RUFORUM trains 50 Gulu University students and staff in Scientific Data Management
The diversity and the complexity of data from scientific experiments and natural systems constitute serious challenges for students and even scientists in the universities and research institutions. Lack of data management and data analysis skills among graduate students often causes a waste of productive time, with negative consequences on successful thesis completion and student graduation. Short training courses in Scientific Data Management provide students with the right analytical skills on application of cutting-edge technologies and solutions for managing and analyzing vast amounts of data, thereby helping them focus on their scientific goals.
The Regional University Forum for Capacity building in Agriculture (RUFORUM), in collaboration with Gulu University organized a five-days training in Scientific Data Management for staff and students drawn from several departments in the Faculty of Agriculture and Environment, the Faculty of Business and Development Studies and the Faculty of Sciences. The training was held from 3rd to 7th April 2018 at the Faculty of Agriculture and Environment of Gulu University, with the objective to improve understanding of various biometrical components pertaining to design and analysis of experiments/surveys; as well as optimal application of various statistical techniques at different stages of research. More specifically, the training provided participants with:
- statistical knowledge in design, analysis and result interpretation, of surveys/experiments applied to agricultural, socio-economics and other biophysical studies;
- skills and ability to manage complex data, analyse data using statistical software and interpret results for scientific production;
In total, 50 staff and students (19 Females and 31 males) were trained and provided with knowledge and hands-on skills in use of software (GenStat & SPSS) in data analysis and presentation of results in a format that would ensure their wide dissemination as in theses and peer reviewed publications. The training was facilitated by a team of three experts: Dr Susan Balaba Tumwebaze and Dr Thomas Lapaka Odong, both from the College of Agricultural and Environmental Sciences (Makerere University), and Dr Vincent Oeba from the Kenyan Forest Institute (Kenya).
The workshop was opened on behalf of the Dean of the Faculty of Agriculture and Environment, by Dr Collins Okello who recognized the importance of the training for both students and staff, but also called for participants’ attention to maximize the benefits from the workshop. The delivery of the courses was mixed mode, including interactive lectures and practicals designed to complement the lecture material. A participatory approach was used, enabling participants to be active learners, and to commit themselves to intensive and critical self-study. In terms of content, participants were taken through several statistical aspects related to (i) data management; (ii) experimental designs; (iii) sampling designs and techniques (elements of sampling, sampling techniques and sampling size, etc); (iv) design of spreadsheet and data checking techniques; (v) data management and analysis; (vi) data exploration and manipulation; (vii) inferential statistics, among others. They were also introduced to the environment of two software packages, namely GenStat & SPSS, and intensively learned how to analyse and interpret results and outputs from the two statistical packages. The training ended with a half-day session on student clinics to deal with specific challenges and issues related to research design and proposal, as raised by individual students.
At the closing ceremony of the workshop, participants were awarded certificates of completion, and provided positive feed backs and were appreciative of RUFORUM support. On behalf of RUFORUM, Dr Sylvanus Mensah, in charge of the Recruitment and Training for mastercard foundation at RUFORUM, expressed his gratitude to Gulu University for hosting the event, but also pointed out the necessity for the participants to build on the acquired statistical knowledge to widen their skills in Scientific Data Management.