cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31130701
title: <b><i>Can Probabilistic Distributions of Sentence and Clause Lengths Differentiate Between Translation Directions</i></b>?

description: This repository includes the complete raw dataset(s), all cleaned/refined datasets used for analysis, the code/scripts for data processing and statistical analyses, and the corresponding original outputs and statistical results.
DOI: 10.6084/m9.figshare.31130701.v1
title: <b><i>Can Probabilistic Distributions of Sentence and Clause Lengths Differentiate Between Translation Directions</i></b>?

description: This repository includes the complete raw dataset(s), all cleaned/refined datasets used for analysis, the code/scripts for data processing and statistical analyses, and the corresponding original outputs and statistical results.
DOI: 10.15468/dl.3up2bg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((90.15928700000001 24.990912, 90.081816 24.985335, 90.00627299999999 24.968743, 89.93453599999999 24.941547, 89.868387 24.904426, 89.80946299999999 24.8583, 89.759218 24.804315, 89.718886 24.74381, 89.68944999999999 24.678281, 89.67162 24.60935, 89.665818 24.538717, 89.672166 24.468122, 89.690487 24.399304, 89.720314 24.333951, 89.760896 24.273667, 89.811228 24.219927, 89.87006599999999 24.174047, 89.935964 24.137146, 90.00731 24.110127, 90.08236100000001 24.093647, 90.15928700000001 24.088109, 90.23621300000001 24.093647, 90.31126399999999 24.110127, 90.38261 24.137146, 90.448508 24.174047, 90.507346 24.219927, 90.557678 24.273667, 90.59826 24.333951, 90.62808699999999 24.399304, 90.64640799999999 24.468122, 90.652756 24.538717, 90.64695399999999 24.60935, 90.629124 24.678281, 90.599688 24.74381, 90.55935599999999 24.804315, 90.509111 24.8583, 90.450187 24.904426, 90.384038 24.941547, 90.31230100000001 24.968743, 90.23675799999999 24.985335, 90.15928700000001 24.990912))"},{"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/0006047-260120142942310/datasets/export for details.
DOI: 10.15468/dl.jeahmm
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Erythrolamprus cursor"
 ]
}

 The dataset includes 12 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006048-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/vgdupb
title: A new species of the genus Smicromyrme THOMSON, 1870 (Hymenoptera: Mutillidae) from India

description: This dataset contains the digitized treatments in Plazi based on the original journal article Terine, Joshua B., Kumar, Girish P. (2023): A new species of the genus Smicromyrme THOMSON, 1870 (Hymenoptera: Mutillidae) from India. Zootaxa 5353 (1): 82-88, DOI: 10.11646/zootaxa.5353.1.6, URL: http://dx.doi.org/10.11646/zootaxa.5353.1.6AbstractSmicromyrme (Eremotilla) williamsi Terine & Girish Kumar, sp. nov. is described based on a female specimen from Kerala, Southern India. A modified key to the S. coromandelica species-group (females only) is given.
DOI: 10.34740/kaggle/dsv/14588769
title: Layline Shareholder activism dataset
DOI: 10.5281/zenodo.17104715
title: Neural Organoid Cell Atlas with Perturbations (NOCAP)

description: The Neural Organoid Cell Atlas with Perturbations (NOCAP) is a large-scale single-cell transcriptomic resource generated from human brain organoids. It comprises 3,643,130 cells spanning neurotypical, genetically perturbed, and patient-derived disease organoids relevant to autism spectrum disorder (ASD) and neurodevelopment. NOCAP enables systematic investigation of cell-type composition, developmental trajectories, and transcriptional responses to genetic perturbations in a human-relevant model system.

The resource is organized into complementary components:



CoreAtlas: Exclusive datasets for normal organoids (14 datasets, 148 samples) containing 1,418,675 cells

ExtendedAtlas: Datasets designed for normal and genetically perturbed organoids or disease organoids derived from patients, containing 2,224,455 cells (26 datasets, 353 samples)

All: Integrated dataset combining both Core and Extended Atlases
DOI: 10.5281/zenodo.17104714
title: Neural Organoid Cell Atlas with Perturbations (NOCAP)

description: The Neural Organoid Cell Atlas with Perturbations (NOCAP) is a large-scale single-cell transcriptomic resource generated from human brain organoids. It comprises 3,643,130 cells spanning neurotypical, genetically perturbed, and patient-derived disease organoids relevant to autism spectrum disorder (ASD) and neurodevelopment. NOCAP enables systematic investigation of cell-type composition, developmental trajectories, and transcriptional responses to genetic perturbations in a human-relevant model system.

The resource is organized into complementary components:



CoreAtlas: Exclusive datasets for normal organoids (14 datasets, 148 samples) containing 1,418,675 cells

ExtendedAtlas: Datasets designed for normal and genetically perturbed organoids or disease organoids derived from patients, containing 2,224,455 cells (26 datasets, 353 samples)

All: Integrated dataset combining both Core and Extended Atlases
DOI: 10.6084/m9.figshare.31130758
title: Additional file 3 of Particulate matter 2.5 promotes bladder cancer cell migration and invasion through the crosstalk between integrin-mediated MAPK/ERK and Wnt/β-catenin pathways

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.31130758.v1
title: Additional file 3 of Particulate matter 2.5 promotes bladder cancer cell migration and invasion through the crosstalk between integrin-mediated MAPK/ERK and Wnt/β-catenin pathways

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.31130757
title: Additional file 1 of Impact of nonpharmaceutical interventions on infectious disease patterns in Yunnan Province across the COVID-19 pandemic phases

description: Supplementary Material 1: Supplementary Table 1. Monthly reported cases of 45 infectious diseases in Yunnan Province, China (2014–2023).
DOI: 10.6084/m9.figshare.31130757.v1
title: Additional file 1 of Impact of nonpharmaceutical interventions on infectious disease patterns in Yunnan Province across the COVID-19 pandemic phases

description: Supplementary Material 1: Supplementary Table 1. Monthly reported cases of 45 infectious diseases in Yunnan Province, China (2014–2023).
DOI: 10.6084/m9.figshare.31130760
title: Additional file 2 of Impact of nonpharmaceutical interventions on infectious disease patterns in Yunnan Province across the COVID-19 pandemic phases

description: Supplementary Material 2: Supplementary Table S2. Annual reported cases of 36 infectious diseases in Yunnan Province, China (2014–2023).
DOI: 10.6084/m9.figshare.31130760.v1
title: Additional file 2 of Impact of nonpharmaceutical interventions on infectious disease patterns in Yunnan Province across the COVID-19 pandemic phases

description: Supplementary Material 2: Supplementary Table S2. Annual reported cases of 36 infectious diseases in Yunnan Province, China (2014–2023).
DOI: 10.15468/dl.qymnw8
title: Occurrence Download

description: A dataset containing 1214 records in GBIF matching the query:
SELECT
 '128' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= -8.098015256935714
 AND decimalLatitude &lt;= -4.045127286108716
 AND decimalLongitude &gt;= -65.25780903919457
 AND decimalLongitude &lt;= -59.958833819396
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 1214 records were derived from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006040-260120142942310/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.31130764
title: Additional file 1 of Global, regional, and national burden of gastric cancer attributable to smoking and a high-sodium diet from 1990 to 2021: a global burden of disease study 2021

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.31130764.v1
title: Additional file 1 of Global, regional, and national burden of gastric cancer attributable to smoking and a high-sodium diet from 1990 to 2021: a global burden of disease study 2021

description: Supplementary Material 1.
DOI: 10.15468/dl.h3jrr9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((90.15928700000001 24.990912, 90.081816 24.985335, 90.00627299999999 24.968743, 89.93453599999999 24.941547, 89.868387 24.904426, 89.80946299999999 24.8583, 89.759218 24.804315, 89.718886 24.74381, 89.68944999999999 24.678281, 89.67162 24.60935, 89.665818 24.538717, 89.672166 24.468122, 89.690487 24.399304, 89.720314 24.333951, 89.760896 24.273667, 89.811228 24.219927, 89.87006599999999 24.174047, 89.935964 24.137146, 90.00731 24.110127, 90.08236100000001 24.093647, 90.15928700000001 24.088109, 90.23621300000001 24.093647, 90.31126399999999 24.110127, 90.38261 24.137146, 90.448508 24.174047, 90.507346 24.219927, 90.557678 24.273667, 90.59826 24.333951, 90.62808699999999 24.399304, 90.64640799999999 24.468122, 90.652756 24.538717, 90.64695399999999 24.60935, 90.629124 24.678281, 90.599688 24.74381, 90.55935599999999 24.804315, 90.509111 24.8583, 90.450187 24.904426, 90.384038 24.941547, 90.31230100000001 24.968743, 90.23675799999999 24.985335, 90.15928700000001 24.990912))"},{"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/0006049-260120142942310/datasets/export for details.
DOI: 10.6084/m9.figshare.31130769
title: Additional file 2 of Informing the redesign of psychiatric seclusion rooms: a mixed-methods pre-evaluation with individuals with lived experience

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.31130769.v1
title: Additional file 2 of Informing the redesign of psychiatric seclusion rooms: a mixed-methods pre-evaluation with individuals with lived experience

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.31130775
title: Additional file 1 of Benchmarking of methods to analyse data derived from GBS-MeDIP

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.31130775.v1
title: Additional file 1 of Benchmarking of methods to analyse data derived from GBS-MeDIP

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.31130823
title: Additional file 3 of PAK2 promotes CTC cluster formation by phosphorylating E-cadherin to enhance cell-cell adhesion in breast cancer

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.31130823.v1
title: Additional file 3 of PAK2 promotes CTC cluster formation by phosphorylating E-cadherin to enhance cell-cell adhesion in breast cancer

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.31130815
title: Additional file 1 of Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

description: Additional file 1: Data S1. Contains results and output files from the DDA case study. Table S1. Comma-separated values (CSV file) includes the identified protein intensities along with the differential expression analysis results. Output S1. (PDF file) is the FP-Analyst generated report with all corresponding visualizations and outputs.
DOI: 10.6084/m9.figshare.31130815.v1
title: Additional file 1 of Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

description: Additional file 1: Data S1. Contains results and output files from the DDA case study. Table S1. Comma-separated values (CSV file) includes the identified protein intensities along with the differential expression analysis results. Output S1. (PDF file) is the FP-Analyst generated report with all corresponding visualizations and outputs.
DOI: 10.6084/m9.figshare.31130818
title: Additional file 2 of Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

description: Additional file 2: Data S2. Contains the results and output files from the DIA case study. Table S2. (CSV file) includes the identified protein intensities and differential expression analysis results, based on the data-independent acquisition workflow. Output S2. (PDF file) is the FP-Analyst generated report with all corresponding visualizations and outputs.
DOI: 10.6084/m9.figshare.31130818.v1
title: Additional file 2 of Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

description: Additional file 2: Data S2. Contains the results and output files from the DIA case study. Table S2. (CSV file) includes the identified protein intensities and differential expression analysis results, based on the data-independent acquisition workflow. Output S2. (PDF file) is the FP-Analyst generated report with all corresponding visualizations and outputs.
DOI: 10.6084/m9.figshare.31130821
title: Additional file 3 of Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

description: Additional file 3. Data S3. Contains the results and output files from the TMT case study. Table S3. (CSV file) provides quantified protein intensities and differential expression analysis results obtained from the tandem mass tag (TMT) experiments. Output S3. (PDF file) is the FP-Analyst generated report with all corresponding visualizations and outputs.
DOI: 10.6084/m9.figshare.31130821.v1
title: Additional file 3 of Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

description: Additional file 3. Data S3. Contains the results and output files from the TMT case study. Table S3. (CSV file) provides quantified protein intensities and differential expression analysis results obtained from the tandem mass tag (TMT) experiments. Output S3. (PDF file) is the FP-Analyst generated report with all corresponding visualizations and outputs.
DOI: 10.6084/m9.figshare.31130836
title: Additional file 8 of Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

description: Additional file 8: File S1. Detailed description of data used from the AALS and MSBB repositories in Fig. 1.
DOI: 10.6084/m9.figshare.31130836.v1
title: Additional file 8 of Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

description: Additional file 8: File S1. Detailed description of data used from the AALS and MSBB repositories in Fig. 1.
DOI: 10.6084/m9.figshare.31130839
title: Additional file 9 of Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

description: Additional file 9: File S2. (A: sheets 1 to 4) Comparison of peptide and protein level outputs generated using the FragPipe GUI and the Nextflow-based Frag’n’Flow pipeline. (B: sheets 5 to 6) Protein level intensity matrices from Frag’n’Flow and quantms, used in benchmarking analysis.
DOI: 10.6084/m9.figshare.31130839.v1
title: Additional file 9 of Frag’n’Flow: automated workflow for large-scale quantitative proteomics in high performance computing environments

description: Additional file 9: File S2. (A: sheets 1 to 4) Comparison of peptide and protein level outputs generated using the FragPipe GUI and the Nextflow-based Frag’n’Flow pipeline. (B: sheets 5 to 6) Protein level intensity matrices from Frag’n’Flow and quantms, used in benchmarking analysis.
DOI: 10.5281/zenodo.17112343
title: Mouse Brain Cell Atlas with Perturbations

description: "Mouse Brain Cell Atlas with Perturbations" provides a comprehensive single-cell transcriptomic atlas of the mouse brain, comprising 194,500 high-quality cells derived from 291 samples across 28 datasets. The atlas encompasses 22 brain regions and developmental stages, spanning from embryonic day 10.5 (E10.5) to postnatal day 100 (P100). It includes normal and genetically perturbed samples, enabling systematic analysis of cell-type composition, developmental trajectories, and transcriptional responses to genetic perturbations in the murine nervous system.
DOI: 10.5281/zenodo.17112344
title: Mouse Brain Cell Atlas with Perturbations

description: "Mouse Brain Cell Atlas with Perturbations" provides a comprehensive single-cell transcriptomic atlas of the mouse brain, comprising 194,500 high-quality cells derived from 291 samples across 28 datasets. The atlas encompasses 22 brain regions and developmental stages, spanning from embryonic day 10.5 (E10.5) to postnatal day 100 (P100). It includes normal and genetically perturbed samples, enabling systematic analysis of cell-type composition, developmental trajectories, and transcriptional responses to genetic perturbations in the murine nervous system.
DOI: 10.6084/m9.figshare.31130847
title: Additional file 2 of Rapid assessment of the factors contributing to the increase in maternal mortality during the COVID-19 pandemic in the Latin American region

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.31130847.v1
title: Additional file 2 of Rapid assessment of the factors contributing to the increase in maternal mortality during the COVID-19 pandemic in the Latin American region

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.31130899
title: Additional file 3 of Stratifying high-risk prediabetes clusters using blood-based epigenetic markers

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.31130899.v1
title: Additional file 3 of Stratifying high-risk prediabetes clusters using blood-based epigenetic markers

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.31130902.v1
title: Additional file 4 of Stratifying high-risk prediabetes clusters using blood-based epigenetic markers

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.31130905
title: Additional file 5 of Stratifying high-risk prediabetes clusters using blood-based epigenetic markers

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.31130905.v1
title: Additional file 5 of Stratifying high-risk prediabetes clusters using blood-based epigenetic markers

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.31130902
title: Additional file 4 of Stratifying high-risk prediabetes clusters using blood-based epigenetic markers

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.31130908
title: Additional file 6 of Stratifying high-risk prediabetes clusters using blood-based epigenetic markers

description: Supplementary Material 6
DOI: 10.6084/m9.figshare.31130908.v1
title: Additional file 6 of Stratifying high-risk prediabetes clusters using blood-based epigenetic markers

description: Supplementary Material 6
DOI: 10.6084/m9.figshare.31130916.v1
title: Additional file 2 of Assessing the spatiotemporal dynamics and driving factors of human brucellosis in Northern Xinjiang, China (2015–2023)

description: Supplementary material 2. VIF value.
DOI: 10.6084/m9.figshare.31130916
title: Additional file 2 of Assessing the spatiotemporal dynamics and driving factors of human brucellosis in Northern Xinjiang, China (2015–2023)

description: Supplementary material 2. VIF value.
DOI: 10.6084/m9.figshare.30509831.v2
title: <b>A 30 m Multi-Year Dataset of Major Crop Distributions in Xinjiang, China (2013–2024) Based on Harmonized Landsat–Sentinel-2 Data</b>

description: <b>Dataset Title:</b> <b>A 30 m Multi-Year Dataset of Major Crop Distributions in Xinjiang, China (2013–2024) Based on Harmonized Landsat–Sentinel-2 Data</b><b>Description:</b>This dataset provides annual maps of major crop distributions across the Xinjiang region of China from 2013 to 2024 at a spatial resolution of 30 m. The maps were generated exclusively using the Harmonized Landsat–Sentinel (HLS) L30 Version 2.0 surface reflectance product, which integrates observations from Landsat 8/9 and Sentinel-2 sensors into a consistent radiometric and geometric framework.The dataset includes major crop types in Xinjiang, such as cotton, maize, wheat, rice, and other agricultural land-use categories, which are organized into eight thematic classes. All crop distribution maps were produced using a unified classification framework implemented on the Google Earth Engine (GEE) cloud computing platform. The processing workflow integrates all available high-quality HLS observations, models crop phenological trajectories using harmonic analysis of NDVI and LSWI time series, and applies a Random Forest classifier to identify crop types at the pixel level.<br><b>File Naming and Data Explanation:</b>File naming format: `year.tif` (e.g., `2019.tif`)Raster values (1-8) and corresponding crop types:1. Spring Maize2. Summer Maize3. Rice4. Winter Wheat5. Spring Wheat6. Cotton7.Other economic crops8. Economic Forest<b>Data Specifications:</b><b>Spatial Coverage:</b> Xinjiang, China<b>Temporal Coverage: </b>2013-2024 (30m),<b>Spatial Resolution: </b>30 meters<b>Data Format: </b>GeoTIFF<b>Map Projection: </b>WGS84 (EPSG:4326)<b>Key Crops: </b>Cotton, Corn, Wheat, Rice, etc. (Full classification listed above).<br>
DOI: 10.6084/m9.figshare.31130947
title: Additional file 1 of Microbial metabolite 5-formamidoimidazole-4-carboxamide ribotide targets METTL1 to inhibit m7G modification of BRCA1 mRNA to inhibit high-grade serous ovarian cancer

description: Supplementary Material 1: Table S1. Prediction information of m7G modification site of BRCA1 mRNA.
DOI: 10.6084/m9.figshare.31130947.v1
title: Additional file 1 of Microbial metabolite 5-formamidoimidazole-4-carboxamide ribotide targets METTL1 to inhibit m7G modification of BRCA1 mRNA to inhibit high-grade serous ovarian cancer

description: Supplementary Material 1: Table S1. Prediction information of m7G modification site of BRCA1 mRNA.
DOI: 10.6084/m9.figshare.31130956
title: Additional file 1 of SKiM-GPT: combining biomedical literature-based discovery with large language model hypothesis evaluation

description: Supporting Information
DOI: 10.6084/m9.figshare.31130956.v1
title: Additional file 1 of SKiM-GPT: combining biomedical literature-based discovery with large language model hypothesis evaluation

description: Supporting Information