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En este primer episodio, Santiago de Pablo y Virginia López de Maturana hablan sobre símbolos vascos. Descubriremos cuáles son los más importantes, su historia y la importancia que tienen para nuestra cultura. Además, conoceremos el origen del lauburu con Isabel Mellén y los fondos que atesora la Fundación Sancho el Sabio sobre el rock radical vasco con Ander Gondra. Gracias a Jabi Soto nos adentraremos en el fascinante mundo de los orígenes de la fotografía. ¡Descubre estos temas y mucho más en este primer episodio de El Podcast de Sancho el Sabio!

Este documento presenta los datos de biomasa en carbono (mgC m-2) de taxones planctónicos y perifíticos de un experimento a escala de mesocosmos. La comunidad planctónica-perifítica fue sometida a tres escenarios de cambio global, con la temperatura y la radiación ultravioleta como factores experimentales testados independientemente. Cada elemento de la comunidad fue identificado a la máxima resolución taxonómica posible. Las abreviaciones y explicaciones sobre los escenarios experimentales, las replicas de cada escenario y los detalles de los compartimentos considerados en cada mesocosmos se muestran en la primera página de este documento., This document presents the data about carbon biomass (mgC m-2) of planktonic and periphytic taxa identified in a mesocosm experiment. The planktonic-periphytic community was subjected to three global change-related scenarios, with temperature and ultraviolet radiation as independently tested factors. Each element of this community was identified at the highest possible taxonomic resolution. Abbreviations about the experimental scenarios, the replicates and the details of the considered compartments in each mesocosm are shown on the first page of the document.

Two databases: Research on happiness and affect during Covid-19 confinement

Esta base de datos contiene los valores de diversos índices topológicos calculados a partir de los nodos funcionales de las comunidades acuáticas plánctónicas-bentónicas de un experimento a escala de mesocosmos. Mediante un diseño experimental que constaba de tres escenarios ambientales con cuatro réplicas cada uno (con la temperatura y la radiación ultravioleta como factores experimentales), se agruparon los organismos presentes en cada réplica (mesocosmos) en nodos funcionales. Con estos nodos se construyeron redes ecológicas tróficas (solo considerando relaciones tróficas entre ellos) y redes multi-interacción (considerando relaciones tróficas y no-tróficas). A cada uno de los nodos en cada versión de la red ecológica se le calcularon diversos índices (topological importance index, toplogical overlap index, closeness centrality y betweenness centrality) que muestran su importancia topológica en la red. Las abreviaciones y explicaciones sobre los escenarios experimentales, las replicas de cada escenario y los detalles sobre los índices y los nodos de las redes están detallados en la primera hoja de esta base de datos., This database contains the values of several topological indices calculated from the functional nodes of the planktonic-benthic aquatic communities from a mesocosm-scale experiment. Using an experimental design consisting of three environmental scenarios with four replicates each (with temperature and ultraviolet radiation as experimental factors), the organisms present in each replicate (mesocosm) were grouped into functional nodes. With these nodes, ecological trophic networks (only considering trophic relationships between them) and multi-interaction networks (considering trophic and non-trophic relations) were constructed. Different indices (topological importance index, topological overlap index, closeness centrality, and betweenness centrality) were calculated for each of the nodes in each version of the ecological network, showing their topological importance in the network. The abbreviations and explanations about the experimental scenarios, the replicates of each scenario, and the details regarding the indices and the nodes of the networks are detailed in the first sheet of this database.

Se trata de un Fichero de datos manejados, referentes a investigación académica médica, con perspectiva de género, It is a dataset, about medical academic research, used with a gender perspective, Datos de investigación académica médica

Los datos aqui presentados comprenden la ocurrencia y abundancia relativa de las diatomeas bentónicas identificadas en sistemas acuáticos de la Serranía de Cuenca (centro de España) durante el verano de 2017. Los taxones se ordenan en la matriz alfabéticamente y se les asigna un código. Las muestras se caracterizan tambien por un código y por información sobre su cuenca de origen (rio Tajo o Júcar), sustrato (mineral o planta) y hábitat (ambientes lénticos o lóticos). Toda esta información contenida en varias página de un documento excel., The data presented in this document comprised the occurrence and relative abundance of benthic diatom taxa identified in Serranía de Cuenca (central Spain) waters during summer of 2017. Taxa are ordered alphabetically in columns and named with a code (see the "Taxa code" sheet) The sample code and information on catchments (Tajo or Júcar rivers), substrate (mineral or plant) and habitat (stream or stagnant water) are reported in the "Sample code" sheet

[Description of methods used for collection/generation of data] We built a genus-level phylogeny using the workflow proposed by Roquet et al. (2013). We downloaded from Genbank three conserved chloroplastic regions (rbcL, matK and ndhF) plus the ITS region for a subset of families, which we aligned separately by taxonomic clustering. [Methods for processing the data] We aligned all coding sequence clusters with MACSE (Ranwez et al. 2011) and non-coding ones with MAFFT (Katoh and Standlye 2013), and trimmed all alignments with TrimAl (Capella-Gutiérrez et al. 2009). We concatenated all alignments to obtain a supermatrix. We then conducted maximum-likelihood (ML) phylogenetic inference analyses with RAxML (Stamatakis 2014), applying the most appropriate partitioning scheme and substitution model obtained with PartitionFinder (Lanfear et al. 2012) and a supertree constraint at the family-level obtained with the online software Phylomatic v.3 (tree R20120829). Specifically, we performed 100 independent tree searches and selected the best ML tree (the one with the highest probability). [Instrument- or software-specific information needed to interpret/reproduce the data] Any software for manipulation or analysis of phylogenies such as R packages ape or picante. [Standards and calibration information, if appropriate] The best ML tree was dated applying the penalized likelihood method in treePL (Smith and O'Meara,2012) and the following node calibrations: we fixed the node corresponding to the ancestor of eudicots at 125 Ma based on the earliest eudicot fossil (Hughes and McDougall 1990), and applied minimum age constraints to 15 nodes based on fossil information extracted from Smith and Beaulieu (2010) and Bell et al. (2010). 5. Environmental/experimental conditions:, Agencia Estatal de Investigación, Project VULVIMON (Reference: CGL2017-90040-R)., Peer reviewed

Volcanic eruptions produce health changes in animals that may be associated with emitted gases and deposited ashes. We evaluated whether the Puyehue–Cordón Caulle volcanic eruption in 2011 produced health changes in the threatened Andean condor (Vultur gryphus) living in the area most affected by the eruption, north-western Patagonia. We studied clinical and biochemical parameters of condors examined before and after the eruption. We also examined concentrations of different metals and metalloids in the blood of individuals sampled after the eruption. The most common clinical abnormality associated with the eruptive process was irritating pharyngitis. In condors sampled after the eruption, blood concentrations of albumin, calcium, carotenoids and total proteins decreased to levels under the reference values reported for this species. We found different chemical elements in the blood of these condors after the eruption, such as arsenic and cadmium, with the potential to produce health impacts. Thus, the health of Andean condors was affected in different ways by the eruption; remaining in the affected area appears to have been costly. However, in comparison to other animal species, the health impacts were not as strong and were mainly related to food shortages due to the decrease in availability of livestock carcasses linked to the eruption. This suggests that condors dealt relatively well with this massive event. Future research is needed to evaluate if the health changes we found reduce the survival of this species, and if the cost of inhabiting volcanic areas has any ecological or evolutionary influence on the condor’s life history., Peer reviewed

[Methods] Dr Carlos Palacin-Lizarbe made the datasets. For further details of the methods see the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). Further details on the denitrification rate measurement method are provided in the publication: Palacin-Lizarbe, C., Camarero, L. & Catalan, J. Estimating sediment denitrification rates using cores and N2O microsensors. J Vis Exp, e58553, doi:doi:10.3791/58553 (2018). Further details on the sediment molecular descriptors (DNA, 16S, nirS, nirK, nosZ1, and nosZ2) are provided in the publication: Palacin-Lizarbe, C. et al. The DNRA-denitrification dichotomy differentiates nitrogen transformation pathways in mountain lake benthic habitats. Front Microbiol 10, 1229, doi:10.3389/fmicb.2019.01229 (2019)., [Usage Notes] Dryad repository - https://doi.org/10.5061/dryad.j6q573n95 This document describes four datasets (tab-separated text format tables) related to measurements of denitrification rates in mountain lake sediments of the Pyrenees. Dr Carlos Palacin-Lizarbe made the datasets, anyone that uses these data should reference this DRYAD repository and the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). See this publication for further details on the methods used. Dataset 1: Sediment_denitrification_rates_DRYAD.txt Dataset containing all measured denitrification rates and incubation conditions. The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, denitrification_rate, nitrate_treatment, temperature_insitu, temperature_incubation, nitrate_actual_plus_added, glucose_added, habitat, site. Dataset 2: table_background_DRYAD_def.txt Dataset containing all measured denitrification rates and the background descriptors (landscape, water, sediment). This dataset contain missing values. The dataset contains the following variables: id_core, yymmdd_lake_sample, actual_denitrification_rate, nitrate_add_7_denitrification_rate, nitrate_add_14_denitrification_rate, nitrate_add_28_denitrification_rate, high_nitrate_add_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrmicroogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. Dataset 3: table_actual_denitrification_rates_modeled_DRYAD.txt Dataset containing the actual denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, actual_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of actual denitrification rates, we use the variables scaled; before being scaled some variables were square root (actual_denitrification_rate, DOC, DNA, X16S, nirS, and nirK) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nosZ1, and nosZ2) transformed to reduce the influence of extreme values. Dataset 4: table_potential_denitrification_rates_modeled_DRYAD.txt Dataset containing the potential (28 microM nitrate added) denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, nitrate_add_28_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of potential denitrification rates, we use the variables scaled; before being scaled some variables were square root (nitrate_add_28_denitrification_rate, DOC, DNA, X16S, and nirS) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nirK, nosZ1, and nosZ2) transformed to reduce the influence of extreme values. Variables contained in the datasets: id_rate: identification code for each denitrification rate measurement is useful as a link between datasets. id_core: identification code for each sediment core is useful as a link between datasets. yymmdd_lake_sample: The datasets are sorted by this variable. Identification code for each sediment core is useful as a link between datasets. Each code corresponds to the date sampled (year, month, and day), and the Lake, and the number of the sediment core. E.g. 130701Llo3 correspond the 3rd core sampled in Llong Lake on 2013, July 1 (13 07 01). The following are the sampled Lakes, with its abbreviation in brackets: Bergus (Be), Bassa de les Granotes (Bgra), Contraix (Co), Gelat de Bergus (GBe), Llebreta (Lle), Llong (Llo), Plan (Pl), Podo (Po), Redon (Re), Redo Aiguestortes (ReAT), and Redon de Vilamos (ReVil). This variable is useful as a link between datasets. denitrification_rate (micromols N2O m-2 h-1): denitrification rate without identifying the nitrate treatment. actual_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured without any substrate addition. nitrate_add_7_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 7 microM nitrate and 1.5 g/L of glucose. nitrate_add_14_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 14 microM nitrate and 1.5 g/L of glucose. nitrate_add_28_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 28 microM nitrate and 1.5 g/L of glucose. high_nitrate_add_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding a high concentration of nitrate (>300 microM) and 1.5 g/L of glucose. nitrate_treatment: treatment of nitrate addition in the denitrification measurements. Treatments: actual (without any nitrate added), Add_7 (7 microM nitrate added), Add_14 (14 microM nitrate added), Add_28 (28 microM nitrate added), High_add (>300 microM nitrate added). temperature_incubation (Celsius degrees): constant temperature during the incubation of the denitrification measurement. nitrate_actual_plus_added (microM): actual (in situ) plus added nitrate concentration in the water overlying the sediment core. glucose_added (mg * L-1): added when nitrate was also added. site: Lake (code): Bergus (B), Contraix (C), Bassa de les Granotes (G), Gelat de Bergus (GB), Llebreta (Le), Llong (Lo), Plan (P), Podo (Po), Redon (R), Redo Aiguestortes (RA), and Redon de Vilamos (RV). latitude (N). longitude (E). altitude (m a.s.l.). lake_area (ha). catchment_area (ha). renewal_time (months): mean time that water spends in the lake, retention time and residence time are synonyms of renewal time. habitat: sediment type (code): littoral sediments from rocky areas (R), helophyte Carex rostrata belts (C), beds of isoetid (I) and elodeid (E) macrophytes, and non-vegetated deep (D) sediments. temperature_insitu (Celsius degrees): temperature measured in the water overlying the sediment core just after the sediment core was retrieved. nitrate (microM): nitrate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. nitrite (microM): nitrite concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. ammonium (microM): ammonium concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. sulphate (microM): sulphate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. DOC (mg*L-1): dissolved organic carbon concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations. water_column_depth (m): depth of the water column in the sediment core sampling point. Coded as 0.5 for littoral habitats. sediment_layer_depth (cm): depth of the sediment layer described by molecular and abiotic features. organic_matter (%): sediment organic matter content determined by Lost on Ignition. nitrogen (%): sediment nitrogen content. d15N (parts per thousand): sediment delta15N. carbon (%): sediment carbon content. d13C (parts per thousand): sediment delta13C. carbon_nitrogen_ratio (a/a): sediment atomic ratio of the two elements. dry_weight_wet_weight_ratio: sediment dry weight to wet weight ratio. sediment_density (g * cm-3). sediment_grain_size (um): sediment, volume diameter of the mean-sized spherical particle (vol_weighted_mean parameter provided by Mastersizer 2000, Malvern Instruments Ltd, UK). DNA (ng * m-2): sediment DNA content, ng of DNA per m2 in the sediment layer (0-0.5 cm). X16S (copies * m-2): 16S rRNA gene copies per m2 in the sediment layer (0-0.5 cm). nirS (copies * m-2) : nirS gene copies per m2 in the sediment layer (0-0.5 cm). nirK (copies * m-2) : nirK gene copies per m2 in the sediment layer (0-0.5 cm). nosZ1 (copies * m-2) : nosZ1 gene copies per m2 in the sediment layer (0-0.5 cm). nosZ2 (copies * m-2) : nosZ2 gene copies per m2 in the sediment layer (0-0.5 cm)., During the last decades, atmospheric nitrogen loading in mountain ranges of the Northern Hemisphere has increased substantially, resulting in high nitrate concentrations in many lakes. Yet, how increased nitrogen has affected denitrification, a key process for nitrogen removal, is poorly understood. We measured actual and potential (nitrate and carbon amended) denitrification rates in sediments of several lake types and habitats in the Pyrenees during the ice-free season. Actual denitrification rates ranged from 0 to 9 μmol N2O m−2 h−1 (mean, 1.5 ± 1.6 SD), whereas potential rates were about 10-times higher. The highest actual rates occurred in warmer sediments with more nitrate available in the overlying water. Consequently, littoral habitats showed, on average, 3-fold higher rates than the deep zone. The highest denitrification potentials were found in more productive lakes located at relatively low altitude and small catchments, with warmer sediments, high relative abundance of denitrification nitrite reductase genes, and sulphate-rich waters. We conclude that increased nitrogen deposition has resulted in elevated denitrification rates, but not sufficiently to compensate for the atmospheric nitrogen loading in most of the highly oligotrophic lakes. However, there is potential for high rates, especially in the more productive lakes and landscape features largely govern this., Ministerio de Ciencia e Innovación, Gobierno de España, Award: CGL2010-19373. Ministerio de Economía, Industria y Competitividad, Gobierno de España, Award: CGL2016–80124-C2-1-P., Peer reviewed