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README.md

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 # ABCDQC Web Server
 
 ## Introduction
-This is one of the projects from the NCBI collaborative biodata science hackathon 
-[http://biohackathons.github.io](http://biohackathons.github.io). Our group is working on a project to automatically QC the [ABCD study data](https://data-archive.nimh.nih.gov/abcd). [Here](https://docs.google.com/presentation/d/1SSinOI-IDNTdZreTARghN799z-Oi2Bn-H6-xFGCnIVc/edit?usp=sharing) is a copy of our initial presentation. 
+This is one of the projects from the February 2019 NCBI collaborative biodata science hackathon [http://biohackathons.github.io]. Our group is working on a project to automatically QC the [ABCD study data](https://data-archive.nimh.nih.gov/abcd) and provide interactive visualizations of the data.
 
-This project is composed of three github repos ([abcdqc_webserver](https://github.com/abcdqc/abcdqc_webserver), [abcdqc_batchserver](https://github.com/abcdqc/abcdqc_batchserver), [abcdqc_hcp_notebooks](https://github.com/abcdqc/abcdqc_hpc_notebooks)) that work on two AWS instances and utilize the NIH high performance computing cluster.
+This project is composed of three github repos ([abcdqc_webserver](https://github.com/abcdqc/abcdqc_webserver), [abcdqc_batchserver](https://github.com/abcdqc/abcdqc_batchserver), [abcdqc_hcp_notebooks](https://github.com/abcdqc/abcdqc_hpc_notebooks)) that work on two AWS instances and utilize the NIH high performance computing cluster. 
 
 This repo contain the code running the NGINX webserver on the AWS client that serves the interactive visualizations from http://abcdqc.org
 
 ## Background
 The Adolescent Brain Cognitive Development (ABCD) study will track approximately 10,000 nine- and ten-year-old children longitudinally throughout adolescence and early adulthood. Approximately half the enrolled participants were identified as likelier to engage in high risk behaviors and/or develop mental health problems during adolescence. It is the largest neuroimaging study of this type, and aims to track the arc of mental health development within a nationally-representative sample. Data are generated by 21 imaging centers throughout the United States, with imaging acquisitions and parameters optimized for better compatibility across 3T scanners. Imaging data include T1-, T2- and diffusion-weighted structural scans and functional MRI. Both resting state and task-based fMRI scans are collected (Casey et al., 2018).
 
-## Purpose
-Coming soon
+In partnership with the NIMH Data Archive (NDA), the ABCD Study releases fast-track data every month since June 2017. The fast-track data contains unprocessed neuroimaging data and rudimentary demographics. Processed and anonymized data including all the assessment criteria are released to the research community annually.
+
+## Project Description
+This project uses both the ABCD fast-track data and the available ABCD annual releases (currently Release 1.1), creates a uniformly bid-formatted release of the data, and runs the data through the MRI Quality Control (MRIQC) tool using the NIH High Performance Compute (HPC) Cluster. MRIQC calculates a variety of image-quality metrics (IQMs) and generates a summary JSON file per subject. On the project's batch server, this data is put into a unified table and sorted by selected variables (including age, sex, drug abuse risk, manual QC score, task type and run number, manufacturer and model, and the IQMs). To preserve participant confidentiality, no identifying information is tranferred from the batch server to the webserver. Instead, Kernel Density Estimates (KDEs) for each combination of variables are calculated and converted into JSONs. On the webserver, these JSONs are converted to interactive violin plots. These interactive visualizations of the QC results are available at [http://abcdqc.org]. Data can be sorted and viewed at different levels to compare different IQMs. 
 
 ## Workflow
 ![ABCDQC Project Schematic](https://raw.githubusercontent.com/abcdqc/abcdqc_batchserver/bd637699f54891a2556c20f1a52cda67324811ad/ABCDQCflowchart.png "Project Schematic")
 
+## Purpose
+This project allows the user to visually compare and analyze the ABCD data while protecting participant confidentiality. There are many potential applications for this tool, including making comparisons by scanner manufacturer or model, analyzing the impact of age, sex, and other variables on iamge quality, comparing the ABCD Study’s IQMs to the IQMs of other publically available datasets, and creating a predictive model for future datasets. 
+
 ## Installation 
 To build the website, `cd abcd-client; npm build` and then place the contents of `abcd-client/build` in your webserver's content directory, e.g., `cp build/* /some/directory/`.