稳定性。 2. 数据核心价值 - 技术优化：通过分析传感器误判、决策延迟等数据，迭代算法模型（如提升复杂天气下的障碍物识别精度、优化拥堵场景跟车策略）。- 安全验证：统计“无接管里程”“风险场景应对成功率”“紧急制动距离偏差”等指标，对标ISO 26262等功能安全标准，量化系统可靠性。- 法规合规：为自动驾驶车辆上路许可提供数据支撑（如美国NHTSA、中国《智能网联汽车道路测试管理规范》要求的风险场景覆盖率、数据可追溯性）。 3. 典型应用场景数据 - 城市道路跟车：重点采集前车距离变化率、转向曲率、加减速频次，用于优化跟车舒适性与防碰撞逻辑。- 无保护左转：关注对向车辆速度、行人横穿速度、决策耗时，提升复杂路口通行效率与安全性。- 雨雾天气行驶：监测传感器信噪比、算法置信度下降幅度，验证恶劣环境下的系统鲁棒性。 4. 数据挑战与应对 - 规模庞大：单辆车单日测试可产生TB级数据，依赖云计算与边缘计算实现实时存储与预处理。- 标注复杂：人工标注海量场景（如“施工路段绕行”“行人手势示意”）需结合半自动化工具（如AI辅助标注）提升效率。- 隐私安全：对道路场景中涉及的车牌、人脸等敏感信息，通过数据脱敏技术（如模糊化、特征替换）符合GDPR等法规要求。 通过系统化采集与分析测试数据，自动驾驶技术正从实验室走向真实道路，逐步实现“安全、智能、高效”的出行目标。
description: 自动驾驶车辆测试数据简介 自动驾驶车辆测试数据是验证其技术安全性与可靠性的核心依据，主要围绕车辆运行、环境感知、决策控制等维度展开，具体内容如下： 1. 数据核心范畴 - 传感器数据：包含摄像头拍摄的道路图像、激光雷达生成的三维点云、毫米波雷达探测的障碍物距离与速度，以及高精度定位系统的实时坐标与姿态信息，用于还原车辆周边动态环境（如行人、车辆、交通信号、道路标线等）。- 车辆运行数据：记录车速、加速度、转向角度、制动状态、动力系统输出等基础参数，以及算法决策日志（如场景识别结果、路径规划轨迹、加减速/转向控制指令），反映车辆实时运行状态与系统响应逻辑。- 环境与交互数据：涵盖天气（晴、雨、雾）、光照、道路材质（干燥、湿滑）等外部环境信息，以及人类驾驶员接管次数、接管响应时间、风险场景等级等交互数据，用于评估系统在复杂场景下的稳定性。 2. 数据核心价值 - 技术优化：通过分析传感器误判、决策延迟等数据，迭代算法模型（如提升复杂天气下的障碍物识别精度、优化拥堵场景跟车策略）。- 安全验证：统计“无接管里程”“风险场景应对成功率”“紧急制动距离偏差”等指标，对标ISO 26262等功能安全标准，量化系统可靠性。- 法规合规：为自动驾驶车辆上路许可提供数据支撑（如美国NHTSA、中国《智能网联汽车道路测试管理规范》要求的风险场景覆盖率、数据可追溯性）。 3. 典型应用场景数据 - 城市道路跟车：重点采集前车距离变化率、转向曲率、加减速频次，用于优化跟车舒适性与防碰撞逻辑。- 无保护左转：关注对向车辆速度、行人横穿速度、决策耗时，提升复杂路口通行效率与安全性。- 雨雾天气行驶：监测传感器信噪比、算法置信度下降幅度，验证恶劣环境下的系统鲁棒性。 4. 数据挑战与应对 - 规模庞大：单辆车单日测试可产生TB级数据，依赖云计算与边缘计算实现实时存储与预处理。- 标注复杂：人工标注海量场景（如“施工路段绕行”“行人手势示意”）需结合半自动化工具（如AI辅助标注）提升效率。- 隐私安全：对道路场景中涉及的车牌、人脸等敏感信息，通过数据脱敏技术（如模糊化、特征替换）符合GDPR等法规要求。 通过系统化采集与分析测试数据，自动驾驶技术正从实验室走向真实道路，逐步实现“安全、智能、高效”的出行目标。
DOI: 10.57760/sciencedb.26360
title: dataset sample
title: dataset sample

description: The dataset provides information about vehicles.
description: The dataset provides information about vehicles.
DOI: 10.25442/hku.29206841
title: <i>Supporting data for “Developments of Advanced 3D Diffusion-Weighted Imaging Techniques”</i>

description: This project aims to develop advanced 3D diffusion-weighted imaging (DWI) techniques. One of the methods, SLIPEN, is designed to address slab boundary artifacts commonly seen in 3D multi-slab DWI. Another technique, 3D AccMUSER, enables accelerated acquisition for both 3D single-slab and 3D multi-slab DWI.
DOI: 10.25442/hku.29206841.v1
title: <i>Supporting data for “Developments of Advanced 3D Diffusion-Weighted Imaging Techniques”</i>

description: This project aims to develop advanced 3D diffusion-weighted imaging (DWI) techniques. One of the methods, SLIPEN, is designed to address slab boundary artifacts commonly seen in 3D multi-slab DWI. Another technique, 3D AccMUSER, enables accelerated acquisition for both 3D single-slab and 3D multi-slab DWI.
DOI: 10.57760/sciencedb.26179
title: SHG data
title: SHG data

description: This prospective cohort study initially enrolled 1,651 patients scheduled for elective gastrointestinal surgery at the Affiliated Hospital of Xuzhou Medical University (Jiangsu Province) between May and December 2024. Through rigorous application of inclusion/exclusion criteria, 765 eligible participants were retained for final analysis. Comprehensive perioperative data collection encompassed demographic characteristics, perioperative blood glucose levels, and postoperative outcomes. Postoperative infections were diagnosed according to the Centers for Disease Control and Prevention(CDC)criteria supplemented by biochemical confirmation (C-reactive protein[CRP]≥190mg/L).
description: This prospective cohort study initially enrolled 1,651 patients scheduled for elective gastrointestinal surgery at the Affiliated Hospital of Xuzhou Medical University (Jiangsu Province) between May and December 2024. Through rigorous application of inclusion/exclusion criteria, 765 eligible participants were retained for final analysis. Comprehensive perioperative data collection encompassed demographic characteristics, perioperative blood glucose levels, and postoperative outcomes. Postoperative infections were diagnosed according to the Centers for Disease Control and Prevention(CDC)criteria supplemented by biochemical confirmation (C-reactive protein[CRP]≥190mg/L).
DOI: 10.57760/sciencedb.26094
title: MD4LC: Multimodal Remote Sensing and Labeled Dataset for Lunar Craters
title: MD4LC: Multimodal Remote Sensing and Labeled Dataset for Lunar Craters

description: The MD4LC primarily consists of two components: a multi-source remote sensing dataset of lunar impact craters and a corresponding label set. The remote sensing dataset mainly contains raster data with spatial coordinate information, which is primarily used to represent the topography, morphology, gravitational anomalies, and material composition of lunar impact craters.The label set is a multi-dimensional knowledge label system formed by team's expert annotators based on the Chain of Thought. Specifically, it comprises age classification labels used to assess the relative ages of craters, subtype classification labels representing the morphological features, and bilingual text labels (Chinese and English) that describe the geological characteristics of impact craters.Additionally, MD4LC includes a global Helium-3 content distribution map produced by our team. It is also one of the important base map data that constitute the MD4LC remote sensing dataset. We are sharing this data here for collaborative use.
description: The MD4LC primarily consists of two components: a multi-source remote sensing dataset of lunar impact craters and a corresponding label set. The remote sensing dataset mainly contains raster data with spatial coordinate information, which is primarily used to represent the topography, morphology, gravitational anomalies, and material composition of lunar impact craters.The label set is a multi-dimensional knowledge label system formed by team's expert annotators based on the Chain of Thought. Specifically, it comprises age classification labels used to assess the relative ages of craters, subtype classification labels representing the morphological features, and bilingual text labels (Chinese and English) that describe the geological characteristics of impact craters.Additionally, MD4LC includes a global Helium-3 content distribution map produced by our team. It is also one of the important base map data that constitute the MD4LC remote sensing dataset. We are sharing this data here for collaborative use.
DOI: 10.57760/sciencedb.26357
title: Raw data for the article "A Cross-Etiology Single-Cell Atlas Reveals Hepatic Stellate Cell Heterogeneity and Dynamics after Liver Injury"
title: Raw data for the article "A Cross-Etiology Single-Cell Atlas Reveals Hepatic Stellate Cell Heterogeneity and Dynamics after Liver Injury"

description: Liver fibrosis, driven by hepatic stellate cell (HSC) activation, remains a major clinical challenge due to elusive heterogeneity and plasticity of HSCs across injury etiologies and stages. This study aimed to decode HSC dynamics through a cross-etiology and -stages single-cell atlas, identify fibrosis-resolution checkpoints, and propose therapeutic strategies.
description: Liver fibrosis, driven by hepatic stellate cell (HSC) activation, remains a major clinical challenge due to elusive heterogeneity and plasticity of HSCs across injury etiologies and stages. This study aimed to decode HSC dynamics through a cross-etiology and -stages single-cell atlas, identify fibrosis-resolution checkpoints, and propose therapeutic strategies.
DOI: 10.57760/sciencedb.17763
title: Research on microbial metagenomics in mud volcanoes
title: Research on microbial metagenomics in mud volcanoes

description: To reveal the metabolism mechanism of AOM-involed microbials in mud volcanoes.
description: To reveal the metabolism mechanism of AOM-involed microbials in mud volcanoes.
DOI: 10.57760/sciencedb.26182
title: IDP Camp DAta
title: IDP Camp DAta

description: Data collected on the sociodemigraphics of Internall displayed persond in IDP camop, Benue State, Nigeria.
description: Data collected on the sociodemigraphics of Internall displayed persond in IDP camop, Benue State, Nigeria.
DOI: 10.7910/dvn/gffmik
title: Language barriers limit clinical trial accessibility for patients with gastrointestinal cancer

description: This dataset is a systematic evaluation of language eligibility criteria in gastrointestinal (GI) cancer clinical trials conducted in the United States from 2019 to 2023. The dataset includes variables extracted from ClinicalTrials.gov, including NCT ID, cancer type, language-related eligibility criteria, study start date, trial location zip codes, and classification of language inclusion/exclusion. It also integrates county-level data on non-English speaking (NES) populations and limited English proficiency (LEP) from the U.S. Census Bureau’s American Community Survey (ACS), detailing the top 10 most commonly spoken non-English languages in the U.S. and NES population estimates by county.
description: Descriptive statistics were used to determine the proportion of trials excluding NES overall and by cancer type. Geographic mapping of trial locations was performed using zip-code to county conversion. U.S. Census ACS B16001 data were joined to assess LEP populations in each county (5-year estimate 2019-2023). Data visualizations include bar charts, bubble plots of language prevalence and LEP rates (1-year estimate 2023), and U.S. county maps colored by NES population. The text document is the configuration for the map in Figure 1C and can be uploaded into MapChart.Net (https://www.mapchart.net/usa-counties.html)
DOI: 10.15468/dl.2aqeph
title: Occurrence Download

description: A dataset containing 1812 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (NSW BioNet Atlas, ALA species sightings and OzAtlas, NatureMapr, Sustainable Rivers Audit, iNaturalist Research-grade Observations, Eremaea, FeralScan, BirdLife Australia, Birdata, EOD – eBird Observation Dataset, IMOS - Animal Tracking Facility - Acoustic Tracking - Quality Controlled Detections (2007 -2021))",
 "Geometry POLYGON((149.3222 -36.63558,149.6414 -36.66994,149.8052 -36.61731,150.08308 -36.30429,150.11087 -36.01299,150.1044 -35.60351,149.89175 -35.4584,149.61362 -35.53277,149.51846 -35.96415,149.3222 -36.63558))",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is VU",
 "OccurrenceStatus is Present",
 "Year 2009-2019"
 ]
}

 The dataset includes 1812 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037624-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.26383
title: Multimodal Feature Analysis Dataset
title: 多模态特征分析数据集

description: To further analyze the impact of multimodal data on forecasting, we tested the effect of 15 different combinations of modal data on carbon emission forecasting performance. The experiments were divided into Group A and Group B. Group A refers to the combination of different modal variables and historical carbon emission data used as input for forecasting. Group B refers to the combination of different modal variables, internal information data (GDP, population, and total energy consumption), and historical carbon emission data used as input for forecasting.
description: To further analyze the impact of multimodal data on forecasting, we tested the effect of 15 different combinations of modal data on carbon emission forecasting performance. The experiments were divided into Group A and Group B. Group A refers to the combination of different modal variables and historical carbon emission data used as input for forecasting. Group B refers to the combination of different modal variables, internal information data (GDP, population, and total energy consumption), and historical carbon emission data used as input for forecasting.
DOI: 10.57760/sciencedb.26381
title: Model forecast performance dataset
title: 模型预测效果数据集

description: The model prediction performance dataset primarily involves model comparison and evaluation, model effectiveness analysis, and spatio-temporal analysis of models. Among these, model comparison and evaluation involves assessing similar prediction models using metrics such as MAPE, SMAPE, MAE, RMSE, and EVS to compare their prediction accuracy for carbon emissions. Model performance analysis involves comparing the percentage improvement in accuracy between two models, which can quantitatively assess the differences in predictive performance between models. This is also a relatively intuitive method for identifying the relative performance of models. Spatio-temporal analysis of models involves the selection of lag steps and prediction steps. The choice of lag steps directly affects the model's ability to capture historical information, while the selection of prediction steps determines the model's ability to predict future time periods.
description: The model prediction performance dataset primarily involves model comparison and evaluation, model effectiveness analysis, and spatio-temporal analysis of models. Among these, model comparison and evaluation involves assessing similar prediction models using metrics such as MAPE, SMAPE, MAE, RMSE, and EVS to compare their prediction accuracy for carbon emissions. Model performance analysis involves comparing the percentage improvement in accuracy between two models, which can quantitatively assess the differences in predictive performance between models. This is also a relatively intuitive method for identifying the relative performance of models. Spatio-temporal analysis of models involves the selection of lag steps and prediction steps. The choice of lag steps directly affects the model's ability to capture historical information, while the selection of prediction steps determines the model's ability to predict future time periods.
DOI: 10.57760/sciencedb.26378
title: Key information granularity Sentiment value
title: 关键信息粒度情感值

description: Sentiment analysis technology can convert textual information into digital representations, facilitating deep integration and interaction between unstructured social media data and structured carbon emissions data. By performing sentiment analysis on textual data containing keyword information, when the sentiment score approaches 0, the textual content reflects a more negative emotional tendency. Conversely, when the sentiment score approaches 1, it indicates that the textual content has a more positive emotional tendency.
description: Sentiment analysis technology can convert textual information into digital representations, facilitating deep integration and interaction between unstructured social media data and structured carbon emissions data. By performing sentiment analysis on textual data containing keyword information, when the sentiment score approaches 0, the textual content reflects a more negative emotional tendency. Conversely, when the sentiment score approaches 1, it indicates that the textual content has a more positive emotional tendency.
DOI: 10.57760/sciencedb.26377
title: In2CuAgSe4
title: In2CuAgSe4

description: Structural and transport data
description: Structural and transport data
DOI: 10.57760/sciencedb.26376
title: Key information granularity subjective and objective values
title: 关键信息粒度主客观值

description: Subjectivity and objectivity are indispensable elements in Chinese public opinion and text mining. By constructing and summarizing knowledge bases of subjective and objective clues in Chinese sentences, we provide a method for determining the subjectivity of Chinese sentences. The larger the output value, the more subjective the text tends to be; the smaller the output value, the more objective the text tends to be.
description: Subjectivity and objectivity are indispensable elements in Chinese public opinion and text mining. By constructing and summarizing knowledge bases of subjective and objective clues in Chinese sentences, we provide a method for determining the subjectivity of Chinese sentences. The larger the output value, the more subjective the text tends to be; the smaller the output value, the more objective the text tends to be.
DOI: 10.57760/sciencedb.26367
title: Functional study of CTNNA1 in HEK293T cells
title: Functional study of CTNNA1 in HEK293T cells

description: We provide comprehensive proteomic profiling of CTNNA1 and its ubiquitylated forms in HEK293T cells, including: (1) Interactome analysis of native and ubiquitylated CTNNA1 via immunoaffinity purification-mass spectrometry (IP-MS); (2) Ubiquitylation site mapping using LC-MS/MS. Dataset includes raw spectra, and processed identification files.Raw data were analyzed using MaxQuant (version 1.6.17.0 and version 2.0.3.0) searched against human FASTA database, or mouse CTNNA1 FASTA (P26231) for CTNNA1 ubiquitylation sites identification. For ubiquitylation sites identification, the (GlyGly) remnants search was enabled. 
description: We provide comprehensive proteomic profiling of CTNNA1 and its ubiquitylated forms in HEK293T cells, including: (1) Interactome analysis of native and ubiquitylated CTNNA1 via immunoaffinity purification-mass spectrometry (IP-MS); (2) Ubiquitylation site mapping using LC-MS/MS. Dataset includes raw spectra, and processed identification files.Raw data were analyzed using MaxQuant (version 1.6.17.0 and version 2.0.3.0) searched against human FASTA database, or mouse CTNNA1 FASTA (P26231) for CTNNA1 ubiquitylation sites identification. For ubiquitylation sites identification, the (GlyGly) remnants search was enabled.
DOI: 10.6084/m9.figshare.29299325
title: Soil MNC for citrus orchards

description: Different land use for soil MNC
DOI: 10.6084/m9.figshare.29299325.v1
title: Soil MNC for citrus orchards

description: Different land use for soil MNC
DOI: 10.15468/dl.xcn5kb
title: Occurrence Download

description: A dataset containing 84 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum, Bupleurum Linnaeus, Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum L., Bupleurum Linnaeus, 1753, Bupleurum C.Linnaeus, 1753, Bupleurum L., 1754, Bupleurum L., 1753, Bupleurum L., 1753, Heldreichia Boiss., Mathiasella Constance & C.L.Hitchc., Tenthredo Linnaeus, 1758, Lasioptera Meigen, 1818, Lasioptera Meigen)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((118 27,123 27,123 31.25,118 31.25,118 27))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 84 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037625-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25442/hku.29209043
title: Supporting Data For: "Deep Learning-based Multi-Camera 3D-Analysis for Automated Quantification of Optomotor Response in Mice"

description: This research project explores the analysis and quantification of Optomotor Response (OMR) in Mice. OMR is a reflex response that is used to analyse vision acuity in preclinical research. The experiment involves placing the animal inside a chamber surrounded by computer screens to create a virtual stimulus that the animal manifests as a cyclinder made up of gratings of customizable widths (spatial frequency). The cylinder rotates in either clock-wise or counter clockwise directions stimulating the left eye or the right eye, respectively. If the animal can discern the movement of the gratings, it reflexively follows the stimulus by shifting its head in the same direction. If The subject does not respond to the stimulus by following the stimulus with its head, it is deem to not be able to perceive the stimulus at that particular spatial frequency. The experiment follows a staircase protocol to test the mouse at multiple spatial frequencies until the maximum spatial frequency at which the animal can track reliably is found. The data here shows the recordings of the animal behaviour and the analysis of its motion features that were obtained by pose estimation and used to develop a machine learning model to classify the whether animals are exhibiting a positive behaviour or negative behaviour. The dataset contains old and young mice behaviour responses that were used to compare age-related changes. The angular velocity and duration of response were one of several features that extracted and compared.
DOI: 10.25442/hku.29209043.v1
title: Supporting Data For: "Deep Learning-based Multi-Camera 3D-Analysis for Automated Quantification of Optomotor Response in Mice"

description: This research project explores the analysis and quantification of Optomotor Response (OMR) in Mice. OMR is a reflex response that is used to analyse vision acuity in preclinical research. The experiment involves placing the animal inside a chamber surrounded by computer screens to create a virtual stimulus that the animal manifests as a cyclinder made up of gratings of customizable widths (spatial frequency). The cylinder rotates in either clock-wise or counter clockwise directions stimulating the left eye or the right eye, respectively. If the animal can discern the movement of the gratings, it reflexively follows the stimulus by shifting its head in the same direction. If The subject does not respond to the stimulus by following the stimulus with its head, it is deem to not be able to perceive the stimulus at that particular spatial frequency. The experiment follows a staircase protocol to test the mouse at multiple spatial frequencies until the maximum spatial frequency at which the animal can track reliably is found. The data here shows the recordings of the animal behaviour and the analysis of its motion features that were obtained by pose estimation and used to develop a machine learning model to classify the whether animals are exhibiting a positive behaviour or negative behaviour. The dataset contains old and young mice behaviour responses that were used to compare age-related changes. The angular velocity and duration of response were one of several features that extracted and compared.
DOI: 10.7910/dvn/bfinpm
title: Available Data for "Inverse Modeling of High Global Warming Potential Perfluorinated Greenhouse Gases in Southeastern China"

description: Observational data (provided as NetCDF format) of SF6, NF3, PFC-14, PFC-116, and PFC-318 from 202110 to 202312 at Xichong Station, Shenzhen, China. All the datasets are collected by using ODS5-pro GC-MS system and validated by GC-werk software of AGAGE network.