cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e227222
title: Data on the analysis of resource allocation efficiency of community health institutions in China

description: This study focuses on
the critical role of Chinese Community health institutions (CHIs) in primary healthcare services, aiming to
systematically evaluate the efficiency level of their resource allocation and
explore optimization strategies aligned with the characteristics of primary
healthcare services.The data used in this
study were obtained from the China Health Statistics
Yearbook (2018-2022), the China Health and Family Planning Statistics Yearbook
(2016-2017), and the China Statistical Yearbook.
DOI: 10.3886/e227222v3-180495
title: Data on the analysis of resource allocation efficiency of community health institutions in China

description: This study focuses on
the critical role of Chinese Community health institutions (CHIs) in primary healthcare services, aiming to
systematically evaluate the efficiency level of their resource allocation and
explore optimization strategies aligned with the characteristics of primary
healthcare services.The data used in this
study were obtained from the China Health Statistics
Yearbook (2018-2022), the China Health and Family Planning Statistics Yearbook
(2016-2017), and the China Statistical Yearbook.
DOI: 10.15468/dl.nx7eme
title: Occurrence Download

description: A dataset containing 1858668 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is one of (China, Chinese Taipei)",
 "DatasetKey is iNaturalist Research-grade Observations"
 ]
}

 The dataset includes 1858668 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001399-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11561633
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.14288/1.0132527
title: Historical Chinese Language Materials in British Columbia: An Electronic Inventory

description: Historical Chinese Language Materials in British Columbia (HCLMBC) was a collaboration between UBC and SFU to digitize historical records and images related to Chinese settlement and life in British Columbia. Topics covered include family photographs, household tool photographs, various reports, and local Chinese-Canadian organizations. The image collection consists of personal photo albums, city photos, and photographs of everyday items from the early 20th century. The catalogue csv files are bilingual compilations of records detailing holdings related to Chinese-Canadian history at various institutions, with call numbers and contact names for further research. Some characters do not render in Excel and are best viewed in another program. This information was collected in Chinese and English on the HCLMBC website which can be seen in archived form here: http://wayback.archive-it.org/org-734/20140129191004/http://burton.library.ubc.ca/hclmbc/index.html
DOI: 10.15468/dl.7zvk3k
title: Occurrence Download

description: A dataset containing 321 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Notholirion bulbuliferum"
 ]
}

 The dataset includes 321 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024648-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z8bzf3
title: Occurrence Download

description: A dataset containing 8345 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Portunus pelagicus (Linnaeus, 1758)"
 ]
}

 The dataset includes 8345 records from 101 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007149-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18150/x1kj3h
title: Estradiol induces global changes in miRNA expression in endometrial cancer cells and upregulates oncogenic miR-182

description: The dataset presents data from the article entitled "Estradiol induces global changes in miRNA expression in endometrial cancer cells and upregulates oncogenic miR-182" published in bioRxiv. This study aimed to assess the role of the miRNA-estrogen axis in endometrial cancer cellsThe dataset presents calculations of relative expression of selected miRNAs which expression was most strongly induced by estradiol in endometrial cancer cells, the expression of selected microRNAs in dissected tissue of endometrial cancer and healthy endometrium, the proliferation of mimic miR-182-transfected 213 Ishikawa cells compared to control, and the relative expression of selected targets in mimic miR-182 and anti-miR-182 -transfected Ishikawa cells.
DOI: 10.15468/dl.8dn6cd
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.76409 24.420142, 88.686971 24.414565, 88.611771 24.397973, 88.540359 24.370778, 88.474507 24.333657, 88.415847 24.287531, 88.365824 24.233545, 88.325669 24.173038, 88.296359 24.107507, 88.27860200000001 24.038572, 88.272819 23.967934, 88.27913100000001 23.897334, 88.297365 23.828508, 88.32705300000001 23.763148, 88.367452 23.702856, 88.417558 23.64911, 88.476135 23.603223, 88.541743 23.566317, 88.61277699999999 23.539293, 88.6875 23.52281, 88.76409 23.517271, 88.84068000000001 23.52281, 88.915403 23.539293, 88.986437 23.566317, 89.05204500000001 23.603223, 89.11062200000001 23.64911, 89.16072800000001 23.702856, 89.201127 23.763148, 89.23081500000001 23.828508, 89.249049 23.897334, 89.25536099999999 23.967934, 89.249578 24.038572, 89.231821 24.107507, 89.202511 24.173038, 89.162356 24.233545, 89.11233300000001 24.287531, 89.053673 24.333657, 88.987821 24.370778, 88.916409 24.397973, 88.84120900000001 24.414565, 88.76409 24.420142))"},{"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 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004516-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ecv9dn
title: Occurrence Download

description: A dataset containing 107 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Calamagrostis arundinacea",
 "Year 1970-2000"
 ]
}

 The dataset includes 107 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004518-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14578448
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.15468/dl.4yqevq
title: Occurrence Download

description: A dataset listing the 2 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chromolaena odorata"
 ]
}

 The dataset's 2 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004507-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qdut8q
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.76409 24.420142, 88.686971 24.414565, 88.611771 24.397973, 88.540359 24.370778, 88.474507 24.333657, 88.415847 24.287531, 88.365824 24.233545, 88.325669 24.173038, 88.296359 24.107507, 88.27860200000001 24.038572, 88.272819 23.967934, 88.27913100000001 23.897334, 88.297365 23.828508, 88.32705300000001 23.763148, 88.367452 23.702856, 88.417558 23.64911, 88.476135 23.603223, 88.541743 23.566317, 88.61277699999999 23.539293, 88.6875 23.52281, 88.76409 23.517271, 88.84068000000001 23.52281, 88.915403 23.539293, 88.986437 23.566317, 89.05204500000001 23.603223, 89.11062200000001 23.64911, 89.16072800000001 23.702856, 89.201127 23.763148, 89.23081500000001 23.828508, 89.249049 23.897334, 89.25536099999999 23.967934, 89.249578 24.038572, 89.231821 24.107507, 89.202511 24.173038, 89.162356 24.233545, 89.11233300000001 24.287531, 89.053673 24.333657, 88.987821 24.370778, 88.916409 24.397973, 88.84120900000001 24.414565, 88.76409 24.420142))"},{"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 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004519-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18328701
title: Dataset for : Realistic Multi-Fault Diagnostics of Millions-Scale Li-ion batteries with Rapid Unsupervised Learning

description: Dataset for : Realistic Multi-Fault Diagnostics of Millions-Scale Li-ion batteries with Rapid Unsupervised Learning

Abstract



The rapid deployment of battery swapping stations necessitates scalable and reliable fault diagnosis, yet massive, sparse operational data and scarce labeled samples make this challenging. Here, we report a rapid unsupervised learning framework for realistic  multi-fault diagnosis in million-scale battery fleets. Our approach employs a double-layer mechanism. First, we rapidly screen for abnormal devices by extracting features from voltage-envelope sequences. Subsequently, we pinpoint faulty cells and types using an enhanced two-stage unsupervised clustering combined with rule-based fault tracing. The framework is validated on a production dataset of over 128,000 devices, achieving 97.33% device-layer and 99.66% cell-layer accuracy. Laboratory tests on recalled batteries further confirm the detection of low-capacity and micro-short-circuit faults. These results demonstrate scalability and robustness under sparse-data conditions, enabling reliable operations for large-scale energy storage systems.


Dataset Structure

 

DataRepo/

├── fullDataset/

│   └── fullDataset.json             # Feature data for all devices

└── predefinedDataset/

    ├── data/                        # Raw data for predefined devices

    └── processedData/

        ├── predefinedFeatures.json  # Extracted features for predefined devices

        ├── device_level_info.csv    # Device-level information extracted from JSON

        └── cell_level_info.csv      # Cell-level information extracted from JSON

Dataset Description

1. Predefined Dataset (predefinedDataset/)

The predefined dataset contains data for a selected set of devices used in preliminary research:

 Raw Data ( data/ )



Contains raw voltage data files for predefined devices

Each file represents voltage measurements from a single device

Data format: CSV files with timestamp and voltage readings for each cell Processed Data ( processedData/ )

Contains feature-extracted data from the raw measurements

predefinedFeatures.json :



JSON file with device filenames as keys

Each value contains comprehensive feature information for the corresponding device

Features include both device-level and cell-level characteristics



device_level_info.csv :



CSV file extracted from the JSON

Contains only device-level information for easier visualization and analysis



cell_level_info.csv :



CSV file extracted from the JSON

Contains only cell-level information for focused analysis




2. Full Dataset (fullDataset/)

The full dataset contains feature-extracted data for all devices:





fullDataset.json :









JSON file with the same structure as predefinedFeatures.json

Contains feature data for all devices in the dataset

Format and feature definitions are consistent with the predefined dataset
DOI: 10.5281/zenodo.18328700
title: Dataset for : Realistic Multi-Fault Diagnostics of Millions-Scale Li-ion batteries with Rapid Unsupervised Learning

description: Dataset for : Realistic Multi-Fault Diagnostics of Millions-Scale Li-ion batteries with Rapid Unsupervised Learning

Abstract



The rapid deployment of battery swapping stations necessitates scalable and reliable fault diagnosis, yet massive, sparse operational data and scarce labeled samples make this challenging. Here, we report a rapid unsupervised learning framework for realistic  multi-fault diagnosis in million-scale battery fleets. Our approach employs a double-layer mechanism. First, we rapidly screen for abnormal devices by extracting features from voltage-envelope sequences. Subsequently, we pinpoint faulty cells and types using an enhanced two-stage unsupervised clustering combined with rule-based fault tracing. The framework is validated on a production dataset of over 128,000 devices, achieving 97.33% device-layer and 99.66% cell-layer accuracy. Laboratory tests on recalled batteries further confirm the detection of low-capacity and micro-short-circuit faults. These results demonstrate scalability and robustness under sparse-data conditions, enabling reliable operations for large-scale energy storage systems.


Dataset Structure

 

DataRepo/

├── fullDataset/

│   └── fullDataset.json             # Feature data for all devices

└── predefinedDataset/

    ├── data/                        # Raw data for predefined devices

    └── processedData/

        ├── predefinedFeatures.json  # Extracted features for predefined devices

        ├── device_level_info.csv    # Device-level information extracted from JSON

        └── cell_level_info.csv      # Cell-level information extracted from JSON

Dataset Description

1. Predefined Dataset (predefinedDataset/)

The predefined dataset contains data for a selected set of devices used in preliminary research:

 Raw Data ( data/ )



Contains raw voltage data files for predefined devices

Each file represents voltage measurements from a single device

Data format: CSV files with timestamp and voltage readings for each cell Processed Data ( processedData/ )

Contains feature-extracted data from the raw measurements

predefinedFeatures.json :



JSON file with device filenames as keys

Each value contains comprehensive feature information for the corresponding device

Features include both device-level and cell-level characteristics



device_level_info.csv :



CSV file extracted from the JSON

Contains only device-level information for easier visualization and analysis



cell_level_info.csv :



CSV file extracted from the JSON

Contains only cell-level information for focused analysis




2. Full Dataset (fullDataset/)

The full dataset contains feature-extracted data for all devices:





fullDataset.json :









JSON file with the same structure as predefinedFeatures.json

Contains feature data for all devices in the dataset

Format and feature definitions are consistent with the predefined dataset
DOI: 10.17617/3.dgojrq
title: HoloRECOMB

description: Chromosomal fissions, fusions and whole-genome duplications propel genome evolution, yet their impact on meiotic recombination is obscured by the centromere constraint, as in monocentric species most large rearrangements are lethal1–4. Holocentric organisms, which distribute kinetochore activity along the entire chromosome, overcome this barrier and therefore offer a unique window onto the interplay between karyotype change and crossover control2. We assembled chromosome-scale genomes for 20 holocentric Rhynchospora species (including 56 haplotypes), representing all major clades of the genus, featuring satellite-based holocentromeres5,6, and integrated single-gamete crossover maps, high-resolution meiotic synapsis cytology and Hi-C chromatin architecture. Breakpoint analysis shows that holocentromeric Tyba satellite arrays6,7 are recurrent hotspots for both chromosome fusions and fissions, driving the genus extraordinary chromosome number variation from 2n = 4 to 36. Crossover landscapes segregate into two classes: strongly distal-biased versus irregularly distributed, which is correlated with divergent patterns of synapsis elongation. Moreover, crossover number scales with chromosome count and meiotic axis length. In contrast, crossover density per megabase is inversely related to chromosome length and to chromatin-loop size. We propose that chromosome fissions create karyotypes with smaller chromosomes folded into shorter loops, thereby increasing the axial substrate accessible for double-strand break formation and elevating recombination frequency. Together, our results provide a structural link between large-scale structural chromosome evolution and meiotic recombination through coupled changes in chromosome number, size, loop geometry, and synapsis dynamics.
DOI: 10.5281/zenodo.18333340
title: Metagenomics-2
DOI: 10.5281/zenodo.18333341
title: Metagenomics-2
DOI: 10.15468/dl.qawtmd
title: Occurrence Download

description: A dataset listing the 4 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ulmus wallichiana"
 ]
}

 The dataset's 4 records were derived from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004495-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bm3u7r
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Calamagrostis epigejos",
 "Year 1970-2000"
 ]
}

 The dataset includes 53 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004523-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17617/3.qtmwum
title: Pan-Holocentromeres

description: Centromeres are essential for chromosome segregation, yet their organisation and evolution remain poorly understood in holocentric species, where kinetochore activity is distributed along entire chromosomes1,2. While monocentric centromeres are often structured by megabase-sized satellite arrays3–5, the role of repetitive DNA in holocentric systems remains enigmatic. Here, we analyse the dynamics of centromeric Tyba satellite DNA repeats and transposable elements across a chromosome-scale pangenome comprising 52 long-read genome assemblies from 20 Rhynchospora species6,7, a plant genus with repeat-based holocentromeres8,9. We identify over 4.6 million monomers of the Tyba satellite repeat, arranged into 43,400 discrete arrays that span all chromosomes. CENH3 ChIP-seq confirms Tyba as the universal centromeric marker across the genus, showcasing the conservation of the same centromeric DNA over 40 million years. We show that Tyba arrays function as modular centromeric units whose number and spacing, but not size, scale with chromosome length. Tyba sequence diversity recapitulates species phylogeny, while higher-order repeat formation and antagonism with transposable elements shape array turnover. A novel synteny-aware algorithm reveals rapid gain, loss, and rearrangement of arrays across homologous chromosomes. Using cytogenetics and polymer simulations, we demonstrate that inter-array spacing governs chromatin loop length and chromatid thickness, linking repeat-based holocentromere organisation directly to chromosome mechanics. Our findings uncover a scalable, modular logic for holocentromere function and establish a framework for understanding the plasticity of repeat-based centromere evolution and genome architecture in eukaryotes.
DOI: 10.15468/dl.yt4w5z
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.76569000000001 24.45489, 88.68855000000001 24.449313, 88.61332899999999 24.432721, 88.541898 24.405526, 88.476028 24.368405, 88.41735199999999 24.322279, 88.367316 24.268293, 88.32715 24.207786, 88.297832 24.142255, 88.280072 24.07332, 88.274287 24.002683, 88.280601 23.932083, 88.29884 23.863258, 88.328537 23.797898, 88.36894700000001 23.737607, 88.419066 23.68386, 88.477659 23.637974, 88.543285 23.601068, 88.61433700000001 23.574045, 88.68908 23.557562, 88.76569000000001 23.552023, 88.84229999999999 23.557562, 88.91704300000001 23.574045, 88.988095 23.601068, 89.053721 23.637974, 89.112314 23.68386, 89.16243299999999 23.737607, 89.202843 23.797898, 89.23254 23.863258, 89.25077899999999 23.932083, 89.257093 24.002683, 89.25130799999999 24.07332, 89.233548 24.142255, 89.20423 24.207786, 89.164064 24.268293, 89.114028 24.322279, 89.055352 24.368405, 88.989482 24.405526, 88.91805100000001 24.432721, 88.84283000000001 24.449313, 88.76569000000001 24.45489))"},{"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 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004522-260120142942310/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.29890229
title: Data for Aritcle "Scalable Semitransparent Organic Solar Cells with Robust Film Thickness Tolerance for Building-Integrated Photovoltaics"

description: The original data in the article “Scalable Semitransparent Organic Solar Cells with Robust Film Thickness Tolerance for Building-Integrated Photovoltaics”
DOI: 10.6084/m9.figshare.29890229.v3
title: Data for Aritcle "Scalable Semitransparent Organic Solar Cells with Robust Film Thickness Tolerance for Building-Integrated Photovoltaics"

description: The original data in the article “Scalable Semitransparent Organic Solar Cells with Robust Film Thickness Tolerance for Building-Integrated Photovoltaics”
DOI: 10.6084/m9.figshare.29890229.v2
title: Data for Aritcle "Scalable Semitransparent Organic Solar Cells with Robust Film Thickness Tolerance for Building-Integrated Photovoltaics"

description: The original data in the article “Scalable Semitransparent Organic Solar Cells with Robust Film Thickness Tolerance for Building-Integrated Photovoltaics”
DOI: 10.34740/kaggle/dsv/14578498
title: Full TMDB Movies Dataset 2024 (1M Movies)