SUMMARY: The Internet, Web 2.0 and Social Networking technologies are enabling citizens to actively participate in ‘citizen science’ projects by contributing data to scientific programmes via the Web. However, the limited training, knowledge and expertise of contributors can lead to poor quality, misleading or even malicious data being submitted. Subsequently, the scientific community often perceive citizen science data as not worthy of being used in serious scientific research—which in turn, leads to poor retention rates for volunteers. In this paper, we describe a technological framework that combines data quality improvements and trust metrics to enhance the reliability of citizen science data. We describe how online social trust models can provide a simple and effective mechanism for measuring the trustworthiness of community‐generated data. We also describe filtering services that remove unreliable or untrusted data and enable scientists to confidently reuse citizen science data. The resulting software services are evaluated in the context of the CoralWatch project—a citizen science project that uses volunteers to collect comprehensive data on coral reef health. Copyright © 2012 John Wiley & Sons, Ltd.

Partial discharge (PD) signals generated within electrical power equipment can be used to assess the condition of the insulation. In practice, testing often results in multiple PD sources. In order to assess the impact of individual PD sources, signals must first be discriminated from one another. This paper presents a procedure for the identification of PD signals generated by multiple sources. Starting with the assumption that different PD sources will display unique signal profiles this will be manifested in the distribution of energies with respect to frequency and time. Therefore the technique presented is based on the comparison of signal energies associated with particular wavelet-decomposition levels. Principal component analysis is adopted to reduce the dimensionality of the data, whilst minimizing lost information in the data concentration step. Physical parameters are extracted from individual PD pulses and projected into 3-dimensional space to allow clustering of data from specific PD sources. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm is chosen for its ability to discover clusters of arbitrary shape in n-dimension space. PD data from individual clusters can then be further analyzed by projecting the clustered data with respect to the original phase relationship. Results and analysis of the technique are compared for experimentally measured PD data from a range of sources commonly found in three different types of high voltage (HV) equipment; ac synchronous generators, induction motors and distribution cables. These experiments collect data using varied test arrangements including sensors with different bandwidths to demonstrate the robustness and indicate the potential for wide applicability of the technique to PD analysis for a range of insulation systems.

The Internet, Web 2.0 and Social Networking technologies are enabling citizens to actively participate in “citizen science” projects by contributing data to scientific programs. However, the limited expertise of contributors can lead to poor quality or misleading data being submitted. Subsequently, the scientific community often perceive citizen science data as not worthy of being used in serious scientific research. In this paper, we describe a technological framework that combines data quality improvements and trust metrics to enhance the reliability of citizen science data. We describe how trust models provide a simple and effective mechanism for measuring the reliability of community-generated data. We also describe filtering services that remove untrustworthy data, and enable confident re-use of the data. The resulting services are evaluated in the context of the Coral Watch project which uses volunteers to collect data on coral reef bleaching.

Summary: Distinguishing specific behavioural modes from data collected by animal-borne tri-axial accelerometers can be a time consuming and subjective process. Data synthesis can be further inhibited when the tri-axial acceleration data cannot be paired with the corresponding behavioural mode through direct observation. Here we explored the use of a tame surrogate (domestic dog) to build a behavioural classification module, and then used that module to accurately identify and quantify behavioural modes within acceleration collected from other individuals/species. Tri-axial acceleration data were recorded from a domestic dog whilst it was commanded to walk, run, sit, stand, and lie-down. Through video synchronisation, each tri-axial acceleration sample was annotated with its associated behavioural mode; the feature vectors were extracted, and used to build the classification module through the application of support vector machines (SVM). This behavioural classification module was then used to identify and quantify the same behavioural modes in acceleration collected from a range of other species (alligator, badger, cheetah, dingo, echidna, kangaroo, and a wombat). Evaluation of the module performance, using a binary classification system, showed there was a high capacity (> 90 %) for behaviour recognition between individuals of the same species. Furthermore, a positive correlation existed between SVM capacity and the extent by which the individual had a spinal length-to-height above the ground ratio (SL:SH) similar to that of the surrogate. The study describes how to build a behavioural classification module and highlights the value of using a surrogate for studying cryptic, rare or endangered species.