EDS 211: Team Science

EDS 211: Team Science, Collaborative Analysis and Project Management

If you want to go fast, go alone. If you want to go far, go together. – African proverb

1 Schedule

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source(here::here("functions.R"))
get_sched() %>% 
  dt_sched()

2 Overview

Welcome to the Team Science course for the Masters in Environmental Data Science! In this class you’ll learn how to work together on code while managing a project. You’ll apply these techniques to the group project from the course Remote Sensing (EDS 220) and they should help you manage your Capstone group project starting winter quarter. The techniques will help guide three basic questions:

  1. What should I work on next?
  2. How long will it take?
  3. How do we adapt to change?

These basic questions are however deceptively difficult to answer, especially as the size of the team and scope of the project increases.

What should I work on next? You’ll learn ‘agile’ software development techniques to divide work into tasks, which can be assigned to various team members and passed between as work progresses. These tasks can be arranged as cards into columns on a ‘Kanban’ board: horizontally according to status (ToDo, In Progress, Done) or time (next week, next month, …); and vertically based on priority (from most to least important, top to bottom). This overview of who is to do what next will be approached with different tools embracing increasing levels of complexity: a Trello board for basic projects without code, a Github Project to integrate Github Issues associated with code level git commit messages, and ZenHub for adding dependencies between issues and clustering into Epics for use with Roadmaps.

How long will it take? You’ll take on the role of an environmental data science consultancy responding to a request for proposal. In the proposal you’ll outline milestones with deliverables and associated dollar amounts based on time estimates and hourly rates of team members. You’ll track time spent on each task and report out at the end with a presentation on time spent over and under expectations, which will help update your understanding for future estimates. You’ll use Clockify to track time and Rmarkdown to generate reports.

How do we adapt to change? Changes are inevitable with technical problem solving and client requirements. As Mark Zuckerberg famously said “move fast and break things.” Iterating quickly within the team and getting feedback from all stakeholders are an important part of the development process and the basis for ‘agile’ project management. You’ll compare ‘traditional’ project management with scoping tasks at the outset of the project with a Gantt chart as a ‘waterfall’ diagram showing various phases of the project (eg data collection, analysis and communication). As you proceed with the project you’ll keep track of tasks that expand and shift with some evaluation after the fact through a burndown report. Other ‘agile’ techniques and roles will be reviewed to adapt to change.

3 Office Hours

Office Hours are Mondays 1-2pm at NCEAS. You can swing by the office across from Jamie’s where I’ll be or better yet slot 10 minutes in advance with this calendar:

4 Grading

Grades will be based on the following percentages:

5 Readings

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library(citr)
md_cite("Bennett", bib_file="eds211-team.bib")
Bastille, Kimberly, Sean Hardison, Lynn deWitt, Jennifer Brown, Jameal Samhouri, Sarah Gaichas, Sean Lucey, et al. 2021. “Improving the IEA Approach Using Principles of Open Data Science.” Coastal Management 49 (1): 72–89. https://doi.org/10.1080/08920753.2021.1846155.
Bennett, L. Michelle, and Howard Gadlin. 2012. “Collaboration and Team Science: From Theory to Practice.” Journal of Investigative Medicine 60 (5): 768–75. https://doi.org/10.2310/JIM.0b013e318250871d.
Larson, Deanne. 2019. “Best Practices in Accelerating the Data Science Process in Python.” In Introduction to Data Science and Machine Learning. IntechOpen.
Lowndes, Julia S. Stewart, Benjamin D. Best, Courtney Scarborough, Jamie C. Afflerbach, Melanie R. Frazier, Casey C. O’Hara, Ning Jiang, and Benjamin S. Halpern. 2017. “Our Path to Better Science in Less Time Using Open Data Science Tools.” Nature Ecology & Evolution 1 (6): 0160. https://doi.org/10.1038/s41559-017-0160.
Perez-Riverol, Yasset, Laurent Gatto, Rui Wang, Timo Sachsenberg, Julian Uszkoreit, Felipe da Veiga Leprevost, Christian Fufezan, et al. 2016. “Ten Simple Rules for Taking Advantage of Git and GitHub.” Edited by Scott Markel. PLOS Computational Biology 12 (7): e1004947. https://doi.org/10.1371/journal.pcbi.1004947.

References