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Megan's Blog: Thriving in a multidisciplinary world: technical & communication skills are key

Notes

Abstract:

Scientists from all domains have something in common – they must continuously analyze data to conduct their research, in what has become known as “data-centric science”. The volume of data involved is usually very large, e.g., when astronomers conduct sky surveys, or chemists run simulations on chemical compounds. In other situations, data can be also scarce, e.g., when archaeologists discover prehistoric human bones, or biologists study a rare species. Whether in large or small volumes, rare or common, scientific data usually comes in many formats and from very many sources. Its management involves experts from distinct domains, working in interdisciplinary and multi-institutional teams.

A challenge for computer scientists is to design new methods and algorithms, and construct software and hardware tools to help these scientists from other domains to manage, analyze and visualize their data. This, in turn, has brought about the need for a new kind of training for computer scientists, who must learn how to work in a multidisciplinary world, and deal with a variety of domain-specific requirements.

The talk will give an overview of some of the challenges of conducting computer science research in this new scenario. It will concentrate on the new skills required (technical and social), with examples from several scientific fields, followed by a case study in agricultural planning.

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Talk overview (outline)

1. Data rich, analysis poor - the deluge

   - Most data being produced will never be seen/understood by humans
     Hence, we need to develop means to condense data and produce useful/usable information 

2. Common needs of all domains

   - Data, models, visualization, cooperation support
     Regardless of the scientific domain (from humanities to mathematical sciences), users have a few common major needs

3. The two-way road

   - Technical skills
   - Socio-emotional skills
   - Transcultural skills

Issues that will be discussed, with examples from large multi-institutional projects coordinated by the speaker (in biodiversity systems, agro-environmental monitoring and urban planning):

a) What are common technical requirements in multidisciplinary projects that involve handling heterogeneous scientific data?

b) What technical skills do computer scientists need to work in such projects?

c) What other very important aspects need to be considered to get good results?

d) And, as a consequence, what challenges must we face in such situations?