cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.35995
title: Data acquisition
title: Data acquisition

description: Relevant data were retrieved from public databases. The transcriptome dataset of BRCA (TCGA-BRCA; access date: February 27, 2025) was obtained from the UCSC database (https://xena.ucsc.edu/) as the training set, comprising 1,113 BRCA and 113 control tissue samples. The GSE42568 dataset from the GPL570 platform (access date: February 27, 2025) was retrieved from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) as a validation set, including 104 BRCA and 17 control samples. The GSE243526 dataset (GPL18573), containing 12 BRCA and 4 control samples, was also obtained from the GEO database as a scRNA-seq dataset.
description: Relevant data were retrieved from public databases. The transcriptome dataset of BRCA (TCGA-BRCA; access date: February 27, 2025) was obtained from the UCSC database (https://xena.ucsc.edu/) as the training set, comprising 1,113 BRCA and 113 control tissue samples. The GSE42568 dataset from the GPL570 platform (access date: February 27, 2025) was retrieved from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) as a validation set, including 104 BRCA and 17 control samples. The GSE243526 dataset (GPL18573), containing 12 BRCA and 4 control samples, was also obtained from the GEO database as a scRNA-seq dataset.
DOI: 10.25612/837.ym0pnxmnng15
title: Public Crash Data Extract from TxDOT (2023)

description: Crash records for H-GAC counties, including crash type, date/time, roadway system, speed limit, roadway type, geographic information, crash severity, and day of the week for 2023.
DOI: 10.57760/sciencedb.35972
title: Assembled genome and annotation for wild Actinidia hubeiensis
title: Assembled genome and annotation for wild Actinidia hubeiensis

description: We present a new assembled genome for wild Actinidia hubeiensis from a new population in Hubei province. The genome assembled by Hicanu software with HiFI reads. This file contains genome sequences, annotations and protein sequences for two haplotypes.
description: We present a new assembled genome for wild Actinidia hubeiensis from a new population in Hubei province. The genome assembled by Hicanu software with HiFI reads. This file contains genome sequences, annotations and protein sequences for two haplotypes.
DOI: 10.15468/dl.5vnhfd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.746509 24.206105, 88.66951899999999 24.200528, 88.59444499999999 24.183936, 88.523152 24.156741, 88.45741 24.11962, 88.39884600000001 24.073494, 88.348906 24.019508, 88.308814 23.959, 88.279551 23.893468, 88.261821 23.824531, 88.256044 23.753892, 88.262344 23.683289, 88.280545 23.614462, 88.31018299999999 23.549099, 88.350514 23.488804, 88.400537 23.435055, 88.459018 23.389165, 88.52452 23.352257, 88.595439 23.325232, 88.670042 23.308748, 88.746509 23.303209, 88.822976 23.308748, 88.89757899999999 23.325232, 88.968498 23.352257, 89.03400000000001 23.389165, 89.09248100000001 23.435055, 89.142504 23.488804, 89.182835 23.549099, 89.212473 23.614462, 89.23067399999999 23.683289, 89.236974 23.753892, 89.23119699999999 23.824531, 89.21346699999999 23.893468, 89.18420399999999 23.959, 89.14411200000001 24.019508, 89.094172 24.073494, 89.035608 24.11962, 88.969866 24.156741, 88.898573 24.183936, 88.823499 24.200528, 88.746509 24.206105))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004034-260120142942310/datasets/export for details.
DOI: 10.15468/dl.a4kudb
title: Occurrence Download

description: A dataset containing 261 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Hong Kong",
 "TaxonKey is Ropalidia"
 ]
}

 The dataset includes 261 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004035-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.35963
title: Warming and nitrogen addition reshape nitrogen–carbon–energy coupling to regulate N2O fluxes in an alpine wet meadow
title: Warming and nitrogen addition reshape nitrogen–carbon–energy coupling to regulate N2O fluxes in an alpine wet meadow

description: Warming and nitrogen addition reshape nitrogen–carbon–energy coupling to regulate N2O fluxes in an alpine wet meadow
description: Warming and nitrogen addition reshape nitrogen–carbon–energy coupling to regulate N2O fluxes in an alpine wet meadow
DOI: 10.57760/sciencedb.35970
title: determinants of international trade
title: determinants of international trade

description: This database analyzes the determinants of international trade in 54 countries between 2000 and 2023. The data in this database is primarily sourced from the World Bank and the World Trade Organization.
description: This database analyzes the determinants of international trade in 54 countries between 2000 and 2023. The data in this database is primarily sourced from the World Bank and the World Trade Organization.
DOI: 10.57760/sciencedb.35654
title: Python codes for calculating temperature–log m(SiO2) phase relations in the system MgO-SiO2-H2O using pyDEW
title: Python codes for calculating temperature–log m(SiO2) phase relations in the system MgO-SiO2-H2O using pyDEW

description: To show how dissolved silica (measured as molality, m(SiO₂)) affects talc stability, I created temperature–log m(SiO₂) phase diagrams in the system MgO-SiO2-H2O for pressures of 10–30 kbar and temperatures of 500–850°C using the pyDEW package and custom Python codes developed in this study. Here, the python codes are reported.
description: To show how dissolved silica (measured as molality, m(SiO₂)) affects talc stability, I created temperature–log m(SiO₂) phase diagrams in the system MgO-SiO2-H2O for pressures of 10–30 kbar and temperatures of 500–850°C using the pyDEW package and custom Python codes developed in this study. Here, the python codes are reported.
DOI: 10.57760/sciencedb.35979
title: A dataset to evaluate the impact of digital intelligence technology on innovation efficiency and innovation quality of specialized, refined, distinctive and innovative enterprises
title: A dataset to evaluate the impact of digital intelligence technology on innovation efficiency and innovation quality of specialized, refined, distinctive and innovative enterprises

description: The data used in this study are obtained from third-party sources and are subject to access restrictions. The firm-level financial data, digital intelligence technology indicators, innovation efficiency measures, and control variables are available from the China Stock Market and Accounting Research (CSMAR) database. The research sample consists of Specialized, Refined, Differential and Innovative (SRDI) enterprises listed on China’s A-share market.Patent data used to measure innovation quality are authorized patent records obtained from the public patent information system of the China National Intellectual Property Administration (CNIPA). Patents were matched to firms primarily using firm names. As some firms changed their registered names during the sample period, manual screening and matching were conducted to ensure consistency across years.The list of SRDI enterprises is publicly available from the Ministry of Industry and Information Technology of the People’s Republic of China (MIIT).Researchers who have legitimate access to the CSMAR database and publicly available patent data from CNIPA, and who follow the sample selection criteria and variable construction methods described in the Methods section, can fully replicate the findings of this study. The authors did not have any special access privileges to the data that other researchers would not have.
description: The data used in this study are obtained from third-party sources and are subject to access restrictions. The firm-level financial data, digital intelligence technology indicators, innovation efficiency measures, and control variables are available from the China Stock Market and Accounting Research (CSMAR) database. The research sample consists of Specialized, Refined, Differential and Innovative (SRDI) enterprises listed on China’s A-share market.Patent data used to measure innovation quality are authorized patent records obtained from the public patent information system of the China National Intellectual Property Administration (CNIPA). Patents were matched to firms primarily using firm names. As some firms changed their registered names during the sample period, manual screening and matching were conducted to ensure consistency across years.The list of SRDI enterprises is publicly available from the Ministry of Industry and Information Technology of the People’s Republic of China (MIIT).Researchers who have legitimate access to the CSMAR database and publicly available patent data from CNIPA, and who follow the sample selection criteria and variable construction methods described in the Methods section, can fully replicate the findings of this study. The authors did not have any special access privileges to the data that other researchers would not have.
DOI: 10.57760/sciencedb.35917
title: Mplus code for data analysis of public sector employees
title: Mplus code for data analysis of public sector employees

description: This study explores the influences and mechanisms of two dimensions of relational job characteristics—job impact on service recipients and job contact with service recipients—on Chinese public sector employees’ proactive service behavior. Specifically, it examines the mediating role of work calling. In addition, it explores public service motivation as a moderator of the relationships between relational job characteristics and work calling. 
description: This study explores the influences and mechanisms of two dimensions of relational job characteristics—job impact on service recipients and job contact with service recipients—on Chinese public sector employees’ proactive service behavior. Specifically, it examines the mediating role of work calling. In addition, it explores public service motivation as a moderator of the relationships between relational job characteristics and work calling.
DOI: 10.57760/sciencedb.35996
title: Spatial transcriptomics data from 10 NSCLC samples
title: Spatial transcriptomics data from 10 NSCLC samples

description: Spatial transcriptomics data from 10 NSCLC samples, saved in a list of Seurat objects
description: Spatial transcriptomics data from 10 NSCLC samples, saved in a list of Seurat objects
DOI: 10.57760/sciencedb.36007
title: Passive Sampling SEM-EDS Dataset of Atmospheric Particulate Matter in Caitun and Xihu Areas of Benxi City, 2023
title: Passive Sampling SEM-EDS Dataset of Atmospheric Particulate Matter in Caitun and Xihu Areas of Benxi City, 2023

description: Atmospheric particulate matter at the sampling sites was collected via settling using passive samplers over a specific time period, followed by sample analysis with scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) to generate the corresponding dataset.Caitun：B5031 2023/1/17-2023/1/27B5033 2023/2/10-2023/2/24B5044 2023/2/24-2023/3/13A2045 2023/8/11-2023/8/21A2107 2023/9/11-2023/9/21A2109 2023/9/22-2023/10/4A2021 2023/10/23-2023/11/3A2026 2023/11/3-2023/11/13A2019 2023/11/20-2023/11/30Xihu：B5034 2023/1/17-2023/1/27B5032 2023/2/10-2023/2/24B5045 2023/2/24-2023/3/13A2043 2023/8/11-2023/8/21A2108 2023/9/11-2023/9/21A2110 2023/9/22-2023/10/4A2017 2023/10/23-2023/11/3A2028 2023/11/3-2023/11/13A2020 2023/11/20-2023/11/30
description: Atmospheric particulate matter at the sampling sites was collected via settling using passive samplers over a specific time period, followed by sample analysis with scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) to generate the corresponding dataset.Caitun：B5031 2023/1/17-2023/1/27B5033 2023/2/10-2023/2/24B5044 2023/2/24-2023/3/13A2045 2023/8/11-2023/8/21A2107 2023/9/11-2023/9/21A2109 2023/9/22-2023/10/4A2021 2023/10/23-2023/11/3A2026 2023/11/3-2023/11/13A2019 2023/11/20-2023/11/30Xihu：B5034 2023/1/17-2023/1/27B5032 2023/2/10-2023/2/24B5045 2023/2/24-2023/3/13A2043 2023/8/11-2023/8/21A2108 2023/9/11-2023/9/21A2110 2023/9/22-2023/10/4A2017 2023/10/23-2023/11/3A2028 2023/11/3-2023/11/13A2020 2023/11/20-2023/11/30
DOI: 10.25612/837.1zq5bznbbwq1
title: Harris County Parcels 2021

description: Parcel shapefiles from Harris Central Appraisal District (HCAD).
DOI: 10.15468/dl.srys2n
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((98.393 4.212163, 98.322548 4.206593, 98.253834 4.190021, 98.188553 4.162855, 98.12831199999999 4.125766, 98.07459799999999 4.079668, 98.02873200000001 4.025697, 97.991845 3.965184, 97.96484100000001 3.89962, 97.948386 3.83062, 97.94288 3.759884, 97.948457 3.689153, 97.964978 3.620169, 97.99203199999999 3.55463, 98.028953 3.494149, 98.07483000000001 3.440213, 98.128533 3.39415, 98.18874 3.357093, 98.25397100000001 3.329953, 98.32262 3.313397, 98.393 3.307833, 98.46338 3.313397, 98.53202899999999 3.329953, 98.59726000000001 3.357093, 98.657467 3.39415, 98.71117 3.440213, 98.757047 3.494149, 98.79396800000001 3.55463, 98.821022 3.620169, 98.837543 3.689153, 98.84312 3.759884, 98.837614 3.83062, 98.82115899999999 3.89962, 98.794155 3.965184, 98.757268 4.025697, 98.71140200000001 4.079668, 98.65768799999999 4.125766, 98.597447 4.162855, 98.532166 4.190021, 98.463452 4.206593, 98.393 4.212163))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004036-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.je58vd
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Polygala lewtonii",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 24 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004037-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zqsfe6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.746509 24.206105, 88.66951899999999 24.200528, 88.59444499999999 24.183936, 88.523152 24.156741, 88.45741 24.11962, 88.39884600000001 24.073494, 88.348906 24.019508, 88.308814 23.959, 88.279551 23.893468, 88.261821 23.824531, 88.256044 23.753892, 88.262344 23.683289, 88.280545 23.614462, 88.31018299999999 23.549099, 88.350514 23.488804, 88.400537 23.435055, 88.459018 23.389165, 88.52452 23.352257, 88.595439 23.325232, 88.670042 23.308748, 88.746509 23.303209, 88.822976 23.308748, 88.89757899999999 23.325232, 88.968498 23.352257, 89.03400000000001 23.389165, 89.09248100000001 23.435055, 89.142504 23.488804, 89.182835 23.549099, 89.212473 23.614462, 89.23067399999999 23.683289, 89.236974 23.753892, 89.23119699999999 23.824531, 89.21346699999999 23.893468, 89.18420399999999 23.959, 89.14411200000001 24.019508, 89.094172 24.073494, 89.035608 24.11962, 88.969866 24.156741, 88.898573 24.183936, 88.823499 24.200528, 88.746509 24.206105))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004039-260120142942310/datasets/export for details.
DOI: 10.15468/dl.2thmk6
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((98.40600000000001 4.217163, 98.335548 4.211593, 98.266834 4.195021, 98.20155099999999 4.167855, 98.141311 4.130766, 98.087596 4.084668, 98.04173 4.030697, 98.004842 3.970184, 97.977839 3.90462, 97.961383 3.83562, 97.955877 3.764883, 97.961455 3.694153, 97.977975 3.625169, 98.00503 3.55963, 98.041951 3.499149, 98.087828 3.445213, 98.141531 3.39915, 98.201739 3.362093, 98.26697 3.334953, 98.33562000000001 3.318397, 98.40600000000001 3.312833, 98.47638000000001 3.318397, 98.54503 3.334953, 98.61026099999999 3.362093, 98.670469 3.39915, 98.724172 3.445213, 98.770049 3.499149, 98.80697000000001 3.55963, 98.834025 3.625169, 98.850545 3.694153, 98.856123 3.764883, 98.850617 3.83562, 98.83416099999999 3.90462, 98.807158 3.970184, 98.77027 4.030697, 98.72440400000001 4.084668, 98.670689 4.130766, 98.610449 4.167855, 98.54516599999999 4.195021, 98.47645199999999 4.211593, 98.40600000000001 4.217163))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004040-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29039445
title: ForceMapping: learning visual-force features from vision for soft objects manipulation

description: To realize delicate manipulation of soft/rigid objects, incorporating some notion of force into the controller is essential. Such forces can either be measured explicitly via onboard sensors or estimated from visual input through an offline-trained network. For visuomotor control, unlike state-based models, visual input (i.e. RGB images) is first encoded into a low-dimensional latent before feeding into some low-level policy. The encoder network is usually trained end-to-end using supervised policy loss or pre-trained with unsupervised RGB reconstruction loss. This work proposes a learning approach that implicitly integrates force-relevant features into the latent image encoding, rather than explicit force integration. Our proposal, ForceMapping, incorporates a supervised force prediction loss as an auxiliary optimization objective alongside unsupervised RGB reconstruction loss or policy loss. For soft bodies, forces are estimated from the raw RGB image input, given the correlation between object deformation and imposed forces. To validate our approach, we designed a soft object stacking task requiring force-aware manipulation. Soft-body deformation was efficiently rendered through a fast and stable pipeline, utilizing states obtained from rigid-body simulation. Our experiments demonstrated that ForceMapping encouraged attention to and encoding of force-relevant features, leading to increased task success rates (from 25% to 75%) and reduced deviations from a target critical load (from 300% to 40%) compared to only-RGB model.
DOI: 10.6084/m9.figshare.29039445.v1
title: ForceMapping: learning visual-force features from vision for soft objects manipulation

description: To realize delicate manipulation of soft/rigid objects, incorporating some notion of force into the controller is essential. Such forces can either be measured explicitly via onboard sensors or estimated from visual input through an offline-trained network. For visuomotor control, unlike state-based models, visual input (i.e. RGB images) is first encoded into a low-dimensional latent before feeding into some low-level policy. The encoder network is usually trained end-to-end using supervised policy loss or pre-trained with unsupervised RGB reconstruction loss. This work proposes a learning approach that implicitly integrates force-relevant features into the latent image encoding, rather than explicit force integration. Our proposal, ForceMapping, incorporates a supervised force prediction loss as an auxiliary optimization objective alongside unsupervised RGB reconstruction loss or policy loss. For soft bodies, forces are estimated from the raw RGB image input, given the correlation between object deformation and imposed forces. To validate our approach, we designed a soft object stacking task requiring force-aware manipulation. Soft-body deformation was efficiently rendered through a fast and stable pipeline, utilizing states obtained from rigid-body simulation. Our experiments demonstrated that ForceMapping encouraged attention to and encoding of force-relevant features, leading to increased task success rates (from 25% to 75%) and reduced deviations from a target critical load (from 300% to 40%) compared to only-RGB model.