Resultados de investigación

7 datasets, This data set contains the stable isotope record of Uvigerina auberiana, Melonis barleeanus and Cibicidoides spp., abundance and fragmentation of planktonic foraminifera, major and trace elements and total inorganic carbon content of ODP Site 202-1242, Peer reviewed

1 file, Supplementary information for the article https://doi.org/10.1038/s41598-020-57501-0, Table S1. Primer sequences used for qPCR.-- Supplementary Figure S1. Antiviral activity of the CRP1-7-depleted supernatants.-- Supplementary Figure S2. Assessment of the ability of CRP-mix to induce the transcription of ifnphi1 and ifnphi2 in vitro.-- Supplementary Figure S3. Assessment of the ability of IL-6 to induce the expression of crp1-7 transcripts in vivo.-- Supplementary Figure S4. Transcriptional modulation of autophagy by SVCV in the ZF4 cells.-- Supplementary Figure S5. Effect of the addition of exogenous cholesterol on intracellular autophagosome levels and SVCV infection in vitro, Peer reviewed

[Related to:] Bode, Antonio; Álvarez, Marta; Ruiz-Villarreal, Manuel; Varela, Marta M (2019): Changes in phytoplankton production and upwelling intensity off A Coruña (NW Spain) for the last 28 years. Ocean Dynamics, 69(7), 861-873, https://doi.org/10.1007/s10236-019-01278-y Bode, Antonio; Alvarez-Ossorio, María Teresa; Cabanas, J; Miranda, Ana; Varela, M (2009): Recent trends in plankton and upwelling intensity off Galicia (NW Spain). Progress in Oceanography, 83(1-4), 342-350, https://doi.org/10.1016/j.pocean.2009.07.025 Bode, Antonio; Alvarez-Ossorio, María Teresa; Miranda, Ana; Ruiz-Villarreal, Manuel (2013): Shifts between gelatinous and crustacean plankton in a coastal upwelling region. ICES Journal of Marine Science, 1-9, https://doi.org/10.1093/icesjms/fss193 Bode, Antonio; Estévez, M Graciela; Varela, Manuel; Vilar, José A (2015): Annual trend patterns of phytoplankton species abundance belie homogeneous taxonomical group responses to climate in the NE Atlantic upwelling. Marine Environmental Research, 110, 81-91, https://doi.org/10.1016/j.marenvres.2015.07.017, [Project(s):] Adaptação Costeira às alterações climáticas: conhecer os riscos e aumentar a resiliência (MarRisk) seRies temporAles De oceanografIA en eL norte de ESpaña (RADIALES), [Coverage:] Latitude: 43.421660 * Longitude: -8.436660, Monthly series of hydrographic (temperature, salinity) and biogeochemical variables (nutrients, dissolved oxygen, particulate organic matter, chlorophyll, primary production), and zooplankton taxa abundance and biomass (dry weight) collected at one shelf station for various periods between 1990 and 2018 at A Coruña (NW Spain). These series are part of the long-term observational project RADIALES (Instituto Español de Oceanografía, IEO, Spain)., Peer reviewed

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12029427., [Code availability] The code used to calculate the indices is available via: https://cran.r-project.org/web/packages/ClimInd/index.html. The R scripts necessary to calculate the different indices, from the ECA&D and ERA5 gridded datasets in a 3-D array format, can be accessed via: https://gitlab.com/fergusrg/indecis_example., Monitoring and management of several environmental and socioeconomic sectors require climate data that can be summarized using a set of standard and meaningful climate metrics. This study describes a newly developed gridded dataset for the whole of Europe, which employed a set of 125 climate indices spanning different periods based on data availability, but mainly 1950–2017 and 1979–2017. This dataset comprehensively summarizes climate variability in Europe for a wide range of climate variables and conditions, including air temperature, precipitation, biometeorology, aridity, continentality, drought, amongst others. Climate indices were computed at different temporal scales (i.e. monthly, seasonal and annual) and mapped at a grid interval of 0.25°. We intend to update these indices on an annual basis. This dataset is freely available to research and end-user communities., This work was supported by the research projects PCIN-2015-220, CGL2017-82216-R and CGL2017-83866-C3-1-R financed by the Spanish Commission of Science and Technology and FEDER; CROSSDRO project financed by the AXIS (Assessment of Cross(X) - sectorial climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and INDECIS which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). Dhais Peña-Angulo is supported by a “Juan de la Cierva” postdoctoral contract (FJCI-2017-33652 Spanish Ministry of Economy and Competitiveness, MEC)., Peer reviewed

[Data availability] The high-resolution SPEI dataset is publicly available from the Centre for Environmental Data Analysis (CEDA) from the following link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a). It covers the whole of Africa at a monthly temporal resolution and 5 km spatial resolution from 1981 to 2016 and is provided with geographic latitude–longitude projection and NetCDF format., Droughts in Africa cause severe problems, such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security on Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectional perspective that includes crops, hydrological systems, rangeland and environmental systems. Such assessments are essential for policymakers, their advisors and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high-resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole of Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time Series (TS) datasets, Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project and root zone soil moisture modelled by GLEAM. Agreement found between coarse-resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with an average correlation coefficient (R) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information for the study of drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) via the following link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a)., This research has been supported by the UK Space Agency's International Partnership Programme (grant no. 417000001429)., Peer reviewed

Sequencing data from various tissues of European seabass (Dicentrarchus labrax).-- Accession: PRJNA1103406. Data Type:Raw sequence reads, Los programas tradicionales de cría se centran en la genética y suelen ignorar la influencia ambiental y epigenética en la variabilidad fenotípica. En este estudio con peces, se analizaron ~500 000 loci CpG de 60 familias para desarrollar biomarcadores epigenéticos (EBs) que ayuden a seleccionar reproductores. Mediante selección cuidadosa de muestras, filtrado estadístico y algoritmos de machine learning, se identificaron tres loci CpG heredables asociados con biomasa, crecimiento rápido de hembras y resistencia a efectos de masculinización por alta temperatura. Con estos EBs se construyó un modelo capaz de predecir los mejores reproductores, validado en tres ensayos independientes y en una cohorte externa, mostrando robustez. Incorporar la selección epigenética aumentó la producción hasta 1,4 veces respecto a la selección genética sola. Esta estrategia ofrece un enfoque económico para integrar selección genética y epigenética, aplicable también a estudios de enfermedades raras y conservación biológica. El conjunto de datos incluye 20 librerias de RRBS con las cuales se desarrollo el método inicial y librerías de MBS hechas para su validación, Este estudio fue financiado por el “Centro para el Desarrollo Tecnológico e Industrial” (CDTI) y el Fondo Europeo Marítimo y de Pesca (FEMP) a través del Proyecto IDI-20220244 para ML, y por los proyectos del Ministerio de Ciencia de España PID2019-108888RB-I00 y PID2022-139096OB-I00 para FP, con financiación del gobierno español mediante la acreditación de “Centro de Excelencia Severo Ochoa” (CEX2019-000928-S). NS contó con una beca predoctoral del Ministerio de Ciencia e Innovación de España (BES-2017-079744), 28 RRBS libraries with pools of 4 individuals per family and sex and 70 individual MBS libraries, Peer reviewed

Data include the voltage and current at the transformer terminals, also the measured current losses from applying various switching strategies. This data serves to reproduce Figure 18 and 19 in the paper.

The dataset contains the following files: (1) readme.txt, it is a text file including details about the content of the dataset; (2) Measurement in ARSD_SMS_Data.xls, it is a MS Excel file containing the data used in the analysis of the primary studies, reported in Section 4 of the paper; and, (3) Metrics in ARSD.pdf, it is pdf file containing the complete list of metrics reported in the different primary studies. The metrics are classified into the following categories: QR metrics, QMIs, and QMI metrics. In the MS Excel file, the data is organised in separated sheets: (a) RQ1 - Literature characterisation; (b) RQ2 - QR Metrics, where QR refers to Quality Requirements; (c) RQ3 - QMI, where QMI refers to Quality Management Indicators; (d) RQ3 - Tools, corresponding to the reported tools used to manage QMIs; and, (e) RQ3 - QMI Metrics., This dataset has been used to generate part of the results included in the manuscript: “Quality Measurement in Agile and Rapid Software Development: A Systematic Mapping”, submitted to the Information and Software Technology journal (IST) at the time of publishing this dataset. The paper reports the results of a systematic mapping study surveying the literature related to quality requirements management through metrics in agile and rapid software development processes. The dataset contains the data extracted from the primary studies that was used in the analysis phase of the study and reported in the results section of the paper. The data extracted from the primary studies has been complemented with some data used for specific purposes, e.g. during the analysis phase, we included the list of companies collaborating in the paper analysing the authors’ affiliations to understand the level of implication of companies in the field. Complementing the data analysed, the dataset includes some detailed results that are not included in the body of the paper, i.e., the list of metrics reported in the selected primary studies.

These data are associated to the paper with the same title and are structured according to the figures in the paper. These data correspond to the research results of the WP3 of the European Project 4D-POLYSENSE., The present dataset reports the scientific results corresponding to a scientific study where a bidimensional (2D) polypropylene flexible mesh material has been converted into a fourth dimension (4D) dynamic system. The platform is composed by a fibres of isotactic polypropylene (iPP) mesh coated with thermosensitive poly(N-isopropylacrylamide-co-N,N'-methylene bis(acrylamide) (PNIPAAm-co-MBA) hydrogel, and undergoes variations in its geometry by reversible folding/unfolding behaviour. Folding and unfolding motion in aqueous solution of bilayer and monolayer systems composed by two or one layer of hydrogel, respectively, are reported in the present dataset. Furthermore, an infrared thermographic camera and an optical microscope were used to evaluate the macroscopic and microscopic structure stimulus response in air. Additionally, PP-g-PNIPAAm meshes showed an increase in the mechanical properties (bursting strength) with respect to the uncoated mesh.

This dataset has been used to generate part of the results included in the manuscript: "Cooperative Learning for End-to-End Delay Modeling in Multi-Domain Networks", submitted to IEEE Transactions on Network and Service Management (TNSM) at the time of publication of this dataset. The dataset contains synthetic end-to-end (e2e) delay and traffic measurements for multidomain paths established in a network consisting in three domains. Paths traverse either two or three domains. Besides e2e monitoring data, routing information of the paths and the models used to compute the delay introduced for each domain (i.e. intra-domain delay components of total e2e delay) are provided. Traffic and delay data were synthetically generated according to the CURSA-SQ methodology. For further details about it, please refer to the following reports: "The Logistic Queue Model", Tech. Rep. UPC-DAC-RR-GEN-2020-1 (Available online: https://people.ac.upc.edu/lvelasco/docs/research/UPC-DAC-RR-GEN-2020-1.pdf) and "Validation of the CURSA-SQ Methodology", Tech. Rep. UPC-DAC-RR-GEN-2020-2 (Available online: https://people.ac.upc.edu/lvelasco/docs/research/UPC-DAC-RR-GEN-2020-2.pdf)