cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.bgb9n6
title: Occurrence Download

description: A dataset containing 1974382 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Quercus arkansana, Quercus alvordiana, Quercus aliena, Quercus acutidens, Quercus austrina, Quercus chapmanii, Quercus alba, Quercus virginiana, Quercus sinuata, Quercus michauxii, Quercus macrocarpa, Quercus bicolor, Quercus stellata, Quercus montana, Quercus garryana, Quercus prinoides, Quercus mohriana, Quercus vaseyana, Quercus rugosa, Quercus pungens, Quercus grisea, Quercus oblongifolia, Quercus turbinella, Quercus lobata, Quercus berberidifolia, Quercus dumosa, Quercus gambelii, Quercus muehlenbergii, Quercus kelloggii, Quercus agrifolia, Quercus arizonica, Quercus geminata, Quercus macranthera, Quercus canariensis, Quercus frainetto, Quercus faginea, Quercus petraea, Quercus dentata, Quercus pyrenaica, Quercus lyrata, Quercus lusitanica, Quercus cerris, Quercus suber, Quercus brantii, Quercus variabilis, Quercus acutissima, Quercus semecarpifolia, Quercus, Quercus salicina, Quercus longinux)",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "Year is greater than or equal to 1970",
 "Year is less than or equal to 2026",
 "CoordinateUncertaintyInMeters is less than or equal to 3000"
 ]
}

 The dataset includes 1974382 records from 1640 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006714-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sgy4k2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bathytroctes Günther, 1878",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((34.9038 27.8147,35.2246 28.8496,26.9648 28.8496,26.9648 19.8496,35.9648 19.8496,35.9648 24.4806,35.4928 25.0593,34.4963 26.7709,34.4829 27.231,34.9038 27.8147))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010341-250525065834625/datasets/export for details.
DOI: 10.34740/kaggle/ds/7567949
title: Autonomic Aging
DOI: 10.15468/dl.8fzv3j
title: Occurrence Download

description: A dataset containing 77001 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Nassella trichotoma (Nees) Hack. & Arechav.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 77001 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014308-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/v7ec-xm79
title: "The prostate MRI dataset is used for deep learning to perform Gleason grading with five classes"

description: "Deep learning is used to analyze prostate MRI images and perform Gleason five-classification. Currently, only two sets of patient images are provided, but additional sets can be made available in the future."
DOI: 10.5281/zenodo.15570820
title: Supporting files for MANAGEMENT OF SUSTAINABLE AND SECURE REGIONAL DEVELOPMENT
DOI: 10.5281/zenodo.15570821
title: Supporting files for MANAGEMENT OF SUSTAINABLE AND SECURE REGIONAL DEVELOPMENT
DOI: 10.15468/dl.tx8gsh
title: Occurrence Download

description: A dataset containing 971 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Arthrosaura Boulenger, 1885"
 ]
}

 The dataset includes 971 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014312-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kbj7zf2dw4
title: ESG_events_Russia

description: ESG related events with quotes downloaded from Yahoo Finance
DOI: 10.17632/m8rvwxsyxz
title: [Dataset] Being an adult or an older caregiver of older people during the pandemic

description: The "Study on the consequences of the COVID-19 outbreak on INFORMAL/FAMILY/UNPAID CARERS across Europe" looks at the effect of COVID on different aspects of the carer experience. The online survey was targeted at the informal carers of older, frail or disabled people across Europe regarding their experience during the COVID-19 crisis. Overall 2,468 questionnaires submitted by informal carers in 16 countries were collected. Individuals caring for someone aged less than 65 (n=988) or not declaring the age of the care recipient (n=30), or not reporting their own age (n=24), were excluded from the analysis. In addition, observations with missing values in the main outcomes (n=36) were also excluded, thus leaving 1,390 subjects available for the analysis
DOI: 10.17632/82wn58ry9p
title: Global COVID-19 Statistics - Jan-2025

description: This dataset, titled "Global COVID-19 Statistics - Jan 2025," contains the latest COVID-19 statistics collected from the Worldometer website on Jan 09, 2025. The data includes crucial metrics such as the total number of cases, deaths, recoveries, and active cases for countries around the world. The information is extracted from the comprehensive table provided by Worldometer, which is widely regarded as a reliable source for real-time coronavirus statistics.
Source and Collection Date
Source: Worldometer Coronavirus Page
Date of Collection: Jan 09, 2025
DOI: 10.6084/m9.figshare.13259888.v1
title: MAVEN/IUVS data

description: This includes data from the IUVS instrument onboard MAVEN.<br>Each product consists on a png file and a json file with the same name. All these products are made by combining different swatchs from IUVS taken in the same IUVS apocenter. The number of the orbit is in the name of each product. Source products are available in NASA (United States Space Administration) PDS (Planetary Data System).<br>All this data is in cilindrical projection.<br><br>The json files contain metadata related to each product, the metadata is:PRNAME: Product namePR_CORNERS: Coordinates for the top left and bottom right corners.PR_WIDTH: Width of the map of Mars in this projection in pixels (i.e., not the width of the image, as it does not cover the whole range of longitudes).SRC_PRS: List of source products as available in NASA PDS.<br>TIME: Mean timestamp for the product. Take into account that the source files might be separated by several minutes.<br><br><br>
DOI: 10.6084/m9.figshare.20000699.v1
title: RNA-Seq raw data

description: Maize seeds was surface disinfected, then germinated under dark conditions. After germination, the seedlings grew in 28±2℃ light and 25±2℃ night in greenhouse. After reaching the 3-leaf stage, half of the seedlings were treated with γ-PGA (10 KD) solution for 3 days, and the maize roots of the treated group and the untreated group were taken for RNAseq . RNA sequencing and primary bioinformatics analysis were performed by BGI Tech Solutions Co., Ltd. (She nzhen, China). Primary sequencing data (raw read) were produced by Illumina HiSeq™ 2000.
DOI: 10.6084/m9.figshare.28604453.v3
title: ACSAformer_data

description: ACSAformer_data
DOI: 10.17632/37dh5d2krt
title: Data for: Strength and failure modes of surface treated CFRP secondary bonded single-lap joints in static and fatigue tensile loading regimes

description: Static and cyclic fatigue test results on tensile lap shear of carbon/epoxy polymer composite bonded single-lap joints with surface treatments of manual sanding, grit blasting, peel-ply plus grit blasting.
DOI: 10.17632/hdxnbtkwb6
title: Finite element analysis of deep-sea type III pressure vessels: raw and processed data

description: This dataset provides the processed (spreadsheets) and raw (abaqus input files) files of a parametric study to optimise the design of a type III pressure vessel in order to withstand 300 bar internal pressure and 600 bar external pressure. Several different designs were created by varying the metal liner (aluminium 7075-T6) thickness and whether or not there is hoop reinforcing layers. The wrapping composite material is T700 fibre/epoxy, with the helical layers stacked in an antisymmetric way and the hoop layers at 90 degrees. After the design optimisation, the selected designs were tested under external hydrostatic pressure to calculate the maximum allowable pressure/depth that these pressure vessels can be submerged.
DOI: 10.6084/m9.figshare.24139365.v1
title: DGRM2022

description: Portuguese registered national fishing fleet from 2022
DOI: 10.6084/m9.figshare.25526458.v1
title: VisCom_data.zip

description: For review
DOI: 10.17632/k7bmn7295r
title: Good and Bad Classification of Coriandrum sativum

description: Data Description: Good and Bad Classification of Coriandrum sativum

This dataset has been developed to support a classification project aiming to distinguish between "Good" and "Bad" quality samples of Coriandrum sativum (commonly known as coriander or cilantro). It contains a total of 2000 labeled images, evenly distributed between two categories:

1000 Good Samples: These images represent healthy, fresh coriander leaves. Characteristics typically include vibrant green coloration, full and intact leaf structures, absence of spots or wilting, and natural moisture. These samples meet visual standards for marketability and consumer acceptance.

1000 Bad Samples: These images depict coriander leaves that are not suitable for use or sale. They may show signs of spoilage such as yellowing, browning, black spots, mold presence, drying, shriveling, insect damage, or mechanical bruising. These defects affect both visual quality and shelf life.

Image Details:

Format: JPEG/PNG

Resolution: Varied (standardized during preprocessing)

Background: Mostly neutral or simple backgrounds to focus on leaf features

Capture Conditions: Mixed lighting and orientations to ensure dataset variability and robustness in model training

Purpose and Use: The dataset is intended for use in training and evaluating machine learning models, particularly in computer vision applications involving classification and quality assessment of agricultural produce. Potential applications include:

Automated sorting systems for coriander in supply chains

Mobile applications for farmers or vendors to assess produce quality

Research in plant disease and spoilage detection using AI

Labeling and Annotation: Each image is labeled as either "Good" or "Bad" based on visual inspection. Labeling was conducted by individuals with basic experience in agricultural product handling, ensuring practical relevance. No bounding boxes or segmentations are included; the task is a simple binary image classification.

Preprocessing Recommendations: To prepare the dataset for machine learning tasks, it is recommended to:

Resize images to a consistent resolution (e.g., 224x224 pixels for CNNs)

Normalize pixel values

Apply data augmentation (rotation, flipping, brightness variation) to improve model generalization

Ethical Considerations: This dataset is created solely for academic and research purposes. It is important to note that quality classification based on visual features may not always align perfectly with internal or chemical quality, and should be used as a complementary tool, not a sole decision-making source.
DOI: 10.17632/nprw2h5tx6
title: JCVI-syn3A qPCR for origin/quarter/terminus ratios

description: qPCR data presented in "Fundamental Cell Behavior Emerges from Whole Cell Simulations of a Minimal Cell by Integrating Experiment and Theory", Thornburg et al., 2021
DOI: 10.6084/m9.figshare.28334954.v1
title: Uetz-Powell 2025 Caribbean Table S1.xlsx

description: Supplementary Table S1 from Uetz & Powell 2025, <b>Etymologies of Caribbean Reptiles</b>, Caribbean Herpetology
DOI: 10.6084/m9.figshare.28544282.v1
title: SSIS-RS Factor Analysis Latino Preschoolers

description: One popular multi-rater social skill instrument is the Social Skills Improvement System Rating Scales (SSIS-RS). The SSIS-RS is designed to evaluate social skills, problem behaviors, and academic competence in children. While previous versions of the SSIS have been widely used in research and practice, including use with multiple translations across languages, few studies have examined the psychometric properties of the most recent iteration of the SSIS-RS with diverse populations. The current study aims to evaluate the construct validity of SSIS-RS with a Mexican-American population using the Spanish Parent and English Teacher versions. Confirmatory factor analysis was conducted with data from 624 parents and 135 teachers rating in total 761 Mexican-American children’s social skills and problem behaviors. Results revealed adequate psychometric properties but varied somewhat from the proposed scale models of the SSIS-RS. It is possible that the differences were due to the cultural uniqueness of Mexican origin compared to the norming sample. Similarities and discrepancies were found in comparing the factor structures between parent and teacher ratings. These results suggest that while parents and teachers share views on the children, environmental factors and the language of the SSIS-RS might influence their responses.
DOI: 10.6084/m9.figshare.21354366.v1
title: 2017-2020 年期间西太平洋沿海和公海环境参数和挥发性卤烃大气浓度数据

description: 为了讨论 ECS、SCS 和西太平洋中挥发性卤烃 （VHC） 的浓度、生产率、排放和来源的差异，以及沿海海域和公海对全球卤素预算的贡献差异。我们测量了 2017 年至 2020 年在东海 （ECS）、南海 （SCS） 和西太平洋巡航期间 VHC 的浓度。该数据集包含 2017-2020 年期间 ECS、SCS 和西太平洋 VHC 的环境参数和大气浓度的原始数据。
DOI: 10.6084/m9.figshare.12746150.v1
title: OxCOVID19 Database: a multimodal data repository for better understanding the global impact of Covid-19

description: These files contain a snapshot of the database described in <strong>MANUSCRIPT</strong>* as of <strong>31-07-2020</strong>. The schema.org file contains a plain text description of the tables. The tables themselves are in the CSV files. The covid19db-figshare-master.zip contains the schemas along the scripts that where used to download these data and some example usage. A complete listing of the variables and their sources can be found in <strong>MANUSCRIPT</strong>. <br><br>Since the OxCOVID19 Database draws from multiple data sources there are different licenses for different tables, please see https://covid19.eng.ox.ac.uk/ for additional details.<br><br>* The manuscript is currently under consideration, this description of the data will be updated when there is a publicly available version. In the meantime, please refer to https://covid19.eng.ox.ac.uk/
DOI: 10.6084/m9.figshare.22817144.v1
title: NETRA Dataset

description: The NETRA dataset is a comprehensive and detailed resource, specifically curated to facilitate the development and evaluation of automated algorithms for glaucoma diagnosis. This dataset originates from the B.P. Koirala Lions Centre for Ophthalmic Studies (BPKLOCS) Hospital in Nepal, a leading institution in the field of ophthalmology. The dataset consists of 519 high-resolution retinal images (3456 x 2304 pixels). Each of these images has been manually annotated by an experienced ophthalmologist. The annotation process, which was meticulously carried out using a tablet and pen, includes various pathological signs such as cup notching, rim thinning, and a fully cupped optic disc. This attention to detail underscores the quality and reliability of the dataset. The NETRA dataset covers an extensive variety of cases, ranging from mild to advanced glaucoma. This wide range enhances the value of the dataset, making it ideal for creating robust and versatile algorithms capable of identifying a broad spectrum of glaucoma conditions. The primary goal of the collection and distribution of the NETRA dataset is to promote the development of next-generation tools for mass glaucoma screening. It is designed to be used for training and evaluating algorithms that can accurately diagnose glaucoma, thereby significantly contributing to advancements in ophthalmology and healthcare.
DOI: 10.6084/m9.figshare.4981145.v2
title: ReadMe _Clinical meta-data

description: Supplemental information about the headings of the columns in the datasets
DOI: 10.17632/z4s9bx4wrn
title: Data for: A New Intelligent Fault Identification Method Based on Transfer Locality Preserving Projection for Actual Diagnosis Scenario of Rotating Machinery

description: (1) The data file contains the MATLAB codes and feature data used to implement the results in "A New Intelligent Fault Identification Method Based on Transfer Locality Preserving Projection for Actual Diagnosis Scenario of Rotating Machinery". (2) Only feature data are given. The original data are provided by the following references: [1] PHM 09 Data Challenge Data. https://www.phmsociety.org/competition/PHM/09/apparatus. [2] CWRU bearing data center. http://csegroups.case.edu/bearingdatacenter/pages/12k-drive-end-bearing-fault-data [3] Eric Bechhoefer, MFPT Bearing Fault Data Sets. http://mfpt.org/fault-data-sets/. (3) The toolbox used in the codes are listed below: [1] libsvm_3.22. https://www.csie.ntu.edu.tw/~cjlin/libsvm/ [2] DeepLearnToolbox-master. https://github.com/rasmusbergpalm/DeepLearnToolbox [3] minFunc_2012. https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html (4) The supported platform should have a Windows system, meanwhile the MATLAB version should be R2017b or later version (R2018a is also tested).
DOI: 10.17866/rd.salford.24998678.v1
title: Sound and Sound Quality Metric Data

description: Sound and Sound Quality Metric information about the audio stimuli used within the listening experiment
DOI: 10.6084/m9.figshare.22691350.v2
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.23669019.v1
title: Microbial feeding link registry

description: Database of microbial feeding links and traits used to train the machine learning models and infer microbial feeding interactions from species lists.RES = resource (prey); CON = consumer (predator)All details about traits are given in the related article.Please cite this article when using the database:Barel, J., Petchey, O., Ghafoulli, A., Jassey, V., 2023. Uncovering Microbial Food Webs Using Machine Learning. Soil Biology and Biochemistry (in press).
DOI: 10.6084/m9.figshare.25672188.v1
title: Elsafty_RBCs_for_Segmentation_and_Detection_Slide_22

description: Each one of (Elsafty_RBCs_for_Segmentation_and_Detection_Slide_1-25) consists of three primary folders: "Cropped images,” "Masks,” and "Segmented images” corresponding to each slide/patient. The naming scheme for the cropped image, mask, and segmented image of every cell adheres to a consistent format, starting with the slide/patient number, followed by the unique patch/field number, and concluding with the (XYWH) coordination on the patch. All these images are conveniently stored in the lossless ".PNG" format.
DOI: 10.6084/m9.figshare.27329620.v1
title: Additional file 1 of Cardiomyopathies in 100,000 genomes project: interval evaluation improves diagnostic yield and informs strategies for ongoing gene discovery

description: Additional file 1: All supplementary tables. Table S1 Candidate gene list used as part of the re-analysis of paediatric cardiomyopathy probands. Table S2 List of genes included in different paediatric or syndromic cardiomyopathy panels. Table S3 List of genes associated with cardiomyopathy identified from literature. Table S4 List of genes in version 1 of the UK PanelApp cardiomyopathy panels. Table S5 Filters used for de novo variant annotations in 100,000 Genomes Project taken from Genomics England Research Documentation, https://re-docs.genomicsengland.co.uk/de_novo_data/. Table S6 Demographic and clinical details of the paediatric and adult cardiomyopathy cohorts when stringent criteria are used to define children (age at consent ≤ 16 years). Table S7 Family structures at enrolment for the paediatric and adult cardiomyopathy cohorts when stringent criteria are used to define children (age at consent ≤ 16 years). Table S8 Number of paediatric and adult cardiomyopathy probands by disease recruitment category in the 100,000 Genome Project and the number and percentage of solved probands, using stringent criteria to define children. Table S9 Recruitment categories used for those paediatric and adult probands (Complex CM probands) not recruited under a cardiomyopathy specific category. Table S10 Variant details for genetically‘solved’ paediatric cardiomyopathy probands following GEL’s initial analysis. Table S11 Variant details for genetically ‘solved’ adult cardiomyopathy probands following GEL’s initial analysis. Table S12 Variant details for paediatric cardiomyopathy probands following a re-analysis.
DOI: 10.26180/27684102.v3
title: Intake24-MY: Malaysian Food List

description: This comprehensive food list was developed by the <b>Intake24-MY team</b> from <b>Monash University Malaysia</b>. It includes the following features:<b>Intake24 Code</b>: A unique identifier for each food item.<b>Descriptions</b>:<b>Local/English</b>: The English name of the food.<b>Local/Malay</b>: Malay translation of the food description.<b>Local/Mandarin</b>: Mandarin translation of the food description.<b>Local/Tamil</b>: Tamil translation of the food description.<br>This food list serves as a reference for dietary studies, nutritional assessments, or food-related research within the Malaysian context.*Nurul and Shaleen are the first authors of this work
DOI: 10.6084/m9.figshare.20644362.v1
title: POMS | Almeida et al. (2019) analysis infiles

description: Input datafiles for running analyses on case-control microbiome datasets (from Almeida et al. 2019; DOI: 10.1038/s41586-019-0965-1), which are presented as validations in the POMS manuscript. <br> Includes: bwa_depth_min25coverage.tsv.gz - MAG depth, processed to have at least a breadth of coverage of 25% per sample to be called as present. kegg_modules/path_abun_unstrat.tsv.gz - KEGG module copy number per MAG kegg_pathways/path_abun_unstrat.tsv.gz - KEGG pathway copy number per MAG kegg_summary.csv.gz - KEGG ortholog copy number per MAG MGS_samples_info_SuppTable1.txt.gz - Metadata of all samples across all samples in meta-analysis raxml_hgr-umgs_phylogeny.nwk - Newick tree of all MAGs in dataset <br>
DOI: 10.6084/m9.figshare.24471919.v5
title: Data for Spatiotemporal co-optimization of agricultural management practices

description: Spatiotemporal co-optimization of agricultural management practices towards climate-smart crop production.
DOI: 10.6084/m9.figshare.24638913.v1
title: DB48_SpectralLibrary.predicted.speclib

description: Spectral library generated <i>in silico</i> via DIA-NN (1.8.1) using a tandem mass spectrometry proteotyping based database from a human faeces sample.
DOI: 10.6084/m9.figshare.9787085.v1
title: Fig 4_b and d_Shear rate ramps test_12-HSA and 12-HSA T80_raw data

description: Shear rate ramps tests for 12-HSA and 12-HSA T80 organogels