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The first Provenance Challenge was set up in order to provide a forum for the community to help understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a Functional Magnetic Resonance Imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarise the participants contributions.

A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0 [13], a modern data standard for collaborative science across the neurophysiology community.

Today, scientific experiments and simulations produce massive amounts of heterogeneous data that need to be stored and analyzed. Given that these large datasets are stored in many files, formats and locations, how can scientists find relevant data, duplicates or similarities? In this context, we concentrate on developing algorithms to compare similarity of time series for the purpose of search, classification and clustering. For example, generating accurate patterns from climate related time series is important not only for building models for weather forecasting and climate prediction, but also for modeling and predicting the cycle of carbon, water, and energy. We developed the methodology and ran an exploratory analysis of climatic and ecosystem variables from the FLUXNET2015 dataset. The proposed combination of similarity metrics, nonlinear dimension reduction, clustering methods and validity measures for time series data has never been applied to unlabeled datasets before, and provides a process that can be easily extended to other scientific time series data. The dimensionality reduction step provides a good way to identify the optimum number of clusters, detect outliers and assign initial labels to the time series data. We evaluated multiple similarity metrics, in terms of the internal cluster validity for driver as well as response variables. While the best metric often depends on a number of factor, the Euclidean distance seems to perform well for most variables and also in terms of computational expense.

Scientific and engineering experiments often produce large volumes of data that must be processed and visualised in near-realtime. An example of this, described in this paper, is microphone array processing of data from wind tunnels for aeroacoustic measurements. The overall turnaround time from data acquisition and movement, to data processing and visualization is often inhibited by factors such as manual data movement, system interoperability issues, manual resource discovery for job scheduling, and disparate physical locality between the experiment and scientist or engineer post-event. Workflow frameworks and runtimes can enable rapid composition and execution of complex scientific workflows. In this paper we explore two approaches based on Windows Workflow Foundation, a component of Microsoft WinFX. In our first approach, we present a framework for users to compose sequential workflows and access Globus grid services seamlessly using a .NET-based Commodity Grid Toolkit (MyCoG.NET). We demonstrate how application specific activity sets can be developed and extended by users. In our second approach we highlight how it can be advantageous to keep databases as central to the complete workflow enactment. These two approaches are demonstrated in the context of a wind tunnel Grid system being developed to help experimental aerodynamicists orchestrate such workflows.