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BeST - Bespoke e-Style Statistical Training for Africa Miranda Y. Mortlock, Vincent A Mellor School of Agriculture and Food Science, Faculty of Science, University of Queensland, Australia Good Design Good Research Figure 1: Field trials of maize in Harare, Zimbabwe– Scientists require good statistical skills for design and anal- ysis of trials Poor field study design and the inadequate analysis of resultant data (Mouyelo-Katoula 2006) can lead to expensive research with little or no useful outcome. In Africa, which is resource rich but wealth poor, this issue is of great importance. Also with the vast size of the continent it is difficult and a major commitment to access trained Statistical Consulting support (Thabane et al. 2008). Our project is designed to produce an end- to-end statistical training programme, easily accessible on both computers and mobile devices, specifically aimed at Agricultural Research in Africa. We structure our modules from initial design methods, all the way to the presenta- tion of results in an accessible manner for all mathematical abilities. Filling a need As the Statistical Training programme was a com- ponent of the larger research on Sustainable Intensi- fication of Maize and Legume Systems for Food Se- curity in Eastern and Southern Africa (SIMLESA) supported by the Australian Centre for International Research (ACIAR), the project would focus mainly on applications to crop science. Before starting to design the modules, a small interest study was un- dertaken, with the results showing strong interest in a statistical training programme are shown below. The Training Programme The development of ‘BeST’, an online course for African early career researchers and scientists, was started in 2014 by a team of Agricultural Re- searchers, some with a background of teaching in Africa, Mathematicians, Physicists and Engineers. With approaches from different fields and back- grounds, the course was designed around the use of R software (R Core Team 2016) using the popular RStudio interface RStudio Team (2015) to provide researchers of variable mathematical ability com- plete methods for designing, then analysing and re- porting their experiments. The focus is on a graphi- cal modular format which provides online and down- loadable resources. As the programme’s primary fo- cus is on those with limited backgrounds, extensions to topics are provided for those wishing to know the “mechanics” behind the methods. The programme has also been designed with an open source approach and is licensed under Creative Commons Modular Design Special Issues In African Internet Affordability is poor and cover- age of technology can be difficult, especially at field stations. With this in mind, this online programme has been designed to be lightweight and provides downloadable resources in the form of pdf ‘Walk- thoughs’ of particular analysis methods along with presentations of key points. When code is presented, the syntax is explained so that it may be used again with different data easily. Modules on Data Collec- tion, Storage and Processing are provided as these are key issues for any study. As time may be lim- ited, the programme’s modular design aims to let re- search learn about specific topics, while seeing how they can link together to give ‘toolbox’ of powerful statistical methods. Acknowledgements This project is part of the research into Sustainable Intensifica- tion of Maize and Legume Systems for Food Security in Eastern and Southern Africa (SIMLESA), and the team would also like to thank the following organisations for the continued support: References Mouyelo-Katoula, M. (2006), ‘Rethinking statistics for na- tional development in Africa’, The African Statistics Jour- nal 2, 140. R Core Team (2016), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Comput- ing, Vienna, Austria. URL: https://www.R-project.org/ RStudio Team (2015), RStudio: Integrated Development En- vironment for R, RStudio, Inc., Boston, MA. URL: http://www.rstudio.com/ Thabane, L., Chinganya, O. & Ye, C. (2008), ‘Training young statisticians for the development of statistics in africa’, The African Statistical Journal 7, 125. Contact Information • Web: http://www.yieldingresults.org • Twitter: @BeST_MYM • Facebook: @BeST4AAR • G+: +YieldingresultsOrg
