cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22002/jn7h0-fhc71
title: NLRP3

description: Lysate from macrophages expressing ASC / Caspase-1-CARD / NLRP3-PYD-NBD
description: <strong>Tilt Series Date:</strong> 2014-03-17</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Macrophage lysate</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 160.0 eV/Å²,  defocus: -10.0 μm,  magnification: 34000x. </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> Hao Wu</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2014-03-17-3/rawdata/</p>NLRP3_34k0003.mrc,  Tilt Series (Pixel Size 0.32 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2014-03-17-3/rawdata/NLRP3_34k0003.mrc"
        > <i class="download icon"></i> Download </a></p>

        NLRP3_34k0003_full.rec,  Reconstruction (Pixel Size 1.28 nm), 712.7 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2014-03-17-3/3dimage_40174/NLRP3_34k0003_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/8v9g0-sqf76
title: Drp1

description: Drp1
description: <strong>Tilt Series Date:</strong> 2013-01-01</p>
<strong>Data Taken By:</strong> Elitza Tocheva</p>
<strong>Species / Specimen:</strong> Drp1</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -6.0 μm,  magnification: 27500x. </p>
<strong>Acquisition Software:</strong> UCSF tomo</p>
<strong>Upload Method:</strong> uploadraptor</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> David Chan</p>
<strong>Purification / Growth Conditions / Treatment:</strong> purified</p>
<strong>Sample Preparation:</strong> quantifoil grid R2/2, 20nm gold, 100% humidity</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2013-01-01-42/rawdata/</p>drp1_275_8defocus0024.mrc,  Tilt Series (Pixel Size 0.4 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2013-01-01-42/rawdata/drp1_275_8defocus0024.mrc"
        > <i class="download icon"></i> Download </a></p>

        24_2k_full.rec,  Reconstruction (Pixel Size 1.6 nm), 1.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2013-01-01-42/3dimage_57474/24_2k_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/5w7hy-k7432
title: Acetonema longum

description: Vegetative septa of A. longum
description: <strong>Tilt Series Date:</strong> 2011-03-28</p>
<strong>Data Taken By:</strong> Elitza Tocheva</p>
<strong>Species / Specimen:</strong> Acetonema longum</p>
<strong>Strain:</strong> None</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -10.0 μm,  magnification: 22500x. </p>
<strong>Acquisition Software:</strong> Leginon</p>
<strong>Upload Method:</strong> uploadraptor</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Eric Matson grew the cells</p>
<strong>Sample Preparation:</strong> quantifoil grid R2/2, 20nm gold, 100% humidity</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2011-03-28-1/3dimage_25332/</p>11mar28a_gr1_003_part.rec,  Reconstruction (Pixel Size 1.9 nm), 524.3 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2011-03-28-1/3dimage_25332/11mar28a_gr1_003_part.rec"
        > <i class="download icon"></i> Download </a></p>

        11mar28a_gr1_003.mrc,  Tilt Series (Pixel Size 0.95 nm), 939.5 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2011-03-28-1/rawdata/11mar28a_gr1_003.mrc"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/n3jvs-cm124
title: C65_HuvecspNL24h_11122012

description: Whole cells were grown on Au finder grids and infected with pNLGFP. Sample frozen 24h post infection
description: <strong>Tilt Series Date:</strong> 2011-11-12</p>
<strong>Data Taken By:</strong> Cora Woodward</p>
<strong>Species / Specimen:</strong> HUVECs</p>
<strong>Strain:</strong> </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: None μm,  magnification: 22500x. </p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> UCSFTomo</p>
<strong>Collaborators and Roles:</strong> pNLGFP proviral construct provided by Irvin Chen UCLA</p>
<strong>Sample Preparation:</strong> manual blotting</p>
description: Files available via S3 at ttps://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/cwl2011-11-12-59/rawdata/</p>12nov10_C65_HuvecspNL24h_AT2aBT10025.mrc,  Tilt Series (Pixel Size 0.45 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/cwl2011-11-12-59/rawdata/12nov10_C65_HuvecspNL24h_AT2aBT10025.mrc"
        > <i class="download icon"></i> Download </a></p>

        12nov10_C65_HuvecspNL24h_AT2aBT10025_full.rec,  Reconstruction (Pixel Size 1.8 nm), 530.0 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/cwl2011-11-12-59/3dimage_23413/12nov10_C65_HuvecspNL24h_AT2aBT10025_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.17632/bb7ctkh5dn
title: The Longitudinal Interplay of Habits, Intentions, and Behaviors in Physical Activity and Screen Time among Chinese Early Adolescents

description: Research data on 692 Chinese middle school students to examine longitudinal interactions between physical activity and screen time habits, intentions and behaviors.
DOI: 10.17632/bb7ctkh5dn.1
title: The Longitudinal Interplay of Habits, Intentions, and Behaviors in Physical Activity and Screen Time among Chinese Early Adolescents

description: Research data on 692 Chinese middle school students to examine longitudinal interactions between physical activity and screen time habits, intentions and behaviors.
DOI: 10.22002/d7fk0-wbx14
title: E.coli "strain8" Tsr-A413G

description: E.coli strain #8 (methylation site mutant locked in a CheA activating state)
description: <strong>Tilt Series Date:</strong> 2011-03-03</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Escherichia coli</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: °, constant angular increment, dosage: 180.0 eV/Å²,  defocus: -10.0 μm,  magnification: 34000x. </p>
<strong>Acquisition Software:</strong> Leginon</p>
<strong>Upload Method:</strong> webload</p>
<strong>Processing Software Used:</strong> IMOD</p>
<strong>Collaborators and Roles:</strong> Mutant from John S. Parkinson </p>
<strong>Purification / Growth Conditions / Treatment:</strong> grown in TB media to log phase, lysed for one hour with penicillin G</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2011-03-03-6/rawdata/</p>11mar03a_coli8_003.st,  Tilt Series (Pixel Size 0.0 nm), 1.0 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2011-03-03-6/rawdata/11mar03a_coli8_003.st"
        > <i class="download icon"></i> Download </a></p>

        11mar03a_coli8_003.rec,  Reconstruction (Pixel Size 0.0 nm), 2.8 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2011-03-03-6/3dimage_15486/11mar03a_coli8_003.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/yt1z1-a1083
title: Myxococcus Xanthus (WT)

description: ATCC #25232
description: <strong>Tilt Series Date:</strong> 2011-11-06</p>
<strong>Data Taken By:</strong> Matt Swulius</p>
<strong>Species / Specimen:</strong> Myxococcus xanthus</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-64.0°, 64.0°), step: 1°, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -10.0 μm,  magnification: 22500x. </p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> uploadraptor</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Grown in CYE medium at 30 degrees celsius to OD of 0.8.</p>
<strong>Sample Preparation:</strong> Plunge frozen from in suspension</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mst2011-11-06-5/rawdata/</p>20111106_MyxXan_30005.mrc,  Tilt Series (Pixel Size 0.95 nm), 1.0 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mst2011-11-06-5/rawdata/20111106_MyxXan_30005.mrc"
        > <i class="download icon"></i> Download </a></p>

        20111106_MyxXan_30005_full.rec,  Reconstruction (Pixel Size 1.9 nm), 1.0 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mst2011-11-06-5/3dimage_16268/20111106_MyxXan_30005_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.17632/ng6tv57j5c
title: FeliBreed – A Multi-Breed Cat Image Dataset

description: FeliBreed – A Multi-Breed Cat Image Dataset

We present the FeliBreed dataset, a carefully curated collection of 300 real-world cat images from a variety of breeds. This dataset is designed to support robust image analysis, especially in tasks related to cat breed classification. Each image has been sourced from real-life settings, captured through fieldwork with cameras, ensuring the data is authentic and representative of different environments.

Breakdown of the Dataset:

• Tabby Cat: 28 images
• Mixed Breed: 41 images
• Local: 59 images
• Bengal Cat: 14 images
• Persian: 158 images

Key Features:

• Real-World Images: All images are sourced from real-life settings, making the dataset highly applicable for model training and validation.
• Multi-Breed Representation: The dataset includes multiple breeds, ensuring diversity in the training data and improving the generalization of models.
• High-Quality Data: The images are captured using professional cameras during fieldwork, guaranteeing high resolution and accuracy for image classification tasks.

FeliBreed is an excellent resource for researchers and developers working on cat breed recognition and image classification tasks. By leveraging this dataset, machine learning models can achieve improved accuracy and robustness when deployed in real-world applications.
DOI: 10.22002/zmf5f-w1y79
title: sporulating MJ007 349

description: MJ007 349
description: <strong>Tilt Series Date:</strong> 2009-09-27</p>
<strong>Data Taken By:</strong> Elitza Tocheva</p>
<strong>Species / Specimen:</strong> Bacillus subtilis </p>
<strong>Strain:</strong> JFG349, ponA null, SpoIIIE73-11-GFP</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -10.0 μm,  magnification: 22500x. </p>
<strong>Acquisition Software:</strong> Leginon</p>
<strong>Upload Method:</strong> uploadraptor</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Purification / Growth Conditions / Treatment:</strong> sporulation medium</p>
<strong>Sample Preparation:</strong> quantifoil grid R2/2, 20nm gold, 100% humidity</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2009-09-27-35/rawdata/</p>09sep27b_et-pos2_006.mrc,  Tilt Series (Pixel Size 0.95 nm), 1.0 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2009-09-27-35/rawdata/09sep27b_et-pos2_006.mrc"
        > <i class="download icon"></i> Download </a></p>

        cell6_b_full.rec,  Reconstruction (Pixel Size 1.9 nm), 849.3 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2009-09-27-35/3dimage_25883/cell6_b_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.17632/ng6tv57j5c.2
title: FeliBreed – A Multi-Breed Cat Image Dataset

description: FeliBreed – A Multi-Breed Cat Image Dataset

We present the FeliBreed dataset, a carefully curated collection of 300 real-world cat images from a variety of breeds. This dataset is designed to support robust image analysis, especially in tasks related to cat breed classification. Each image has been sourced from real-life settings, captured through fieldwork with cameras, ensuring the data is authentic and representative of different environments.

Breakdown of the Dataset:

• Tabby Cat: 28 images
• Mixed Breed: 41 images
• Local: 59 images
• Bengal Cat: 14 images
• Persian: 158 images

Key Features:

• Real-World Images: All images are sourced from real-life settings, making the dataset highly applicable for model training and validation.
• Multi-Breed Representation: The dataset includes multiple breeds, ensuring diversity in the training data and improving the generalization of models.
• High-Quality Data: The images are captured using professional cameras during fieldwork, guaranteeing high resolution and accuracy for image classification tasks.

FeliBreed is an excellent resource for researchers and developers working on cat breed recognition and image classification tasks. By leveraging this dataset, machine learning models can achieve improved accuracy and robustness when deployed in real-world applications.
DOI: 10.22002/scr9p-tf491
title: Acetonema longum

description: Vegetative septa of A. longum
description: <strong>Tilt Series Date:</strong> 2011-03-28</p>
<strong>Data Taken By:</strong> Elitza Tocheva</p>
<strong>Species / Specimen:</strong> Acetonema longum</p>
<strong>Strain:</strong> None</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -10.0 μm,  magnification: 22500x. </p>
<strong>Acquisition Software:</strong> Leginon</p>
<strong>Upload Method:</strong> uploadraptor</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Eric Matson grew the cells</p>
<strong>Sample Preparation:</strong> quantifoil grid R2/2, 20nm gold, 100% humidity</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2011-03-28-13/3dimage_25356/</p>11mar28a_gr1_017_part.rec,  Reconstruction (Pixel Size 1.9 nm), 524.3 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2011-03-28-13/3dimage_25356/11mar28a_gr1_017_part.rec"
        > <i class="download icon"></i> Download </a></p>

        11mar28a_gr1_017.mrc,  Tilt Series (Pixel Size 0.95 nm), 947.9 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2011-03-28-13/rawdata/11mar28a_gr1_017.mrc"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.17632/68nsrz8r76
title: Parameter files for GeoWEPP simulation and Observed data for calibration

description: Research data for the paper titeled "Application of GeoWEPP to a cabbage monocropping region: developing agricultural strategies to mitigate water erosion under short-duration intense rainfall".

On August 26, 2025, we corrected the dataset because incorrect data for observed sediment yield had been published.
DOI: 10.17632/68nsrz8r76.3
title: Parameter files for GeoWEPP simulation and Observed data for calibration

description: Research data for the paper titeled "Application of GeoWEPP to a cabbage monocropping region: developing agricultural strategies to mitigate water erosion under short-duration intense rainfall".

On August 26, 2025, we corrected the dataset because incorrect data for observed sediment yield had been published.
DOI: 10.22002/f077s-96989
title: CB15N, 0.1mM Galactose,high salt

description: Raw data files of CB15N, 0.1mM Galactose,high salt
description: <strong>Tilt Series Date:</strong> 2009-10-22</p>
<strong>Data Taken By:</strong> Ariane Briegel</p>
<strong>Species / Specimen:</strong> Caulobacter crescentus</p>
<strong>Strain:</strong> NA1000</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: °, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -12.0 μm,  magnification: 27500x. </p>
<strong>Acquisition Software:</strong> Leginon</p>
<strong>Upload Method:</strong> webload</p>
<strong>Processing Software Used:</strong> IMOD</p>
<strong>Purification / Growth Conditions / Treatment:</strong> synchronized in 20x M2 salts, 0.1mM Galactose</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2009-10-22-9/rawdata/</p>09oct22a_MT310Auglu50_009.st,  Tilt Series (Pixel Size 0.774 nm), 1.0 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2009-10-22-9/rawdata/09oct22a_MT310Auglu50_009.st"
        > <i class="download icon"></i> Download </a></p>

        09oct22a_MT310Auglu50_009.rec,  Reconstruction (Pixel Size 1.548 nm), 135.0 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aba2009-10-22-9/3dimage_8848/09oct22a_MT310Auglu50_009.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.15468/dl.myxahs
title: Occurrence Download

description: A dataset containing 196875 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((4.875163624300908 51.599426133396506, 4.873873162356314 51.60819803981957, 4.869858093939974 51.61664449856167, 4.863271408437672 51.62444065906323, 4.854365344563541 51.63128652887406, 4.843481832259853 51.636918561610685, 4.831039402664756 51.64111985550994, 4.817517061336768 51.64372855988398, 4.803435750882645 51.64464415556829, 4.789338135097214 51.64383135873629, 4.775767511541359 51.64132149179611, 4.763246698666988 51.63721126639522, 4.75225774474566 51.63165902733748, 4.743223268899636 51.62487860780457, 4.73649017173602 51.61713104115084, 4.732316348822548 51.608714458586796, 4.730860910581973 51.59995257180056, 4.732178264309547 51.5911821923097, 4.736216255624934 51.58274027331997, 4.742818405343209 51.57495097425611, 4.751730120459581 51.568113242977994, 4.762608610671327 51.56248938685182, 4.77503610939802 51.55829506282544, 4.788535884166076 51.55569106048536, 4.802590427926886 51.5547771831528, 4.816661151882081 51.5555884530502, 4.830208852518404 51.558093780231935, 4.842714201195085 51.562197144188936, 4.8536975039205235 51.56774124471131, 4.86273700190575 51.57451348763613, 4.869485029958249 51.58225408442305, 4.873681419413366 51.590665964959314, 4.875163624300908 51.599426133396506))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 196875 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047061-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bpns8vzwrj
title: Behavioral Data-Driven Prediction of Suicide Risk Using Machine Learning Approaches

description: The Suicide Risk Prediction Dataset comprises 1,128 structured records collected from multiple medical institutions and open sources in Bangladesh. This dataset has been curated to support machine learning and statistical modeling for suicide risk assessment and prediction. Each record captures critical demographic, behavioral, and psychological features, enabling effective feature analysis and risk factor identification for suicidal behavior.

Data Collection Details

Collected From:

1.Dhaka Medical College & Hospital – 38 records
2.Bangladesh Medical University (BMU) – 19 records
3.Enam Medical College & Hospital – 27 records
4.Shaheed Tajuddin Ahmed Medical College – 36 records
5.Online Survey (Google Form) – 358 records
6.Open-source data (GitHub) – 650 records


Collection Period: February 2025 – July 2025

Collection Methods:

Hospital records supervised by mental health professionals

Online survey conducted through Google Forms

External open-source datasets carefully selected from GitHub

Dataset Statistics

Total Records: 1,128

Features per Record: 16 structured features + 1 dependent variable

Features Include:

1.Age

2.Gender

3.Religion

4.Occupation

5.Civil Status

6.Level of Education

7.Psychological Session

8.Past Attempt

9.Disorder

10.Illness

11.Alcohol

12.Anger

13.Sleep Problem

14.Isolation

15.Humiliation

16.Sad/Weary

Dependent Variable:

Attempted? (Binary indicator: history of suicide attempt or not)

Ethical Considerations

All data were collected under ethical approval and professional supervision. Mental health experts from BMU, Dhaka Medical College, and other institutions were directly involved to ensure accuracy, patient confidentiality, and clinical relevance.

Key Applications

Risk Factor Analysis: Identifying major behavioral and psychological triggers behind suicidal tendencies.

Predictive Modeling: Training machine learning algorithms for suicide risk prediction.

Healthcare Decision Support: Assisting clinicians in early detection of high-risk patients.

Public Health Policy: Supporting targeted interventions and preventive strategies.

Academic Research: Providing a benchmark dataset for behavioral health and suicide prevention studies.