BUSCADOR RECOLECTA

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., 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., 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., 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., Peer reviewed

The data folder is includes the five different data sets used in the paper along with a metadata file, where the different features are explained., This dataset contains python scripts to calibrate tropospheric ozone sensor data obtained in the H2020 Captor project using sensor fusion techniques. Four different models are implemented; Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN),Random Forest(RF) and Support Vector Regression (SVR). The methodology consists of first applying the PLS procedure to derive orthogonal components (to avoid multicollinearity problems). Afterwards, the components are used as features in the machine learning algorithms, so the models are trained. The scripts available in this repository have been used in the elaboration of the paper: "Multi-sensor data fusion calibration in IoT air pollution platforms" submitted to the IEEE Internet of Things journal., National Spanish funding; Regional Project; EU H2020 CAPTOR Project; AGAUR SGR44; 10.13039/501100011033-Agencia Estatal de Investigación; Spanish Ministry of Economy, Industry and Competitiveness, Peer reviewed

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

These data are associated to the paper with the same title of the authors, and are structured according to the figures in the paper: http://hdl.handle.net/2117/335727, The use of Cold Atmospheric Plasma (CAP) in oncology is being recently studied as a novel potential anti-cancer therapy. However, the beneficial effects of CAP for treating osteosarcoma have mostly been demonstrated in 2-dimensional cultures of cells, which do not mimic the complexity of the 3-dimensional (3D) bone microenvironment. In order to evaluate the effects of CAP in a relevant context of the human disease, we developed a 3D tissue-engineered model of osteosarcoma using a bone-like scaffold made of collagen type I and hydroxyapatite nanoparticles. Human osteosarcoma cells cultured within the scaffold showed a high capacity to infiltrate and proliferate and to exhibit osteomimicry in vitro. As expected, we observed significantly different functional behaviors between monolayer and 3D cultures when treated with Cold Plasma-Activated Ringer’s Solution (PAR). Our data reveal that the 3D environment not only protects cells from PAR-induced lethality by scavenging and diminishing the amount of reactive oxygen and nitrogen species generated by CAP, but also favours the stemness phenotype of osteosarcoma cells. This is the first study that demonstrates the negative effect of PAR on cancer stem-like cell subpopulations in a 3D biomimetic model of cancer. These findings will allow to suitably re-focus research on plasma-based therapies in future

This dataset is related to the protocol developed by the authors on the evaluation of the effects of cold atmospheric plasma and plasma-treated liquids in cancer cell cultures. More specifically, the data correspond to the anticipated results presented in the paper submitted to Nature Protocols, The present dataset corresponds to the anticipated results that can be obtained following the protocol described in the article. In particular, dataset corresponding to Figure 8 presents the absorbance values from the WST-1 cytotoxicity assay as a function of the well size where the experiment is performed. Data from Figure 9b show absorbance values from the WST-1 cytotoxicity assay as a function of the treatment time. Data from figure 9c-e show the absorbance or fluorescence values obtained in the experiments to quantify RONS, together with the values from the calibration curves. Data fromfigure 10 a provide the list and values of mRNA fold induction of the genes analyzed, and data from figure 10c show the pixel intensity obtained for the phospho-kinase antibody array.