Course Start Date: Introduction to Citizen Artist and Art-Based Research Methods​

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  • 12/05/2022 All day

    Opens on December 5, 2022, and closes on February 26, 2023 (begin anytime)

     

    No instructor support:

    • Early Bird Course Fee: $350 professional / $250 student

    • Regular Course Fee: $400 professional / $300 student

      Instructor support:

    • Early Bird Course Fee: $500 professional / $400 student

    • Regular Course Fee: $550 professional / $450 student

  • 12/05/2022 All day

    Full instructor support

    • Early bird course fee (ends December 5): $500 professional / $400 student

    • Regular course fee (after December 5): $550 professional / $450 student

    No instructor support

    • Early bird course fee (ends December 5): $375 professional / $275 student

    • Regular course fee (after December 5): $400 professional / $300 student

  • 12/05/2022 11:21 AM - 12/05/2022 11:21 AM

    ull instructor support

    • Early bird course fee (ends December 5): $500 professional / $400 student

    • Regular course fee (after December 5): $550 professional / $450 student

    No instructor support

    • Early bird course fee (ends December 5): $375 professional / $275 student

    • Regular course fee (after December 5): $400 professional / $300 student

  • 12/05/2022 All day

    Dates: December 5, 2022, - February 26, 2023 (begin anytime)

    An introductory course for students interested in applying GIS as a tool to help answer important questions in the natural sciences, or for those with ArcGIS experience looking to transition to an Open-Source platform. This course presents the concepts upon which GIS technology is based including the following fundamentals: cartography, geodesy, coordinate systems, and projections. Conceptual overview and hand-on experience of vector data analyses and table queries are introduced. Students will use QGIS to classify data, query tables, analyze spatial relationships, set map projections, build spatial databases, edit data, and create map layouts. Lectures are followed by hands-on activities to develop and reinforce methodologies for GIS analyses.

    You have two options when enrolling in this course:

    (1)  Instructor support. Reach out to your instructor over a 1-month period to get help as you work through prerecorded lectures, problem sets, and your own personal work. You then have access to the course for an additional 2 months. Instructor support includes emailing your instructor, accessing live discussion threads, and scheduling one-on-one appointments (Zoom or phone) about course material, your research, datasets from work, etc. You MUST select this option if you want to take the course for academic credit at your home institution or you would like to work with an instructor on a dataset from school or work.

    • Early bird course fee (ends Nov 17th): $500 professional / $400 student

    • Regular course fee (after Nov 17th): $550 professional / $450 student

    (2)  NO instructor support. Sign up anytime over a 3-month period and learn at your own pace as you work through prerecorded lectures and problem sets. Be aware that at any time during the first month, you may upgrade to receive Instructor support.

    • Early bird course fee (ends Nov 17th): $350 professional / $250 student

    • Regular course fee (after Nov 17th): $400 professional / $300 student

  • 12/18/2022 All day
    • Early bird course fee (before Dec 18): $550 professional / $450 student

    • Regular course fee (after Dec 18): $600 professional / $500 student

  • 12/19/2022 All day

    DATES: Dec 19, 2022 - Jan 15, 2023

  • 12/31/2022 All day

    Course start date

    Get started immediately! Course content will be accessible upon enrollment.

    Registration period

    Registration for this virtual workshop series will remain open through December 31, 2023 at 11:55 pm ET unless notified otherwise.

  • 01/02/2023 All day

    Dates: Begins January 2, 2023 (instructor support begins in March)

    Species presence/absence is a fundamental concept used in many areas of ecology (e.g., species distributions, habitat modeling, monitoring, and metapopulation studies), however imperfect detection can lead to false absences. Unaccounted for false absences can lead to misleading inferences about patterns and dynamics of species occurrence and the factors that influence them. We will learn methods for accounting for imperfect detection with species detection/non-detection data and also discuss important study design considerations. Exercises are completed using Program PRESENCE and RPresence.

    You have two options when enrolling in this course:

    (1) Instructor support. Reach out to your instructor over a 1-month period to get help as you work through prerecorded lectures, problem sets, and your own personal work. You then have access to the course for an additional 2 months. Instructor support includes emailing your instructor, accessing live discussion threads, and scheduling one-on-one appointments (Zoom or phone) about course material, your research, datasets from work, etc. You MUST select this option if you want to take the course for academic credit at your home institution or you would like to work with an instructor on a dataset from school or work.

    • Early bird course fee (ends December 5): $600 professional / $500 student

    • Regular course fee (after December 5): $650 professional / $550 student

    (2) No instructor support. Sign up anytime over a 3-month period and learn at your own pace as you work through prerecorded lectures and problem sets. Be aware that at any time during the first month, you may upgrade to receive Instructor support.

    • Early bird course fee (ends December 5): $375 professional / $275 student

    • Regular course fee (after December 5): $400 professional / $300 student

  • 01/02/2023 All day

    Dates: Begins January 2, 2023 (instructor support begins in March)

    Bayesian methods for analyzing data are now widely used in ecology and wildlife management. The Bayesian approach involves specifying the “prior” distribution, which represents the uncertainty about the model parameters before we collect the data, and the likelihood, which represents the plausibility of different parameters values based solely on the data. These are combined via Bayes’ Rule to obtain the “posterior” distribution for the parameters, which represents the uncertainty about the parameters after we have analyzed the data. Using the Bayesian approach gives you more flexibility in the type of models that you can fit, compared to the classical frequentist approach, as the model is typically specified in the same way that you would write it down mathematically. Once you have become familiar with fitting a Bayesian model in R, you will appreciate the extra flexibility and the more intuitive way in which the results can be presented.

    You have two options when enrolling in this course:

    (1) Instructor support. Reach out to your instructor over a 1-month period to get help as you work through prerecorded lectures, problem sets, and your own personal work. You then have access to the course for an additional 2 months. Instructor support includes emailing your instructor, accessing live discussion threads, and scheduling one-on-one appointments (Zoom or phone) about course material, your research, datasets from work, etc. You MUST select this option if you want to take the course for academic credit at your home institution or you would like to work with an instructor on a dataset from school or work.

    • Early bird course fee (ends December 5): $500 professional / $400 student

    • Regular course fee (after December 5): $550 professional / $450 student

    (2) No instructor support. Sign up anytime over a 3-month period and learn at your own pace as you work through prerecorded lectures and problem sets. Be aware that at any time during the first month, you may upgrade to receive Instructor support.

    • Early bird course fee (ends December 5): $375 professional / $275 student

    • Regular course fee (after December 5): $400 professional / $300 student

  • 01/02/2023 All day

    Dates: Course #1 - Jan 2-16, 2023

    Scent detection dogs are increasingly used in conservation research to improve the frequency or probability of finding biological samples or focal organisms, including rare or cryptic species. We offer a series of three short courses (Course #1-2 online; & Course #3 field) designed to provide an appreciation for this emerging field and the science behind it, while presenting real-world examples, logistics, and considerations underscoring the potential benefits of this approach.

    Course #1: Intro to Working with Conservation Canines

    Ideal for students, researchers, and others new to learning theory, working dogs, and scent detection training. Using an online platform to illustrate key concepts via lectures with video clips and interactive discussions, it provides an overview of scent detection applications, animal training basics, general principles of scent detection training, and dog selection considerations.

    Students will take the course largely at their own pace over a two-week period. Activities for the online course consist of pre-recorded lectures, synchronous and asynchronous mid-week discussions based on articles from the primary and secondary literature, as well as weekly real-time discussions around themed topics and applied training scenarios, and an online Q&A forum for other professional development and related training questions.

    • Early bird course fee: $500 professional / $400 student

    • Regular course fee: $550 professional / $450 student

  • 01/03/2023 12:13 PM - 01/03/2023 12:13 PM

    Opens on January 3, 2022, and closes on March 26, 2023

    INSTRUCTORS: Drs. Gina Himes Boor & Jack Hopkins

  • 01/03/2023 All day

    Dates: January 3, 2023 - February 26, 2023 (enroll anytime)

    Analyses of wildlife home range and habitat use are used in wildlife-focused disciplines to inform population and habitat management practices. The geographical space used by animals is largely driven by habitat selection, where habitat can be described by spatially explicit site factors (e.g., land cover). Geospatial modeling is the process of synthesizing geographically referenced field measurements and spatial data layers representing those site factors. In this course, we will process raw point location data, estimate home ranges, and conduct habitat assessments using raster land cover data and the open-source software applications QGIS and R.

    You have two options when enrolling in this course:

    (1)  Instructor support. Reach out to your instructor over a 1-month period to get help as you work through prerecorded lectures, problem sets, and your own personal work. You then have access to the course for an additional month with less consistent support. Instructor support includes emailing your instructor, accessing live discussion threads, and scheduling one-on-one appointments (Zoom or phone) about course material, your research, datasets from work, etc. You MUST select this option if you want to take the course for academic credit at your home institution. It is highly RECOMMENDED that you select this option if you would like to work with an instructor on a dataset from school or work.

    • Early bird course fee (ends Nov 27th): $550 professional / $450 student

    • Regular course fee (after Nov 27th): $600 professional / $500 student

    (2)  NO instructor support. Sign up anytime over a 2-month period and learn at your own pace as you work through prerecorded lectures and problem sets. Be aware that at any time during the first month, you may upgrade to receive Instructor support.

    • Early bird course fee (ends Nov 27th): $400 professional / $300 student

    • Regular course fee (after Nov 27th): $450 professional / $350 student