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Traceability, reproducibility, and scalability in Integrated Ecosystem Assessments: July 2013 ECO-OP is supported by NSF Grant #0955649 PIs: Peter Fox.

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Presentation on theme: "Traceability, reproducibility, and scalability in Integrated Ecosystem Assessments: July 2013 ECO-OP is supported by NSF Grant #0955649 PIs: Peter Fox."— Presentation transcript:

1 Traceability, reproducibility, and scalability in Integrated Ecosystem Assessments: July 2013 ECO-OP is supported by NSF Grant #0955649 PIs: Peter Fox (RPI) and Andrew Maffei (WHOI) NEFSC Collaborators: Jon Hare and Mike Fogarty Software programmer: Massimo Di Stefano Informatics and metadata: Stace Beaulieu stace@whoi.edu

2 Traceability, reproducibility, and scalability in Integrated Ecosystem Assessments: Adopting a provenance model for a collaborative report July 2013 ECO-OP is supported by NSF Grant #0955649 PIs: Peter Fox (RPI) and Andrew Maffei (WHOI) NEFSC Collaborators: Jon Hare and Mike Fogarty Software programmer: Massimo Di Stefano Informatics and metadata: Stace Beaulieu stace@whoi.edu

3 Traceability, reproducibility, and scalability in Integrated Ecosystem Assessments: Adopting a provenance model for a collaborative report July 2013

4 Traceability, reproducibility, and scalability in Integrated Ecosystem Assessments: Adopting a provenance model for a collaborative report July 2013 Metadata for data and workflow provenance (i.e., the marine ecosystem indicators and the collaborative report)

5 Use Case: Northeast Shelf Large Marine Ecosystem Ecosystem Status Report “traceability, repeatability, explanation, verification, and validation” for ecosystem data and information products in the NEFSC Ecosystem Status Report (ESR) Goal:

6 Page from 2009 ESR Section on Climate Forcing Figures available for download as PDF or image files – but without access to data or metadata

7 Page from 2009 ESR Section on Climate Forcing Figures available for download as PDF or image files – but without access to data or metadata Note: NOAA directive for ISO 19115 metadata, but these are not sufficient to describe time-series indicators

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9 Software design to track provenance M. Di Stefano

10 Software design to track provenance M. Di Stefano

11 PROV Data Model http://www.w3.org/TR/prov-dm/ W3C Recommendation 30 April 2013 Core Structures (types and relations)

12 PROV Data Model http://www.w3.org/TR/prov-dm/ W3C Recommendation 30 April 2013 Core Structures (types and relations) Entity may be a single data product, or a chapter containing several data products

13 PROV-O: The PROV Ontology (expresses PROV-DM using OWL2) http://www.w3.org/TR/prov-o/ PROV Data Model http://www.w3.org/TR/prov-dm/ W3C Recommendation 30 April 2013 Core Structures (types and relations) Entity may be a single data product, or a chapter containing several data products

14 http://ipython.org/ Screenshot of IPython Notebook used to track both data and workflow provenance

15 http://ipython.org/ Screenshot of IPython Notebook used to track both data and workflow provenance Code in Python, Matlab, R, other

16 http://ipython.org/ Screenshot of IPython Notebook used to track both data and workflow provenance Code in Python, Matlab, R, other

17 http://ipython.org/ Screenshot of IPython Notebook used to track both data and workflow provenance Notebook can be shared, or output as script, HTML, PDF, other

18 PDF output of IPython Notebook with clickable links to data and code

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20 Screenshot of csv file at GitHub

21 Having access not only to the data that are plotted, but also to provenance metadata increases the (re-) usability of the data


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