Short presentation on the lifecycle of scientific data and how it relates to the Glastir Monitoring and Evaluation Programme. The GMEP is effectively a "real-time" healthcheck system for the new Welsh agri-environment scheme Glastir.

1. Processing of scientific data
From field capture to web delivery
Hector Quintero Casanova
Postgraduate in e-Science

2. Why e-Science? Data-intensive
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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.

3. Why e-Science? Metadata
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NERC's data policy says it all
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“It is essential that metadata are submitted”
Metadata = context information about data
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Provenance = who, when, where, how
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Workflow = how. Essential if using models
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Exposes data relationships → traceability
Enables reproducing outcome → repeatability
Exactly what information depends on the stage.

4. Data collection
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Raw data from the field
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Metadata: method, calibration, place, units...

5. Data analysis
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Information products: e.g. data from models
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Metadata: name, conditions, where it applies

6. Data analysis
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Workflow metadata avoids costly reruns
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Identify model output needed → reuse
But not enough for cross-organisation collab.
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13 institutions in Glastir.
Differences in storage structure, metadata defs...
Need extra layer(s) for seamless access
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Web already offers tools needed.

7. Publication: linked data
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HTTP for generic retrieval of resources
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URIs for unique identification of those resources
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E.g. http://www.ceh.ac.uk
Both can be used to build web services
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Amount to remote functions.
Eg: seamless recording of workflows across institutions.
Semantics for automated reasoning
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Acts as standardised metadata aimed at machines.

8. … We've come full circle!
¿?

9. Thank you
www.hqcasanova.com
Hector Quintero Casanova
Postgraduate in e-Science