TY - JOUR
T1 - The ISCB Student Council Internship Program
T2 - Expanding computational biology capacity worldwide
AU - Anupama, Jigisha
AU - Francescatto, Margherita
AU - Rahman, Farzana
AU - Fatima, Nazeefa
AU - Deblasio, Dan
AU - Shanmugam, Avinash Kumar
AU - Satagopam, Venkata
AU - Santos, Alberto
AU - Kolekar, Pandurang
AU - Michaut, Magali
AU - Guney, Emre
N1 - Funding: FR is funded by HPC Wales in conjunction with Fujitsu European Lab and Graduate Research program of University of South Wales. DD is funded by NSF Grant CCF-1256087, NSF Grant CCF-1319998, NIH Grant R01HG00 7104, and Gordon and Betty Moore Foundation Grant GBMF4554, all to Carl Kingsford. And EG by the Beatriu de Pinos Fellowship from AGAUR - Generalitat de Catalunya. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Education and training are two essential ingredients for a successful career. On one hand, universities provide students a curriculum for specializing in one’s field of study, and on the other, internships complement coursework and provide invaluable training experience for a fruitful career. Consequently, undergraduates and graduates are encouraged to undertake an internship during the course of their degree. The opportunity to explore one’s research interests in the early stages of their education is important for students because it improves their skill set and gives their career a boost. In the long term, this helps to close the gap between skills and employability among students across the globe and balance the research capacity in the field of computational biology. However, training opportunities are often scarce for computational biology students, particularly for those who reside in less-privileged regions. Aimed at helping students develop research and academic skills in computational biology and alleviating the divide across countries, the Student Council of the International Society for Computational Biology introduced its Internship Program in 2009. The Internship Program is committed to providing access to computational biology training, especially for students from developing regions, and improving competencies in the field. Here, we present how the Internship Program works and the impact of the internship opportunities so far, along with the challenges associated with this program.
AB - Education and training are two essential ingredients for a successful career. On one hand, universities provide students a curriculum for specializing in one’s field of study, and on the other, internships complement coursework and provide invaluable training experience for a fruitful career. Consequently, undergraduates and graduates are encouraged to undertake an internship during the course of their degree. The opportunity to explore one’s research interests in the early stages of their education is important for students because it improves their skill set and gives their career a boost. In the long term, this helps to close the gap between skills and employability among students across the globe and balance the research capacity in the field of computational biology. However, training opportunities are often scarce for computational biology students, particularly for those who reside in less-privileged regions. Aimed at helping students develop research and academic skills in computational biology and alleviating the divide across countries, the Student Council of the International Society for Computational Biology introduced its Internship Program in 2009. The Internship Program is committed to providing access to computational biology training, especially for students from developing regions, and improving competencies in the field. Here, we present how the Internship Program works and the impact of the internship opportunities so far, along with the challenges associated with this program.
U2 - 10.1371/journal.pcbi.1005802
DO - 10.1371/journal.pcbi.1005802
M3 - Article
C2 - 29346365
AN - SCOPUS:85041386662
SN - 1553-734X
VL - 14
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 1
M1 - e1005802
ER -