BUSCADOR RECOLECTA

Esta base de datos contiene la densidad de las microalgas encontradas en 30 lagunas costeras de la costa pacífica de Nicaragua y Costa Rica en tres periodos de su año hidrológico: comienzo de las lluvias (junio 2010), plena inundación (septiembre 2010) y sequía (enero 2011). La recolección fue subsuperficial. En esta matriz de datos las filas son las diferentes especies encontradas y las columnas la muestra (laguna y fecha). Información geográfica y limnológica de las lagunas se puede encontrar en Sasa et al. (2015: Limnetica vol 34).

Esta matriz de datos contiene: a) el biovolumen de las especies encontradas en el punto llamado Molemocho del humedal de Las Tablas de Daimiel desde 1996 a 2002 muestreado mensualmente y b) el área de inundación del humedal en esos años. Los métodos de toma de muestras y observación, clasificación, recuento y cálculo del biovolumen, así como más información sobre el humedal se pueden encontrar, entre otras publicaciones en Rojo et al. 2012 (Trade-offs in plankton species richness arising from drought: insights from long-term data of a National Park wetland (central Spain). Biodversity and Consrvation. http://dx.doi.org/10.1007/s10531-012-0307-1). La información de a) está ordenada en filas (cada especie) y columnas (cada fecha). Las unidades son micras cúbicas por mililitro. La información de b) se encuentra como dos filas últimas (rótulo de fecha y hectáreas de inundación) siendo las columnas de nuevo la fecha de la obtención del dato. El formato es CVS.

Esta base de datos contiene los valores de 24 variables (geográficas y limnológicas) de 30 lagunas costeras de la costa pacífica de Nicaragua y Costa Rica en tres periodos de su año hidrológico: comienzo de las lluvias (junio 2010), plena inundación (septiembre 2010) y sequía (enero 2011). En esta matriz de datos las filas son los cuerpos de agua muestreados y las columnas las variables. Cada periodo hidrológico muestreado se presenta en una hoja aparte. Descripción de estas lagunas basada en esta y en otra información se puede encontrar en Sasa et al. (2015: Limnetica vol 34).

Datos que se publicarán en el artículo

Research data from the thesis Structure of lexicon in the automatic acquisition of lexical information. MONO-DATASETS: contains files of the single-sense English data sets extracted for each lexical semantic class studied in the thesis (COMMUNICATION_OBJECT, EVENT, HUMAN, LOCATION and ORGANIZATION). Each data set contains a list of nouns and their class membership information; and (ii) LEXICO-SYNTACTIC-REs: contains files of the linguistic cues and corresponding lexical-syntactic patterns formalized as Regular Expressions for each of the single-sense English lexical semantic classes studied in this thesis (COMMUNICATION_OBJECT, EVENT, HUMAN, LOCATION and ORGANIZATION). This folder also contains a file of the unmarked contexts that were identified, as well as their corresponding lexical-syntactic patterns formalized as Regular Expressions.

Corpus d'anàlisi utilitzat en l'estudi lexicomètric de la tesi doctoral "Morales A. Estudi lexicomètric del vocabulari del procés d'aprovació de l'Estatut d'autonomia de Catalunya (2006) [tesi]. Barcelona: Universitat Pompeu Fabra; 2015. 600 p.".

A needle-plot (aka stem-plot or lollipop-plot) plots each data point as a big dot and adds a vertical line that makes it appear like a needle.

Turkish şarkı vocal dataset is a collection of recordings of compositions from the vocal form şarkı. The recordings/nare selected from a musicBrainz collection of Turkish music/n/nhttp://musicbrainz.org/collection/544f7aec-dba6-440c-943f-103cf344efbb /n/nThe collection has annotations with lyrics. Each lyrical phrase is aligned to its corresponding segment in the audio.

This repository contains the necessary tools to reproduce the experiments of the paper: G. Santatmaría, V. Gómez (2015) Convex inference for community discovery in signed networks. NIPS 2015 Workshop: Networks in the Social and Information Sciences ------The method first maps the MAP problem on the Potts model as a hinge-loss minimization problem (see the paper for details). To run the code you need to install psl (included here) and if you want to additionally compare with other inference methods, such as max prod belief propagation or junction tree, you need to install the libDAI library (also included here) --------The directory europeanCongressData/ (~500 Mb) contains the votings of the EU parlament, including 300 votings events from the actual term, from May 2014 to June 2015, obtained from http://www.votewatch.eu/ * data/ : json files with the european votes * network.net : signed network built from the votes * political_parties.txt : "ground truth" party * community_results/ : results for different number of communities and initial vertices * dataComputations.py : used to build the signed network * dataProcessing.py : used to build the signed network We would appreciate if you cite the paper after using the data or the code. --------DEPENDENCIES-------- The code has been tested in Linux Mint 18.1 Serena and Ubuntu 14.04 - For PSL library, you need to have java 1.8 you may need to export JAVAHOME='/usr/lib/jvm/YOURJAVA1.8FOLDER' maven 3.x - For libDAI you will need: make doxygen graphviz libboost-dev libboost-graph-dev libboost-program-options-dev libboost-test-dev libgmp-dev cimg-dev libgmp-dev --------CODE TO RUN THE FOLLOWING EXPERIMENTS:-------- Compare the performance in terms of structural balance of max prod bp and our method against an exact inference method (junction tree), with different number of communities --------INSTALL-------- To install the experiments you have to follow the next steps: 1 Build the libdai library by doing: make -B on the folder (libdai) 2 Generate the class path of the groovy project: mvn clean install mvn dependency:build-classpath-Dmdep.outputFile=classpath.out on the psl root folder (You need to have java 1.8 and maven 3.x installed) 3 Grant exec permissions to the run.sh script --------Options-------- The main python file to run the experiments is evaluatebalanceon_sn.py. It accepts the following parameters: 1 (Int) Nodes of the graph. In order to run the junction tree we recommend to set this paremeter to 150 or less 2 (Int) The number of underlying communities 3 (Float) The maximum amount of unbalance for the experiments. We recommend 0.45 4 (Bool) Whether to use an heuristic to find the initial node for each community or to use directly random nodes from the ground truth communities. This heuristic looks alternatively for the nodes with highest negative degree and highest positive degree. For the case when the number of communities is equal to 2 (Ising Model), the heuristic is used by default. An example of execution would be: python evaluate_balance_on_sn.py 120 3 0.45 True True The results of the experiments are save in the folder results/ Scripts The main script of the hinge-loss method can be found in the folder psl/psl-example/src/main/java/edu/umd/cs/example/PottsCommunities.groovy --------For further questions, please contact vicen.gomez@upf.edu

Dades associades a l'article publicat: Massana i Raurich, J., Pous i Sabadí, C., Burgas Nadal, Ll., Meléndez i Frigola, J., Colomer Llinàs, J. (2015). Short-term load forecasting for non-residential buildings contrasting artificial occupancy attributes. Energy and Buildings, vol. 130, p. 519-531. Disponible a https://doi.org/10.1016/j.enbuild.2016.08.081, Software: 1 Fitxer Zip; 2 Fitxers xlsx, Data related to electrical consumption and employment indicators of 2 buildings of the University of Girona, Dades relacionades amb els consums elèctrics i els indicadors d’ocupació de 2 edificis de la Universitat de Girona, This research project has been partially funded through BR-UdG Scholarship ofthe University of Girona granted to Joaquim Massana Raurich. Work developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016) and the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R), European Union’s Horizon2020 Research and Innovation Programme under grant agreementNo 680708