Tuesday's UDL and Accessibility scenario: 

Scenario

Designing a PowerPoint for an introductory lesson

• Student who uses English as a second language
• Student with Dyslexia
• Student who is blind/low-vision

Instructions

• Form small groups (3-4 people)
• Discuss learning goals, slide design, and presentation techniques
• Share back to large group

Attached

• UDL Guidelines from CAST (pdf)
• Online UDL Guidelines (link)
• College STAR Case Study, “When Lecture is Necessary” (link)
• University of Colorado, “Universal Design Measures Checklist – Microsoft PowerPoint” (link)
• York St John University, “A guide to dyslexia-friendly Powerpoint” (link/pdf)
• DeafTEC, “Best Practices on Visuals/Referencing” (link)

We first discuss ‘data literacy’ as an emerging concept within a much longer historical narrative of literacy promotion. History sheds light on how defining and promoting literacy—who was literate and who was not—has been often entrenched with the constructs and perpetuation of power structures within societies—at odds with the notion of literacy as a necessarily empowering and enlightenment force. There is a risk that the same processes may play out in the age of data, at a speed and scope commensurable with those of the spread of data as a social phenomenon.

This online file contains information and additional resources related to the Data Science session at the BioQUEST summer 2018.

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent.

National Academies of Sciences, Engineering, and Medicine (2018). Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press. https://doi.org/10.17226/25104

Report Highlights - https://www.nap.edu/resource/25104/RH-dataundergrad.pdf

Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills.

Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved.

Max Shron talk at NYC data science meetup

Talk for O'Reilly

This is an example of how Shiny can be used to quickly engage students in data exploration, visualization, and analysis. This tool allows you to explore the dataset associated with,  "PanTHERIA: a species‐level database of life history, ecology, and geography of extant and recently extinct mammals".

This app draws on a large species-level dataset with metabolic, life history, and ecological traits of most living and recently extinct mammal species. Users can select and plot traits, fit linear models to the data, and query displayed datapoints.

The bioinformatics competencies that NIBLSE recommends undergraduate life sciences students have by the time they graduate. As discussed in the narrative, they are informed by the results of the national NIBLSE survey, analysis of ninety syllabi with bioinformatics content, and the cumulative expertise and experience of the authors. Following each competency is a list of three representative examples illustrating the competency. 

The Data Management Skillbuilding Hub contains resources for better data management and is open to community input and update. These resources are adaptable across a range of contexts and intended for use by researchers, teachers, librarians, or anyone who wants to learn better data management practices. Each tile below contains a lesson in slide format with annotations, a one page handout that distills the main message, and a hands-on exercise. 

Also see the data life cycle at DataONE.

swirl teaches you R programming and data science interactively, at your own pace, and right in the R console!

This is an Open Educational Resource written on the PressBooks platform. This textbook has great resources and concise chapters you can assign to students.

Since 1998, Software Carpentry has been teaching researchers the computing skills they need to get more done in less time and with less pain. Our volunteer instructors have run hundreds of events for more than 34,000 researchers since 2012. All of our lesson materials are freely reusable under the Creative Commons - Attribution license.

The Software Carpentry Foundation and its sibling project, Data Carpentry, have merged to become The Carpentries, a fiscally sponsored project of Community Initiatives, a 501(c)3 non-profit incorporated in the United States.

Contains a link to the CC-BIOME site that will be discussed during the session and a link to a forum discussion where you can provide feedback on the CC-BIOME site.

These are two manuscripts and PowerPoint slides that outline a framework for models and modeling that we will discuss in the session.

Only allows you to use the most common 1000 words in the English language.  

Educational use here - Introducing Students to the Challenges of Communicating Science by Using a Tool That Employs Only the 1,000 Most Commonly Used Words

The Data Management Skillbuilding Hub contains resources for better data management and is open to community input and update. These resources are adaptable across a range of contexts and intended for use by researchers, teachers, librarians, or anyone who wants to learn better data management practices. Each tile below contains a lesson in slide format with annotations, a one page handout that distills the main message, and a hands-on exercise. 

Also see the data life cycle at DataONE.

Human biospecimens have played a crucial role in scientific and medical advances. Although the ethical and policy issues associated with biospecimen research have long been the subject of scholarly debate, the story of attention of a much broader audience. The story has been a catalyst for policy change, including major regulatory changes proposed in the United States surrounding informed consent. These proposals are premised in part on public opinion data, necessitating a closer look at what such data tell us. The development of biospecimen policy should be informed by many considerations—one of which is public input, robustly gathered, on acceptable approaches that optimize shared interests, including access for all to the benefits of research. There is a need for consent approaches that are guided by realistic aspirations and a balanced view of autonomy within an expanded ethical framework.

Data for Democracy is partnering with Bloomberg and BrightHive to develop a code of ethics for data scientists. This code will aim to define values and priorities for overall ethical behavior, in order to guide a data scientist in being a thoughtful, responsible agent of change. The code of ethics is being developed through a community-driven approach.

By hosting discussions among data scientists, we hope to better capture the diverse interests, needs, and concerns that are at play in the community, and put together a code that is truly created by data scientists, for data scientists.

This code of ethics for data sharing is created and proposed for adoption by the data science community to reflect the behaviors and principles for the responsible and ethical use and sharing of data by data scientists. 

As a community-driven crowdsourced effort, you can join the the discussion and contribute to the next version of the Community Principles on Ethical Data Sharing.