HealthCare | Data | Publicly available biomedical data

The Indonesian Family Life Survey (IFLS)

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The Indonesian Family Life Survey (IFLS) is an on-going longitudinal survey in Indonesia. The sample is representative of about 83% of the Indonesian population and contains over 30,000 individuals living in 13 of the 27 provinces in the country. It contains data about participants’ information including demographics, health status, healthcare utilization, morbidity and mortality, family structure, income, expenditure, employment, retirement, and education. The study contains 5 waves.

 

The first wave of the IFLS (IFLS1) was conducted in 1993/94 by RAND in collaboration with Lembaga Demografi, University of Indonesia. 

 

IFLS2 and IFLS2+ were conducted in 1997 and 1998, respectively, by RAND in collaboration with UCLA and Lembaga Demografi, University of Indonesia. IFLS2+ covered a 25% sub-sample of the IFLS households. 

 

IFLS3, which was fielded in 2000 and covered the full sample, was conducted by RAND in collaboration with the Population Research center, University of Gadjah Mada. 

 

The fourth wave of the IFLS (IFLS4), fielded in 2007/2008 covering the full sample, was conducted by RAND, the center for Population and Policy Studies (CPPS) of the University of Gadjah Mada and Survey METRE. 

 

The fifth wave of the IFLS (IFLS-5) was fielded 2014-15.

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ADRD | Data | Publicly available biomedical data

The Minimal Interval Resonance Imaging in Alzheimer’s Disease (MIRIAD)

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The MIRIAD dataset is a database of volumetric MRI brain-scans of Alzheimer’s sufferers and healthy elderly people. This study aims to identify structural and functional changes of the patient’s brain particularly in the early stages of Alzheimer’s Disease. The database contains a variety of data, including structural MRI, diffusion tensor imaging, functional MRI, clinical, cognitive assessment, and demographic data. To access the MIRIAD database, researchers need to go through an application process and agree to the data use and sharing restrictions.

 

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ADRD | Data | Publicly available biomedical data

The National Alzheimer’s Coordinating Center (NACC)

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The National Alzheimer’s Coordinating Center was established in 1999 by the National Institute on Aging/NIH to facilitate collaborative research. The center contains various types of dataset. The Uniform Data Set (UDS) is a longitudinal dataset, collected using a prospective, standardized, and longitudinal clinical evaluation of subjects in the National Institute on Aging’s ADRC Program. The Neuropathology Data Set is an autopsy dataset, which contains autopsy data for a subset of both Minimum Data Set (a dataset before UDS) and UDS participants and Minimum Data Set subjects. The collection of datasets in the center also includes MRI and PET imaging data, CSF biomarker data, and genotypic and genomic data.

 

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HealthCare | Data | Publicly available biomedical data

The National Health and Aging Trends Study (NHATS)

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National Health and Aging Trend Study (NHATS) has conducted annual in-person interviews with a nationally representative sample of Medicare beneficiaries aged 65 or older. The study is designed as a platform for scientific study of late-life disability trends and trajectories and has supported research in disability reduction, independent functioning maximization, and quality of life enhancement at older ages. From the interviews, the study collected longitudinal data of participants including health status data, physical function data, cognitive function data, social factors data, and healthcare utilization data. The study is conducted by investigators at Johns Hopkins University and sponsored by the National institute of Aging.

 

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HealthCare | Publicly available biomedical data | Data

The National Study of Caregiving (NSOC)

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National Study of Caregiving (NSOC) periodically interviews thousands of family members, friends, and unpaid caregivers who have helped participants in National Health and Aging Trend Study (NHATS). NSOC I (2011) and NSOC II (2015) provide cross-sectional data about caregiving to older adults, and NSOC III (2017) contains both cross-sectional sample and longitudinal follow-up with caregivers identified in NSOC II (2015). 

 

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AutoML | Software | Technology: Tools, Hardware, and Software | All Resources

Tree-based Pipeline Optimization Tool (TPOT)

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Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there. TPOT is built on top of scikit-learn, so all of the code it generates should look familiar… if you’re familiar with scikit-learn, anyway.

 

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