Assignment week 1

Week 1

Assignment 1.1 – Journal club

Expectation:

• Everybody should read all the abstracts
• By group of 3, read carefully and prepare a 3-4 slides presentation about the paper / book chapter you have been assigned
• Participate in the discussion during the Journal club

Reading Resources

1. Borer, et al, 2009. Some Simple Guidelines for Effective Data Management. https://doi.org/10.1890/0012-9623-90.2.205

2. Fegraus, et al, 2005. Maximizing the Value of Ecological Data with Structured Metadata: An Introduction to Ecological Metadata Language (EML) and Principles for Metadata Creation. https://doi.org/10.1890/0012-9623(2005)86[158:MTVOED]2.0.CO;2

3. Hampton, et al, 2015. The Tao of open science for ecology. Ecosphere 6, art120. https://doi.org/10.1890/ES14-00402.1

4. Heidorn, P.B., 2008. Shedding Light on the Dark Data in the Long Tail of Science. Library Trends 57, 280–299. https://doi.org/10.1353/lib.0.0036

5. Michener, W.K., 2015. Ten Simple Rules for Creating a Good Data Management Plan. PLOS Computational Biology 11, e1004525. https://doi.org/10.1371/journal.pcbi.1004525

6. Recknagel, F., Michener, W.K., 2018. Ecological Informatics: An Introduction, in: Recknagel, F., Michener, W.K. (Eds.), Ecological Informatics: Data Management and Knowledge Discovery. Springer International Publishing, Cham, pp. 3–10. https://doi.org/10.1007/978-3-319-59928-1_1

7. Michener, W.K., 2018. Ecological Informatics: Data Discovery, in: Recknagel, F., Michener, W.K. (Eds.), Ecological Informatics: Data Management and Knowledge Discovery. Springer International Publishing, Cham, pp. 3–10. https://doi.org/10.1007/978-3-319-59928-1_1

8. Schildhauer M.S., 2018. Ecological Informatics: Data Integration, in: Recknagel, F., Michener, W.K. (Eds.), Ecological Informatics: Data Management and Knowledge Discovery. Springer International Publishing, Cham, pp. 3–10. https://doi.org/10.1007/978-3-319-59928-1_1

9. Wilkinson, et al, 2016. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018. https://doi.org/10.1038/sdata.2016.18

Journal Club – Teams

library(tidyverse)
library(DT)

# Set the random seed to make this reproducible
set.seed(12)

# Read the roster 
students <- read.csv("data/eds213_roster.csv")

# Randomly create the groups
groups <- students %>% 
  slice(sample(1:n())) %>% # randomly arrange the data frame
  group_by((row_number()-1) %/% (n()/9)) %>%  # create 9 Groups
  nest %>% pull() %>% bind_rows(.id = "Group") %>%  # group number as column
  datatable(options = list(pageLength = 25))   # Display

groups

Due date:

Wed 09/29/2021

Assignment 1.2 – Define your group project question

See here for more background information about the group project

Expectation:

• Brainstorm as a group about your group project question
• Open an GitHub issue on this repository describing:
  • Your environmental question
  • The type of data sets you are planning to search for on ADC, KNB and/or EDI repositories via DataONE
  • Link to your Group project repository on GitHub

Due date:

Wed 10/06/2021 at 12PM (Noon)