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  1. Free, publicly-accessible full text available March 31, 2022
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  3. This method is adapted and updated from methods originally published in Grottoli et al. (2004) and is based on the original methods of Folch & Stanley (1957), and Bligh & Dyer (1959). There are five parts to extracting lipids from ground corals: 1) grind and sub-sample the coral and store at -80 °C until ready to extract, 2) freeze-dry the sample, 3) extract the lipids from the freeze-dried samples, 4) standardize the lipid concentration to ash-free dry weight (AFDW), and 5) resuspend the extracted lipid for long-term storage and possible later analysis of lipid classes or isotopes. The lipid extractionmore »procedure must be conducted in a fume hood with the sash as low as possible with the researcher wearing protective eyewear, gloves, and lab coat at all times. Important considerations regarding lipid analysis were gained from reading Chapter 1.3 “Lipid extraction, storage, and sample handling” from the textbook Lipid Analysis by Christie (2003). This method was originally developed by Andréa Grottoli and refined by Rowan McLachlan (06-11-18) with the guidance of Dr. Agus Muñoz-Garcia at The Ohio State University. This protocol was written by Rowan McLachlan (03-12-2020). dx.doi.org/10.17504/protocols.io.bc4qiyvw« less
  4. As technology increases in the global arena and the necessity for a more diverse group of individuals to fulfill engineering and computing roles increases, it is important to engage more students in computing majors and roles. Identity has proven to be an important lens through which researchers can better understand how to engage students in these fields. In particular, our framing for computing identity includes students’ self-perceptions about recognition, interest, and performance/competence. Using survey data, this study examines the computing identity of high achieving underserved students in computer science (CS), computer engineering (CE), and information technology (IT). For these students,more »we compare the constructs between men and women, computing fields, and freshmen and juniors/seniors. Based on preliminary data, results show that female participants had less of a computing identity than male students, specifically with respect to computing recognition and overall computing identity. Students in IT programs had less of an overall computing identity than students in CS and CE. Finally, freshmen were lower on their overall computing identities and specifically performance/competence beliefs and interest. These results suggest that even within computing programs, students differ in their computing identities. Furthermore, there are different constructs related to computing identity that vary for different subgroups of students. While these results are insightful, future work will compare the computing identities of high achieving underserved students with a larger population of computing students as well as strategies for building their computing identities.« less