Processing of scientific dataFrom field capture to web delivery

Hector Quintero CasanovaPostgraduate in e-Science

● GMEP ticks all the boxes:

✔ Highly multidisciplinary: social, landscape, water, birds

plants...

✔ Large volumes of data: covers the whole of Wales.

✔ Cross-organisational collaboration: 13 institutions.

Why e-Science? Data-intensive

Why e-Science? Metadata

● NERC's data policy says it all

– “It is essential that metadata are submitted”

● Metadata = context information about data

– Provenance = who, when, where, how

● Exposes data relationships → traceability

– Workflow = how. Essential if using models

● Enables reproducing outcome → repeatability

● Exactly what information depends on the stage.

● Raw data from the field– Metadata: method, calibration, place, units...

Data collection

● Information products: e.g. data from models– Metadata: name, conditions, where it applies

Data analysis

Data analysis

● Workflow metadata avoids costly reruns

– Identify model output needed → reuse

● But not enough for cross-organisation collab.

– 13 institutions in Glastir.

– Differences in storage structure, metadata defs...

● Need extra layer(s) for seamless access

– Web already offers tools needed.

Publication: linked data

● HTTP for generic retrieval of resources

● URIs for unique identification of those resources

– E.g. http://www.ceh.ac.uk

● Both can be used to build web services

– Amount to remote functions.

– Eg: seamless recording of workflows across institutions.

● Semantics for automated reasoning

– Acts as standardised metadata aimed at machines.

… We've come full circle!

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Hector Quintero Casanova Postgraduate in e-Science

Thank youwww.hqcasanova.com