Processing of scientific data: from field capture to web delivery

794 views

Published on

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
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
794
On SlideShare
0
From Embeds
0
Number of Embeds
46
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

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

×