Sarah Grace
Not Alvsce
45 Matching Annotations
- Aug 2023
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viz.datascience.arizona.edu viz.datascience.arizona.edu
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Rachel Laura
Accept if space?
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Eung Joo Lee
Already an expert!
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Bryan Roxas
Admit. Did incubator with group.
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Renee Grambihler
Seems like good opportunity to network with Biosphere 2 & build DS capacity there. However, already pretty advanced practicioner.
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Rachel Leih
Admit. Good answers, stands to benefit a lot
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Parker Geffre
Admit.
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Mary Ahern
Admit
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Madeleine deBlois
Admit. Good fit. Terrace's co-worker.
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Madeleine Wallace
Admit. ALVSCE, good answers
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Likith Kumar Dundigalla
Cut. Already highly skilled, answers dont' match course description
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Katherine King
Not ALVSCE, but admit if space
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Jordan Gunning
Admit.
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Giovanni Melandri
Admit? Curious if he'll really have time
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Eldridge Wisely
Not in ALVSCE, but admit if space
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Edwin Alvarado-Mena
Not in ALVSCE, but admit if space
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Ryan
Cut. Not at UAZ
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Bryan Blue
Admit. ALVSCE, good answers, stands to benefit
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Ajay Perumbeti
Cut? Already highly skilled, not in ALVSCE
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Zoe Scott
Admit. Will benefit, good answers, in CALES
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YIJYUN LIN
I kept the duplicate application because her answers are quite different LOL
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YIJYUN LIN
Accept. I've worked with YiJyun and she would benefit from this course
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1: Not able to perform task
This should be a 2. See answers below
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SQL for phenotyping
How?
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Simone Williams
Accept. I've been working with Simone and she would benefit from this course
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Rohit Hemaraja
Cut? Vague answers
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Rachel Gildersleeve
Cut. No R
This is Terrace's co-worker. We should follow up personally with resources to get started in R so they can take this course next year.
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Michael Hernandez
Cut. Answers don't match workshop description
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Meccah Jarrah
Cut. No R experience
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Mariam Hovhannisyan
Cut? Already has a lot of skills and not in ALVSCE
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Kylie Boyd
Cut. No R
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Joshua Oyekanmi
Cut? Answers are terse, low R skill
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Damian Barraza
Cut. His answer to "how" doesn't match the course description.
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- Aug 2022
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fw8051statistics4ecologists.netlify.app fw8051statistics4ecologists.netlify.app
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However, it is also common to only include a subset of principal component scores when building regression models
This is probably rarely a good idea. If ecologically relevant variables are not the ones that contribute to co-variation, they will be lost. In fact, principal component regression is rarely a good idea, especially since there are many supervised multivariate analysis techniques to deal with multicollinearity in regression like problems (e.g. RDA, CCA, PLSR). For more detailed discussion of why PCA regression is probably almost always the wrong choice for ecological data, see Scott & Crone 2021
Scott, Eric R., and Elizabeth E. Crone. “Using the Right Tool for the Job: The Difference between Unsupervised and Supervised Analyses of Multivariate Ecological Data.” Oecologia 196 (February 12, 2021): 13–25. https://doi.org/10.1007/s00442-020-04848-w.
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- Jul 2022
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localhost:4594 localhost:4594Untitled1
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xecutable code
change this to something else
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- Apr 2022
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sortee-github-hackathon.github.io sortee-github-hackathon.github.io
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https://ourcodingclub.github.io/, https://www.openscapes.org/
convert to citations
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(Box 1)[#box-1-definitions])
Markdown issue
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General background about GitHub
We should comment out any headers that we don't actually want showing up in the rendered doc
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The source code and data for this manuscript are available at https://github.com/SORTEE-Github-Hackathon/manuscript.
Add Zenodo DOI.
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German Centre for Integrative Biodiversity Research (iDiv).
change to citation
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[[22]][7].
Typo here in formatting citation
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Ecologists who write code often use the R programming language, and the rOpenSci community has a well-established software peer review process that involves both rOpenSci’s staff software engineers and the broader R user community. Their software review GitHub repository provides instructions for submitting an R package for review as well as guidelines for code reviewers. rOpenSci’s efforts have resulted in many well-used R packages for ecology research including rfishbase [21] and taxize [22].
rOpenSci review is mentioned earlier in the Peer-Review section. I suggest moving this up and merging
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GitHub can bGitHub
Typo
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- Mar 2022
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sortee-github-hackathon.github.io sortee-github-hackathon.github.io
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The standard GitHub licensing options are best suited for software. If your code is intended only for your specific analysis, consider a Creative Commons License. The Choose a License website can offer further guidance. If you wish to allow anyone to re-use your code, consider a CC0 1.0 public domain dedication. If you wish to receive attribution for any reuse of your code, consider a CC BY 4.0 license, which requires attribution upon reuse. If you have build an app, tool, package, or other product that you would like others to use and would like attribution for any reuse of your code, consider the GNU General Public License v3. This license also prohibits the re-user from making their re-used version private. If you do not wish to receive attribution and are open to private use, consider the MIT license.
I think probably less detail is needed here. Distill down to most important points
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Box 2
This should just be Table 1 I think
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