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1. Project Report (Wiki/Read the docs | Due Dec. 17th @ Noon)


In progress 


  1. Abstract: 

    • Overview of project 

    • analysis workflow 

    • computational system 

    • results management 

  2. Project Description - Documentation (Emmanuel) 

    • What is the gantry phenotyping platform

    • What sensors does it have

    • How much data does it produce

    • Why do we need to process data coming from this machine ASAP?

    • Overview of workflow and the main features of it

      • Scalable, flexible, modular, extensible, high performant, high reliability (checks and retries errors)

  3. Team Members (names and roles) (Galen) 

    • Overview of the team and team responsibilities

    • List teams

    • Team lead(s)

    • Team members

    • Role of each person (a couple of words)

  4. Input Data - Jennen, Naomi

    • Description of data: which sensors, what do they do

    • How much data

    • Which where 

    • Where/how were they obtained.

    • What was done to clean them for processing.

    • Link, description, and instructions of using code to get and clean data.

  5. Analysis Workflow/Code - Workflow team (John:Cloud & Michele: HPC )

    • Description of analytical workflow.

    • Description of computational framework (master/worker with Workqueue and Makeflow)

    • Instructions for use

    • Description of datasets

    • Describe scaling methods

    • Description of how workflow can be modified

  6. Results - Jiatian & Jialiang Wang

    • How are results managed

    • How are results evaluated for faults  - Sami/Derek 

    • Where can researchers find the results

  7. Project plan and timeline - Sateesh & Emmanuel

  8. Detailed benchmarks - Jiatian wang & Jialiang Wang

    • How long it took to run the full dataset:

    • Software installation

    • Data Staging

    • Data Processing

    • Workflow monitoring

    • Results deposition

    • How much would this cost to do on AWS (e.g., for each workflow, approximate cost to process one day’s worth of data)

  9. Post-Mortem Analysis (SubPage) Documentation

    • One for each team!

    • Focus on team processes; not technical problems

    • What worked well

    • What didn't work well

    • What you would do differently

2. Project Code  (GitHub) and Documentation (Read the Docs)

  • Overview

  • System requirements

  • Getting Started

  • How to use

  • How to scale (what is needed)

  • Description of output

  • Warnings and caveats

  • License

  • Author description

  • Where do get additional help

3. Client Presentation (In-Class | Due Dec. 17 @ 8AM)

  • Introduction (Sateesh Peri)

    • Introduce people presenting

      • Kyle Strokes, Erika Tapia, 

  • Overview of project ( Terraref - Erika, CCtools-Kyle )

  • Overview of workflow ( 

  • Overview of solution (

  • Overview of results (

  • Details on:

    • Data: obtaining, cleaning, about the final dataset.

    • Analysis workflow: overview, obtaining, how to use

    • Benchmarks: Scaling, full analysis time

    • Results and result management

  • Code:

    • Where is it?

    • What does it take to rerun it?

    • Show an example of making a modification to workflow (adding a new extractor) an rerunning

  • Documentation and training materials

    • How would a new person take the code and get to work?

  • Live demo (John Xu & Class)

    • Show us the system in action!

  • Conclusion (Sateesh Peri)

    • You have done some amazing work, here is your chance to put a bow on it

  • Thank you (Sateesh Peri)

    • Team members, roles and responsibilities 

    • Help from external teams (cctools, David LB group, class clients, CyVerse people, etc.)  Note -- always make sure to give credit (sometimes extra credit) for anyone who has helped make the project possible

    • Computational infrastructure:  UA HPC, CyVerse, XSEDE, JetStream

4. Teammate Evaluations

  • You will lose 50% of your final score if this is not completed. 

  • This is for each sub team


  • Note: Feel free to review team members in other teams (especially if someone has gone above and beyond to help)