Assignments

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

Random selection:

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)

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Week2

Create a GitHub repository to store your codes of assignment 2.1 and 2.2. Use your group project repository for 2.3.

Assignment 2.1 – dataRetrieval

1. Add the Santa Paula Creek gauge to exercise we did in class
2. What is the best way to query multiple sites using this R package?
3. Create a plot showing any stream flow response to the rain of last night (10/04). Determine at what time did the stream flow peaked (if any) at the two locations?

Assignment 2.2 – metajam

Finish the exercise we started during the lab session to use metajam to download the Alaskan household languages data from DataONE.

For inspiration Here is the code sample we started during the lab: https://brunj7.github.io/EDS-213-metadata/codes/metajam_session.html

Push all your codes to a GitHub repository and add me (brunj7) as a collaborator to your repository.

Assignment 2.3 – Use an API to retrieve data for your project

As your group project team:

1. Based and what we discussed this week, find an API that you could use to retrieve data you are using in your group project.

2. In your Group project repository, write a script to retrieve the data relying on this API. Don’t forget to capture any information about how you found the data as well as any metadata in the Rmarkdown / jupyter notebook so an external user can understand the data set(s) and track their source / data provider.

Due date:

Wed 10/13/2021 at 12PM (Noon)