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wkdaytopicthemewilling instructorsco-instructorprimary learning outcomes
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11intro to serversfundamental open science skillsmatt,
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11unix toolsmatt, julien
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Social aspects of collaboration, high-performing groups, data policies
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11(R)markdownintro to markdown and its R flavorjulienfrom code to notebook: learn the markdown syntax, what is R markdown and how to render it and why should we use it. Jupyter notebook as an alternative
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11Git and GitHubcode versioning and sharingmatt, julienlearn git basic commands and be able to integrate it into their own workflow; (include Git/GitHub integration in R Studio?)
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12Data summarization and cleansingChecking data
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12Data Management
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12Facilitating group discussions
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12bash shell scripting
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13Overview of Data models, esp the relational modelto enhance understanding of how data are organized (or not!) according to specific conceptual models.
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13Data wrangling in Rdata modeling concepts (should it be a separate session?) and data manipulations using Rjulien,to get exposition to data normalization concepts, wide vs long formats and tools to implement them in R (more likly tidyverse, but also data.table)
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Message box 2: Group exchange and feedback
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14SQL and databases
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14DataONE R client
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14advanced unix tools and regexmatt, julien
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15meta-analysisadvanced synthesischrisJuliento be able to do a meta-analysis in R, to appreciate the challenges associated with scraping primary data from the lit, to be able to do a transparent synthesis workflow
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21Data wrangling in Rtidy databrycechriswork within tiyverse and dplyr to be able to use pipes, filter data and combine datasets
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2Debugging with Rstudiooverview of the debugger interfacejulienknow how to debugg a code
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2intro to OOfunctions, scope and modular codebest practices awareness (DRY, KISS,..), creating modular code through functions, what is a scope
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2Code optimizationprofiling, vectorization and multiprocessingjulienintro to profiling (system.time, microbenchmark, profvis), apply functions and multiprocessing (foreach, mclapply)
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Reproducible scienceclarify what is "Open Science", differences among repeatability, reproducibility, replicability, transparency, etc. Outline both methods and products involved w Open Science
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22Data semantics and annotationto enhance understanding of how data are assertions of "facts" typically involving "measurements" of "characteristics" of "entities". To enhance understanding of how Web-resolvable unique identifers can lead to greater clarity in referencing types of facts and measurements-- essential to enabling synthesis and integration across information resources
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2intro to GISLeah
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2intro to RSLeah
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HDF5?? Leah ?Matt mentioned this - i think if we do remote sensing we could do hdf5 or 4 depending on what products we use. accessing them is different in R.
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3geospatial analysis
Leah (if it's not spatial statistics - someone else should cover stats like Ben)
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3video story tellingchris
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3Data visualizationData visualization and interactive toolsLeah, Chrisggplot, plotly, html widgets, shiny
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31bioinformaticstracy
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NOTE from Leah: not sure were to put this however i'm open to staying a week or a bit more than a week and maybe it would make sense to have that be the secondish half of week 2 into mid week 3? that way i could help the students and also potentially help with the data viz and video stuff which i enjoy!! i'd rather stay over the weekend than travel 2 weekends given my schedule.
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