On the integration of self-tracking data amongst quantified self members

Self-tracking, the process of recording one's own behaviours, thoughts and feelings, is a popular approach to enhance one's self-knowledge. While dedicated self-tracking apps and devices support data collection, previous research highlights that the integration of data constitutes a barrier for users. In this study we investigated how members of the Quantified Self movement---early adopters of self-tracking tools---overcome these barriers. We conducted a qualitative analysis of 51 videos of Quantified Self presentations to explore intentions for collecting data, methods for integrating and representing data, and how intentions and methods shaped reflection. The findings highlight two different intentions---striving for self-improvement and curiosity in personal data---which shaped how these users integrated data, i.e. the effort required. Furthermore, we identified three methods for representing data---binary, structured and abstract---which influenced reflection. Binary representations supported reflection-in-action, whereas structured and abstract representations supported iterative processes of data collection, integration and reflection. For people tracking out of curiosity, this iterative engagement with personal data often became an end in itself, rather than a means to achieve a goal. We discuss how these findings contribute to our current understanding of self-tracking amongst Quantified Self members and beyond, and we conclude with directions for future work to support self-trackers with their aspirations.

Integration of data for nowcasting of harmful algal blooms

Harmful algal blooms (HABs) are a significant and potentially expanding problem around the world. Resource management and public health protection require sufficient information to reduce the impacts of HABs by response strategies and through warnings and advisories. To be effective, these programs can best be served by an integration of improved detection methods with both evolving monitoring systems and new communications capabilities. Data sets are typically collected from a variety of sources, these can be considered as several types: point data, such as water samples; transects, such as from shipboard continuous sampling; and synoptic, such as from satellite imagery. Generation of a field of the HAB distribution requires all of these sampling approaches. This means that the data sets need to be interpreted and analyzed with each other to create the field or distribution of the HAB. The HAB field is also a necessary input into models that forecast blooms. Several systems have developed strategies that demonstrate these approaches. These range from data sets collected at key sites, such as swimming beaches, to automated collection systems, to integration of interpreted satellite data. Improved data collection, particularly in speed and cost, will be one of the advances of the next few years. Methods to improve creation of the HAB field from the variety of data types will be necessary for routine nowcasting and forecasting of HABs.

Integration of submersible transect data and high-resolution multibeam sonar imagery for a habitat-based groundfish assessment of Heceta Bank, Oregon

In the face of dramatic declines in groundfish populations and a lack of sufficient stock assessment information, a need has arisen for new methods of assessing groundfish populations. We describe the integration of seafloor transect data gathered by a manned submersible with high-resolution sonar imagery to produce a habitat-based stock assessment system for groundfish. The data sets used in this study were collected from Heceta Bank, Oregon, and were derived from 42 submersible dives (1988–90) and a multibeam sonar survey (1998). The submersible habitat survey investigated seafloor topography and groundfish abundance along 30-minute transects over six predetermined stations and found a statistical relationship between habitat variability and groundfish distribution and abundance. These transects were analyzed in a geographic information system (GIS) by using dynamic segmentation to display changes in habitat along the transects. We used the submersible data to extrapolate fish abundance within uniform habitat patches over broad areas of the bank by means of a habitat classification based on the sonar imagery. After applying a navigation correction to the submersible-based habitat segments, a good correlation with major boundaries on the backscatter and topographic boundaries on the imagery were apparent. Extrapolation of the extent of uniform habitats was made in the vicinity of the dive stations and a preliminary stock assessment of several species of demersal fish was calculated. Such a habitat-based approach will allow researchers to characterize marine communities over large areas of the seafloor.

Integration of genomic and phenotypic data

Clare, A. (2005) Integration of genomic and phenotypic data. In Data Analysis and Visualization in Genomics and Proteomics, Eds. Francisco Azuaje and Joaquin Dopazo, Wiley, London. ISBN: 0-470-09439-7

Are Prorocentrum hoffmannianum and Prorocentrum belizeanum (Dinophyceae, Prorocentrales), the same species? An integration of morphological and molecular data.

The taxonomic assignment of Prorocentrum species is based on morphological characteristics; however, morphological variability has been found for several taxa isolated from different geographical regions. In this study, we evaluated species boundaries of Prorocentrum hoffmannianum and Prorocentrum belizeanum based on morphological and molecular data. A detailed morphological analysis was done, concentrating on the periflagellar architecture. Molecular analyses were performed on partial Small Sub-Unit (SSU) rDNA, partial Large Sub-Unit (LSU) rDNA, complete Internal Transcribed Spacer Regions (ITS1-5.8S-ITS2), and partial cytochrome b (cob) sequences. We concatenated the SSU-ITS-LSU fragments and constructed a phylogenetic tree using Bayesian Inference (BI) and maximum likelihood (ML) methods. Morphological analyses indicated that the main characters, such as cell size and number of depressions per valve, normally used to distinguish P. hoffmannianum from P. belizeanum, overlapped. No clear differences were found in the periflagellar area architecture. Prorocentrum hoffmannianum and P. belizeanum were a highly supported monophyletic clade separated into three subclades, which broadly corresponded to the sample collection regions. Subtle morphological overlaps found in cell shape, size, and ornamentation lead us to conclude that P. hoffmanianum and P. belizeanum might be considered conspecific. The molecular data analyses did not separate P. hoffmannianum and P. belizeanum into two morphospecies, and thus, we considered them to be the P. hoffmannianum species complex because their clades are separated by their geographic origin. These geographic and genetically distinct clades could be referred to as ribotypes: (A) Belize, (B) Florida-Cuba, (C1) India, and (C2) Australia.

Workflow data exchange semantics, object discovery & integration architecture for cross media production & distribution

There has been a clear lack of common data exchange semantics for inter-organisational workflow management systems where the research has mainly focused on technical issues rather than language constructs. This paper presents the neutral data exchanges semantics required for the workflow integration within the AXAEDIS framework and presents the mechanism for object discovery from the object repository where little or no knowledge about the object is available. The paper also presents workflow independent integration architecture with the AXAEDIS Framework.

Integration of a 3D Variational data assimilation scheme with a coastal area morphodynamic model of Morecambe Bay

This paper describes the implementation of a 3D variational (3D-Var) data assimilation scheme for a morphodynamic model applied to Morecambe Bay, UK. A simple decoupled hydrodynamic and sediment transport model is combined with a data assimilation scheme to investigate the ability of such methods to improve the accuracy of the predicted bathymetry. The inverse forecast error covariance matrix is modelled using a Laplacian approximation which is calibrated for the length scale parameter required. Calibration is also performed for the Soulsby-van Rijn sediment transport equations. The data used for assimilation purposes comprises waterlines derived from SAR imagery covering the entire period of the model run, and swath bathymetry data collected by a ship-borne survey for one date towards the end of the model run. A LiDAR survey of the entire bay carried out in November 2005 is used for validation purposes. The comparison of the predictive ability of the model alone with the model-forecast-assimilation system demonstrates that using data assimilation significantly improves the forecast skill. An investigation of the assimilation of the swath bathymetry as well as the waterlines demonstrates that the overall improvement is initially large, but decreases over time as the bathymetry evolves away from that observed by the survey. The result of combining the calibration runs into a pseudo-ensemble provides a higher skill score than for a single optimized model run. A brief comparison of the Optimal Interpolation assimilation method with the 3D-Var method shows that the two schemes give similar results.

Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes

Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.

Agents and data mining: mutual enhancement by integration

This paper tells a story of synergism of two cutting edge technologies — agents and data mining. By integrating these two technologies, the power for each of them is enhanced. Integrating agents into data mining systems, or constructing data mining systems from agent perspectives, the flexibility of data mining systems can be greatly improved. New data mining techniques can add to the systems dynamically in the form of agents, while the out-of-date ones can also be deleted from systems at run-time. Equipping agents with data mining capabilities, the agents are much smarter and more adaptable. In this way, the performance of these agent systems can be improved. A new way to integrate these two techniques –ontology-based integration is also discussed. Case studies will be given to demonstrate such mutual enhancement.