BIDS Project Hack Day, December 7, 2017 === Project Hack Day is an opportunity for BIDS Fellows and Researchers to devote an entire day to project work with collaborators - and the projects are entirely up to you. We'll have the 190 Doe workspace available to us + invited guests. **Edit this hack pad by clicking on the pencil in the upper right.** Questions? Email [Diya]( ## How to participate 0. Sign up [here]( so we can get you breakfast & lunch! 1. Do you have a project you'd like help with? Write up a description in the project section! We'll have 2 min pitches on the day of the event before we get started. 2. Do you want to join forces with someone? Read the project descriptions and think about which project(s) interest you. ## Schedule 09:00–09:30 Breakfast 09:30–10:30 Project pitches 10:30–12:30 Hacking 12:30–13:00 Lunch 13:00–14:00 Fellows Talk (by Marla Stuart) & Discussion 14:00–16:15 Hacking 16:15–16:30 Tea 16:30–end 5-10 min project (progress) presentations Happy hour, off-campus (?) ## Projects - **Ecology Challenge** [Stéfan van der Walt] Data Science Challenge in which we use remote sensing data from low flying airplanes to infer the location and type of trees in forests. Interested? Please read [this overview]( Participants: Chris Kennedy, Deborah Sunter,[you?] - **Eventbrite Public Dashboard** [Diya Das] Eventbrite doesn't have a *public-facing* dashboard to share high-level registration/check-in information (for promoting events), but they do have an API. *Interested? Add your name here*: Raymond Yee ## Notes ### Ecology Challenge > Dear perspective ECODSE participant, Thank you for your interest in participating in the NIST Data Science Evaluation on “Plant Identification with NEON Remote Sensing Data”. The competition deadline is December the 15th. If you are interested in participating please send us asap your team name, the name of the members of your team (it's totally fine if it's just you), and the email address your team will use for submissions. You can send them by email at at <redacted> or <redacted>. Full information about the scope, data, timeline, and evaluation of the three tasks at In short, you can work on any subset of the 3 tasks you are interested in, you do not have to work on all of them. Task 3 is the most straight forward task and can be completed using common classification methods on the provided data table. Task 2 is likewise based mostly on tabular data. Task 1 is the most complicated because it involves a combination of spatial and image data. Also, we are planning on writing up the results of this competition for publication, either in a single paper involving active participants or as a series of small papers with each competing team writing up their methods and results as a short paper that accompanies an overall summary.