Ict4 d rhul talk
0 likes279 views
The document discusses the opportunities and challenges of data science in low and middle-income countries (LMIC), highlighting the increasing availability of open data and the need for improved infrastructure and training. It presents solutions such as the establishment of summer schools and access to cloud computing resources to build a cohort of data-savvy researchers. Specifically, it emphasizes the importance of tailored training programs and collaborations between organizations to empower local communities and enhance research capabilities.
Ict4 d rhul talk
- 1. Research Data Science and its potential impact in Low and Middle Income countries Hugh Shanahan Hugh.Shanahan@rhul.ac.uk @hughshanahan
- 2. Opportunities for LMIC Researchers from the Data Deluge In many research fields, data becoming more open Public data gradually becoming more open as well Placement of sites in LMIC - Square Kilometre Array
- 3. Example - Bioinformatics PBytes of Omic data freely available Good basic Science can be done just by analysing these data sets Clear results from test species could be applied to local species
- 4. Square Kilometre Array Array of Radio Telescopes Most of these to be South Africa South Africa wants to own this not just be the site 1 PByte of data per day
- 5. Challenges Absence/deficit of infrastructure Equipment / Electricity / IT Support; Internet access - though improving Online collaboration and communities Absence/deficit of awareness (leading to funding gap) Absence/deficit of education and training
- 6. Solutions Provide training through Summer School system to create cohort of Professionals who cognisant in Research Data Science Provide access to cloud computing resources that have the data
- 7. Organisations Co-chair of Working Group RDA/CODATA Summer Schools in Data Science and Cloud Computing in the Developing World RDA - young organisation (<3 years) for Data sharing CODATA - 40 year old organisation with deep interest in developing world research
- 8. Schools in Research Data Science Give attendees an introduction to principles behind Data Science and how it can be applied. Aim to make this a professional qualification Focus on standards avoid reinvention of the wheel sharing data
- 10. Vanilla covers the basics for anyone with BSc/BA Machine Learning/Statistics Software Carpentry Data Carpentry Infrastructures Visualisation Curriculum
- 11. Cover topics that represent issues specific to disciplines Flavoured schools Examples :- Extreme Data Life Sciences Databases/Geospatial
- 12. Partners so far
- 13. Cloud Computing Technical solution to infrastructure problem Cost for individual LMIC researcher is barrier Free at the point of use cloud facilities
- 14. Use case - North African Bioinformatics Potential user community based in Morocco,Tunisia and Egypt Suggestion - Federated clouds to find resources in “the cracks”.