The GEP: Crowd-Sourcing Big Data Analysis with Undergraduates

Elgin, Sarah C. R.; Hauser, Charles R.; Holzen, Teresa M.; Jones, Christopher J.; Kleinschmit, Adam J.; Leatherman, Judith L.

doi:10.1016/j.tig.2016.11.004

Cited by 33 publications

(35 citation statements)

References 8 publications

(9 reference statements)

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“…We deliberately use the term "crowd-training" instead of "crowdsourcing" to acknowledge that a training component is required before individuals could perform the specific type of neuroanatomical mapping techniques we describe here. Given that crowd-trained individuals (students in CUREs) have helped advance big-data projects in the field of genome annotation (Chen et al, 2005;Call et al, 2007;Elgin et al, 2016), and that certain projects within the subdomains of pathology (Della Mea et al, 2014) and neuroanatomy (Roskams & Popović, 2016;Irshad et al, 2017; also see Oleson, 2011) have been crowdsourced successfully outside of a formal CURE environment, we reasoned that it was possible to develop, implement, and evaluate a CURE that provided students the means to conduct authentic scientific research in atlas-based neuroanatomical mapping. Accordingly, with the support of a grant from the Howard Hughes Medical Institute (HHMI) (Grant 52008125), we created an introductory biology CURE called Brain Mapping & Connectomics (BM&C), the first CURE of its kind in the country.…”

Section: Introductionmentioning

confidence: 99%

Cognitive and non-cognitive outcomes associated with student engagement in a novel brain chemoarchitecture mapping course-based undergraduate research experience

D’Arcy

Martínez

Khan

et al. 2019

Preprint

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Course-based undergraduate research experiences (CUREs) engage emerging scholars in the authentic process of scientific discovery, and foster their development of content knowledge, motivation, and persistence in the science, technology, engineering, and mathematics (STEM) disciplines. Importantly, authentic research courses simultaneously offer investigators unique access to an extended population of students who receive education and mentoring in conducting scientifically relevant investigations and who are thus able to contribute effort toward big-data projects. While this paradigm benefits fields in neuroscience, such as atlas-based brain mapping of nerve cells at the tissue level, there are few documented cases of such laboratory courses offered in the domain.Here, we describe a curriculum designed to address this deficit, evaluate the scientific merit of novel student-produced brainatlasmapsofimmunohistochemically-identifiednervecellpopulations for the rat brain, and assess shifts in science identity, attitudes, and science communication skills of students engaged in the introductory-level Brain Mapping and Connectomics (BM&C) CURE. BM&C students reported gains in research and science process skills following participation in the course. Furthermore, BM&C students experienced a greater sense of science identity, including a greater likelihood to discuss course activities with non-class members compared to their non-CURE counterparts. Importantly, evaluation of student-generated brain atlas maps indicated that the course enabled students to produce scientifically valid products and make new discoveries to advance the field of neuroanatomy. Together, these findings support the efficacy of the BM&C course in addressing the relatively esoteric demands of chemoarchitectural brain mapping.

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Section: Introductionmentioning

confidence: 99%

Cognitive and non-cognitive outcomes associated with student engagement in a novel brain chemoarchitecture mapping course-based undergraduate research experience

D’Arcy

Martínez

Khan

et al. 2019

Preprint

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“…This real-time data-abundance revolution has positively impacted public health (Khoury et al, 2014; Murdoch et al, 2013) and public health policy (Athey, 2017; Beam et al, 2018). As a result, the university community has escalated its graduate and undergraduate data-science curriculum to include big-data analysis tools to advance the understanding of the trends in social lifestyle diseases (Elgin et al, 2017; De Veaux et al, 2017). …”

Section: Introductionmentioning

confidence: 99%

Data Talks: Obesity-Related Influences on US Mortality Rates

D’Souza

Bautista

Wentzien

2018

RHS

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Background: In the US, obesity is an epidemiologic challenge and the population fails to comprehend this complex public health issue. To evaluate underlying obesity-impact patterns on mortality rates, we data-mined the 1999–2016 Center for Disease Control WONDER database’s vital records. Methods: Adopting SAS programming, we scrutinized the mortality and population counts. Using ICD-10 diagnosis codes connected to overweight and obesity, we obtained the obesity-related crude and age-adjusted causes of death. To understand divergent and prevalence trends we compared and contrasted the tabulated obesity-influenced mortality rates with demographic information, gender, and age-related data. Key Results: From 1999 to 2016, the obesity-related age-adjusted mortality rates increased by 142%. The ICD-10 overweight and obesity-related death-certificate coding showed clear evidence that obesity factored in the male age-adjusted mortality rate increment to 173% and the corresponding female rate to 117%. It also disproportionately affected the nation-wide minority population death rates. Furthermore, excess weight distributions are coded as contributing features in the crude death rates for all decennial age-groups. Conclusions: The 1999–2016 data from ICD-10 death certificate coding for obesity-related conditions indicate that it is affecting all segments of the US population.

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“…The concept of crowdsourcing using accurate sensor data was successfully conveyed through the RLO. Crowdsourcing, where the public can help increase the density of data, is an increasingly important method of collecting and analyzing "big data" for scientific research (Elgin et al, 2017). Through the RLO and classroom exercise, students not only learned about crowdsourcing, but learned through participation in this type of data collection as part of the active learning exercise.…”

Section: Discussionmentioning

confidence: 99%

Teaching Sensor Technology and Crowdsourcing with Reusable Learning Objects

Mikhailova¹,

Stiglitz²,

Post³

et al. 2018

Natural Sciences Education

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Core Ideas Reusable learning objects are self‐contained digital modules. Reusable learning objects were developed for teaching soil color determination using a color sensor application and crowdsourcing. Reusable learning objects were effective teaching tools. Students positively described their perception of the sensor‐based method of soil testing. Reusable learning objects (RLOs) are self‐contained digital modules commonly utilized in e‐learning. The purpose of this study is to develop RLOs for teaching soil color determination using a color sensor application: soil color, Soil Scanner application, global positioning system (GPS) location, color conversion, and crowdsourcing. Each RLO is a self‐contained learning unit with specific learning goals, educational materials, quiz, and assessment. Navigation of each object is controlled by the participant via tabs to allow the user to control the pace of the RLO. The quality of each RLO is assessed by a learning object review instrument (LORI) framework rubric. Online quizzes at the end of each RLO are used to examine the learning outcomes. This study also used the web‐based survey tool Qualtrics before and after the laboratory‐based activity to systematically measure various constructs including familiarity with sensors, crowdsourcing, and perception of the sensor‐based method of soil testing. Reusable learning objects were effective teaching tools as demonstrated by excellent scores (A) received by the students for all RLO quizzes. Each RLO scored well for each category of the LORI model framework assessment. Additional comments suggest that students were receptive to the RLOs as a learning tool. Students positively described their perception of the sensor‐based method of soil testing compared with the traditional method, a Munsell color chart.

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The GEP: Crowd-Sourcing Big Data Analysis with Undergraduates

Cited by 33 publications

References 8 publications

Cognitive and non-cognitive outcomes associated with student engagement in a novel brain chemoarchitecture mapping course-based undergraduate research experience

Cognitive and non-cognitive outcomes associated with student engagement in a novel brain chemoarchitecture mapping course-based undergraduate research experience

Data Talks: Obesity-Related Influences on US Mortality Rates

Teaching Sensor Technology and Crowdsourcing with Reusable Learning Objects

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