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Social Science Workshop Overview

Home Page: https://datacarpentry.org/socialsci-workshop/

License: Other

Makefile 8.57% R 7.41% Python 82.48% Shell 0.66% Ruby 0.89%
carpentries data-carpentry workshop english social-sciences stable

socialsci-workshop's Introduction

DOI

Social Sciences Workshop

A workshop for teaching data management and analysis for social science research including best practices for data organization,reproducible data cleaning, , and data analysis and visualization. Please see https://datacarpentry.org/socialsci-workshop/ for a rendered version of this material, the lesson template documentation for instructions on formatting, building, and submitting material, or run make in this directory for a list of helpful commands.

Code of Conduct

All participants should agree to abide by the Carpentries Code of Conduct.

Authors

The Social Sciences workshop overview is authored and maintained by the Curriculum Advisory Committee.

Citation

Please cite as:

Data Carpentry Social Sciences Workshop. May 2018.

socialsci-workshop's People

Contributors

angela-li avatar bencomp avatar brownsarahm avatar erinbecker avatar fmichonneau avatar katrinleinweber avatar laurabotzet avatar lemythe avatar likeajumprope avatar maneesha avatar marasedlins avatar noghte avatar petersmyth12 avatar thorkellmoon avatar tobyhodges avatar tracykteal avatar zkamvar avatar

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socialsci-workshop's Issues

clarify on the difference between R and RStudio

In the "Before we Start" episode, the materials did a very good job in explaining what is R and RStudio. But I do think the explanation is too technical for social scientists. I persoanlly a political scientist. I was very confused about why I need to install both R and RStudio to make it work. We should emphasize more on the difference between R and RStudio. I think a lot of people in social science have very limited language in coding so just explaining RStudio as an IDE--Integrated Development Environment does not make a lot of sense to me when I started to learn R. People should know R actually is still runninng when they run their codes on RStudio.

1 day or 2 days worth of material?

The home page for the Social Sciences Data Carpentry says the curriculum is designed for two days of instruction. However the schedule of Data + OpenRefine + R seems to only take one day - are the lesson time estimates accurate?

SAFI farms cognitive load

The SAFI farms dataset has quite a high cognitive load for those without regional, development or farming knowledge. The lessons may work better for a wider audience with a more general social dataset, e.g. a census.

Lesson release checklist

Lesson Release checklist

For each lesson release, copy this checklist to an issue and check off
during preparation for release

Scheduled Freeze Date: 2018-04-27
Scheduled Release Date: 2018-04-30

Checklist of tasks to complete before release:

  • check that the learning objectives reflect the content of the lessons
  • check that learning objectives are phrased as statements using action words
  • check for typos
  • check that the live coding examples work as expected
  • if example code generates warnings, explain in narrative and instructor notes
  • check that challenges and their solutions work as expected
  • check that the challenges test skills that have been seen
  • check that the setup instructions are up to date (e.g., update version numbers)
  • check that data is available and mentions of the data in the lessons are accurate
  • check that the instructor guide is up to date with the content of the lessons
  • check that all the links within the lessons work (this should be automated)
  • check that the cheat sheets included in lessons are up to date (e.g., RStudio updates them regularly)
  • check that languge is clear and free of idioms and colloquialisms
  • make sure formatting of the code in the lesson looks good (e.g. line breaks)
  • check for clarity and flow of narrative
  • update README as needed
  • fill out “overview” for each module - minutes needed for teaching and exercises, questions and learning objectives
  • check that contributor guidelines are clear and consistent
  • clean up files (e.g. delete deprecated files, insure filenames are consistent)
  • update the release notes (NEWS)
  • tag release on GitHub

Two small suggestions for "Introduction to R"

Two small suggestions, see if this makes sense!
(1) For the part of "Functions and their arguments", it might be helpful to quickly go through different rounding functions (i.e., ceiling, floor, and trunc), after the function "round".
(2) For the "Missing data" part, one example that might be useful & meaning to add is how to change all missing values into a particular value, such as 999.

June 2019 Lesson Release checklist

If your Maintainer team has decided not to participate in the June 2019 lesson release, please close this issue.

To have this lesson included in the 18 June 2019 release, please confirm that the following items are true:

  • Call out boxes (exercises, discussions, tips, etc) render correctly
  • Setup instructions are up-to-date, correct, clear, and complete
  • File structure is clean (e.g. delete deprecated files, ensure filenames are consistent)
  • Some Instructor notes are provided
  • Lesson links work as expected

When all checkboxes above are completed, this lesson will be added to the 18 June lesson release. Please leave a comment on carpentries/lesson-infrastructure#26 or contact Erin Becker with questions ([email protected]).

Feedback from Instructors on pilot workshops

This issue is meant to collect feedback from Instructors (and helpers, etc) running pilot workshops. Leave open through January 2019.

Instructors, please add any comments you have about how your pilot workshop went. In particular:

  • What type of an audience did you have? What was their background and skill level?
  • How long did it take to teach each lesson? Were there any parts you had to leave out? Please be specific about what you left out.
  • If you left out material, how did it seem to affect your learners? Was it ok to remove that material? Did you have to come back to it later? Was it confusing to skip certain concepts?
  • What problems did your learners have with the installation? What solutions did you find? If you are comfortable doing so, please also put this information into the Instructor Notes for the relevant lesson(s).
  • Do you have any specific tips for other Instructors teaching these lessons? If you are comfortable doing so, please also put this information into the Instructor Notes for the relevant lesson(s).

You don't need to answer all of the questions above! Please share any and all information that you think will be helpful for future Instructors of these lessons. I will be reading through this issue before the next Curriculum Advisory Committee meeting and will raise issues to that committee for discussion. Please also feel free to leave specific issues on the individual lessons. The Maintainers and Curriculum Advisors really appreciate your feedback!

Include a sample schedule for workshops

This general suggestions follows up from a conversation on #33, which appears to be outdated. It still seems like a good idea to have sample schedule, even if the suggestion in #33 cannot be included at this time for technical reasons.

Currently the "Schedule" section on the workshop page is empty, because it is generated by the Carpentries theme within https://github.com/carpentries/carpentries-theme/blob/main/_includes/syllabus.html and there are no episodes in this pseudo-lesson that would fill the schedule. It looks "broken".

Although I have write access to this repository, I don't feel I have a good solution to offer.
If we resolved the merge conflict(s) in the existing #33 and perhaps update the suggested timings in that schedule, we would still have the empty generated schedule.
Alternatively, if we added pseudo-episodes to the pseudo-lesson, that would probably show up nicely, but mean more work.
Yet another potential solution could be to use a different template so that the schedule would not be generated. But then it could make sense to try to migrate to The Workbench – and I believe at some point pseudo-lessons like this one will be migrated (maybe to a different system than the Workbench).

Transition to standardized GitHub labels

The lesson infrastructure committee unanimously approved the proposal of using the same set of labels across all our repositories during its last meeting on May 23rd, 2018.

This repository has now been converted to use the standard set of labels.

If this repository used the previous set of recommended labels by Software Carpentry, they have been converted to the new one using the following rules:

SWC legacy labels New 'The Carpentries' labels
bug type:bug
discussion type:discussion
enhancement type:enhancement
help-wanted help wanted
newcomer-friendly good first issue
template-and-tools type:template and tools
work-in-progress status:in progress

The label instructor-training was removed as it is not used in the workflow of certifying new instructors anymore. The label question was left as is when it was in use, and removed otherwise. If your repository used custom labels (and issues were flagged with these labels), they were left as is.

The lesson infrastructure committee hopes the standard set of labels will make it easier for you to manage the issues you receive on the repositories you manage.

The lesson infrastructure committee will evaluate how the labels are being used in the next few months and we will solicit your feedback at this stage. In the meantime, if you have any questions or concerns, please leave a comment on this issue.

-- The Lesson Infrastructure subcommittee

PS: we will close this issue in 30 days if there is no activity.

Data sets varied across & figshare

The python lesson in particular uses both multiplel datasets and different data than the rest.

Also a note about figshare and why we use it should be added (re 7/25 lesson infrastructure committee meeting) @fmichonneau

Add lesson for qualitative data analysis

Would it be useful to add lessons for doing qualitative data analysis (of interview or conversation data that needs hand-coding for example) in R and Python. I know of an R package that exists for this (RQDA; http://rqda.r-forge.r-project.org/) in which people can have/create a code book and uses a GUI within R to code data. Does something like this exist in Python? This is something that would probably add about half a day's worth of time.

update datasets

If the SAFI dataset is the only one used across the lessons, the data section to be updated to only include its description.

grand_liv listed twice

grand_liv ("Did your grandparents live in this village or neighboring village?") is listed twice in the list of variables.

describe data

The index.md page has a place for introducing the data that will be used in this workshop, but it is currently empty. @PeterSmyth12 could you please add a short description of the source of the data, similar to how the data is introduced for the genomics workshop here?

Transition to The Carpentries Workbench

Hi @datacarpentry/socialsci-workshop-maintainers

The Curriculum Team is preparing to transition all of the workshop/curriculum overview sites to the Workbench. @zkamvar has set a tentative migration date of Monday 18th September. This change will bring all of the overview sites, including this one, back in line with all of the official lessons that were transitioned to the new infrastructure in May.

What do you need to do?

To help us prepare for a smooth transition, we invite you to:

  1. explore the preview of the transitioned version of the overview site: https://fishtree-attempt.github.io/socialsci-workshop/
  2. process any pull requests made to the repository, ensuring that they are all merged or closed before the transition takes place. Any open pull requests on this repository will be invalidated by the transition. There are currently Zero (0) open pull requests on the repository. 🎉
  3. post the questions and concerns you have about the transition here, tagging @tobyhodges.

What happens next?

After the transition, Maintainers will temporarily lose their write access to the repository as a safety measure, ensuring that the old project history cannot be accidentally pushed back into the repository. The steps to restore access are relatively straightforward (see an example from the completed transition of another lesson) and you can expect detailed instructions and further support from the Curriculum Team when the transition takes place.

Beyond that, you can expect to continue maintaining the repository as you were before, while taking advantage of the simplified layout and syntax that the new infrastructure offers. main will become the default branch of the repository (currently gh-pages), and files currently located in _extras/ will be divided between two new folders: learners/ and instructors/. You can read more about the changes to repository structure/organisation and source file syntax in the Workbench Transition Guide.

mislabeled variable in data description?

On the data description page one row appears to be wrong, but I'm not sure where the source data is to find the correction

| liv_count | Own poultry? |

In the actual datasets, the liv_count variable is an integer, so i would guess it's the count of livestock whereas "Own poulty?" suggests the answer to the question would be boolean.

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