Computational Thinking in Life Science Education

Rubinstein, Amir; Chor, Benny

doi:10.1371/journal.pcbi.1003897

Cited by 84 publications

(63 citation statements)

References 16 publications

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“…In addition to formal workshops, UAL's Data Science Specialist (an author on this work, JCO) provides in-depth consults in R data analyses and visualization, focusing largely on support in the life sciences (the Data Science Specialist's own background is in bioinformatics and ecology). The largest audience for these consults is graduate students, who often have little to no formal training in computational skills (Valle & Berdanier, 2012;Rubinstein & Chor, 2014).…”

Section: Computational Literacymentioning

confidence: 99%

Data Science Support at the Academic Library

Oliver

Kollen

Hickson

et al. 2019

Journal of Library Administration

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Data science is a rapidly growing field with applications across all scientific domains. The demand for support in data science literacy is outpacing available resources at college campuses. The academic library is uniquely positioned to provide training and guidance in a number of areas relevant to data science. The University of Arizona Libraries has built a successful data science support program, focusing on computational literacy, geographic information systems, and reproducible science. Success of the program has largely been due to the strength of library personnel and strategic partnerships with units outside of the library. Academic libraries can support campus data science needs through professional development of current staff and recruitment of new personnel with expertise in data-intensive domains.

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Section: Computational Literacymentioning

confidence: 99%

Data Science Support at the Academic Library

Oliver

Kollen

Hickson

et al. 2019

Journal of Library Administration

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“…Thus, Rubinstein and Chor (2014) argue that life science students need a basic course on programming and software tool handling before being given a course on computational thinking. For instance, a procedural course on how to use a particular tool (R, Python, etc.)…”

Section: Delineatingmedicalctmentioning

confidence: 99%

Emerging Research, Practice, and Policy on Computational Thinking

Rich¹,

Hodges²

2017

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“…Bioinformatics skills have become intrinsic to life-science research, particularly to 'omic'-based research (proteomics, genomics, metagenomics, etc .). While rudimentary programming, use of bioinformatics tools and databases, and statistical principles are beginning to appear in some undergraduate (UG) life-science degree curricula (Goodman & Dekhtyar, 2014;Libeskind-Hadas & Bush, 2013;Rubinstein & Chor, 2014), basic skills in data analysis and interpretation, and especially in data management, are still taught relatively rarely within traditional UG programmes, even though such skills are essential to most research projects today. Many students thus progress to postgraduate (PG) life-science degrees without adequate foundations in computational science.…”

Section: Introductionmentioning

confidence: 99%

A global perspective on bioinformatics training needs

Attwood

2017

Preprint

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In the last decade, life-science research has become increasingly data-intensive and computational. Nevertheless, basic bioinformatics and data stewardship are still only rarely taught in life-science degree programmes, creating a widening skills gap that spans educational levels and career roles. To better understand this situation, we ran surveys to determine how the skills dearth is affecting the need for bioinformatics training worldwide. Perhaps unsurprisingly, we found that respondents wanted more short courses to help boost their expertise and confidence in data analysis and interpretation. However, it was evident that most respondents appreciated their need for training only after designing their experiments and collecting their data. This is clearly rather late in the research workflow, and suboptimal from a training perspective, as skills acquired to address a specific need at a particular time are seldom retained, engendering a cycle of low confidence in trainees. To ensure that such skill gaps do not continue to create barriers to the progress of research, we argue that universities should strive to bring their life-science curricula into the digital-data era. Meanwhile, the demand for point-of-need training in bioinformatics and data stewardship will grow. While this situation persists, international groups like GOBLET are increasing their efforts to enlarge the community of trainers and quench the global thirst for bioinformatics training.

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Computational Thinking in Life Science Education

Cited by 84 publications

References 16 publications

Data Science Support at the Academic Library

Data Science Support at the Academic Library

Emerging Research, Practice, and Policy on Computational Thinking

A global perspective on bioinformatics training needs

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