Capturing and sharing our collective expertise on climate data: the CHARMe project

For users of climate services, the ability to quickly determine the datasets that best fit one's needs would be invaluable. The volume, variety and complexity of climate data makes this judgment difficult. The ambition of CHARMe ("Characterization of metadata to enable high-quality climate services") is to give a wider interdisciplinary community access to a range of supporting information, such as journal articles, technical reports or feedback on previous applications of the data. The capture and discovery of this "commentary" information, often created by data users rather than data providers, and currently not linked to the data themselves, has not been significantly addressed previously. CHARMe applies the principles of Linked Data and open web standards to associate, record, search and publish user-derived annotations in a way that can be read both by users and automated systems. Tools have been developed within the CHARMe project that enable annotation capability for data delivery systems already in wide use for discovering climate data. In addition, the project has developed advanced tools for exploring data and commentary in innovative ways, including an interactive data explorer and comparator ("CHARMe Maps") and a tool for correlating climate time series with external "significant events" (e.g. instrument failures or large volcanic eruptions) that affect the data quality. Although the project focuses on climate science, the concepts are general and could be applied to other fields. All CHARMe system software is open-source, released under a liberal licence, permitting future projects to re-use the source code as they wish.

The BALTIMOS (BALTEX Integrated Model System) fi eld experiments: A comprehensive atmospheric boundary layer data set for model validation over the open and ice- covered Baltic Sea

A comprehensive atmospheric boundary layer (ABL) data set was collected in eight fi eld experiments (two during each season) over open water and sea ice in the Baltic Sea during 1998–2001 with the primary objective to validate the coupled atmospheric- ice-ocean-land surface model BALTIMOS (BALTEX Integrated Model System). Measurements were taken by aircraft, ships and surface stations and cover the mean and turbulent structure of the ABL including turbulent fl uxes, radiation fl uxes, and cloud conditions. Measurement examples of the spatial variability of the ABL over the ice edge zone and of the stable ABL over open water demonstrate the wide range of ABL conditions collected and the strength of the data set which can also be used to validate other regional models.

Extracting Causal Graphs from an Open Provenance Data Model

The open provenance architecture (OPA) approach to the challenge was distinct in several regards. In particular, it is based on an open, well-defined data model and architecture, allowing different components of the challenge workflow to independently record documentation, and for the workflow to be executed in any environment. Another noticeable feature is that we distinguish between the data recorded about what has occurred, emphprocess documentation, and the emphprovenance of a data item, which is all that caused the data item to be as it is and is obtained as the result of a query over process documentation. This distinction allows us to tailor the system to separately best address the requirements of recording and querying documentation. Other notable features include the explicit recording of causal relationships between both events and data items, an interaction-based world model, intensional definition of data items in queries rather than relying on explicit naming mechanisms, and emphstyling of documentation to support non-functional application requirements such as reducing storage costs or ensuring privacy of data. In this paper we describe how each of these features aid us in answering the challenge provenance queries.

Profiles of steroid hormones in canine X-Linked muscular dystrophy via stable isotope dilution LC-MS/MS

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Um estudo acerca dos recursos audiovisuais no contexto do Linked Data

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Um estudo acerca dos recursos audiovisuais no contexto do Linked Data

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Influence of landscape evolution on the distribution of floristic patterns in northern Amazonia revealed by delta 13C data

The Amazonian lowlands include large patches of open vegetation which contrast sharply with the rainforest, and the origin of these patches has been debated. This study focuses on a large area of open vegetation in northern Brazil, where d13C and, in some instances, C/N analyses of the organic matter preserved in late Quaternary sediments were used to achieve floristic reconstructions over time. The main goal was to determine when the modern open vegetation started to develop in this area. The variability in d13C data derived from nine cores ranges from -32.2 to -19.6 parts per thousand, but with nearly 60% of data above -26.5 parts per thousand. The most enriched values were detected only in ecotone and open vegetated areas. The development of open vegetation communities was asynchronous, varying between estimated ages of 6400 and 3000 cal a BP. This suggests that the origin of the studied patches of open vegetation might be linked to sedimentary dynamics of a late Quaternary megafan system. As sedimentation ended, this vegetation type became established over the megafan surface. In addition, the data presented here show that the presence of C4 plants must be used carefully as a proxy to interpret dry paleoclimatic episodes in Amazonian areas. Copyright (c) 2012 John Wiley & Sons, Ltd.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

Frame-driven Extraction of Linked Data and Ontologies from Text

Ontology design and population -core aspects of semantic technologies- re- cently have become fields of great interest due to the increasing need of domain-specific knowledge bases that can boost the use of Semantic Web. For building such knowledge resources, the state of the art tools for ontology design require a lot of human work. Producing meaningful schemas and populating them with domain-specific data is in fact a very difficult and time-consuming task. Even more if the task consists in modelling knowledge at a web scale. The primary aim of this work is to investigate a novel and flexible method- ology for automatically learning ontology from textual data, lightening the human workload required for conceptualizing domain-specific knowledge and populating an extracted schema with real data, speeding up the whole ontology production process. Here computational linguistics plays a fundamental role, from automati- cally identifying facts from natural language and extracting frame of relations among recognized entities, to producing linked data with which extending existing knowledge bases or creating new ones. In the state of the art, automatic ontology learning systems are mainly based on plain-pipelined linguistics classifiers performing tasks such as Named Entity recognition, Entity resolution, Taxonomy and Relation extraction [11]. These approaches present some weaknesses, specially in capturing struc- tures through which the meaning of complex concepts is expressed [24]. Humans, in fact, tend to organize knowledge in well-defined patterns, which include participant entities and meaningful relations linking entities with each other. In literature, these structures have been called Semantic Frames by Fill- 6 Introduction more [20], or more recently as Knowledge Patterns [23]. Some NLP studies has recently shown the possibility of performing more accurate deep parsing with the ability of logically understanding the structure of discourse [7]. In this work, some of these technologies have been investigated and em- ployed to produce accurate ontology schemas. The long-term goal is to collect large amounts of semantically structured information from the web of crowds, through an automated process, in order to identify and investigate the cognitive patterns used by human to organize their knowledge.

Linked Data e bibliometriche: un indice di multidisciplinarieta nel Semantic Publishing

Nel presente lavoro si introduce un nuovo indice per la valutazione dei prodotti della ricerca: l'indice di multidisciplinarieta`. Questa nuova metrica puo` essere un interessante parametro di valutazione: il panorama degli studi multidisciplinari e` vasto ed eterogeneo, ed all'interno di questo sono richieste necessarie competenze trasversali. Le attuali metriche adottate nella valutazione di un accademico, di un journal, o di una conferenza non tengono conto di queste situazioni intermedie, e limitano la loro valutazione dell'impatto al semplice conteggio delle citazioni ricevute. Il risultato di tale valutazione consiste in un valore dell'impatto della ricerca senza una connotazione della direzione e della rilevanza di questa nel contesto delle altre discipline. L'indice di multidisciplinarieta` proposto si integrerebbe allora all'interno dell'attuale panorama delle metriche di valutazione della ricerca, offrendo -accanto ad una quantificazione dell'impatto- una quantificazione della varieta` dei contesti disciplinari nei quali si inserisce.

Arald: an architecture for name resolution, disambiguation and linking of linked data through semantics

La capacità di estrarre entità da testi, collegarle tra loro ed eliminare possibili ambiguità tra di esse è uno degli obiettivi del Web Semantico. Chiamato anche Web 3.0, esso presenta numerose innovazioni volte ad arricchire il Web con dati strutturati comprensibili sia dagli umani che dai calcolatori. Nel reperimento di questi temini e nella definizione delle entities è di fondamentale importanza la loro univocità. Il nostro orizzonte di lavoro è quello delle università italiane e le entities che vogliamo estrarre, collegare e rendere univoche sono nomi di professori italiani. L’insieme di informazioni di partenza, per sua natura, vede la presenza di ambiguità. Attenendoci il più possibile alla sua semantica, abbiamo studiato questi dati ed abbiamo risolto le collisioni presenti sui nomi dei professori. Arald, la nostra architettura software per il Web Semantico, estrae entità e le collega, ma soprattutto risolve ambiguità e omonimie tra i professori delle università italiane. Per farlo si appoggia alla semantica dei loro lavori accademici e alla rete di coautori desumibile dagli articoli da loro pubblicati, rappresentati tramite un data cluster. In questo docu delle università italiane e le entities che vogliamo estrarre, collegare e rendere univoche sono nomi di professori italiani. Partendo da un insieme di informazioni che, per sua natura, vede la presenza di ambiguità, lo abbiamo studiato attenendoci il più possibile alla sua semantica, ed abbiamo risolto le collisioni che accadevano sui nomi dei professori. Arald, la nostra architettura software per il Web Semantico, estrae entità, le collega, ma soprattutto risolve ambiguità e omonimie tra i professori delle università italiane. Per farlo si appoggia alla semantica dei loro lavori accademici e alla rete di coautori desumibile dagli articoli da loro pubblicati tramite la costruzione di un data cluster.

Service Descriptions and Linked Data for Integrating WSNs into Enterprise IT

This paper presents our ongoing work on enterprise IT integration of sensor networks based on the idea of service descriptions and applying linked data principles to them. We argue that using linked service descriptions facilitates a better integration of sensor nodes into enterprise IT systems and allows SOA principles to be used within the enterprise IT and within the sensor network itself.

Simulated Bromoform emission and concentration using the ocean biogeochemistry model MPIOM/HAMOCC forced by 6-hourly NCEP data

Bromoform (CHBr3) is one important precursor of atmospheric reactive bromine species that are involved in ozone depletion in the troposphere and stratosphere. In the open ocean bromoform production is linked to phytoplankton that contains the enzyme bromoperoxidase. Coastal sources of bromoform are higher than open ocean sources. However, open ocean emissions are important because the transfer of tracers into higher altitude in the air, i.e. into the ozone layer, strongly depends on the location of emissions. For example, emissions in the tropics are more rapidly transported into the upper atmosphere than emissions from higher latitudes. Global spatio-temporal features of bromoform emissions are poorly constrained. Here, a global three-dimensional ocean biogeochemistry model (MPIOM-HAMOCC) is used to simulate bromoform cycling in the ocean and emissions into the atmosphere using recently published data of global atmospheric concentrations (Ziska et al., 2013) as upper boundary conditions. Our simulated surface concentrations of CHBr3 match the observations well. Simulated global annual emissions based on monthly mean model output are lower than previous estimates, including the estimate by Ziska et al. (2013), because the gas exchange reverses when less bromoform is produced in non-blooming seasons. This is the case for higher latitudes, i.e. the polar regions and northern North Atlantic. Further model experiments show that future model studies may need to distinguish different bromoform-producing phytoplankton species and reveal that the transport of CHBr3 from the coast considerably alters open ocean bromoform concentrations, in particular in the northern sub-polar and polar regions.