Ecoblender hosting a workshop: An Introduction to R and Generalized Linear Models

Full details are provided here.
https://afilazzola.github.io//YorkU.GLM.2017-04-28/

General Information

The purpose of this workshop is to provide tools for a new/novice analyst to more effectively and efficiently analyse their data in R. This hands-on workshop will introduce the basic concepts of R and use of generalized linear models in R to describe patterns. Participants will be encouraged to help one another and to apply what they have learned to their own problems.

Who: The course is aimed at R beginners and novice to intermediate analysts. You do not need to have any previous knowledge of the tools that will be presented at the workshop.

Where: 88 Pond Road, York University. Room 2114 DB (TEL). Google maps

Requirements: Participants should bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) with administrative privileges. If you want to work along during tutorial, you must have R studio installed on your own computer. However, you are still welcome to attend because all examples will be presented via a projector in the classroom. Coffees and cookies provided for free.

 

How to use colour in manuscripts

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I thought this was a very helpful guide on using colour in figures. There are a few rules, but one comment from the whole document stands out. “If colour serves a purpose, but something other than colour would do the job better, avoid using it”.

http://www.perceptualedge.com/articles/visual_business_intelligence/rules_for_using_color.pdf

Here are the simple rules:

1. If you want different objects of the same color in a table or graph to look
the same, make sure that the background—the color that surrounds
them—is consistent.

2. If you want objects in a table or graph to be easily seen, use a background
color that contrasts sufficiently with the object.

3. Use color only when needed to serve a particular communication goal.

4. Use different colors only when they correspond to differences of meaning
in the data.

5.  Use soft, natural colors to display most information and bright and/or dark
colors to highlight information that requires greater attention.

6.  When using color to encode a sequential range of quantitative values,
stick with a single hue (or a small set of closely related hues) and vary
intensity from pale colors for low values to increasingly darker and brighter
colors for high values.

7.  Non-data components of tables and graphs should be displayed just
visibly enough to perform their role, but no more so, for excessive salience
could cause them to distract attention from the data.

8.  To guarantee that most people who are colorblind can distinguish groups
of data that are color coded, avoid using a combination of red and green in
the same display.

9. Avoid using visual effects in graphs.

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A funny story about external HD failures for #animalcamera and #pollinator imagery and videos

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Funny story on drive failures
I have had the external HD from Panoche Hills Ecological Reserve with records of background animal cam monitoring on my desk for about a month.  About to back up, had call with Ally, and she told me her nightmare experiences with external drive failures this season. Righteously, I held up this particular ‘x’ brand hard drive and said ‘ohya these are way better, never fail’.
After call, I am thinking, well time to use that unlimited google drive folder and back up these animal cam photos.  Plug in drive, opens up, spins for less than a minute, then promptly fails.  We do not have backup so this was only copy….
I am like are you kidding, here I am about to finally back up to cloud, and it crashes!!
Spoke with NCEAS informatics team.  Apparently, there are just two types of drives – those about to fail, those that have failed. Also, the failure rate for mechanical drives can be up to 45% when there many, many small files versus lower volume, larger files. Of course, we have exactly the worst case scenario (a million small file-sized animal pics from the Carrizo this season). Mechanical drives, in trying to read many little files, jump around a lot.
 #irony
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So, again, we need to shoot for this bare minimum rule.
Three backups of all video/animal cam raw imagery.
TWO external HDs in different places.
ONE cloud back up.
Workflow for archiving
1. Copy from external HD to HD of a local, dedicated lab machine (doing directly from external introduces too much lag).
2. Use ethernet not wifi to move to cloud drive.
3. Put in appropriate folder on drive with subfolders.
4. Google drive does not support folder dropping with Safari so use Chrome or Opera.
5. PUT a raw.imagery.data.csv or googlesheet file in same master folder as your raw imagery explaining folder and file names and replication so that ANYONE could go to that master folder and understand the raw imagery. Meta-data make the world go round and science reproducible, verifiable, and open.
Final note, publish imagery data to data journal before you write paper.  Taylor did this via GigaScience, but we could also try for Zooniverse or Nature Scientific Data.
After all, these data are precious.
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