The VectorBiTE Training Workshop materials

• The VectorBiTE Training Workshop materials
  • Schedule
  • The Teaching Tools
  • How to prepare for the Workshop
• For Instructors
  • General instructions
  • Planned Topics

Schedule

(Training Team: Leah Johnson (LJ); Samraat Pawar (SP): Marta Shocket (MS); Fadoua El Moustaid (FE); Matthew Watts (MW))

Date	Time	Topic	Lead Instructor
Monday, 11th June	08:30 - 09:00	General Intro and Setting up	SP
	09:00 - 10:30	Data principles/wrangling	SP
	10:30 - 11:00	Break	--
	11:00 - 12:00	Intro to Model Fitting Lecture	SP
	12:00 - 13:00	Lunch	--
	13:00 -14:00	Principles: Probability, Likelihood	LJ
	14:00 - 15:00	Intro to Bayesian Methods	LJ
	15:00 - 15:30	Break	--
	15:30 - 17:00	Traits: Linear Models + NLLS + MLE	SP
	17:00 - 17:30	Intro to challenge/exercise	SP
Tuesday, 12th June	08:30 - 09:00	Q & A	All
	09:00 - 10:30	Traits: (Group) challenge/exercise*	MS-FE-MW
	10:30 - 11:00	Break	--
	11:00 - 12:00	Traits: (Group) challenge/exercise*	MS-FE-MW
	12:00 - 13:00	Lunch	--
	13:00 - 14:00	Abundance/Incidence: Time series	LJ
	14:00 - 15:00	A/I: Linear Models + NLLS + MLE	SP
	15:00 --15:30	Break	--
	15:30 --17:00	Basics of Bayesian Analyses with JAGS	LJ
	17:00 --17:30	Intro to challenge/exercise	LJ
Wednesday, 13th June	08:30 - 09:00	Q & A	All
	09:00 - 10:30	A/I: (Group) challenge/exercise*	MS-FE-MW
	10:30 - 11:00	Break	--
	11:00 - 12:00	(Group) challenges discussion	All
	12:00 - 13:00	Lunch	--
	13:00 --15:00	Working Group Preliminaries
	15:00 --15:30	Break
	15:30 --17:30	Registration/socializing for open session

*These will involve model fitting/selection

All the teaching materials, including the lectures, jupter notebooks, code, and data are at this git repository.

The Teaching Tools

We will be using R. The materials will be delivered through lectures and Jupyter notebooks. If you would like to use jupyter (not required for the workshop), have a look at this Intro to Jupyter notebooks, or something else online.

How to prepare for the Workshop

• We are assuming familarity with R basics. Here are some resources for brushing up (and there are many more online -- pick something that suits your learning style):
  • Samraat's notes - The Intro to R Chapter, but more if you want
  • try R
  • https://ismayc.github.io/rbasics-book/
  • https://www.datacamp.com/courses/free-introduction-to-r/?tap_a=5644-dce66f&tap_s=10907-287229
  • https://kingaa.github.io/R_Tutorial/

A good bit of the traing will focus on data visualization. In particular, you might want to have a look at ggplot. There is a section on this in the Workshop notes, but plenty of other online resources are available --- just google ggplot!

• Bring your laptop. Any operating system/platform will do.

• Have R (version 3.2 or higher) installed.

• Have some code editor installed. RStudio is a great option, as it is a good code editor + a inuitive GUI.

• Inculcate the coding Jedi inside of you - or the Sith - whatever works.

For Instructors

General instructions

• Please add your files to lectures, notebooks, code, or data directories, as needed; all workshop sessions will share these directories

• If you want to share old materials taht will be up-cycled to the current workshop, please put them in the old_materials directory, under a subdirectory

  • Each of these should ideally include working examples, each with its own code and data etc directories as needed/appropriate.

• If there are Readings (papers, reports, etc) or Resources (cheat-sheets, etc) put them in those named directories at the same level as code, data, etc.

• We are using R. Minimize from using too many special packages, unless you want to teach how to use them.

• We will be using jupyter, but you may include the training material as an R-MarkDown file (.Rmd), outputting a pdf, which we will covert to jupyter.

• Start each discrete session/tutorial by stating its goal and pre-requisites (including packages needed), all under a clear Introduction section.

• In each session, try to include a few small exercises. These give breathing space, allowing people to catch up, and reinforce their understanding.

• each session will with more substantial exercises that the students will solve in groups in a hackathon format (these will be used in the challenge/exercise sessions), listed in in the Schedule. We have budgeted 1-2 hrs for each of these followed by a discussion.

Planned Topics

The plan is to prepare lectures and jupyter notebooks that cover the following materials (see example here):

• Modelling, Probability, Statisics background

  • Intro to data management and basics of visualization - Samraat
  • Intro to modelling and fitting models to data - Samraat
  • Probability distributions and likelihoods, basics of bayesian statistics - Leah

• Traits: Use VecTraits data (Thermal performance curves and metabolic scaling)

  • Fit trait data to mechanistic and statistical models
    • Linear models / regression for simple trait models - allometric scaling data (log-log)
    • Non-linear Least Squares (NLLS) fitting in R - compare Briere and Polynomial using AIC/BIC - TPC data from Martha
    • MLE/Bayesian fitting of simple models - to both

• Population abundances: Use VecDyn data

  • Fit population dynamics models - Logistic growth (Bacteria data)
    • NLLS
    • MLE and Bayesian
  • Fit statistical models (time series analyses) - basic AR models; time series as regression (including MLE)
    • Abundance (VecDyn) data
    • Dengue data