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Research Data Management

Research Data Management. Conny Bokgobelo Juanri Fouché Lindelwa Khumalo Jerry Komane Nkosana Mabuza Johannes Magagula Mandisa Majola Mashamaite Magret Nyalungu Precious Zuma. Introduction. Arriving at CSIR UP information science students High expectations

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Research Data Management

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  1. Research Data Management ConnyBokgobelo JuanriFouché LindelwaKhumalo Jerry Komane NkosanaMabuza Johannes Magagula MandisaMajola Mashamaite MagretNyalungu Precious Zuma

  2. Introduction • Arriving at CSIR • UP information science students • High expectations • Expectations (kept at the back of our minds) • Rules and regulations • Difficult work task and nervousness • Fun and playing on computers • Office layout with every employee in their office • Hard project leaders • Friendly staff • Research project to be exciting

  3. Introduction (continued) • Work Instruction • Introduction to the project leader Martie van Deventer • The signing of contract to ensure the understating to rules and regulations • All project that students will partake in. • All project leaders to take their team in their offices. • Introduction to CSIR • Project leader explained what the CSIR does (The CSIR is one of the leading scientific and technology research, development and implementation organisations in Africa.) • The CSIR is committed to supporting innovation in South Africa to improve national competitiveness in the global economy. • The CSIR has eight research area’s which include the biosciences and laser technology.

  4. CSIR’s Biosciences Research Unit • Biosciences is one of the research areas of CSIR. Drug Discovery & Development. Expression Systems. Plant Bio Tech Systems biology Biosciences Discovery Chemistry. Structural biology Aptamer Technology. Bio Prospecting. Product & Process Development. Synthetic Biology. Enzyme technologies. Agro processing Gene expression Bioenergetics. Chemical technology. Bioprocess

  5. Terms and definitions

  6. The Research Cycle

  7. The Research Cycle (continued) • Scientific Workflow: • Conducting the Structured Interview • Training and Mentoring: • Continuous feedback and constructive criticism, webinar, workshops • Real-time Communication: • Face-to-face communication and various technology based mediums e.g. E-mail, telephone, web 2.0 etc. • Report Writing: • This was done in order to provide feedback on our project and to help with the presentation. • Dissemination of Facts: • No analysis of the datasets were required, but recommendations and demographical information is available

  8. Soccer Friday • Friday the eleventh: • Introduction to our projects • You do not get paid if you do not do your assigned task. • Remember that you know nothing that happens in biosciences.

  9. Soccer Friday (continued) • Introduction to structured interviews • The H building. • Okay, all this and then…

  10. 2nd day • Experience • Team • Members & Interns • Collaboration • Confident & Comfortable • Culture • CSIR (Bioscience) • Understanding Researchers

  11. 2nd DAY • Work Environment • Life in Office Environment • Respect • Responsible • Getting used to time • Big Picture • Happy and interested

  12. Modderfontein (ECXITEMENT) • Little trip to CSIR in Modderfontein • Having a new partner for the interview and that meant having to reconnect: • New partnership • New environment • New faces (researchers) • Same questionnaire • The interviews went smoothly and quickly (familiar with terms and the questionnaire itself)...(PTA) • More work was done (researchers gave us their time and they were accommodating and welcoming) • SDMS (scientific data management system) machine (software that allows researchers to browse the novel network {search for data}, link and index data, create back up’s, collect data in all databases and produce reports)

  13. “Fluffy” • The day went by very quickly “time flies when you having fun”, couldn’t believe the day was over..all of us wished we could go back. • On our way home we met “fluffy”... he is the snake skin we found under the tree and one of the members took him home (souvenir) and named him. • “IRONY” New beginnings for the staff in Modderfontein as we were told they will be moving to PTA soon (out with the old “skin”, in with the new.)

  14. Findings (Bad) • Researchers ignore information management • Single license for software • Old records are inaccessible • Restrictions on data access

  15. Findings (Good) • Datasets are archived • Effective sharing of data

  16. Comparing Findings to Literature • Researchers ignore information management • “The Biotechnology and Biological Sciences Research Council does not prescribe particular approaches, recognizing variance of practice within the fields it funds, but does emphasize data quality…” (Ball, 2010). • Old records are inaccessible; they are in formats that cannot be managed efficiently. • “Interoperability of metadata for scientific observations .” (Gold, 2010) • Single license for software • Restrictions on data access • Pienaar and van Deventer (2009) identified a need for repositories.

  17. Comparing Findings to Literature • Datasets are archived • “Contributing as data scientists to ongoing research teams by advising on and developing team practices and policies to support both immediate and future data curation, reflecting domain practices and needs..” (Gold, 2010) • Effective sharing of data • “Consulting with individual researchers and research groups on best practices for data management… “ (Gold, 2010)

  18. Lessons & Experiences • Some of the more important experiences gained were: • Transition • Intercultural interactions • Contractual conduct • Collaboration • Interruptions / First Webinar • Web 2.0 Technologies • Feedback • Appreciative Inquiry

  19. Lessons & Experiences (continued) • Some of the more important lessons learnt were: • Working under pressure • Group work • Communication skills • Time management • Meeting objectives • Don’t be disregarded • Self-motivation • Research process

  20. Conclusion • Effective data management requires: • File Management Systems(FMS) • Data Management Systems(DMS) • Right knowledge to effectively use technology. • Recommendations for CSIR Biosciences: • Standardised guidelines for the management of information • Implementation of an electronic lab book system. • A larger storage base • Easier process and system for filing and submitting proposals and publications. • A dedicated maintenance team

  21. Conclusion • A great learning experience • Recommendations for experiential learning: • More time (about a month) • Transportation • Experiencing different projects (exchange) • Time to understand and share experience with other groups

  22. Thank You Bruno Dr van Deventer

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