Showing posts with label data. Show all posts

Wednesday, July 2, 2025

national family survey of pregancy

https://www.cdc.gov/nchs/nsfg/index.htm

todo: request to restrickted access variables.

Monday, March 31, 2025

Asian single-cell genomics data with age-related metadata

The Chan Zuckerberg Initiative (CZI) supports several projects involving Asian single-cell genomics data with age-related metadata through its CELLxGENE Discover platform and affiliated research networks. Here are key resources:

---

## **Asian Immune Diversity Atlas (AIDA)**

**Focus**: Immune cell variation across six Asian populations (Chinese, Japanese, Korean, Indian, Malay, Thai)[1][4].

**Methods**:

- Single-cell transcriptomics (5’ scRNA-seq)

- TCR/BCR profiling

- Enhancer RNA analysis

**Cohort**:

- Over 2,000 healthy individuals spanning 20 ancestrally diverse Asian groups

- Includes non-urban and underrepresented populations

- Analyzes effects of **age**, ancestry, sex, and environmental factors on immune phenotypes[4].

**Access**: Data will contribute to the Human Cell Atlas and likely integrate with CZ CELLxGENE Discover[1][4].

---

## **CZ CELLxGENE Discover Platform**

**Key Features**:

- Hosts >85 million single cells from 1,317 datasets

- Includes metadata filters for **age**, cell type, and tissue[5][6].

- Tools for analysis, visualization, and AI model training

**Relevant Data**:

- Search using filters like `organism: Homo sapiens`, `tissue: blood`, and `ethnicity: Asian`

- Age metadata available for many datasets (e.g., developmental stages, adult vs. aging)[5][6][9].

---

## **Billion Cells Project**

**Partners**: 10x Genomics, Ultima Genomics

**Scope**:

- Includes primary human cell models, potentially covering Asian cohorts

- Focuses on genetic perturbations across cell types and tissues[2][7].

---

## **How to Access**

1. Visit [CZ CELLxGENE Discover](https://chanzuckerberg.github.io/cellxgene-census/)

2. Use the Census API or PyTorch loaders for large-scale analysis[6][9].

3. Filter datasets using variables like `donor_age` and `ethnicity`.

For AIDA-specific data, monitor updates via the [Human Cell Atlas-Asia network](https://chanzuckerberg.com/science/programs-resources/cell-science/ancestry-networks/)[1][4].

Citations:

[1] https://chanzuckerberg.com/science/programs-resources/cell-science/seednetworks/asian-immune-diversity-atlas/

[2] https://www.insideprecisionmedicine.com/topics/precision-medicine/chan-zuckerberg-initiative-10x-genomics-and-ultima-genomics-launch-billion-cells-project/

[3] https://chanzuckerberg.com/science/programs-resources/cell-science/seednetworks/

[4] https://chanzuckerberg.com/science/programs-resources/cell-science/ancestry-networks/immune-cell-atlas-of-asian-populations/

[5] https://www.nature.com/articles/d41586-024-01217-y

[6] https://chanzuckerberg.github.io/cellxgene-census/

[7] https://www.prnewswire.com/news-releases/chan-zuckerberg-initiative-launches-billion-cells-project-with-10x-genomics-and-ultima-genomics-to-advance-ai-in-biology-302369647.html

[8] https://www.genomeweb.com/genetic-research/chan-zuckerberg-initiative-launches-new-york-biohub-immune-cell-based-early

[9] https://www.biorxiv.org/content/10.1101/2023.10.30.563174v1.full.pdf

[10] https://www.science.org/doi/10.1126/science.abf1970

[11] https://www.science.org/doi/10.1126/science.abf3041

---

Answer from Perplexity: pplx.ai/share

https://pmc.ncbi.nlm.nih.gov/articles/PMC9871912/

. 2023 Jan 10;13:1081889. doi: 10.3389/fimmu.2022.1081889

Single-cell RNA sequencing reveals the molecular features of peripheral blood immune cells in children, adults and centenarians

Jinjie Zhong ^1,^2,³, Rong Ding ⁴, Huimin Jiang ^1,^2,³, LongFei Li ⁴, Junli Wan ^1,², Xiaoqian Feng ^1,^2,³, Miaomiao Chen ^1,², Liping Peng ^1,^2,³, Xiaoqin Li ^1,², Jing Lin ^1,², Haiping Yang ^1,^2,³, Mo Wang ^1,^2,³, Qiu Li ^1,^2,^3,^*, Qilin Chen ^1,^2,^3,^*

PMCID: PMC9871912 PMID: 36703979

Age groups in single-cell genomics studies are often categorized based on developmental and aging stages. Here are examples from relevant datasets:

### **Age Groups in Single-Cell Genomics Studies**

1. **Multi-Age PBMC Profile (PMC9871912)**

- **Children (Group 1, G1)**: 1–12 years old.

- **Young Adults (Group 2, G2)**: 20–30 years old.

- **Middle-Aged Adults (Group 3, G3)**: 30–60 years old.

- **Aged Adults (Group 4, G4)**: 60–80 years old.

- **Supercentenarians (Group 5, G5)**: >110 years old[4].

2. **Transcriptomic Age Profiling Study**

- Age groups were used to predict the chronological age of cells. Differences were observed between adolescents, adults, and aged adults across various cell types[1].

3. **PanSci Dataset**

- Includes cellular data across five life stages in mice, showing age-specific shifts in cell populations across organs. This approach might be mirrored in human studies for similar age group classifications[6].

These age groups provide a framework for analyzing cellular changes and aging processes across different life stages.

Citations:

[1] https://www.nature.com/articles/s42003-024-07094-5

[2] https://virtualcellmodels.cziscience.com/dataset/0192d920-34c0-70a6-971c-5c24943f5a13

[3] https://www.genengnews.com/topics/omics/the-100-million-cell-challenge-announces-winning-projects/

[4] https://pmc.ncbi.nlm.nih.gov/articles/PMC9871912/

[5] https://cellxgene.cziscience.com

[6] https://www.rockefeller.edu/news/36993-study-reveals-how-unexpected-shifts-in-cell-populations-are-revising-our-understanding-of-the-aging-process/

[7] https://pubmed.ncbi.nlm.nih.gov/40112801/

[8] https://www.biorxiv.org/content/10.1101/2023.10.30.563174v1.full.pdf

[9] https://www.ega-archive.org/studies/phs003848

---

Answer from Perplexity: pplx.ai/share

Friday, September 6, 2024

standford aging wave 44, 60 data set

Thursday, August 1, 2024

cyberbully and mental health

Several datasets exist that explore the relationship between mental health and cyberbullying. These datasets are often collected through surveys and studies conducted by academic institutions, government agencies, and non-profit organizations. Below are some notable datasets that might be useful:

1. Youth Risk Behavior Surveillance System (YRBSS)

The YRBSS, conducted by the Centers for Disease Control and Prevention (CDC), monitors health-related behaviors in youth, including experiences with cyberbullying and its impact on mental health.

Link: CDC YRBSS

2. Health Behavior in School-Aged Children (HBSC)

The HBSC study, a cross-national research study conducted in collaboration with the World Health Organization, collects data on adolescents' health and well-being, including experiences with cyberbullying and mental health outcomes.

Link: HBSC

3. The National Longitudinal Study of Adolescent to Adult Health (Add Health)

Add Health is a nationally representative study that explores the health behaviors of adolescents and young adults in the U.S. It includes data on cyberbullying and mental health.

Link: Add Health

4. European School Survey Project on Alcohol and Other Drugs (ESPAD)

ESPAD collects data on substance use and other health-related behaviors among European adolescents, including questions related to cyberbullying and mental health.

Link: ESPAD

5. National Crime Victimization Survey: School Crime Supplement (NCVS-SCS)

The NCVS-SCS, conducted by the Bureau of Justice Statistics, collects data on school-related victimization, including experiences with cyberbullying and its impact on students' mental health.

Link: NCVS-SCS

6. Global Kids Online

Global Kids Online is an international research project that provides data on children's online activities, risks, and opportunities, including cyberbullying and its effects on mental health.

Link: Global Kids Online

Home

7. The Youth Internet Safety Survey (YISS)

YISS, conducted by the Crimes Against Children Research Center, explores online experiences of youth, including cyberbullying and its psychological impact.

Link: YISS

Accessing and Utilizing Datasets

These datasets are valuable for researchers interested in studying the relationship between cyberbullying and mental health. To access these datasets, researchers typically need to follow specific protocols, which may include applying for access, providing a research proposal, and agreeing to terms of use.

Exploring these datasets can provide insights into the prevalence and effects of cyberbullying across different populations, helping to inform prevention and intervention strategies aimed at mitigating its impact on mental health.

Monday, July 15, 2024

data sources for AI in heathcare

https://www.nimhd.nih.gov/resources/schare/

Jackson Heart Study (JHS):
- The JHS focuses on cardiovascular disease (CVD) causes among African Americans.
- With over 5,300 African American participants in Jackson, Mississippi, it’s one of the largest initiatives in this field.
- The dataset covers diverse domains relevant to CVD, including demographics, anthropometrics, medication usage, conditions (e.g., hypertension, diabetes), lipid profiles, biomarkers, genetics, and more.
ScHARe Data Ecosystem:
- This ecosystem merges JHS data with area-level SDoH variables.
- SDoH factors (like community resilience, socioeconomic status, and environmental conditions) play a crucial role in health outcomes.
- By incorporating these factors, your AI/ML models can account for biases and enhance fairness.

1. **Social determinants of health data**:

- URL: [Social Determinants of Health Data](https://healthdata.gov/dataset/social-determinants-health)

2. **Genomic data**:

- URL: [National Center for Biotechnology Information (NCBI)](https://www.ncbi.nlm.nih.gov/)

- URL: [Ensembl Genome Browser](https://www.ensembl.org/)

3. **Data on PTSD and burnout among clinicians**:

- URL: [National Institute of Mental Health (NIMH) Data Archive](https://nda.nih.gov/)

4. **Referral networks data**:

- This data may be more specific and proprietary, typically found through healthcare providers or specific research collaborations.

5. **Psychiatric patient data**:

- URL: [National Institute of Mental Health (NIMH) Data Archive](https://nda.nih.gov/)

6. **Health records**:

- URL: [HealthData.gov](https://www.healthdata.gov/)

- URL: [Centers for Medicare & Medicaid Services (CMS)](https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems)

7. **Clinical trial data**:

- URL: [ClinicalTrials.gov](https://clinicaltrials.gov/)

8. **Population health data**:

- URL: [World Health Organization (WHO) Global Health Observatory](https://www.who.int/data/gho)

- URL: [HealthData.gov](https://www.healthdata.gov/)

9. **Electronic health records**:

- Typically proprietary, but some de-identified data sets can be found at:

- URL: [MIMIC-III Clinical Database](https://mimic.physionet.org/)

10. **Survey data**:

- URL: [CDC Behavioral Risk Factor Surveillance System (BRFSS)](https://www.cdc.gov/brfss/index.html)

- URL: [National Health Interview Survey (NHIS)](https://www.cdc.gov/nchs/nhis/index.htm)

11. **Public health datasets**:

- URL: [HealthData.gov](https://www.healthdata.gov/)

- URL: [Centers for Disease Control and Prevention (CDC) Data and Statistics](https://www.cdc.gov/datastatistics/)

12. **Machine learning datasets**:

- URL: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)

- URL: [Kaggle Datasets](https://www.kaggle.com/datasets)

13. **Biomedical data**:

- URL: [Bioinformatics.org](https://www.bioinformatics.org/)

- URL: [NIH Database of Genotypes and Phenotypes (dbGaP)](https://www.ncbi.nlm.nih.gov/gap)

14. **Hospital records**:

- Typically proprietary, but aggregated data can be found at:

- URL: [HealthData.gov](https://www.healthdata.gov/)

- URL: [American Hospital Association (AHA) Data](https://www.aha.org/data)

15. **Mental health data**:

- URL: [National Institute of Mental Health (NIMH) Data Archive](https://nda.nih.gov/)

16. **AI-generated data**:

- This data is typically generated within research projects or specific AI applications, but some examples can be found in open repositories:

- URL: [OpenAI](https://openai.com/)

- URL: [Hugging Face Datasets](https://huggingface.co/datasets)

National Institute of Mental Health Data Archive

https://nda.nih.gov/

Welcome to the NIMH Data Archive

The National Institute of Mental Health Data Archive (NDA) makes available human subjects data collected from hundreds of research projects across many scientific domains. NDA provides infrastructure for sharing research data, tools, methods, and analyses enabling collaborative science and discovery. De-identified human subjects data, harmonized to a common standard, are available to qualified researchers. Summary data are available to all.

The NDA mission is to accelerate scientific research and discovery through data sharing, data harmonization, and the reporting of research results.

NIMH common data elements now available: Go to NIMH Common Data Elements