niklasbuschmann · Jan 10, 2022 · Jan 10, 2022 · Jan 10, 2022 · Jan 10, 2022 · Jan 10, 2022
diff --git a/_config.yml b/_config.yml
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-title: "Blog Title"
-author: "Blog Author"
-description: "Made with <b style=\"color: #f45;\">&lt;3</b>"
-permalink: /:title/
+title: "ARSC 5040 Qualitative Data Tools"
+author: "Jordan Wrigley and Nickoal Eichmann-Kalwara"
+description: "University of Colorado Boulder"
+permalink: /home.md/
 lang: "en"
 excerpt_separator: "\n\n\n"
 date_format: "%B %d, %Y"
 
 # Layout
 
-show_excerpts: true        # show article excerpts on the home page
+show_excerpts: true       # show article excerpts on the home page
 show_frame: true           # adds a gray frame to the site
-show_sidebar: false        # show a sidebar instead of the usual header
-minimal: false             # use a dark header
+show_sidebar: true        # show a sidebar instead of the usual header
+minimal: false            # use a dark header
 
 # Menu
 
 navigation:                # accepts {file, title, url, icon, sidebaricon}
-  - {file: "index.html"}
-  - {file: "README.md"}
+  - {title: "Home", file: "home.md", icon: none}
+  - {title: "Course Schedule", file: "modules.md", icon: none}
+  - {title: "Final Project", file: "finalproject.md", icon: none}
 
 external:                  # shows a footer with social links - for available icons see fontawesome.com/icons
-  - {title: Mail, icon: envelope, url: "mailto:niklasbuschmann@users.noreply.github.com"}
-  - {title: Github, icon: github, url: "https://github.com/niklasbuschmann/contrast"}
-  - {title: Subscribe, icon: rss, url: "/feed.xml"}
+  - {title: Email Us, icon: envelope, url: "mailto:nickoal.eichmann@colorado.edu;jordan.wrigley@colorado.edu"}
 
 comments:
 #  disqus_shortname: ""    # see https://disqus.com/

diff --git a/_includes/home.html b/_includes/home.html
@@ -1,7 +1,7 @@
 {%- assign posts = paginator.posts | default: site.posts -%}
 {% for post in posts %}
   <article>
-    {% include meta.html post=post preview=true %}
+    {% include meta.html post=post preview=false %}
     {{ post.excerpt }}
     <div class="more"><a href="{{ post.url | relative_url }}">read more</a></div>
   </article>

diff --git a/_posts/2017-01-01-advanced-examples.md b/_posts/2017-01-01-advanced-examples.md
diff --git a/_posts/2017-02-01-markdown-examples.md b/_posts/2017-02-01-markdown-examples.md
diff --git a/finalproject.md b/finalproject.md
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+# Final Project
+
+> Develop an overview of a proposed or hypothetical project including: data types, sources, and collection instruments; selected analysis methods and tools; initial and exploratory findings and visualizations. In Week 5, you will receive peer feedback and initiate discussion around our projects.
+
+- **Use what media is most comfortable for you. Thank about what output would be most useful to you in your work. Some example might be...**
+  - A 5-6 slide deck is good if your are thinking of making a public proposal of your project. 
+  - A 1-2 page double-spaced write-up is great if you need to submit and written proposal or woud like to do some initial writing for a scholarly paper.
+  - An audio/video recording such as a screen cast narrating you walking through a tool and exploring whether or not it's meet your research needs may be useful for those testing and learning new tools.
+
+## Final project examples:
+
+### Example #1: 
+- Outline of lifecycle for a real or hypothetical project
+  - Create data or reuse data? Which data sources?
+  - What ethical considerations may there be?
+  - Which tools are most appropriate and why?
+  - Which methods are most appropriate and why?
+
+### Example #2:
+- Explore analysis in a specific 1-2 tools you might be considering
+  - What worked and what did not?
+  - Where were the sticky points?
+  - What skills might you need to aquire to use the tool fully?
+  - Does the required upskilling fit within your research timeline?
+
+## Final Projects due Thursday, 10 February
diff --git a/home.md b/home.md
@@ -0,0 +1,56 @@
+# ARSC 5040: Qualitative Data Tools
+Spring 2022, University of Colorado Boulder
+Tuesdays, 10am-12:30pm Mountain Time
+
+## Instructors
+
+### Nickoal Eichmann-Kalwara (call me Nickoal, she/her)
+nickoal.eichmann@colorado.edu 
+Assistant Professor, Digital Scholarship Librarian
+Director of Digital Scholarship, Center for Research Data & Digital Scholarship
+
+### Jordan Wrigley (call me Jordan, she/her)
+jordan.wrigley@colorado.edu
+Data Librarian and Free-range Data Consultant
+Center for Research Data & Digital Scholarship
+
+## Course Description
+Qualitative data research and analysis includes growing technical aspects with numerous tools. This is a practical crash course in qualitative data tools focused on maximizing the efficacy of tool selection and usage through critical assessment of research goals for analysis. Topics will include data collection through the lens of planned analysis, QDA open-source and paid tools, and visualization for data reporting. Learners will complete a final project portfolio of resources and exploratory tool analyses.
+
+### Objectives:
+- Define "qualtitative data" in your field/domain and apply exploratory research methods.
+- Design a research workflow for your qualitative data project.
+- Evaluate appropriate tools and potential methods for your data.
+
+## Required Materials
+None other than devices that will enable you to remotely attend and participate in class. Please contact us if you’re concerned about your tech equipment. In following the data practices of of open scholarship and science, all materials will be available for free online or via the University Libraries, etc. They all will be hyperlinked in this syllabus. For some materials, you will need to have your proxy settings setup for off-campus access. You are not required to purchase anything from the CU Bookstore.
+
+## Communication
+COMMUNICATION: We're using Slack: [arsc5040quali-use2223.slack.com](arsc5040quali-use2223.slack.com)
+
+## Topics / Modules 
+- Week 1: [Qualitative Types and Considerations](/week1.md)
+- Week 2: [Qualitative Data Collection](/week2.md)
+- Week 3: [Qualitative Data Analysis](/week3.md)  
+- Week 4: [Communicating QDA](/week4.md)
+- Week 5: [Project Presentations and Peer Feedback](/week5.md)
+
+## Assignments
+- See [Modules](/modules.md)
+- Final Project: Learners will propose a real or hypothetical project including: data types, sources, and collection instruments; selected analysis methods and tools; initial and exploratory findings and visualizations. 
+
+## COVID 19
+As your instructors, our first responsibility is to provide you a safe environment in which to learn, which is why we have chosen to hold this course remotely. If you anticipate this online class will cause you difficulties, please reach out and we will collaborate on solutions. If you experience pandemic-related (or other!) challenges to your ability to participate in this course, please also let us know how we can support you, regardless of what point in the semester issues arise. We are in a continuing global crisis. Be on **Team You**.
+
+## Accomodation for Disabilities
+Above all else, we honor your humanity and respect your privacy. You are not required to self-disclose your disabilities. Since not all disabilities are recognized by Disability Services, please know we are willing to support you and accommodate as much as possible.
+
+"If you need accommodations for a disability, it is recommended that you submit an accommodation letter from Disability Services. Disability Services determines accommodations based on documented disabilities in the academic environment.  Information on requesting accommodations is located on the Disability Services website. Contact Disability Services at 303-492-8671 or dsinfo@colorado.edu for further assistance.  If you have a temporary medical condition or injury, see Temporary Medical Conditions under the Students tab on the Disability Services website."
+
+## Classroom Behavior
+Data science and qualitative data analysis has always been an evolving field, making us constant learners. We aim to honor us as co-learners as we grow in this area together.
+
+"Students and faculty each have responsibility for maintaining an appropriate learning environment. Those who fail to adhere to such behavioral standards may be subject to discipline. Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with race, color, national origin, sex, pregnancy, age, disability, creed, religion, sexual orientation, gender identity, gender expression, veteran status, political affiliation or political philosophy.  Class rosters are provided to the instructor with the student's legal name. I will gladly honor your request to address you by an alternate name or gender pronoun. Please advise me of this preference early in the semester so that I may make appropriate changes to my records.  For more information, see the policies on classroom behavior and the Student Code of Conduct."
+
+## Honor Code
+"All students enrolled in a University of Colorado Boulder course are responsible for knowing and adhering to the Honor Code. Violations of the policy may include: plagiarism, cheating, fabrication, lying, bribery, threat, unauthorized access to academic materials, clicker fraud, submitting the same or similar work in more than one course without permission from all course instructors involved, and aiding academic dishonesty. All incidents of academic misconduct will be reported to the Honor Code (honor@colorado.edu; 303-492-5550). Students who are found responsible for violating the academic integrity policy will be subject to nonacademic sanctions from the Honor Code as well as academic sanctions from the faculty member. Additional information regarding the Honor Code academic integrity policy can be found at the Honor Code Office website."
diff --git a/index.html b/index.html
@@ -1,10 +1,10 @@
 ---
 layout: default
-title: "Home"
+title: "ARSC 5040: Qualitative Data Tools"
 ---
 
-{% if site.show_excerpts %}
-  {% include home.html %}
-{% else %}
-  {% include archive.html title="Posts" %}
-{% endif %}
+This course is offered via CARTSS' Interdisciplinary Training in the Social Sciences (ITSS) and the Center for Research Data & Digital Scholarship, at the University of Colorado, Boulder (Spring 2022).
+
+- [Home](https://nickoal.github.io/ARSC5040/home/)
+- [Modules](https://nickoal.github.io/ARSC5040/modeules/)
+
diff --git a/modules.md b/modules.md
@@ -0,0 +1,20 @@
+# Modules
+## [Week 1](/week1.md): 
+- Qualitative Types and Considerations
+- What research approaches and types of data require which tools? This session will examine the planning phase of qualitative data research methods and how to develop processes which guide ethical data collection, analysis, and dissemination.
+
+## [Week 2](/week2.md): 
+- Qualitative Data Collection
+- Qualitative data collection methods and tools are numerous. Selecting ethical and effective collection sources,  instruments, and tools requires parallel critical assessment of project goals, data, and resources. This session will overview exploratory collection methods and tools including webscraper.io, APIs, and others. 
+
+## [Week 3](/week3.md): 
+- Qualitative Data Analysis 
+- Analysis is often a technical and tool heavy phase of modern qualitative data research. A variety of tools may be applicable to a given qualitative research project. This session will tour several survey-based, text mining, qualitative coding, and other tools including R and Python packages. This will also include a deeper dive into particular tools and techniques to upskill learners' practical knowledge.
+
+## [Week 4](/week4.md):
+- Communicating Qualitative Data Analysis
+- Being able to communicate complex qualitative concepts in data findings is a key aspect of research dissemination. This session will examine current common and popular tools for qualitative data visualization and reporting. It will also provide examples of data communication platforms (ex. story maps) and language for presentations and manuscripts. 
+
+## [Week 5](/week5.md): 
+- Project Presentations and Peer Feedback
+- Learners will present a lightning talk presentation overviewing their project including: data types, sources, and collection instruments; selected analysis methods and tools; initial and exploratory findings and visualizations. Presenters will receive peer feedback and initiate discussion around their projects. 
diff --git a/week1.md b/week1.md
@@ -0,0 +1,42 @@
+# Week 1: 11 Jan - Qualitative Types + Considerations
+
+> What research approaches and types of data require which tools? This session will examine the planning phase of qualitative data research methods and how to develop processes which guide ethical data collection, analysis, and dissemination. <br>
+
+## Week 1: [Jamboard](https://jamboard.google.com/d/1pAUGwgTsyXikkiDc8ee8qO7yMYwZE98VYI94qSvXezk/edit?usp=sharing)
+
+### Readings - explore content of interest to you
+1. [Sage’s Little Blue Books](https://methods.sagepub.com/Search/Results?booktypes=qrm&IncludeSegments=false&IncludeParts=false&sort=pubyear&keywords[0].Field=FullText&keywords[0].Text=&noback=true)
+2. [Sage’s QDA Methods Map](https://methods.sagepub.com/methods-map/qualitative-data-analysis)
+
+[*VPN access required](https://oit.colorado.edu/vpn-virtual-private-network)
+
+### In class text/sources (we'll look at these together but feel free to preview on your own!)
+- [The Research Data Lifecycle](https://youtu.be/OL_Vd9dd-AQ) ~00:05:21
+- [QDA 101 Tutorial](https://youtu.be/OL_Vd9dd-AQ) ~00:25:25
+- [Qualitative Data Collection](https://youtu.be/nVR9MHWD7AU) ~00:10:30
+
+### Topics
+
+**Housekeeping**
+- Syllabus and Community Practices
+-  Learning as colleagues
+    - [Bloom's Taxonomy](http://www.talentquest.com/wp-content/uploads/2017/06/Blooms-Taxonomy.pnghttp://www.talentquest.com/wp-content/uploads/2017/06/Blooms-Taxonomy.png)
+    - It's still a pandemic
+    - Communicatio is key
+    - Slack?
+    - GitHub
+    - Jamboards
+ ---
+- Why qualitative data and analysis?
+    - What research scenarios does QDA lend to?
+    - Qualitative, quantitative, and mixed-methods
+- Qualitative data methods and data types
+- Critical ethics in qualitative research data choice-making
+    -  Planning and reflecting
+    -  Good and just practices
+    -  Data humanism and humanitarianism
+      -   Ex. Data visualization: [Mona Chalabi](https://www.instagram.com/monachalabi/)
+
+## For Week 2, bring:
+- Qualitative data collection tool or methods you are interested in or might use in a project, or
+- An example, reading, or tutorial of the method or tool with URL or link.