BUSCADOR RECOLECTA

This item is made of 3 files: the dataset in netcdf format, a Readme.txt file including a small description of the computed variables, and two figures (cartesian and polar format) representing the mean spectrum.-- Dataset contributed to the Project STRAMIX (CTM2012-35155), 28118 Directional wave spectra obtained from ADCP currents between June 2013 and August 2014 in the Ría de Vigo (NW Iberia, Atlantic Ocean), STRAMIX project. First, last and mean spectra were included separately. Waves Monitor Software (RDI) was used to obtain the 28118 individual wave spectra. Criteria applied to compute parameters were: 20 minutes bursts with tilt and current correction every 10 minutes, maximum wave period of 28.6 s, sea-swell transition period of 7.3 s, 256 frequency bands, and 180 angles, Funding for this study was provided by the Spanish Ministry of Economy and Competitiveness under the STRAMIX (CTM2012-35155) research project. Another project contributing to the processing of this dataset was the Spanish Ministry of Science and Innovation project “STRAUSS” (PID2019-106008RB-C21). N.Villacieros-Robineau was funded by the Spanish Ministry of Science and Innovation through a Juan de la Cierva-Formación postdoctoral fellowship (FJCI‐2017–34290), No

Data Mixing Dynamics dataset, Peer reviewed

[Description of methods used for collection/generation of data] Collection of data from extraction of articles retrieved from the literature (Web of Knowledge and SCOPUS, accessed July 2015 and updated May 2021). Papers reporting estimates of the effect of coastal plants on current and wave attenuation in vegetated coastal habitats identified using search terms: “Seagrass*” [All Fields] OR “Mangrove*” [All Fields] OR “Salt marsh*” [All Fields] OR “Macrophyte*” [All Fields] AND “engine*” [All Fields] OR “wave attenuation” [All Fields] OR “flow modification” [All Fields]. The in total 963 papers retrieved were analyzed for quantitative estimates, supplemented with papers and documents containing data meeting the requirements of the analyses contained within the references of the papers retrieved, resulting in a data set containing a total of 1372 estimates derived from 95 individual articles with a temporal cover from 1982 to 2020., [Methods for processing the data] Results from field and laboratory studies were used, but not numerical models. When information was given for multiple observations with different vegetation parameters and/or hydrodynamic parameters, we included several data points per study, but only included 1 measurement (max. distance) when the same structural parameters had repeated measurements for different distances within the vegetation. Where authors reported values for current reduction these were used directly, always making sure a non-vegetated (bare) reference value was used to calculate reduction in the vegetation. When data was (re)calculated from separate reported values the formulas used for current reduction, dU, were calculated as: dU/U0 = (U0-Uv)/U0 With U as the current speed over a reference unvegetated region U0 and through a vegetated region Uv in m s-1 respectively. Where the information was provided in the selected studies, we calculated the wave energy reduction, dE, defined as (Knutson et al. 1982): dE/E0 = ((E0-Ev))/E0 Where E is the energy without vegetation (E0) and within the vegetation (Ev) respectively. The wave height reduction per meter r (Mazda et al 1997) was calculated as: r = dH/(H0x) = ((H0-Hv))/(H0x) Where x is the length of the vegetation field. When multiple measurements were done with the same vegetation settings (i.e. density, water height) at different distances into the vegetation, we took the maximum distance evaluated. The effect of vegetation on current and wave attenuation was represented by the decay coefficients, KiH, (Kobayashi et al., 1993) and KiU (m-1), representing the relative decrease in significant wave height (KiH), and current velocity (KiU) with distance into the vegetated fringe (x, bed length) calculated as, kiH=1/x ln(1-dH/H0 )=1/x ln(Kt ) and kiU=1/x ln(1-dU/U0 ) Where Kt is the wave transmission coefficient. We used the same literature sources that were used for the data were collection, to compile relevant vegetation structural parameters, specifically, shoot or stem density and emergence ratio (defined as hveg/h). For stiffness we used Young’s bending modulus (E, in N mm-2), when this parameter was not available from the same source, we completed the data with species specific values from literature (e.g. Zhu et al. 2020 for salt marshes, de los Santos et al. 2016; La Nafie et al. 2012; Soissons et al. 2017 for seagrasses and van Hespen et al. 2021 for mangroves). When no value was known, the value for the family was used or an average for the group (i.e., saltmarsh, seagrass, etc.) obtained from the compiled values., [Relationship between files] Readme provides background information for xlsx datafile., [People involved with sample collection, processing, analysis and/or submission] https://casrai.org/credit . Idea and concept C.M.D and I.J.L, design and discussion of content during workshops I.E.H., N.M., B.v.W., T.J.B., I.J.L, C.M.D. Database compilation I.E.H, M.M., A.G.M and N.M. Analysis of data I.E.H.. All authors contributed to the writing and editing of the manuscript., Funding for this data collection supplied by the MedShift project, CGL2015-71809-P (MINECO/FEDER) and baseline funding from King Abdullah University of Science and Technology to C.M.D. I.E.H. was supported by grant RYC-2014-15147, co-funded by the Conselleria d'Innovació, Recerca i Turisme of the Balearic Government (Pla de ciència, tecnologia, innovació i emprenedoria 2013-2017) and the Spanish Ministry of Economy, Industry and Competitiveness., Data_coastal_vegetation_adaptation.xlsx, readme.txt, Peer reviewed

[EN] Descriptive statistical data of the review, acceptance and publication dates of a sample of 21890 articles from 326 Ibero-American scientific journals from all subject areas and countries included in the Latindex Catalogue 2.0 and published between 2018 and 2020. Number of cases, average, median, mode, maximum and standard deviation of the evaluation, publication and total days, disaggregated by document type, periodicity, subject area and country are included in the dataset. Also data about the number of journals by subject area and country used to build the analysed sample database are included., [ES] Datos estadísticos descriptivos de las fechas de revisión, aceptación y publicación de una muestra de 21890 artículos de 326 revistas científicas iberoamericanas de todas las áreas temáticas y países incluidos en el Catálogo Latindex 2.0 y publicados entre 2018 y 2020. En el conjunto de datos se incluye el número de casos, la media, la mediana, la moda, el máximo y la desviación estándar de los días de evaluación, publicación y total, desagregados por tipo de documento, periodicidad, área temática y país. También se incluyen datos sobre el número de revistas por área temática y país utilizados para construir la base de datos de la muestra analizada., No

The dataset is composed of several images named as: type of crop_date_number.png., [Methodological information] The data were acquired using the RGB camera TRI016S-CC RGB from Lucid Vision Labs equipped with the SV-0614V lens (resolution: 1.6 MP; FoV: 54.6° × 42.3°)., [Environmental/experimental conditions] The data were acquired by manually operating a mobile platform during different time periods and weather conditions in the same season., [People involved with sample collection, processing, analysis and/or submission] Jesus Herrera-Diaz (Methodology), Luis Emmi (Investigation), Pablo Gonzalez-de-Santos (Supervision)., This dataset is part of a the WeLASER project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101000256., Peer reviewed

The dataset is composed of several images named as: type of crop_date_number.png., [Methodological information] The data were acquired using the RGB camera TRI016S-CC RGB from Lucid Vision Labs equipped with the SV-0614V lens (resolution: 1.6 MP; FoV: 54.6° × 42.3°)., [Environmental/experimental conditions] The data were acquired by manually operating a mobile platform during different time periods and weather conditions in the same season., [People involved with sample collection, processing, analysis and/or submission] Jesus Herrera-Diaz (Methodology), Luis Emmi (Investigation), Pablo Gonzalez-de-Santos (Supervision)., This dataset is part of a the WeLASER project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101000256., Peer reviewed

[Links/relationships to ancillary data sets] To generate these metadata, datasets with health care or research data from SAS, IACS, UNIGE, UCSC, UP was used., [Relationship between files] - SAS CapabilityStatement, SAS DocumentManifest, SAS Provenance; was generated in SAS facilities. - IACS CapabilityStatement, IACS DocumentManifest, IACS Provenance; was generated in IACS facilities. - UNIGE CapabilityStatement, UNIGE DocumentManifest, UNIGE Provenance; was generated in UNIGE facilities. - UCSC CapabilityStatement, UCSC DocumentManifest, UCSC Provenance; was generated UCSC SAS facilities. - UP CapabilityStatement, UP DocumentManifest, UP Provenance; was generated in UP facilities., [Description of methods used for collection/generation of data] The FAIRification workflow designed by the FAIR4Health project, detailed here: https://doi.org/10.1055/s-0040-1713684 - http://hdl.handle.net/10261/236308., [Standards and calibration information] The files have JSON format, and are HL7 FHIR compliance., [Usage Licenses/restrictions placed on the data] - SAS, IACS. CC BY-NC (Attribution-NonCommercial). https://creativecommons.org/licenses/by-nc/4.0/ - UNIGE, UCSC. CC BY-SA (Attribution-ShareAlike). https://creativecommons.org/licenses/by-sa/4.0/ - UP. CC BY-NC-ND (Attribution-NonCommercial-NoDerivs). https://creativecommons.org/licenses/by-nc-nd/4.0/, This repository contains the metadata of the FAIR4Health project which was generated during the FAIRification processes of the clinical partners. According to the FAIR4Health Common Data Model, the FAIRification process results in FAIRified datasets which are managed by an onFHIR Repository. FAIR4Health Common Data Model utilizes the profiling approach of HL7 FHIR to ensure a satisfactory level of FAIR principles for health research datasets. Each clinical partner of the FAIR4Health project applied the FAIRification Workflow using the FAIR4Health software and the associated metadata was generated automatically at each onFHIR repository. Under each folder, the respective data owner's metadata is presented with FHIR's DocumentManifest, Provenance and CapabilityStatement resources., This work was supported by the FAIR4Health project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666., File List: - SAS CapabilityStatement - SAS DocumentManifest - SAS Provenance - IACS CapabilityStatement - IACS DocumentManifest - IACS Provenance - UNIGE CapabilityStatement - UNIGE DocumentManifest - UNIGE Provenance - UCSC CapabilityStatement - UCSC DocumentManifest - UCSC Provenance - UP CapabilityStatement - UP DocumentManifest - UP Provenance, Peer reviewed

The dataset compiles seagrass upper thermal limits (Tlimit) for survival, growth or biomass loss published in the literature and obtained by conducting a search on Web of Knowledge using the keywords combinations seagrass AND (temperature OR warming) and seagrass AND ("thermal limit" OR "thermal threshold" OR "critical temperature" OR "thermal niche”). The reference lists of the papers obtained with these searches were screened for additional relevant data. The dataset only includes data of seagrass populations growing submersed within their native geographical range. Tlimit are derived from empirical observations of seagrass die-off events attributed to heat waves, in combination with other simultaneous stressors (hypersalinity, Carlson et al 2018; low light availability, Moore and Jarvis 2008, Moore et al., 2014), or mesocosm experiments. Seagrasses in mesocosm experiments were exposed to at least 2 temperature treatments above average in situ summer temperature that extended the experimental thermal range beyond the Tlimit. Seagrasses were exposed to experimental temperatures for 6 to 120 days depending on the study. The Tlimit was defined as: a) the upper temperature at which shoot survival, shoot growth or biomass above optimal temperature started to decline in experimental studies; or b) the seawater temperature during the heat wave that triggered die-off events. For each study, the compiled dataset includes the species name, location and coordinates of the population studied, the Tlimit, the approach (i.e. experimental or empirical), the year the study was conducted and the data source. For experimental studies, the dataset also includes the temperature treatments seagrasses were exposed to. For each population studied, we obtained mean annual seawater temperature values for the 5 years before the thermal tolerance experiment or observation was conducted from the ORAS4 ocean reanalysis (Balmaseda, Mogensen, Weaver, 2013), which provides monthly 3D temperature global fields from 1958 to present with a spatial resolution of 1 degree in the horizontal and ~10 m in the vertical. Those temperatures aim at representing the regional characteristics, rather than the local features which cannot be captured by the coarse spatial resolution, [Relationship between files] The file "variables_Marbà_et_al_ 2022.xlsx" defines the variables used in the dataset. The full references of the sources of data compiled in the dataset are provided in the file "References_Dataset_Marba_et_al_2022.docs"., [Environmental/experimental conditions] The dataset includes target experimental temperatures and average annual seawater temperature natural populations were exposed to, calculated for the 5 years before conducting the experiment or the occurrence of seagrass mass-mortality event., Dataset of seagrass upper thermal limits for survival, growth or biomass loss derived from empirical observations of seagrass die-off events attributed to heat waves or mesocosm experiments., This work was funded by the Spanish Ministry of Economy, Industry and Competivness with the projects MedShift (CGL2015-71809-P), SumaEco (RTI2018-095441-B-C21) and Clifish (CTM2015-66400-C3-2-R), the European Union’s Horizon 2020 SOCLIMPACT project (grant agreement No 776661) and the King Abdullah University of Science and Technology (KAUST subaward number 3834). S.B. was supported by a Juan de la Cierva Formación contract funded by the Spanish Ministry of Economy, Industry and Competitiveness., File List: - variables_Marbà_et_al_ 2022.xlsx - dataset_Marbà_et_al_2022_(seagrass thermal limits).xlsx - References_Dataset_Marba_et_al_2022.docs, Peer reviewed

[Description of methods used for collection/generation of data] Splicing functional assays of ATM variants by hybrid minigenes, [Instrument- or software-specific information needed to interpret/reproduce the data] - Sequence analysis (.ab1 files) with appropriate software, such as SnapGene Viewer (https://www.snapgene.com/snapgene-viewer/). - Fluorescent Fragment analysis (.fsa files) with Peak Scanner v1.0 (https://www.thermofisher.com/order/catalog/product/4381867#/4381867)., [Standards and calibration information] Fragment analysis electrophoresis was run with Liz1200 as size standard., [Environmental/experimental conditions] As indicated in the submitted manuscript., [People involved with sample collection, processing, analysis and/or submission] Bueno-Martínez, Elena (E.B.-M); Valenzuela-Palomo, Alberto (A.V.-P.); Sanoguera-Miralles, Lara (L.S.-M.); Velasco Sampedro, Eladio A (E.A.V.-S.) Conceptualization, E.A.V.-S.; Data curation, E.B.-M., L.S.-M., A.V.-P.; Formal analysis, E.B.-M., L.S.-M., A.V.-P. and E.A.V.-S.; Funding acquisition, E.A.V.-S.; Investigation, E.B.-M., L.S.-M., A.V.-P. and E.A.V.-S.; Methodology, A.V.-P., L.S.-M., E.B.-M., A.G.-A., and E.A.V.-S.; Supervision, E.A.V.-S., Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). This only allows people to download and share your work for no commercial gain and for no other purposes., This dataset corresponds to a systematic splicing analysis of splice-site variants of the breast cancer susceptibility gene ATM, which had been sequenced in 113,000 women who took part of the large-scale sequencing project BRIDGES. A set of 381 variants at the intron-exon boundaries were identified, 128 of which were predicted spliceogenic. After further filtering, we ended up selecting 56 variants for splicing analysis in four minigene constructs. Forty-eight variants impaired splicing, 29 of which were classified as pathogenic/likely pathogenic variants according to ACMG/AMP-based guidelines, so that carrier patients and families may benefit from tailored prevention protocols and personalized therapies., Eladio A. Velasco (EAV) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 634935. EAV lab is supported by grants from the Spanish Ministry of Science and Innovation, Plan Nacional de I+D+I 2013-2016, ISCIII (PI17/00227 and PI20/00225) co-funded by FEDER from Regional Development European Funds (European Union) and from the Consejería de Educación, Junta de Castilla y León, ref. CSI242P18 (actuación cofinanciada P.O. FEDER 2014-2020 de Castilla y León). Elena Bueno-Martínez is a postdoctoral researcher funded by the University of Valladolid (POSTDOC-UVA05, 2022-2025).Alberto Valenzuela-Palomo is supported by a predoctoral fellowship from the Consejería de Educación, Junta de Castilla y León (2018–2022). Lara Sanoguera-Miralles is supported by a predoctoral fellowship from the AECC-Scientific Foundation, Sede Provincial de Valladolid (2019–2023)., Folder: cDNA_SEQUENCES. Transcript Sequencing. Sub-Folders: • Sequences_ATM 4-9 • Sequences_ATM 11-17 • Sequences_ATM 17-22 • Sequences_ATM 25-29 • Sequences_ATM 49-52 Folder: FRAGMENT_ANALYSIS. Fluorescent Fragment Analysis. Sub-Folders: • Fragment_Analysis_ATM 4-9 • Fragment_Analysis_ATM 11-17 • Fragment_Analysis_ATM 25-29 • Fragment_Analysis_ATM 49-52, Peer reviewed

This data set contains the underlying data of two scientific publications: Lobo Prieto, Ana; Tena, Noelia; Aparicio-Ruiz, Ramón; Morales, María T.; García-González, Diego L.; 2020; Tracking sensory characteristics of virgin olive oils during storage: Interpretation of their changes from a multiparametric perspective. Molecules 25, 1686. Food Control 123, 107823. https://doi.org/10.3390/molecules25071686. Lobo-Prieto, Ana; Tena, Noelia; Aparicio-Ruiz, Ramón; García-González, Diego L.; Sikorska, Ewa; 2020. Monitoring virgin olive oil shelf-life by fluorescence spectroscopy and sensory characteristics: A multidimensional study carried out under simulated market conditions. Foods 9, 1846. https://doi.org/10.3390/foods9121846. Virgin olive oil is inevitably subject to an oxidation process during storage that can affect its quality due to off-flavors that develop before the oil surpasses its ‘best before’ date. Due to the complexity of the oxidation process at moderate conditions, where many parameters are involved, a multiparametric study is necessary to understand globally the physical-chemical changes and sensory quality degradation in a real storage experiment. In this context, a storage experiment of 27 months was performed for four monovarietal virgin olive oils, bottled in transparent 500-mL PET bottles and subjected to conditions close to a supermarket scenario (1000 lx with 12 hours of light/dark cycles and 25 °C). Physical-chemical quality parameters, phenolic compounds, α-tocopherol concentration and the degradation products of chlorophyll a were determined every month. Simultaneously, an accredited sensory panel assessed their sensory characteristics. The different physical-chemical quality parameters studied (peroxide value, free acidity, K232 and K270) were determined following the International Olive Council (COI) standard methods. The concentration of total phenols was determined by solid phase extraction and HPLC analysis, following the method described by Aparicio-Ruiz et al. (2016), and the α-tocopherol concentration was determined using the method ISO 9936:2016. Finally, the degradation compounds of chlorophyll a (pheophytin a and pyropheophytin a) were measured using the method specified by ISO 29841:2012. The dataset presents the results of all the parameters mentioned above for the four monovarietal virgin olive oils subjected to storage at moderate conditions during 27 months., Project Info: “Nuevas estrategias de verificación del tiempo de vida útil y la fecha de consumo preferente del aceite de oliva virgen mediante indicadores espectroscópicos” and “Materiales de referencia de notas sensoriales positivas de aceites de oliva vírgenes extra basado en marcadores volatiles y estudios de interacción aroma-aroma“, funded by State Research Agency. Proyectos AGL2015-69320-R and RTI2018-101546-B-C21/22 financiado por MCIN/ AEI /10.13039/501100011033/ FEDER “Una manera de hacer Europa”., The dataset consists of: a numerical quantitative file saved both in .xlsx and .ods formats (“VOO_storage_xlsx.xlsx” - “VOO_storage _ods.ods”. A README file “VOO_storage_README_rtf.rtf”, Peer reviewed