Resultados de investigación

Data obtained in oceanographic surveys conducted off the Catalan coast (from 40.15˚N to 42˚N and from 0.5˚E to 3.6˚E). Vessel Name: García del Cid, This dataset gives information on fish eggs and larval diversity and abundances, integrated through the water column, off the Catalan coast in two oceanographic cruises in summer 2011 and 2012. The investigation showed the associations between larval fish assemblages and oceanographic conditions in the upper ocean (top 200 m). In addition, the study investigates the possible pathways for the jellyfish, Pelagia noctiluca, intrusions over the shelf to understand the interactions between jellyfish and fish larvae, Role of vertical mixing processes and dense water formation in the spawning strategy and recruitment of winter breeding fishes in the NW Mediterranean. Ministerio de Ciencia e Innovación (MICINN) CTM2010-18874/MAR, Peer reviewed

Detection of spatial areas where biodiversity is at risk is of paramount importance for the conservation and monitoring of ecosystems. Large terrestrial mammalian herbivores are keystone species as their activity not only has deep effects on soils, plants, and animals but also shapes landscapes, as large herbivores act as allogenic ecosystem engineers. One key landscape feature that indicates intense herbivore activity and potentially impacts biodiversity is the formation of grazing trails. Grazing trails are formed by the continuous trampling activity of large herbivores that can produce complex networks of tracks of bare soil. Here, we evaluated different algorithms based on machine learning techniques to identify grazing trails. Our goal is to automatically detect potential areas with intense herbivory activity, which might be beneficial for conservation and management plans. We have applied five semantic segmentation methods combined with fourteen encoders aimed at mapping grazing trails on aerial images. Our results indicate that in most cases the chosen methodology successfully mapped the trails, although there were a few instances where the actual trail structure was underestimated. The UNet architecture with the MambaOut encoder was the best architecture for mapping trails. The proposed approach could be applied to develop tools for mapping and monitoring temporal changes in these landscape structures to support habitat conservation and land management programmes. This is the first time, to the best of our knowledge, that competitive image segmentation results are obtained for the detection and delineation of trails of large herbivorous mammals., This work was supported by the Asturias Biodiversity Complementary Program BIO06 (Next Generation EU/PRTR), Strategic Projects Oriented Towards Ecological and Digital Transition (TED2021-131388B-100), Spanish Knowledge Generation Projects (PID2023-146074OB-I00) funded by EU Next Generation and Spanish Research Agency, Spanish Research Council Tenured Scientist Incorporation Grants 2022 (202230I041). J.F. Díez-Pastor was supported by a mobility grant PRX22/00634 from the Spanish Ministry of Universities and Ministerio de Ciencia e Innovación (ID2023-146074OB-I00)., Peer reviewed

Datos de una encuesta que se realizó a 82 jóvenes ganaderos. La encuesta se realizó en 2016. Se muestran los datos de base territorial y tenencia de la tierra, tipo de ganado, tipo de trabajo y comercialización y diversificación del ingreso. Así como la formación y educación recibida por el joven, las ayudas y subvenciones obtenidas, su acceso al crédito, las innovaciones realizadas o en proceso, los problemas y/o barreras a los que se enfrenta el joven agricultor y, sus perspectivas de futuro. Data from a survey carried out on 82 young ranchers. The survey was carried out in 2016. Data on territorial base and land ownership, type of livestock, type of work and marketing and income diversification are shown. As well as the training and education received by the young person, the aid and subsidies obtained, their access to credit, the innovations carried out or in process, the problems and/or barriers faced by the young farmer and their future prospects.

In this package we include the following: logonFinal20130315_4matetools361.model; parse_ESCAsentences_mate.sh; freeling_spaMate.sh; toconll2006.py; prueba.txt (test file: 4 sentences for testing); prueba.conll.conll06 (output in CoNLL06); prueba.conll.conll09 (output in CoNLL09); anna-3.61.jar, the version of mate parser used to generate the output. And also: metadata_mateparser_module_ES.xml; metadata_mateparser_module_ES.xml-Authors.txt; readme-espmate.txt

The cancer bioMarkers database is curated and maintained by several clinical and scientific experts in the field of precision oncology supported by the European Union’s Horizon 2020 funding. This database is currently being integrated with knowledge databases of other institutions in a collaborative effort of the Global Alliance for Genomics and Health.

Gitools is a framework for analysis and visualization of multidimensional genomic data using interactive heat-maps.

Long noncoding RNAs (lncRNAs, which comprise 68% of the human transcriptome with average length of 1,000 nt) interact with various RNA-binding proteins (RBPs) to mediate cellular functions. Here we introduce Global Score as a tool to predict protein interactions with lncRNAs (http://service.tartaglialab.com/new_submission/globalscore). We used enhanced CLIP (eCLIP) to test the binding of the lncRNA Xist to the RBPs HnrnpK (Global Score of 0.99), Ptbp1 (0.99), Lbr (0.79), HnrnpU (Saf-A) (0.66), Spen (Sharp) (0.59) and negative control Dkc1 (0.01). Global Score prediction correlates with the eCLIP binding profile (Pearson correlation = 0.93). As for the binding sites, Spen and HnrnpK, Ptbp1, and Lbr interact respectively with Xist A, B, and E repeats and adjacent regions, while HnrnpU binds across the whole transcript, and Dkc1 does not interact with Xist.

Research data from the thesis "Computational Analysis of Audio Recordings and Music Scores for the Description and Discovery of Ottoman-Turkish Makam Music".

https://github.com/arvoConsultores/Open-Peer-Review-Module/wiki, http://www.openscholar.org.uk/developing-the-first-open-peer-review-module-for-institutional-repositories/, The Open Peer Review Module Consortium include: Open Scholar, Arvo Consultores (a DSpace registered service provider), The Artificial Intelligence Research Institute of the Spanish National Research Council (IIIA-CSIC) in Catalonia, the institutional repository of the Spanish National Research Council (DIGITAL.CSIC), the repository of the Spanish Oceanographic Institute (e-IEO) and The Multidisciplinary Laboratory of Library and Computer Sciences (SECABA) in Granada., Open Peer Review Logo. With the support of OpenAIRE, Open Scholar has coordinate a consortium of five partners to develop the first Open Peer Review Module (OPRM) for open access repositories. The OPRM partners include: the institutional repository of the Spanish National Research Council (DIGITAL.CSIC), the repository of the Spanish Oceanographic Institute (e-IEO), the Artificial Intelligence Research Institute (IIIA) in Catalonia, the Multidisciplinary Laboratory of Library and Computer Sciences (SECABA) in Granada, and a company of DSpace professional development and services (ARVO)., This work was funded by OpenAIRE 2020, EU-Horizon2020 Grant ID 643410., No

Imágenes elaboradas para la web de la Red de Bibliotecas y Archivos del CSIC y la web de Digital.CSIC., En 2015 la comunidad científica del CSIC publicó 471 artículos acogiéndose al programa institucional de apoyo a la publicación en acceso abierto. Este esquema institucional está coordinado por la Unidad de Recursos de Información Científica para la Investigación (URICI), No