Drosophila Neurobiology: No Escape from ‘Big Data’ Science

Dierick, Herman A.; Gabbiani, Fabrizio

doi:10.1016/j.cub.2015.05.019

Cited by 7 publications

(4 citation statements)

References 19 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The natural and social science fields are increasingly using data science approaches to answer important questions (Marx 2013 ). Technological advances that allow for the acquisition of increasingly large amounts of data are becoming common across disciplines such as ecology (Michener and Jones 2012 , Hampton et al 2013 ), wildlife biology (Lewis et al 2018 ), evolutionary biology (Muñoz and Price 2019 ), environmental science (Gibert et al 2018 ), genomics (Stephens et al 2015 ), and neurobiology (Dierick and Gabbiani 2015 ). Data science is inherently interdisciplinary (De Veaux et al 2017 ), and data science skills are valuable for students to learn before graduating from colleges and universities (Johnson 2018 , National Academies of Sciences, Engineering, and Medicine 2018 ).…”

mentioning

confidence: 99%

Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training

et al. 2021

View full text Add to dashboard Cite

There is a clear demand for quantitative literacy in the life sciences, necessitating competent instructors in higher education. However, not all instructors are versed in data science skills or research-based teaching practices. We surveyed biological and environmental science instructors (n = 106) about the teaching of data science in higher education, identifying instructor needs and illuminating barriers to instruction. Our results indicate that instructors use, teach, and view data management, analysis, and visualization as important data science skills. Coding, modeling, and reproducibility were less valued by the instructors, although this differed according to institution type and career stage. The greatest barriers were instructor and student background and space in the curriculum. The instructors were most interested in training on how to teach coding and data analysis. Our study provides an important window into how data science is taught in higher education biology programs and how we can best move forward to empower instructors across disciplines.

show abstract

mentioning

confidence: 99%

Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training

et al. 2021

View full text Add to dashboard Cite

show abstract

“…A large proportion of Google searches on these keywords returns results that are irrelevant to their intrinsic semantics and scope, or simply repeat familiar arguments about the needs of data science and existing phenomena. In many such findings [5], [54], [17], [29], [2], [25], [50], [39], [40], [18], [55], [45], [49], [23], [36], [41], [48], [34], [35], [43], [21], [15], [53], [31], [33], big data is described as being simple, data science has nothing to do with the science of data, and advanced analytics is the same as classic data analysis and information processing. There is a lack of deep thinking and exploration of why, what and how these new terms should be defined, developed and applied.…”

Section: A About Data Science Conceptsmentioning

confidence: 99%

Data Science: Nature and Pitfalls

Cao

2016

IEEE Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…In other words, many disciplines – such as law, history and even nursing – have adopted data science because they deal with data-intensive and big data. Other examples of such disciplines include astronomy (Borne et al, 2009), media and entertainment (Gold et al, 2013), climate change (Faghmous and Kumar, 2014), neurobiology (Dierick and Gabbiani, 2015), physical medicine and rehabilitation (Ottenbacher et al, 2019), tourism (Egger, 2022) and other related disciplines as introduced by Cao (2018) in his book Data Science Thinking: The Next Scientific, Technological and Economic Revolution .…”

Section: Introductionmentioning

confidence: 99%

Data science education programmes in Middle Eastern institutions: A survey study

Zakaria

2022

IFLA Journal

View full text Add to dashboard Cite

In response to the current trends in dealing with data in academia, various research institutions and commercial entities around the world are building new programmes to fill the gaps in workforce demand in specific disciplines, including data curation, big data, data management, data science and data analytics. Thus, the aim of the present study was to reveal the reality of data science education in the Middle East and to determine the opportunities and challenges for teaching data science in the region. Thirteen countries in the Middle East were offering 48 data science programmes at the time of the study. The results reveal that these data science programmes significantly use the words ‘data’ and ‘analytics’ in their names. With regard to the academic affiliations of the data science programmes, the study found that they are offered in a variety of schools, especially computer science, information technology and business. Moreover, the study found that computer science is the dominant trend in the programmes. Data science programmes have a significant overlap with other programmes, especially statistics and computer science, because of the interdisciplinary nature of this field. Data science schools in the Middle East differ in terms of their programme titles, programme descriptions, course catalogues, curriculum structures and course objectives. Broadly, this study may be useful for those who are seeking to establish a data science programme or to strengthen data science curricula at both the undergraduate and postgraduate levels.

show abstract

Drosophila Neurobiology: No Escape from ‘Big Data’ Science

Abstract: Combining a variety of large-scale, data-intensive techniques, a recent study has unraveled the neural pathways involved in Drosophila larval escape from a parasitoid wasp invasion.

Cited by 7 publications

References 19 publications

Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training

Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training

Data Science: Nature and Pitfalls

Data science education programmes in Middle Eastern institutions: A survey study

Contact Info

Product

Resources

About