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Processing of scientific data
From field capture to web delivery

Hector Quintero Casanova

Postgraduate in e-Science
Why e-Science? Data-intensive

●

GMEP ticks all the boxes:
✔ Highly multidisciplinary: social, landscape, water, birds
pl...
Why e-Science? Metadata
●

NERC's data policy says it all
–

●

“It is essential that metadata are submitted”

Metadata = ...
Data collection
●

Raw data from the field
–

Metadata: method, calibration, place, units...
Data analysis

●

Information products: e.g. data from models
–

Metadata: name, conditions, where it applies
Data analysis
●

Workflow metadata avoids costly reruns
–

●

Identify model output needed → reuse

But not enough for cro...
Publication: linked data
●

HTTP for generic retrieval of resources

●

URIs for unique identification of those resources
...
… We've come full circle!

¿?
Thank you
www.hqcasanova.com

Hector Quintero Casanova
Postgraduate in e-Science
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Processing of scientific data: from field capture to web delivery

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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.

Published in: Technology, Business
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Transcript of "Processing of scientific data: from field capture to web delivery"

  1. 1. Processing of scientific data From field capture to web delivery Hector Quintero Casanova Postgraduate in e-Science
  2. 2. Why e-Science? Data-intensive ● 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. 3. 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 ● – Workflow = how. Essential if using models ● ● Exposes data relationships → traceability Enables reproducing outcome → repeatability Exactly what information depends on the stage.
  4. 4. Data collection ● Raw data from the field – Metadata: method, calibration, place, units...
  5. 5. Data analysis ● Information products: e.g. data from models – Metadata: name, conditions, where it applies
  6. 6. 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.
  7. 7. 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.
  8. 8. … We've come full circle! ¿?
  9. 9. Thank you www.hqcasanova.com Hector Quintero Casanova Postgraduate in e-Science
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