Publishing Linked Data - There is no One-Size-Fits-All Formula

Publishing Linked Data is a process that involves several design decisions and technologies. Although some initial guidelines have been already provided by Linked Data publishers, these are still far from covering all the steps that are necessary (from data source selection to publication) or giving enough details about all these steps, technologies, intermediate products, etc. Furthermore, given the variety of data sources from which Linked Data can be generated, we believe that it is possible to have a single and uni�ed method for publishing Linked Data, but we should rely on di�erent techniques, technologies and tools for particular datasets of a given domain. In this paper we present a general method for publishing Linked Data and the application of the method to cover di�erent sources from di�erent domains

World-class long- distance running performances are best predicted by volume of easy runs and deliberate practice of short interval and tempo runs

The aim of this novel study was to analyze the effect of deliberate practice (DP) and easy runs completed by elite-standard and world-class long-distance runners on competitive performances during the first 7 years of their sport careers. Eighty-five male runners reported their best times in different running events and the amounts of different DP activities (tempo runs and short and long interval sessions) and 1 non-DP activity (easy continuous runs) after 3, 5 and 7 years of systematic training. Pearson’s correlations were calculated between performances (calculated using the IAAF scoring tables) and the distances run for the different activities (and overall total). Simple and Multiple Linear Regression Analysis calculated how well these activities predicted performance. Pearson’s correlations showed consistently large effects on performance of total distance (r ≥ 0.75, P < 0.001), easy runs (r ≥ 0.68, P < 0.001), tempo runs (r ≥ 0.50, P < 0.001) and short interval training (r ≥ 0.53, P < 0.001). Long interval training was not strongly correlated (r ≥ 0.22). Total distance accounted for significant variance in performance (R2 ≥ 0.57, P < 0.001). Of the training modes, Hierarchical Regression Analysis showed that easy runs and tempo runs were the activities that accounted for significant variance in performance (P < 0.01). Although DP activities, particularly tempo runs and short interval training, are important for improving performance, coaches should note that the non-DP activity of easy running was crucial in better performances, partly because of its contribution to total distance run

GeoLinked Data. An application case / Un caso de aplicación

In this paper we present the process that has been followed for the development of an application that makes use of several heterogeneous Spanish public datasets that are related to three themes of INSPIRE Directive, specifically Administrative Units, Hydrography, and Statistical Units. Our application aims at analysing existing relations between the Spanish coastal area and different statistical variables such as population, unemployment, dwelling, industry, and building trade. Besides providing ethodological guidelines for the generation, publishing and exploitation of Linked Data from such datasets, we provide an important innovation with respect to other similar processes followed in other initiatives by dealing with the geometrical information of features

The IGN-E case: Integrating through a hidden ontology

National Geographic Institute of Spain (IGN-E) wanted to integrate its main information sources for building a common vocabulary reference and thus to manage the huge amount of information it held. The main problem of this integration is the great heterogeneity of data sources. The Ontology Engineering Group (OEG) is working with IGN-E to attain this objective in two phases: first, by creating automatically an ontology using the semantics of catalogues sections, and second, by discovering mappings automatically that can relate ontology concepts to database instances. So, these mappings are the instruments to break the syntactic, semantic and granularity heterogeneity gap. We have developed software for building a first ontology version and for discovering automatically mappings using techniques that take into account all types of heterogeneity. The ontology contains a set of extra-attributes which are identified in the building process. The ontology, called PhenomenOntology, will be reviewed by domain experts of IGN-E. The automatic mapping discovery will be also used for discovering new knowledge that will be added to the ontology. For increasing the usability and giving independence to different parts, the processes of each phase will be designed automatically and as upgradeable as possible

Interrelaciones entre las tecnologías de la Información Geográfica y la ingeniería ontológica para la mejora de la gestión de los recursos geoespaciales

La incorporación de las Técnicas de Información Geográfica (TIGs) en los procesos de planificación territorial es un hecho evidente, en muchos casos reflejado como una mera forma de presentar cartografías digitales, más que como autén-ticas herramientas para la toma de decisiones. Los principales factores que originan este hecho son la ausencia de forma-ción técnica y cierta ausencia del desarrollo metodológico-técnico a nivel de los Sistemas de Información Geográfica (SIG) y de las actuales Infraestructuras de Datos Espaciales (IDEs). Actualmente, derivada de la importancia de los geodatos y de la necesidad de una gestión eficaz de la Información Ge-ográfica (IG) son cada vez más frecuentes términos como “interoperabilidad”. Esta difícilmente será alcanzable, en su sentido más amplio, si no se establece un cuerpo básico (vocabularios comunes y compartidos) en el que los distintos agentes que intervienen en el territorio estén de acuerdo sobre los propios contenidos (conceptos) del mismo. La presente comunicación aborda la utilización de la Ingeniería ontológica – y las ontologías como una de sus herra-mientas clave- y su interrelación con las TIGs para mejorar la gestión de los recursos geo-espaciales. La interrelación de estas técnicas supone un avance incuestionable en la gestión y análisis derivado de la IG

An Inner Disk in the Large Gap of the Transition Disk SR 24S

We report new Atacama Large Millimeter/sub-millimeter Array (ALMA) Band 3 observations at 2.75 mm of the TD around SR 24S with an angular resolution of $\sim$0.11''$\times$ 0.09'' and a peak signal-to-noise ratio of $\sim24$. We detect an inner disk and a mostly symmetric ring-like structure that peaks at $\sim$0.32'', that is $\sim$37 au at a distance of $\sim$114.4 pc. The full width at half maximum of this ring is $\sim$28 au. We analyze the observed structures by fitting the dust continuum visibilities using different models for the intensity profile, and compare with previous ALMA observations of the same disk at 0.45 mm and 1.30 mm. We qualitatively compare the results of these fits with theoretical predictions of different scenarios for the formation of a cavity or large gap. The comparison of the dust continuum structure between different ALMA bands indicates that photoevaporation and dead zone can be excluded as leading mechanisms for the cavity formation in SR 24S disk, leaving the planet scenario (single or multiple planets) as the most plausible mechanism. We compared the 2.75 mm emission with published (sub-)centimeter data and find that the inner disk is likely tracing dust thermal emission. This implies that any companion in the system should allow dust to move inwards throughout the gap and replenish the inner disk. In the case of one single planet, this puts strong constraints on the mass of the potential planet inside the cavity and the disk viscosity of about $\lesssim$5 $M_{\rm{Jup}}$ and $\alpha\sim10^{-4}-10^{-3}$, respectively

Rivière or Fleuve? Modelling Multilinguality in the Hydrographical

The need for interoperability among geospatial resources in different natural languages evidences the difficulties to cope with domain representations highly dependent of the culture in which they have been conceived. In this paper we characterize the problem of representing cultural discrepancies in ontologies. We argue that such differences can be accounted for at the ontology terminological layer by means of external elaborated models of linguistic information associated to ontologies. With the aim of showing how external models can cater for cultural discrepancies, we compare two versions of an ontology of the hydrographical domain: hydrOntology. The first version makes use of the labeling system supported by RDF(S) and OWL to include multilingual linguistic information in the ontology. The second version relies on the Linguistic Information Repository model (LIR) to associate structured multilingual information to ontology concepts. In this paper we propose an extension to the LIR to better capture linguistic and cultural specificities within and across language

Correlation between impact factor and public availability of published research data in Information Science and Library Science journals

The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-016-1868-7[EN] Scientists continuously generate research data but only a few of them are published. If these data were accessible and reusable, researchers could examine them and generate new knowledge. Our purpose is to determine whether there is a relationship between the impact factor and the policies concerning open availability of raw research data in journals of Information Science and Library Science (ISLS) subject category from the Web of Science database. We reviewed the policies related to public availability of papers and data sharing in the 85 journals included in the ISLS category of the Journal Citation Reports in 2012. The relationship between public availability of published data and impact factor of journals is analysed through different statistical tests. The variable "statement of complementary material" was accepted in 50 % of the journals; 65 % of the journals support "reuse"; 67 % of the journals specified "storage in thematic or institutional repositories"; the "publication of the manuscript in a website" was accepted in 69 % of the journals. We have found a 50 % of journals that include the possibility to deposit data as supplementary material, and more than 60 % accept reuse, storage in repositories and publication in websites. There is a clear positive relationship between being a top journal in impact factor ranking of JCR and having an open policy.This work has benefited from assistance by the National R+D+I of the Ministry of Economy and Competitiveness of the Spanish Government (CSO2012-39632-C02).Aleixandre-Benavent, R.; Moreno-Solano, L.; Ferrer Sapena, A.; Sánchez Pérez, EA. (2016). Correlation between impact factor and public availability of published research data in Information Science and Library Science journals. Scientometrics. 107(1):1-13. https://doi.org/10.1007/s11192-016-1868-7S1131071Aleixandre-Benavent, R., Vidal-Infer, A., Alonso-Arroyo, A., Valderrama-Zurián, J. C., Bueno-Cañigral, F., & Ferrer-Sapena, A. (2014). Public availability of published research data in substance abuse journals. International Journal of Drug Policy, 25, 1143–1146.Alsheikh-Ali, A. A., Qureshi, W., Al-Mallah, M. H., & Ioannidis, J. P. A. (2011). Public availability of published research data in high-impact journals. PLoS One, 6(9), e24357.Anderson, B. J., & Merry, A. F. (2009). Data sharing for pharmacokinetic studies. Paediatr Anaesthes, 19(10), 1005–1010.Blahous, B., Gorraiz, J., Gumpenberger, C., Lehner, O., Stein, B., & Ulrych, U. (2015). Research data policies in scientific journals—An empirical study. Zeitschrift fur Bibliothekswesen und Bibliographie, 62(1), 12–24.Borrego, A., & Garcia, F. (2013). Provision of supplementary materials in Library and Information Science scholarly journals. Aslib Proceedings, 65(5), 503–514.Cech, T. R. (2003). Sharing publication-related data and materials: responsibilities of authorship in the life sciences. www.nap.edu/books/0309088593/html . Accessed 24 November 2015CODATA. (2015). http://www.codata.org . Accessed 21 February 2015Conradie, P., & Choenni, S. (2014). On the barriers for local government releasing open data. Government Information Quarterly, 31, S10–S17.De Castro, P., Calzolari, A., Napolitani, F., Maria Rossi, A., Mabile, L., Cambon-Thomsen, A., & Bravo, E. (2013). Open data sharing in the context of bioresources. Acta Informatica Medica, 21(4), 291–292.Digital Curation Centre (DCC). (2015). http://www.dcc.ac.uk . Accessed 4 March 2015European Commission. (2015). Guidelines on open access to scientific publications and research data in Horizon 2020. European Commission, 2013. http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf . Accessed 15 March 2015Fear, K. (2015). Building outreach on assessment: Researcher compliance with journal policies for data sharing. Bulletin of the Association for Information Science and Technology, 41(6), 18–21.González, L. M., Saorín, T., Ferrer, A., Aleixandre-Benavent, R., & Peset, F. (2013). Gestión de datos de investigación: infraestructuras para su difusión. Professional Information, 22, 414–423.Jones, R. B., Reeves, D., & Martinez, C. S. (2012). Overview of electronic data sharing: Why, how, and impact. Current Oncology Reports, 14(6), 486–493.Kaye, J. (2012). The tension between data sharing and the protection of privacy ingenomics research. Annual Review of Genomics and Human Genetics, 13, 415–431.Leonelli, S., Smirnoff, N., Moore, J., Cook, C., & Bastow, R. (2013). Making open data work for plant scientists. Journal of Experimental Botany, 64(14), 4109–41017.National Institutes of Health (NIH). (2015). Data sharing policy. http://grants.nih.gov/grants/policy/data_sharing/index.htm . Accessed 3 March 2015National Science Foundation (NSF). (2014). Dissemination and sharing of research results. NSF Data Sharing Policy. http://www.nsf.gov/bfa/dias/policy/dmp.jsp . Accessed 21 November 2014Nelson, B. (2009). Data sharing: Empty archives. Nature, 461(7261), 160–163.Open Knowledge Foundation. (2015). https://okfn.org/ . Accessed 3 March 2015Pisani, E., & AbouZahr, C. (2010). Sharing health data: Good intentions are not enough. Bulletin of the World Health Organization, 88(6), 462–466.Piwowar, H. A., Day, R. S., & Fridsma, D. B. (2007). Sharing detailed research data is associated with increased citation rate. PLoS One, 2(3), e308.Piwowar, H. A., & Chapman, W.W. (2008). A review of journal policies for sharing research data. http://precedings.nature.com/documents/1700/version/1.hdl:10101/npre.2008.1700.1 . Accessed 11 December 2015Piwowar, H. A., & Todd, J. (2013). Data reuse and the open data citation advantage. PeerJ, 1, e175.Registry of Research Data Repositories (re3data). (2015). http://www.re3data.org/2014/03/re3data-org-from-funding-to-growing/ . Accessed 5 March 2015Savage, C. J., & Vickers, A. J. (2009). Empirical study of data saharing by authors publishing in PLOS journals. PLoS One, 4(9), e7078.Sayogo, D. S., & Pardo, T. A. (2013). Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data. Government Information Quarterly, 30, S19–S31.Spencer, H. (2015). Thoughts on the sharing of data and research materials and the role of journal policies. http://web.stanford.edu/~vcs/Nov21/hilary_spencer_rdcscsJan2010.pdf . Accessed 11 December 2015Sturges, P., Bamkin, M., Anders, J., & Hussain, A. (2014). Journals and their policies on research data sharing. https://jordproject.wordpress.com/reportsand-article/journals-and-their-policies-on-research-data-sharing/ . Accessed 24 November 2015Tenenbaum, J. D., Sansone, S. A., & Haendel, M. (2007). A sea of standards for omics data: sink or swim? Journal of the American Medical Informatics Association, 21(2), 200–203.Tenopir, C., Allard, S., Douglass, K., Aydinoglu, A. U., Wu, L., Read, E., et al. (2011). Data sharing by scientists: Practices and perceptions. PLoS One, 6(6), e21101.The Royal Society Publishing. (2015). http://royalsocietypublishing.org/data-sharing . Accessed 15 March 2015Toronto International Data Release Workshop Authors. (2009). Prepublication data sharing. Nature, 461(7261), 168–170.Van Noorden, R. (2013). Data-sharing: Everything on display. Nature, 500, 243–245.Wellcome Trust. (2015). Data sharing. http://www.wellcome.ac.uk/About-us/Policy/Spotlight-issues/Data-sharing/ . Accessed 21 January 201

Combinando Linked Data con servicios geoespaciales

La Web de Linked Data supone un nuevo paradigma que pretende explotar la Web como un espacio global de información. La aplicación de los principios de esta nueva Web a la información geoespacial superará la integración de información tradicional, logrando una articulación semántica de los datos que haga desaparecer los silos de datos presentes en las actuales Infraestructuras de Datos Espaciales. Ante esta propuesta, en este artículo se describe el trabajo desarrollado en el marco de un caso de uso utilizando una parte de los datos del SIGNA. En este caso de uso se ha llevado a cabo un proceso de generación y publicación de los mencionados datos conforme a los principios de Linked Data y estos se combinan con diversos servicios de la IDEE y CartoCiudad para explotar el componente geoespacial

Pertinencia de Aplicación de la Ingeniería Ontológica a la Planificación Hidráulica del Territorio

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