We’re pleased to announce a two-part workshop aimed at rapidly bringing participants to a high level of proficiency in the management and analysis of multidimensional biodiversity data. Part I serves as an introduction to data management and visualization, and serves as a crash course in the data methods that will be necessary to participate in Part II, which focuses on analyzing biodiversity data using process-based models and machine learning. For those with extensive familiarity with biodiversity data, enrolling only in Part II might make sense.
This workshop is organized in conjunction with Data Carpentry and the Evolution 2023 conference in Albuquerque, NM, USA. Significant funding is available to support travel and lodging for the duration of the workshop. Participants may also apply for funding to extend their stay and join the Evolution 2023 conference (Evolution registration fee waivers may also be requested).
To attend our workshop and apply for funding support, please complete the application form (use for both Parts I and/or II). Application deadline is March 24, 2023. Decisions will be made by March 31.
Spots are limited, and acceptance into the program will be competitive and based on your application. We encourage applicants from all backgrounds and especially welcome individuals from minoritized populations. To contribute to the goal of broadening participation in the study of biodiversity, we will use diversity, equity, inclusion, and justice principles in addition to other criteria in selecting applicants. Computational expertise and financial need will not be selection criteria.
This workshop is made possible by funding provided by the National Science Foundation in the following grants:
- A Rules Of Life Engine (RoLE) Model to Uncover Fundamental Processes Governing Biodiversity (Award #1927319)
- RCN: Cross-Scale Processes Impacting Biodiversity (Award #1745562)
Part I: Multidimensional biodiversity data: management and analysis
Date: June 17-18, 2023
Location: Albuquerque, NM (Hotel Andaluz)
Biodiversity researchers must work with an array of data types, including community composition and abundance information, trait, phylogenetic and genetic data. Traditionally, studies in ecology and evolution have worked with only one or a few of these data types. To successfully advance the study of biodiversity across different levels of organization, biodiversity scientists are nonetheless finding the need to integrate multiple of these disparate data types into the same analytical workflow. This workshop will promote learning in the use of multidimensional data streams, gathering biodiversity scientists and students from different sub-disciplines to help facilitate integrative, cross-specialization research.
Goals of the Workshop: This two-day workshop will introduce different data types used by biodiversity scientists in an integrative framework. We will cover common approaches for working with abundance, trait, phylogenetic, and genetic data separately, and proceed to methods for working with multiple dimensions of biodiversity data simultaneously. Finally, we will explore motivations and platforms for archiving, sharing, and accessing multidimensional biodiversity datasets as part of the wider scientific community (e.g. GEOME).
Target Audience: Our target audience is broad. We welcome senior undergrads with interest or experience in Ecology and Evolution, graduate students at any stage, and postdocs and PIs with an interest in expanding their skill set towards working with new-to-them data types. Some computational skills may be helpful (e.g. familiarity with R and at least one type of biodiversity data), but we expect that most participants will have little to no familiarity with at least some of the data types we work with. We especially welcome scientists and student-scientists from groups traditionally excluded from the biodiversity and computer sciences, including (but not limited to) women, Latinx, Black, Native American, LGBTQIA+, and scientists with disabilities.
Part II: Multidimensional biodiversity data: Process-based modeling and statistical inference
Date: June 20-21, 2023
Location: Albuquerque, NM (Hotel Andaluz)
Process-based models are a powerful framework for generating theoretical expectations and exploring hypothetical scenarios that can then be linked directly to multidimensional biodiversity data streams (e.g. joint data on genetic and species diversity) for hypothesis testing and statistical inference. While several excellent simulation modeling platforms have been developed in recent years, these models can be technically sophisticated and difficult to work with for new users. This workshop will provide an introduction to a process-modeling approach and hands-on experience working through a complete workflow, using a user-friendly process model implemented in R: the Rules of Life Engine (RoLE) model. RoLE is a process-based eco-evolutionary simulation model which incorporates all relevant biodiversity processes (drift, migration, selection, speciation) and makes joint predictions of multiple dimensions of biodiversity data.
Goals of the Workshop: This two-day workshop will provide an introduction to process based modeling using the RoLE model. We will cover the philosophy and motivation behind process modeling, the use of simulation models to explore hypotheses and develop theoretical intuition, and statistical methods for using empirical data to test the hypotheses generated through process modeling.
Target Audience: We welcome senior undergrads, graduate students at any stage, and postdocs and PIs who are comfortable with multidimensional biodiversity datasets and are interested in developing skills with modeling and machine learning-based inference.) is required. Participants should be familiar with the focal biodiversity data axes (abundances, trait data, phylogenies, population genetic data) and have some computational proficiency (e.g. familiarity with R), or attend Part I if not. We especially welcome scientists and student-scientists from groups traditionally excluded from the biodiversity and computer sciences, including (but not limited to) women, Latinx, Black, Native American, LGBTQIA+, and scientists with disabilities.