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Ontology for UDL and Related Content

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

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STEM Inclusive Teaching Practices Webinar Series: Episode One

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.

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Universal Design for Learning Guidelines

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.

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B(ui)LDS: Biological, Universal, and Inclusive Learning in Data Science

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.

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Center for Scientific Collaboration and Community Engagement

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.

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AGU Ethics and Equity Center

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.

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Academic Data Science Alliance

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.

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CSCCE blog posts and tip sheets on Diversity, Equity, and Inclusion

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. 

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Scientific Meetings for All

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

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NSF INCLUDES Coordination Hub

Presenter: Gary Silverstein, Westat and Coordination Hub Team

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National Ecological Observatory Network: Data & Infrastructure to Understand Changing Ecosystems

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?

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Summary of the National Academies Report on Data Science for Undergraduates

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.

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Make Student Thought Process Visible Using Video Recording

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.

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Understanding the Dynamics of Socio-Epidemiological Systems

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.

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Asset Mapping: A Simple Tool for Recruiting & Retaining Underrepresented Populations in STEM

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.

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UW Data Science for Social Good

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.

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Actually, Data Science CAN Be Accessible: Barriers to Inclusion of People with Disabilities

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.

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In Pursuit of Inclusive Excellence in the Environmental Sciences

Presenter: Melvin Hall, Northern Arizona University

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Centering Historically Underrepresented Voices in the Salish Sea

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.

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Building a Diverse Undergraduate Community of Learners in Data Science & Biology

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.

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Data Science Teaching Alternatives from The Carpentries

Presenters: Tracy Teal, Kari L. Jordan, and SherAaron Hurt, The Carpentries

AbstractTraining for data skills is more critical now than ever before. In the past decade, we've seen the creation of certification and graduate programs for data science, as well as a plethora of interactive, self-paced online learning platforms. Today's learners are often learning on the job and need the flexibility of short, or self-paced learning experiences. Research results, however, stress the importance of guided instruction and learner-instructor interaction. We've taken a distinctive approach to this problem, combining the power of guided instruction with the flexibility of short, focused learning experiences. Two-day, interactive, hands-on coding workshops train researchers to work with data, and have impacted over 27,500 researchers, ranging from biologists to physicists to engineers and economists. Researchers have benefited from evidence-based teaching approaches to learning data organization (spreadsheets), cleaning (OpenRefine), management (SQL), analysis and visualization (R and Python). This talk focuses on implications and growth opportunities for incorporating data science curriculum at the university level, from the perspective of The Carpentries. We explore tips and best-practices in data science curriculum development including assessment strategies, accessibility, and equity and inclusion.

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Integration of Traditional Ecological Knowledge with Big Data & Retaining Indigenous Students

Presenter: Marco Hatch, Western Washington University  

Abstract: One common barrier to STEM engagement in underserved and underrepresented communities is a feeling of disconnection from mainstream science. This attitude is rooted in a history of researchers and decision-makers collecting, analyzing and interpreting data without engaging community members as true partners and equals. Spanning this boundary between ecological research and communities impacted by environmental change is foundational to moving toward a more equitable future focused on solutions that serve under-resourced communities facing the brunt of environmental degradation and climate change. Great strides have been made toward the goals of democratizing conservation science, empowering local communities to engage with mainstream research on a level playing field. However, these initiatives are subject to a few common pitfalls such as, projects that do not fully account for the social-cultural context of the community, projects that fail to understand the foundationally different worldview of Indigenous communities. These pitfalls can lead to partnerships with the unstated goal of “making them like us”, where the actions of the partnership are structured such that the decision-making power and authority is retained within the STEM disciplines, and if community members want access to that authority, they must conform their worldview to mainstream science. We believe that spanning this boundary between local communities and mainstream science will increase social justice, increase the relevance of conservation science, and open new opportunity spaces for all involved. Central to the success of this vision are boundary spanners.

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What is big data for?

Presenters: Jennifer Balch, Earth Lab/University of Colorado-Boulder and Kirsten Rowell, University of Colorado-Boulder

Abstract: What are we actually harnessing the data revolution for? It's for humanity. Ultimately, big data should help improve people's lives and help society live more sustainably with our planet. It's not anyone's data, it's everyone's data. This makes it critical to involve, encourage, and support a diversity of people in owning the data and ultimately owning the solutions that come from that data.

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Impact of Urban Development in DC

Presenters: Edem Yevoo, University of Maryland and Travis Belote, The Wilderness Society

Abstract: As the global population continues to increase, more people are moving from rural to urban areas. In the next decades, most of the world's population will be living in urban areas. Utilizing geospatial data from the United States Geological Survey (USGS), a predictive visual model was created using geographic information systems (GIS) software. The model was used to predict the change in the District of Columbia's (DC) urban landscape over time. The use of GIS and data analysis systems hold the key to tackling current and future environmental issues. I will discuss the use of spatial data and how it can be used to impact policy, climate change, and socioeconomic conditions in our urban environments.

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Using Spatial Data and GIS for Remote Internships Through the EcologyPlus Program

Presenters: Travis Belote, The Wilderness Society, Edem Yevoo, University of Maryland, and Teresa Mourad, Ecological Society of America 

Abstract: The EcologyPlus program recruits and supports a cohort of diverse students to pursue a variety of professional development opportunities through a diverse network of organizations. The Wilderness Society, as a partner organization, hosted three EcologyPlus student interns in the fall of 2018. The internship began with a week-long trip to Montana to visit Yellowstone National Park, meet local scientists, receive a short-course on geographic information systems (GIS) and spatial data, and develop research questions. Each student developed separate but related questions requiring various spatial datasets, data management, and analytical approaches. The students completed most of the work at their home institutions throughout the fall semester of 2018. The remote internship included biweekly check-ins including “shadowing” via screenshares to work through data analysis challenges. Students presented their work during a one-hour presentation and webinar at the end of the semester. We will discuss the value of spatial data and GIS in undergraduate education and provide recommendation for a successful remote internship. Our key recommendations include spending time together in-person for a kick-off event, regular check-in meetings with video conferencing and screenshares, and developing clear deliverables (report or professional presentation) to bookend the experience.

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