Abstract Detail

Education and Outreach

Hansen, Sara [1], Linton, Debra [2], Monfils, Anna [2].

Building Biodiversity Datasets: A BLUE module teaching core biodiversity data skills.

Biodiversity science is becoming increasingly data-driven and reliant on FAIR (Findable, Accessible, Interoperable, and Reusable) data and Open Science. Creating a well-rounded, twenty-first century workforce will require education in both biological systems and data proficiency. Scientists must have the essential data skills needed to facilitate each stage of the data lifecycle, from creation to dissemination. Efforts among the Global Biodiversity Information Facility (GBIF), the Carpentries, Biodiversity Information Standards (TDWG), and others have been addressing this need through workshops, seminars, and online educational resources that explain the processes of data discovery, cleaning, analysis, and publication. Users and providers of biodiversity data, including managers, conservation professionals, and researchers, need a core understanding of data collection and structure in order to ask questions and utilize these resources. We aimed to develop educational materials that specifically address the questions: How are datasets built, and what should they look like? We created “Building Biodiversity Datasets,” an Open Education Resource developed by Biodiversity Literacy in Undergraduate Education (BLUE). By completing the module, students will learn to apply the essential vocabulary of tabular datasets, identify and create “tidy” and “untidy” datasets, and translate a field protocol for vegetation sampling to a data collection template. The subject matter is a research project centered around the invasive aquatic plant, European frog-bit (Hydrocharis morsus-ranae L.) in the Laurentian Great Lakes, which provides context and relevant, scalable knowledge that can be applied to other emergent biodiversity issues. The module equips learners to create their own datasets and evaluate whether they are meeting the needs of a research project, while adhering to best practices for data structure and file management. By gaining the underlying knowledge and vocabulary associated with biodiversity data, users of the module will be better prepared to meaningfully participate in biodiversity data science. For example, familiarity with tabular data structures will allow individuals to utilize publicly-available biodiversity data, collect their own data in ways that will be compatible with standards such as Darwin Core, and integrate and archive data from past projects. Additionally, a shared understanding of the conventions of dataset creation and file management will facilitate clearer communication and the development of more robust data management plans that fulfill grantor requirements and ensure the longevity and reusability of data. We will present an overview of the module and highlight the specific needs addressed throughout, as well as explain the ways the module supports and builds upon initiatives in the biodiversity data science community.

1 - Central Michigan University, Mount Pleasant, MI, 48858, USA
2 - Central Michigan University, Department of Biology, Mount Pleasant, MI, 48858, USA

none specified

Presentation Type: Oral Paper
Number: EO2014
Abstract ID:688
Candidate for Awards:None

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