Course developed by Carl T. Bergstrom and Jevin West. The aim of the course is to teach you how to think critically about the data and models that constitute evidence in the social and natural sciences.

An EDSIN partner focused on the exchange of ideas and resources supporting biological and environmental data science education, grounded in practices of universal design for learning and inclusive pedagogy. As part of our commitment to accessible, affordable student-driven education that connects learners to a larger world outside of their classrooms, and inspires them to be transformative; we promote the creation and use of Open Educational Resources (openly licensed learning materials), and the incorporation of Open Pedagogy and Open Science into our teaching practices.

Presentation by EDSIN member Drew Hasley that draw on personal experience as a student and professional with a severe visual impairment and knowledge gained from colleagues and friends during ongoing efforts in the area of accessible teaching in quantitative biology. This was a keynote presentation at the 2019 EDSIN conference.

Inclusive pedagogy for life science education tagging ontology developed by EDSIN members Carrie Diaz Eaton, Sharon Rivera, and Bryan Dewsbury

A list of material in various platforms that help to inform developing thoughts on the ways in which social structure affects the nature of teaching and learning.

This is a description of tagging ontology that can be used for Universal Design for Learning (UDL) resources created by EDSIN member Drew Hasley

Episode One was a conversation with Bryan Dewsbury (University of Rhode Island) one of the authors of the CBE-LSE Inclusive Pedagogy guide moderated by Carrie Diaz Eaton. We talked with him about inclusive teaching practices, and he answered questions about implementation in the STEM classroom. We appreciate that all of our personal and professional lives have been disrupted by the pandemic, so we will also spend time talking about how to think about inclusion in the times of COVID-19.

The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a framework to improve and optimize teaching and learning for all people based on scientific insights into how humans learn. Learn more about the Universal Design for Learning framework from CAST. The UDL Guidelines can be used by educators, curriculum developers, researchers, parents, and anyone else who wants to implement the UDL framework in a learning environment. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

We are a community for the exchange of ideas and resources supporting biological and environmental data science education, grounded in practices of universal design for learning and inclusive pedagogy. As part of our commitment to accessible, affordable student-driven education that connects learners to a larger world outside of their classrooms, and inspires them to be transformative; we promote the creation and use of Open Educational Resources (openly licensed learning materials), and the incorporation of Open Pedagogy and Open Science into our teaching practices.

The Center for Scientific Collaboration and Community Engagement (CSCCE) is a research and training center to support and study the emerging field of scientific community engagement.

The AGU Ethics and Equity Center provides resources to educate, promote and ensure responsible scientific conduct and establish tools, practices, and data for organizations to foster a positive work climate in science. They can help you meet your ethics goals, whether you are an individual scientist looking for resources or professional ethics development, a leader looking to implement best practices at your organization, or an institution wanting to update your code of conduct. The Center is led by AGU.

The Academic Data Science Alliance (ADSA) was launched in May 2019 to provide an independent national resource network for academic data science leaders, practitioners, and educators to connect and exchange ideas, and to advance the uptake of data science best practices in higher education. ADSA seeks to support university researchers in their efforts to learn, use, and teach data-intensive tools and responsible applications. Our ultimate goal is to bring about the institutional changes needed to integrate data science into university research and training. By building networks of academic data science practitioners (including faculty, students, staff, and administrators), ADSA enables better sharing of knowledge, ideas, and lessons learned.

Diversity, Equity and Inclusion (DEI) should be central considerations when planning and supporting any scientific community. The Center for Scientific Collaboration and Community Engagement's (CSCCE) resources on this topic include blog posts and tip sheets (i.e., inclusive marketing, member recruitment, childcare at scientific meetings, decolonizing international research collaborations) to help you to nurture more inclusive communities. 

A publication in EOS on inclusive scientific meetings that features the 2019 EDSIN conference.

Presenter: Gary Silverstein, Westat and Coordination Hub Team

Presenter: Megan A. Jones, Battelle/NEON  

Abstract: NEON, the National Ecological Observatory Network, is an NSF-funded large science facility, operated by Battelle, is designed to collect extensive ecological and environmental data from across the U.S. for the next 30 years. A primary product of NEON is freely available, open access data for use by the scientific research community as well as by students and others who will explore the petabytes of information that will be available during the lifetime of the Observatory. Using NEON data, however, may not be an easy task for researchers, faculty, and students who are not familiar with “big data” access, management and analytic methods. Many ecologists are transitioning to using data science and big data methods including programming-based data management and analysis, complex data portals and APIs that provide access to lots of different types of data, and diverse analytical methods beyond those classically used in their individual research area. Furthermore, ecological data is usually messy in the sense that variability and uncertainty are important components of data analysis and interpretation. Given these challenges, how do we accelerate the use of big data in ecological research and education? How to we create equitable opportunities for data and resource access to all interested individuals? How do we engage communities that have not traditionally been drawn to careers in ecology but for which data science focused careers may provide new opportunities?

Presenter: Louis Gross, University of Tennessee  

Abstract: Under the auspices of the National Academies, a Committee developed a consensus report regarding means to enhance undergraduate programs in the emerging discipline of data science. Building upon advice from a variety of on-line and in-person interactions with a broad spectrum of experts, the report provides a collection of findings and recommendations for initiating, developing and evaluating programs that prepare students for careers in data science as well as encouraging methods for all undergraduates to be exposed to basic concepts in this field. I will summarize the suggestions made regarding development of data acumen, incorporation of real-world examples, enhancing teamwork and communication, ethical considerations, and assessment and evaluation of data science programs. I will emphasize the potential for such programs to broaden participation in quantitative science and the benefit of utilizing environmental data and examples that align with the interests of diverse students.

Presenter: Hong Qin, University of Tennessee at Chattanooga

Abstract: Dr. Qin presents his experience of integrate screen-recording to enhance student learning experience of computational biology. Students were required to screen-recording their process of solving computational problems. These screen-recordings can be used to identify the learning hurdles of students and improve student learning experiences.

Presenter: Carlos Castillo-Chavez

Abstract: The spread of fads, scientific ideas and the growth and stability of communities can also be understood as contagions. In this talk, I would focus on contagion in all its glory, including its role on building communities of mentors and understanding the role that initial conditions should play in our definition of meritocracy.

Presenters: Adrienne Smith, Cynosure Consulting and Rebecca Zulli Lowe, Cynosure Consulting

Abstract: Asset maps serve as a simple, yet impactful tool for helping underrepresented groups connect with important people, programs, and resources that would support their recruitment and retention in STEM. In contrast to a traditional deficit-focused mindset, asset mapping was born out of an approach that seeks out existing strengths and works to build capacity by leveraging current resources as a foundation for further innovation. At the end of this talk, individuals will walk away with a list of steps that they can use to develop a comprehensive map that could be distributed immediately to current and future STEM (including high school seniors). These steps include identifying current assets within an array of existing categories (e.g., tutoring centers, individual faculty mentors, local chapters of STEM associations) designed to help mappers think expansively about existing supports. Additional steps involve reviewing contact lists and asking others to assist in the identification of assets, performing internet searches of the school/organization website looking for key words, and reading through the university directory to highlight offices that work on diversity issues or support the individuals targeted. The assets can be plotted directly onto a campus map and supplied to underrepresented groups, so they are aware of and can locate the resources and supports available to them. Additionally, the formation of the maps can be a beneficial exercise for departments to use to assess their own assets and strategically plan for the development of new assets.

Presenters: Sarah Stone and Anissa Tanweer, University of Washington

Abstract: Launched in Summer 2015, the UW Data Science for Social Good (DSSG) program partners eScience Data Scientists and Student Fellows from across the country with Project Leads from academia, government, and the private sector to find data-driven solutions to pressing societal challenges. Previous projects (15 over the past 4 summers) have involved data analysis and visualization on topics such as transportation, public health, sustainable urban planning, homelessness, and disaster response. Several projects have led to long-term collaborations and funding opportunities. Integrated project-based discussions and training around data science ethics, human-centered design and stakeholder collaboration are keystones of our DSSG program. Differences in prior experience and training among student fellows can pose a challenge, but often become a strength in the context of project work. Our experience running this program supports the notion that DSSG programs can both effectively impact social good and provide "real world" data science training for students from diverse disciplinary backgrounds.

Presenter: Drew Hasley  

Abstract: If you were designing a course or employee training that introduces participants to writing and executing code, statistical analyses, and data visualization, what would you do to make it inclusive of participants with disabilities? What sorts of accessibility issues might you anticipate? How might you address them before you even know who the participants are? Why shouldn’t you just wait until you have a participant with a disability and work with them directly to make accommodations? Confidently answering such questions can be daunting. It requires knowing what disability is, awareness of some barriers to participation in data science by people with disabilities, some knowledge of tools and strategies for lowering those barriers, and above all, creativity. In this talk, I will address each of these, drawing on personal experience as a student and professional with a severe visual impairment, and knowledge gained from colleagues and friends during ongoing efforts in the area of accessible teaching in quantitative biology. Audience members will leave this talk with a better understanding of barriers to recruitment and training of people with disabilities in data science and some tools and strategies to lower them. They will also learn about areas requiring more attention. My primary goal is to leave audience members with the confidence that they can indeed help address the substantial underrepresentation of people with disabilities in this vital, growing field.

Presenter: Melvin Hall, Northern Arizona University

Presenter: Melissa Watkinson, Salish Sea DEI Community of Practice

Abstract: Addressing inequity and working toward environmental justice is essential to a successful environmental movement. Currently, there are significant disparities in the representation, content, and processes for implementing diversity, equity, and inclusion (DEI) within the environmental field across the Salish Sea and the Pacific Northwest Coast. Although professionals in this field are aware and concerned about issues related to DEI, there is an overall lack of understanding for how to integrate these concepts into the environmental workforce. Addressing inequity and working toward environmental justice is essential to a successful environmental movement. We believe that by creating and fostering a Salish Sea DEI Community of Practice (CoP), we can begin to build this critical foundation together.

Presenter: Sarah R. Supp, Denison University

Abstract: This talk will use two examples to discuss inclusive pedagogical strategies for training in data science skills. Building a new interdisciplinary program for undergraduates in data analytics, we have a project-based pedagogy, and as a unit have considered ways in which we can spark interest and build academic successes for students more broadly, including students that are traditionally underrepresented in the Computer Sciences, or other related technical fields. This talk will also discuss an ongoing project to address gaps in training for undergraduate instructors, to enhance data education in biology curriculum, thus also broadening access to technical skills for students in these courses.