cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15575511
title: Digital Screen | Television Dataset

description: Description:







The dataset comprises over 1000+ original Television/TV images, captured and crowdsourced from over 400+ urban and rural areas. Each image has been manually reviewed and verified by computer vision professionals at DataCluster Labs, ensuring high quality and accuracy. This dataset presents a significant challenge for machine learning models due to its diverse conditions and settings. Digital Screen Television Dataset







Download Dataset









Dataset Features



Size: 1000+ images

Contributors: Over 1000+ crowdsource contributors

Resolution: 98% of the images are HD and above (1920×1080 and higher)

Geographical Diversity: Captured across 400+ cities throughout India

Lighting Conditions: Includes various lighting scenarios such as day and night, and different distances and viewpoints

Device Used: Captured using mobile phones during 2022-2023

Usage: Ideal for enhancing real-world applications such as smart home systems, retail analytics, and security monitoring


Available Annotation Formats



COCO

YOLO

PASCAL-VOC

Tf-Record


Ownership and Licensing

DataCluster Labs exclusively owns the images in this dataset, which were not sourced from the internet. A license can be purchased to access a larger portion of the training dataset for research and commercial purposes.

Applications and Use Cases



Smart Home Systems: Improve the accuracy of TV detection and interaction in smart home environments.

Retail Analytics: Utilize the dataset to analyze consumer behavior and optimize in-store experiences.

Security Monitoring: Enhance security systems by improving the detection and recognition of TVs in various settings.

Computer Vision Research: Provide a challenging and diverse dataset for developing advanced computer vision algorithms.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15278541
title: TABLE 1 in Two cryptic new species of the plant bug genus Campylomma recently discovered in Japan and Singapore (Hemiptera: Heteroptera: Miridae: Phylinae)

description: TABLE 1. Measurements for Campylomma species. (Abbreviations: F = female, FM = femur, HEM = maximum width across hemelytra, HT = holotype, L = length, LBM = labium, M = male, PRN = pronotum, PT = paratype, TB = tibia, VTX = vertex or interocular space, W = width)
BodyHeadVTXPRNHEMAntennomere LLBMMetaleg LLWWWWIIIIIIVLFMTBCampylomma marinumHT M2.080.580.260.750.910.460.290.210.750.710.93PT M2.210.570.260.750.910.480.290.200.770.720.99PT M2.160.550.260.740.820.480.320.260.780.690.95PT M2.210.570.270.750.90―――0.770.740.99PT F1.960.550.270.780.890.460.290.240.770.750.98PT F1.890.510.290.670.860.420.270.210.720.600.87PT F2.110.550.290.770.980.460.290.250.790.690.96C. salaciella [from Thailand]PT M2.280.600.260.770.930.600.350.220.780.751.10PT M2.450.600.260.790.960.630.330.210.770.781.13PT M2.400.620.300.770.980.600.360.240.860.771.17PT F2.280.550.290.781.020.600.350.300.910.781.10PT F2.250.550.300.751.010.540.300.260.900.771.14PT F2.230.570.290.770.960.590.320.240.890.811.05C. singapuraHT M2.030.590.270.770.950.440.300.240.720.620.90PT M1.910.570.270.720.900.420.300.260.750.650.87PT M1.910.550.260.720.830.440.300.220.620.690.89PT F1.960.550.290.750.900.440.300.240.720.650.91PT F2.110.580.280.770.990.440.320.270.750.740.99PT F2.210.600.300.791.010.440.300.260.690.670.96
across hemelytra, HT = holotype, L = length, LBM = labium, M = male, PRN = pronotum, PT = paratype, TB = tibia, VTX = vertex or interocular space, W = width)
description: Published as part of Yasunaga, Tomohide, Yap, Ee-Hean, Ito, Reo & Hwang, Wei-Song, 2025, Two cryptic new species of the plant bug genus Campylomma recently discovered in Japan and Singapore (Hemiptera: Heteroptera: Miridae: Phylinae), pp. 541 in Zootaxa 5609 (4) on page 541, DOI: 10.11646/zootaxa.5609.4.5, http://zenodo.org/record/15215469
DOI: 10.20351/29sg20250423
title: TRIDENT-2 Cruise, RV Sarmiento de Gamboa 

description: TRIDENT-2 Cruise (29SG20250423) carried out on the Research Vessel Sarmiento de Gamboa in 2025
DOI: 10.15468/dl.bxc9ke
title: Occurrence Download

description: A dataset containing 19651 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Puma concolor (Linnaeus, 1771))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 19651 records from 327 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013358-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15478977
title: LaMEM and ASPECT input and data files corresponding to Exploiting Physics-Based Machine Learning to Quantify Geodynamic Effects – Insights from the Alpine Region

description: The repository contains input files for LaMEM and aspect simulation presented in "Exploiting Physics-Based Machine Learning to Quantify Geodynamic Effects – Insights from the Alpine Region" by Denise Degen, Ajay Kumar, Magdalena Scheck-Wenderoth, Mauro Cacace , DOI:https://doi.org/10.5281/zenodo.14755256.

ASPECT simulation files, geometry and input files are in aspect_input. Please see the README in this folder.

LaMEM simulation files, geometry (marker distributions) and input files, are in LaMEM_input. Please see the README in this folder.

We provide required datafiles and input file for a sample simulation. All the simulations results can be produced by changing the density, viscosities in the input files from the following files in the Surrogate_model_prep_data folder:1. Varying Densities1.1 TrainingParameterAlps_LHS_100_density_only.txt : Training models1.2 ValidationParameterAlps_random_15_density_only.txt : Validation models

2. Varying Densities and Viscosities2.1 TrainingParameterAlps_LHS_100_vary_viscosity_density.txt : Training models2.2 ValidationParameterAlps_random_15_vary_viscosity_density.txt : Validation models

3. Varying Viscosities3.1 TrainingParameterAlps_LHS_reduced_range_fixed_density.txt : Training models3.1 ValidationParameterAlps_random_20_reduced_range_fixed_density.txt : Validation models
DOI: 10.15468/dl.zsnvnw
title: Occurrence Download

description: A dataset containing 10120 records in GBIF matching the query:
SELECT speciesKey, species, COUNT(*) AS occurrences FROM occurrence WHERE occurrenceStatus = 'PRESENT' AND continent = 'EUROPE' AND "year" &gt;= 2024 AND kingdomKey = 5 AND hasCoordinate = TRUE AND (coordinateUncertaintyInMeters &lt;= 1000 OR coordinateUncertaintyInMeters IS NULL) 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 "month" IS NOT NULL GROUP BY speciesKey, species

 The dataset's 10120 records were derived from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006437-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22033/esgf/cmip6.3445
title: CAS CAS-ESM1.0 model output prepared for CMIP6 CMIP piControl

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.CMIP.CAS.CAS-ESM2-0.piControl' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The CAS-ESM 2.0 (Chinese Academy of Sciences Earth System Model version 2.0) climate model, released in 2019, includes the following components:
 aerosol: IAP AACM, atmos: IAP AGCM 5.0 (Finite difference dynamical core; 256 x 128 longitude/latitude; 35 levels; top level 2.2 hPa), atmosChem: IAP AACM, land: CoLM, ocean: LICOM2.0 (LICOM2.0, primarily 1deg; 362 x 196 longitude/latitude; 30 levels; top grid cell 0-10 m), ocnBgchem: IAP OBGCM, seaIce: CICE4. 
The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.22033/esgf/cmip6.4330
title: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP ssp534-over

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.ScenarioMIP.CSIRO.ACCESS-ESM1-5.ssp534-over' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The Australian Community Climate and Earth System Simulator Earth System Model Version 1.5 climate model, released in 2019, includes the following components:
 aerosol: CLASSIC (v1.0), atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m), land: CABLE2.4, ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: WOMBAT (same grid as ocean), seaIce: CICE4.1 (same grid as ocean). 
The model was run by the Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia (CSIRO) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.15468/dl.jnh44p
title: Occurrence Download

description: A dataset containing 14504 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panthera leo (Linnaeus, 1758)"
 ]
}

 The dataset includes 14504 records from 174 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012520-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yqdahu
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is A revision of the genus Arenopontia Kunz, 1937 (Copepoda, Harpacticoida, Arenopontiidae), including the description of five new species",
 "Country is Croatia"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006719-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.45jtz3
title: Occurrence Download

description: A dataset containing 86 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phellodendron amurense Rupr."
 ]
}

 The dataset includes 86 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006720-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r22zbd
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Lichen herbarium, The Arctic University Museum of Norway (TROM)",
 "Country is Croatia"
 ]
}

 The dataset includes 34 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006721-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q5exaq
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Bryophyte Herbarium (H): Herbarium Generale",
 "Country is Croatia"
 ]
}

 The dataset includes 51 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006722-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00011403
title: DATA CHALLENGE- MDRisk Stratify: AI-driven MDR Prediction for Optimized Antibiotic Use in LMICs

description: MDRisk Stratify: AI-Driven MDR Prediction for Optimized Antibiotic Use in LMICs

Antimicrobial resistance (AMR) poses a severe threat, particularly in low- and middle-income countries (LMICs), where limited diagnostics and empirical antibiotic prescribing fuel multidrug resistance (MDR). MDR, the ability of microbes to resist multiple antibiotics, including critical classes like cephalosporins and carbapenems, is exacerbated by socioeconomic barriers and inadequate surveillance. By 2050, AMR could cause 1.91 million direct deaths and be linked to 8.22 million deaths globally, with LMICs facing the highest mortality rates.

MDRisk Stratify is an innovative machine learning (ML) platform designed to predict individualized MDR risks and antibiotic resistance profiles to guide tailored antibiotic prescribing in LMICs, reducing MDR spread. It leverages datasets from Vivli’s AMR Register, including the LMIC-focused SOAR dataset and ATLAS global data on pathogens like K. pneumoniae and E. coli. The methodology involves preprocessing data by encoding categorical variables (e.g., gender, infection type), normalizing continuous variables (e.g., age), and using SMOTE to address class imbalance. ML models, including Random Forest, Gradient Boosting, and Logistic Regression, will be trained on 80% of the data to predict MDR and resistance to key antibiotics. Performance will be evaluated using F1-score, AUROC, and G-mean, with SHAP analyses ensuring interpretability. A 20% dataset reserved for LMIC contexts, such as Uganda, will validate generalizability.

The platform prioritizes accessibility through a low-bandwidth Streamlit web application and a USSD interface, enabling use in remote areas with limited internet. MDRisk Stratify aims to reduce inappropriate antibiotic prescribing by 20%, achieve over 85% prediction accuracy, enhance clinician accessibility, and inform WHO’s AMR policies. Building on CAMO-Net’s ongoing work, this initiative addresses diagnostic gaps, promotes equitable healthcare, and supports global AMR management.
DOI: 10.15468/dl.ddgk73
title: Occurrence Download

description: A dataset containing 1630 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Rhyacotriton variegatus Stebbins & Lowe, 1951)"
 ]
}

 The dataset includes 1630 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003336-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cqh2hb
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhodochorton tenue",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003335-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bu6t3d
title: Occurrence Download

description: A dataset containing 17017 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Hyla chrysoscelis Cope, 1880)"
 ]
}

 The dataset includes 17017 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003334-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/ggh8z-xss97
title: V. cholerae RT4236 (stationary)

description: Classical strain with ctxA deletion.Cell harbors pMT5 plasmid (inducible toxT).
description: <strong>Tilt Series Date:</strong> 2015-04-09</p>
<strong>Data Taken By:</strong> Yi-Wei Chang</p>
<strong>Species / Specimen:</strong> Vibrio cholerae</p>
<strong>Strain:</strong> O395-N1</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.0°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -6.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> reupload_downloaded</p>
<strong>Processing Software Used:</strong> SIRT</p>
<strong>Collaborators and Roles:</strong> Strain from Ronald K. Taylor</p>
<strong>Purification / Growth Conditions / Treatment:</strong> ON cultures of Vibrio cholerae O395 NI pMT5 grown in LB pH 6.5, 1 mM IPTG and 100 ug/mL ampicilin</p>
<strong>Sample Preparation:</strong> Vitrobot (MK iv) settings: 100% humidity, 4µl sample, 10 mg/ml BSA treated gold. Blot force 6, blot time 2.5 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2015-04-09-6/3dimage_73101/</p>20150409_AK005_10006_SIRT_2k.rec,  Reconstruction (Pixel Size 0.0 nm), 5.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2015-04-09-6/3dimage_73101/20150409_AK005_10006_SIRT_2k.rec"
        > <i class="download icon"></i> Download </a></p>

        20150409_AK005_10006.mrc,  Tilt Series (Pixel Size 0.39 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2015-04-09-6/rawdata/20150409_AK005_10006.mrc"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/bmare-shg22
title: Coxiella_burnetii_ wtACCMD_pH4d5

description: Coxiella burnetii Nine Mile phase II clone 4 (NMIIC4) strain. Wild-type cells day-5 culture. Cells grown in acidic media ACCMD and pH 4.75
description: <strong>Tilt Series Date:</strong> 2017-10-17</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Coxiella burnetii</p>
<strong>Strain:</strong> Nine Mile phase II clone 4</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 130.0 eV/Å²,  defocus: -7.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent to us by Robert Heinzen. Data collected and processed by Debnath Ghosal and Mohammed Kaplan using Caltech tomography pipeline.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells were grown in Robert Heinzen's lab @NIAID and ~50 ul cells were sent to us.</p>
<strong>Sample Preparation:</strong> Refer to Heinzen lab. Freezing: 100% humidity, 4µl sample, Blot force 6, blot time 4 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Add gold to cells prior to freezing: 1ul of gold to 4 ul of cells, freeze.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-10-17-64/rawdata/</p>Cb_d5pH4_20171017c0008.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-10-17-64/rawdata/Cb_d5pH4_20171017c0008.mrc"
        > <i class="download icon"></i> Download </a></p>

        Cb_d5pH4_20171017c0008_full.rec,  Reconstruction (Pixel Size 1.56 nm), 445.4 MB  
        
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        > <i class="download icon"></i> Download </a></p>