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3rd Annual Digital Data Conference, Yale: Difference between revisions
3rd Annual Digital Data Conference, Yale (view source)
Revision as of 19:45, 9 June 2019
, 9 June 2019→Wednesday, 12 June 2019
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| colspan="4" style="text-align:left; vertical-align:top; background-color:#eed1a9;"|'''Workshop 1- Mobilizing museum collections and citizen science data to predict species distribution<br>Facilitators: Erica Stuber and Walter Jetz, Yale University<br>Location & Time: | | colspan="4" style="text-align:left; vertical-align:top; background-color:#eed1a9;"|'''Workshop 1- Mobilizing museum collections and citizen science data to predict species distribution<br>Facilitators: Erica Stuber and Walter Jetz, Yale University<br>Location & Time: 9:00 a.m.-Noon, Sudler Room'''<br> | ||
Species distribution models (SDM) are powerful tools for inferring species ecology, response to environmental change, and biodiversity at multiple spatial and temporal scales. However, understanding species’ ranges at landscape or global scales typically requires moving beyond designed studies, which are relatively small scale, to capitalize on museum collections and citizen science projects which can represent data spanning entire species ranges across current and historic habitats. Understanding species’ range-wide distribution is a requirement for developing evidence-based conservation plans and predicting species’ response to global change, facilitated by digitization efforts and open-source observations databases. In this hands-on workshop, participants will review the state of the science in species distribution modelling, consider best-practices in mobilizing collections-based and citizen-science data sources for distribution modeling, and fit a basic SDM (i.e., access species and environmental data, process, and fit a statistical distribution model). Participants should bring their own laptop; hands-on sessions will use the free R programming environment. Example code and data for modeling will be provided, although some previous practice working with R will be useful.<br> | Species distribution models (SDM) are powerful tools for inferring species ecology, response to environmental change, and biodiversity at multiple spatial and temporal scales. However, understanding species’ ranges at landscape or global scales typically requires moving beyond designed studies, which are relatively small scale, to capitalize on museum collections and citizen science projects which can represent data spanning entire species ranges across current and historic habitats. Understanding species’ range-wide distribution is a requirement for developing evidence-based conservation plans and predicting species’ response to global change, facilitated by digitization efforts and open-source observations databases. In this hands-on workshop, participants will review the state of the science in species distribution modelling, consider best-practices in mobilizing collections-based and citizen-science data sources for distribution modeling, and fit a basic SDM (i.e., access species and environmental data, process, and fit a statistical distribution model). Participants should bring their own laptop; hands-on sessions will use the free R programming environment. Example code and data for modeling will be provided, although some previous practice working with R will be useful.<br> | ||
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| colspan="4" style="text-align:left; vertical-align:top; background-color:#eed1a9;"|'''Workshop 2 - Basic Biodiversity Data Manipulation in R<br>Facilitator: Katelin Pearson <br>Location & Time: 9:00 a.m.-Noon'''<br>Not all of us entered biological research with the intent of learning to code, yet coding skills are increasingly crucial in our work. In this workshop, we will discuss and demonstrate basic workflows in working with biodiversity data in R, including downloading, cleaning, preparing, and analyzing data from iDigBio and GBIF. Basic data structure, useful functions, and tips and tricks will be addressed. Please indicate any specific questions or topics you would like to see covered during this session when you register. This is a hands-on workshop, so bring a laptop with R or R Studio already installed. The workshop leader will be demoing in R Studio. | | colspan="4" style="text-align:left; vertical-align:top; background-color:#eed1a9;"|'''Workshop 2 - Basic Biodiversity Data Manipulation in R<br>Facilitator: Katelin Pearson <br>Location & Time: Room 116, 9:00 a.m.-Noon'''<br>Not all of us entered biological research with the intent of learning to code, yet coding skills are increasingly crucial in our work. In this workshop, we will discuss and demonstrate basic workflows in working with biodiversity data in R, including downloading, cleaning, preparing, and analyzing data from iDigBio and GBIF. Basic data structure, useful functions, and tips and tricks will be addressed. Please indicate any specific questions or topics you would like to see covered during this session when you register. This is a hands-on workshop, so bring a laptop with R or R Studio already installed. The workshop leader will be demoing in R Studio. | ||
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| colspan="4" style="text-align:left; vertical-align:top; background-color:#eed1a9;"|'''Workshop 3 - Tools and Best Practices for Biodiversity Data Science: A Data Carpentries Introduction with Python<br>Facilitators: Holly Little, Deb Paul and Mike Trizna<br>Location & Time: | | colspan="4" style="text-align:left; vertical-align:top; background-color:#eed1a9;"|'''Workshop 3 - Tools and Best Practices for Biodiversity Data Science: A Data Carpentries Introduction with Python<br>Facilitators: Holly Little, Deb Paul and Mike Trizna<br>Location & Time: Room 208, 9:00 a.m.- 2:00 p.m.'''<br/>[https://smithsonianworkshops.github.io/2019-06-12-Yale/ Carpentries Workshop Page] and [https://smithsonianworkshops.github.io/2019-06-12-Yale/ Software Download Instructions]<br>The Carpentries (comprised of Data Carpentry, Library Carpentry, and Software Carpentry) is a project whose mission is to teach foundational computational and data science skills to researchers and others who create, manage, and use data. Specifically, Data Carpentry lessons are designed to be picked up by learners who do not have any previous programming experience. Data Carpentry workshops are typically organized in a 2-day format, but we will compress the lessons in this workshop to 1 day to focus on learning Python and Jupyter notebooks to work with data files in a reproducible manner. Before covering Python, we will go through a lesson using Excel to learn about the "tidy data" format, and best practices for working with tabular data files. We will also spend some time discussing community-building around the Carpentries, and how we can partner together to build biodiversity data science literacy at our organizations. | ||
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