Resultados de investigación

Á.H.L.: predoctoral contract of Junta de Castilla y León with also European Social Funds (ESF; Operational Programme for Castilla y León); -A.C., J.J.V.: Junta de Castilla y León (Project BU235P18) with also European Fund for Economic and Regional Development (EFRD) funding; -Á.H.L., J.J.V.: Spanish Ministry of Economy and Competitiveness (MINECO) with also EFRD funding (Project CGL2016-77560); -C.M., C.M.H., Á.H.L., A.C.: The Leakey Foundation (Neandertal Fire Technology Project) -C.M.: ERC Consolidator Grant project PALEOCHAR – 648871 https://erc.europa.eu/funding/consolidator-grants; -C.M., C.M.H., Á.H.L.: MINECO and EFRD funding (HAR2015-68321-P Project).

Regional Government of Castilla y Leon (Junta de Castilla-León) (Ref. BU034U16) through the European Regional Development Fund, and from the Spanish Ministry of Economy, Industry and Competitiveness, under the I+D+i State Program Challenges for Society (Ref. ENE-2014–54601-R)

Meteorological Database for CIE standard sky classification. Location: Burgos, Spain, the Regional Government of Castilla y León under the “Support Program for Recognized Research Groups of Public Universities of Castilla y León” (BU021G19) and the Spanish Ministry of Science & Innovation under the I+D +i state program “Challenges Research Projects” (RTI2018-098900-B-I00). Diego Granados López express his thanks to Junta de Castilla-León for economic support (PIRTU Program, ORDEN EDU/556/2019).

The dataset is made up of eleven networks, one corresponding to each city (Madrid, Barcelona, Valladolid, Burgos, Salamanca, León, Palencia, Zamora, Ávila, Segovia and Soria). The nodes represent retail stores listed in the Yellow Pages. Each node contains the category code to which they are classified according to 68 codes of The North American Industry Classification System for small businesses (NAICS). There is a link between both nodes if the stores are within a linear distance of 100 meters., Spanish Ministry of Science, Innovation and Universities (excellence networks HAR2017-90883-REDC and RED2018?102518?T, and project PGC2018-094754-B-C21), the Junta de Castilla y León - Consejería de Educación through BDNS 425389 and the predoctoral grant awarded to Virginia Ahedo (supported by the European Social Fund), the Generalitat de Catalunya through grant 2017SGR-896 and the Universitat Rovira i Virgili through grant 2019PFR-URV-B2-41

Higher Education is the driving force behind the social and economic development of countries, the ultimate aim of which is to provide quality academic training. At present, teaching-learning models in virtual environments imply important challenges, specifically in the current situation of the COVID-19 health crisis. Some of these challenges will be addressed in this paper. In this study we worked with 224 third-year undergraduate students in Health Sciences degrees over two academic years during the pandemic situation. The objectives were 1) to check whether there were significant differences in student satisfaction with the teaching process in the first year of the pandemic (e-Learning teaching) vs. the second year (b-Learning teaching); 2) to check whether there were significant differences in academic results between the two groups. A quantitative research (a 2x2 factorial design and ANOVA and ANCOVA analyses were applied) and a qualitative research (a comparative design and categorisation analysis and co-occurrence analysis were applied) were carried out. The results indicate differences in some aspects of satisfaction and learning outcomes in favour of teaching in the second year. Students rated positively the use of active methodologies and technological resources, although they concluded that these required more work time., Junta de Castilla y León a los Grupos de Investigación reconocidos, call 2019 Nº BU032G19 GIR DATAHES y Proyect “Self-Regulated Learning in SmartArt Erasmus+ Adult Education” financiado por la European Commission, call 2019 Nº 2019-1-ES01-KA204-095615

The current Matlab file records a neural network to for CIE standard sky classification. Location: Burgos, Spain.

The dataset is made up of three different filtration experiences on wastewater producer on a single slaghterhouse.

The dataset contains all raw data of the work "Smart sensory polymer for straightforward Zn(II) detection in pet food samples", Authors gratefully acknowledge the financial support provided by the Spanish Agencia Estatal de Investigación (State Research Agency) (grant PID2020-113264RB-I00/AEI/10.13039/501100011033).