An open source approach to enable the reproducibility of scientific workflows in the OCEAN SCIENCES

Presented at the AGU Fall Meeting 2013

Abstract:

Every scientist should be able to rerun data analyses conducted by his or her team and regenerate the figures in a paper. However, all too often the correct version of a script goes missing, or the original raw data is filtered by hand and the filtering process is undocumented, or there is lack of collaboration and communication among scientists working in a team.

Here we present 3 different use cases in ocean sciences in which end-to-end workflows are tracked. The main tool that is deployed to address these use cases is based on a web application (IPython Notebook) that provides the ability to work on very diverse and heterogeneous data and information sources, providing an effective way to share the and track changes to source code used to generate data products and associated metadata, as well as to track the overall workflow provenance to allow versioned reproducibility of a data product. Use cases selected for this work are:

1) A partial reproduction of the Ecosystem Status Report (ESR) for the Northeast U.S. Continental Shelf Large Marine Ecosystem. Our goal with this use case is to enable not just the traceability but also the reproducibility of this biannual report, keeping track of all the processes behind the generation and validation of time-series and spatial data and information products. An end-to-end workflow with code versioning is developed so that indicators in the report may be traced back to the source datasets.

2) Realtime generation of web pages to be able to visualize one of the environmental indicators from the Ecosystem Advisory for the Northeast Shelf Large Marine Ecosystem web site.

3) Data and visualization integration for ocean climate forecasting. In this use case, we focus on a workflow to describe how to provide access to online data sources in the NetCDF format and other model data, and make use of multicore processing to generate video animation from time series of gridded data.

For each use case we show how complete workflows - from data source through results publication - are constructed and transparently published via the IPython Notebook. Our current work in development includes the incorporation of the W3C PROV provenance standard into the metadata of the JavaScript Object Notation (JSON) file of each Notebook. We are sharing our design principles for the granularity of these linked data provenance records with others in NOAA and NASA data communities. We conclude by reporting on end-user experience and satisfaction with these new capabilities.

1 http://nbviewer.ipython.org/6153235
2 http://nbviewer.ipython.org/6171893
3 http://nbviewer.ipython.org/5678081

History

DateCreated ByLink
February 23, 2014
12:36:09
Massimo Di Stefano Download
December 10, 2013
17:38:07
Patrick WestDownload

Related Projects:

ECO-OPEmploying Cyber Infrastructure Data Technologies to Facilitate IEA for Climate Impacts in NE & CA LME's (ECO-OP)
Principal Investigator: Peter Fox
Co Investigator: Andrew Maffei
Description: The purpose of this INTEROP proposal is to facilitate the deployment of an Integrated Ecosystem Approach (IEA) to management in the Northeast and California Current Large Marine Ecosystems (LMEs). The direct result of the proposed activity will be application-level data and information enhanced communication for developing the consensus networks to define the specific components of interest to support the implementation of NOAA’s Driver-Pressure-State-Impact Response framework (DPSIR) decision framework and the cyberinfrastructure technologies to ensure data interoperability and reuse.

Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

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Concepts: eScience
Semantic eScience
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.

As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.

Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: eScience
X-informatics
Lead Professor: Peter Fox
Description: In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems.
Concepts: , eScience