Architectural styles and the design of network-based software architectures
Architectural styles and the design of network-based software architectures
Querying and Creating Visualizations by Analogy
IEEE Transactions on Visualization and Computer Graphics
ManyEyes: a Site for Visualization at Internet Scale
IEEE Transactions on Visualization and Computer Graphics
Provenance and scientific workflows: challenges and opportunities
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Provenance for Computational Tasks: A Survey
Computing in Science and Engineering
VisComplete: Automating Suggestions for Visualization Pipelines
IEEE Transactions on Visualization and Computer Graphics
Towards Provenance-Enabling ParaView
Provenance and Annotation of Data and Processes
Guest Editors' Introduction: Reproducible Research
Computing in Science and Engineering
Future Generation Computer Systems
VisMashup: Streamlining the Creation of Custom Visualization Applications
IEEE Transactions on Visualization and Computer Graphics
Harnessing the Information Ecosystem with Wiki-based Visualization Dashboards
IEEE Transactions on Visualization and Computer Graphics
Cyber-Enabled Simulations in Nanoscale Science and Engineering
IEEE Design & Test
Managing rapidly-evolving scientific workflows
IPAW'06 Proceedings of the 2006 international conference on Provenance and Annotation of Data
Computational reproducibility: state-of-the-art, challenges, and database research opportunities
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
(Re)Use in public scientific workflow repositories
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Detecting common scientific workflow fragments using templates and execution provenance
Proceedings of the seventh international conference on Knowledge capture
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Managing and understanding the growing volumes of scientific data is one of the most challenging issues scientists face today. As analyses get more complex and large interdisciplinary groups need to work together, knowledge sharing becomes essential to support effective scientific data exploration. While science portals and visualization Web sites have provided a first step towards this goal, by aggregating data from different sources and providing a set of predesigned analyses and visualizations, they have important limitations. Often, these sites are built manually and are not flexible enough to support the vast heterogeneity of data sources, analysis techniques, data products, and the needs of different user communities. In this paper we describe CrowdLabs, a system that adopts the model used by social Web sites, allowing users to share not only data but also computational pipelines. The shared repository opens up many new opportunities for knowledge sharing and re-use, exposing scientists to tasks that provide examples of sophisticated uses of algorithms they would not have access to otherwise. CrowdLabs combines a set of usable tools and a scalable infrastructure to provide a rich collaborative environment for scientists, taking into account the requirements of computational scientists, such as accessing high-performance computers and manipulating large amounts of data.