‚ÄčTrack 8 Thur 28 May: Data and tools for water applications

Track contributions

How does 'more data' impact education and capacity development?

Started 9 months ago

Due to new technologies, improved infrastructures but also enormously driven by the SDGs the amount of data increases day by day.

Does every water scientist or expert in national authorities have to be a computer or data scientist in the future?

Certainly not, specific knowledge and experience in the different sectors of water research will be equally important in the future, too. However, such skills will become more and more important and the concept and implications of FAIR data principles have to be implemented in teaching world-wide.

How does this influence the higher education but also the capacity development?

Does every water scientist or expert in national authorities have to be a computer or data scientist in the future?

It probably depends on the type of job. However, for mid-career professionals from the water sector some basic knowledge is data management is important. Therefore, I teach in all IHE Delft MSc programmes about Open Data, Spatial Data Infrastructures (SDI), OGC standards. I think that a basic knowledge of that is essential if you work with data in an authority as well as if you are a decision maker in an authority that collects data.

It is important to teach good practice with spatial data for example. In traditional GIS classes you work with shapefiles on your own computer. Teach them what the advantages are of spatial databases and SDI which enable not only sharing of data, but also sharing of metadata (very important) and re-use of data through open standards. Ideally students should already experience this in class by using an SDI with course data for example. Furthermore, teach them where to find important global datasets for water applications, including citizen science, remote sensing and in situ data.

The education to water professionals should often be closer to the water application than to computer or data science. In class examples of applications with open data that can easily be implemented can be used, such as using the FAO WaPOR Water Productivity data in a GIS or even in a field data collection app without much technical knowledge.

Students who specialise more in modelling have to learn more open source tools for data analysis. A solid basis in scripting with Python or R becomes essential instead of only learning to push the right buttons of a specific model. Python and R libraries are also important for, gap filling, aggregation, conversions, batch processing, etc. This unfortunately is not mainstreamed in education everywhere, while free tutorials are available on the internet.

Being an expert or data scientist who is able to use the data is not enough on its own. It is important to understand what the data are telling you about the environment. For this you need to know how the environment functions and whether the data have been collected from the appropriate place and at the appropriate times to give you the information you need. The UNEP GEMS/Water MSc in Freshwater Quality Monitoring and Assessment gives water professionals an opportunity to gain this knowledge and understanding while continuing in their role.

How does 'more data' impact education and capacity development? I think Educators and Capacity Development experts also need to start using a more data-driven approach. There is more and more data available about what knowledge water professionals are interested about, what content is more sought after and by who. This data should shape also the agenda on capacity development in the water sector. This data should become more actively part of the capacity development needs assessments