PanConverter - a tool for data conversion and recalculation, written to convert special data collections to standard import format used by PANGAEA

Supported file formats: - CrossRef XML file(s) - TRiDaS (Tree Ring Data Standard, http://www.tridas.org). Example: hdl:10013/epic.42747.d001 - IMMA (International Maritime Meteorological Archive). Used by the project CLIWOC (García-Herrera et al. 2007, http://doi.pangaea.de/10.1594/PANGAEA.743343) - NOAA IOAS (International Ocean Atlas Series). Example: hdl:10013/epic.42747.d008 - SOCAT (Surface Ocean CO2 Atlas, Bakker et al. 2014, http://doi.pangaea.de/10.1594/PANGAEA.811776) - CHUAN (Comprehensive Historical Upper-Air Network, Stickler et al. 2013, http://doi.pangaea.de/10.1594/PANGAEA.821222). Example: hdl:10013/epic.42747.d003 - Thermosalinograph (TSG) data. Format developed by Gerd Rohardt. Example: hdl:10013/epic.42747.d002 - Columus GPS Data Logger V-900 format to KML or GPX. Example: hdl:10013/epic.42747.d006

Building ontologies from folksonomies and linked data: Data structures and Algorithms

We present the data structures and algorithms used in the approach for building domain ontologies from folksonomies and linked data. In this approach we extracts domain terms from folksonomies and enrich them with semantic information from the Linked Open Data cloud. As a result, we obtain a domain ontology that combines the emergent knowledge of social tagging systems with formal knowledge from Ontologies.

A Comparison of different Uncertainty Activation Cross-Section Data Libraries and collapsed values for different neutron spectra: ADS, FISSION and FUSION

PART I:Cross-section uncertainties under differentneutron spectra. PART II: Processing uncertainty libraries

Physical data acquisition and PWM signal generation using a microcontroller allowing control for DC motor rotation speed

El objetivo de este proyecto es diseñar un sistema capaz de controlar la velocidad de rotación de un motor DC en función del valor de temperatura obtenido de un sensor. Para ello se generará con un microcontrolador una señal PWM, cuyo ciclo de trabajo estará en función de la temperatura medida. En lo que respecta a la fase de diseño, hay dos partes claramente diferenciadas, relativas al hardware y al software. En cuanto al diseño del hardware puede hacerse a su vez una división en dos partes. En primer lugar, hubo que diseñar la circuitería necesaria para adaptar los niveles de tensión entregados por el sensor de temperatura a los niveles requeridos por ADC, requerido para digitalizar la información para su posterior procesamiento por parte del microcontrolador. Por tanto hubo que diseñar capaz de corregir el offset y la pendiente de la función tensión-temperatura del sensor, a fin de adaptarlo al rango de tensión requerido por el ADC. Por otro lado, hubo que diseñar el circuito encargado de controlar la velocidad de rotación del motor. Este circuito estará basado en un transistor MOSFET en conmutación, controlado mediante una señal PWM como se mencionó anteriormente. De esta manera, al variar el ciclo de trabajo de la señal PWM, variará de manera proporcional la tensión que cae en el motor, y por tanto su velocidad de rotación. En cuanto al diseño del software, se programó el microcontrolador para que generase una señal PWM en uno de sus pines en función del valor entregado por el ADC, a cuya entrada está conectada la tensión obtenida del circuito creado para adaptar la tensión generada por el sensor. Así mismo, se utiliza el microcontrolador para representar el valor de temperatura obtenido en una pantalla LCD. Para este proyecto se eligió una placa de desarrollo mbed, que incluye el microcontrolador integrado, debido a que facilita la tarea del prototipado. Posteriormente se procedió a la integración de ambas partes, y testeado del sistema para comprobar su correcto funcionamiento. Puesto que el resultado depende de la temperatura medida, fue necesario simular variaciones en ésta, para así comprobar los resultados obtenidos a distintas temperaturas. Para este propósito se empleó una bomba de aire caliente. Una vez comprobado el funcionamiento, como último paso se diseñó la placa de circuito impreso. Como conclusión, se consiguió desarrollar un sistema con un nivel de exactitud y precisión aceptable, en base a las limitaciones del sistema. SUMMARY: It is obvious that day by day people’s daily life depends more on technology and science. Tasks tend to be done automatically, making them simpler and as a result, user life is more comfortable. Every single task that can be controlled has an electronic system behind. In this project, a control system based on a microcontroller was designed for a fan, allowing it to go faster when temperature rises or slowing down as the environment gets colder. For this purpose, a microcontroller was programmed to generate a signal, to control the rotation speed of the fan depending on the data acquired from a temperature sensor. After testing the whole design developed in the laboratory, the next step taken was to build a prototype, which allows future improvements in the system that are discussed in the corresponding section of the thesis.

A Reuse-based Lightweight Method for Developing Linked Data Ontologies and Vocabularies

The uptake of Linked Data (LD) has promoted the proliferation of datasets and their associated ontologies for describing different domains. Par-ticular LD development characteristics such as agility and web-based architec-ture necessitate the revision, adaption, and lightening of existing methodologies for ontology development. This thesis proposes a lightweight method for ontol-ogy development in an LD context which will be based in data-driven agile de-velopments, existing resources to be reused, and the evaluation of the obtained products considering both classical ontological engineering principles and LD characteristics.

A note on data-parallelism and (and-parallel) prolog

Abstract is not available.

Combining data mining and ontology engineering to enrich ontologies and linked data

In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.

A data-globe and immersive virtual reality environment for upper limb rehabilitation after spinal cord injury

While a number of virtual data-gloves have been used in stroke, there is little evidence about their use in spinal cord injury (SCI). A pilot clinical experience with nine SCI subjects was performed comparing two groups: one carried out a virtual rehabilitation training based on the use of a data glove, CyberTouch combined with traditional rehabilitation, during 30 minutes a day twice a week along two weeks; while the other made only conventional rehabilitation. Furthermore, two functional indexes were developed in order to assess the patient’s performance of the sessions: normalized trajectory lengths and repeatability. While differences between groups were not statistically significant, the data-glove group seemed to obtain better results in the muscle balance and functional parameters, and in the dexterity, coordination and fine grip tests. Related to the indexes that we implemented, normalized trajectory lengths and repeatability, every patient showed an improvement in at least one of the indexes, either along Y-axis trajectory or Z-axis trajectory. This study might be a step in investigating new ways of treatments and objective measures in order to obtain more accurate data about the patient’s evolution, allowing the clinicians to develop rehabilitation treatments, adapted to the abilities and needs of the patients.

Guidelines for Linked Data generation and publication: an example in building energy consumption

Linked Data is the key paradigm of the Semantic Web, a new generation of the World Wide Web that promises to bring meaning (semantics) to data. A large number of both public and private organizations have published their data following the Linked Data principles, or have done so with data from other organizations. To this extent, since the generation and publication of Linked Data are intensive engineering processes that require high attention in order to achieve high quality, and since experience has shown that existing general guidelines are not always sufficient to be applied to every domain, this paper presents a set of guidelines for generating and publishing Linked Data in the context of energy consumption in buildings (one aspect of Building Information Models). These guidelines offer a comprehensive description of the tasks to perform, including a list of steps, tools that help in achieving the task, various alternatives for performing the task, and best practices and recommendations. Furthermore, this paper presents a complete example on the generation and publication of Linked Data about energy consumption in buildings, following the presented guidelines, in which the energy consumption data of council sites (e.g., buildings and lights) belonging to the Leeds City Council jurisdiction have been generated and published as Linked Data.

Singular value decomposition for genome-wide expression data processing and modeling

We describe the use of singular value decomposition in transforming genome-wide expression data from genes × arrays space to reduced diagonalized “eigengenes” × “eigenarrays” space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Data assimilation and its applications

In data assimilation, one prepares the grid data as the best possible estimate of the true initial state of a considered system by merging various measurements irregularly distributed in space and time, with a prior knowledge of the state given by a numerical model. Because it may improve forecasting or modeling and increase physical understanding of considered systems, data assimilation now plays a very important role in studies of atmospheric and oceanic problems. Here, three examples are presented to illustrate the use of new types of observations and the ability of improving forecasting or modeling.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.