BUSCADOR RECOLECTA

Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR)., Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR)., Peer reviewed

Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179., European Commission: CAPTOR - Collective Awareness Platform for Tropospheric Ozone Pollution (688110), Peer reviewed

Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2017 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179., European Commission: CAPTOR - Collective Awareness Platform for Tropospheric Ozone Pollution (688110), Peer reviewed

Datasets are available in CSV format, being the field delimiter « , » (comma). For further clarification, please look at the README files of every data subset., Part B of this dataset is available at http://hdl.handle.net/2445/139554 under embargoed access, This dataset refers to PERCEIVE tasks 2.2, 2.3, 2.4 and 2.5. These tasks analyze the data sets developed by PERCEIVE task 2.1 in order to identify, quantify and synthetize the most important variables to asses identification with EU by citizens. The results feed an update of the data set developed in task 2.1. Finally, these data is be used to model convergence of EU citizens' identification in the case study regions. Data consists of the quantitative statistical information used to produce all the results of WP2 and the indicators regarding the variables of interest obtained by means of different statistical techniques. Moreover, data on absorption of EU funds, provided by the EC and other research centres and collected in task 2.5, is be gathered in this data set, together with the results of convergence analysis. This data set is be used to evaluate the EU citizens’ perception of the EU project in relation to regional performance of the cohesion policy and institutional quality. WP2 uses primary data from the survey developed in WP1 (“PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Task1.3: Survey at citizen level” data set) and also comparative case studies on EU countries and regions, coming also from WP1 (”PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Task1.1: The framework for the comparative analysis” and ”PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Task1.2: Focus group with Cohesion Policy practitioners” data sets). The data set incorporates data from existing sources including Eurostat and information resulting from the Survey conducted at WP1 of the PERCEIVE’s project. As regards secondary data, no additional treatment is needed. Info about the sources of such data, including references to technical details, is provided in the dataset's codebook.

Datasets are available in CSV format, being the field delimiter « , » (comma). For further clarification, please look at the README files of every data subset., Part A of this dataset is publicly available at http://hdl.handle.net/2445/137318, This dataset refers to PERCEIVE tasks 2.4 and 2.5. These tasks analyze the data sets developed by PERCEIVE task 2.1 in order to identify, quantify and synthetize the most important variables to asses identification with EU by citizens. The results feed an update of the data set developed in task 2.1. Finally, these data is be used to model convergence of EU citizens' identification in the case study regions. Data consists of the quantitative statistical information used to produce all the results of WP2 and the indicators regarding the variables of interest obtained by means of different statistical techniques. Moreover, data on absorption of EU funds, provided by the EC and other research centres and collected in task 2.5, is be gathered in this data set, together with the results of convergence analysis. This data set is be used to evaluate the EU citizens’ perception of the EU project in relation to regional performance of the cohesion policy and institutional quality. WP2 uses primary data from the survey developed in WP1 (“PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Task1.3: Survey at citizen level” data set) and also comparative case studies on EU countries and regions, coming also from WP1 (”PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Task1.1: The framework for the comparative analysis” and ”PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Task1.2: Focus group with Cohesion Policy practitioners” data sets). The data set incorporates data from existing sources including Eurostat and information resulting from the Survey conducted at WP1 of the PERCEIVE’s project. As regards secondary data, no additional treatment is needed. Info about the sources of such data, including references to technical details, is provided in the dataset's codebook.

We provide the code for producing Functional Urban Areas by means of using OSM and Google maps data., Dades associades a l'article: Obaco A., Moisés; Royuela Mora, Vicente (2019): Identifying functional urban areas in Ecuador using a varying travel time approach,. Publicat a: Geographical Analysis, 2020, vol. 52, n. 1. (https://onlinelibrary.wiley.com/doi/10.1111/gean.12190). DOI: https://doi.org/10.1111/gean.12190., Podeu consultar l'article associat a: http://hdl.handle.net/2445/154578, Identifying integrated urban areas is an important issue for urban analysis and policy evaluation. In this paper, we provide a set of FUAs suitable for monitoring urbanization in Ecuador, a less developed country without commuting data. We do so substituting such socioeconomic flows by available information on road structure, which allow us to work with accessibility based on a varying travel time. In order to build Ecuadorean FUAs, we calibrate our methodology considering Colombian data. We perform several sensitivity analysis and robustness checks to Ecuador with alternative sources of socioeconomic flows. We conclude that the use of GIS data and the calibrated thresholds provide a set of FUAs close to the ones that would result of using commuting information.