The H2020 CAPTOR project deployed three testbeds in Spain, Italy and Austria with low-cost sensors for the measurement of tropospheric ozone (O3). The aim of the H2020 CAPTOR project was to raise public awareness in a project focused on citizen science. Each testbed was supported by an NGO in charge of deciding how to raise citizen awareness according to the needs of each country. The data presented in this document correspond to the raw data captured by the sensor nodes in the Spanish testbed using SGX Sensortech MICS 2614 metal-oxide sensors. The Spanish testbed consisted of the deployment of twenty-five nodes. Each sensor node included four SGX Sensortech MICS 2614 ozone sensors, one temperature sensor and one relative humidity sensor. Each node underwent a calibration process by co-locating the node at an EU reference air quality monitoring station, followed by a deployment in a sub-urban or rural area in Catalonia, Spain. All nodes spent two to three weeks co-located at a reference station in Barcelona, Spain (urban area), followed by two to three weeks co-located at three sub-urban reference stations near the final deployment site. The nodes were then deployed in volunteers' homes for about two months and, finally, the nodes were co-located again at the sub-urban reference stations for two weeks for final calibration and assessment of potential drifts. All data presented in this paper are raw data taken by the sensors that can be used for scientific purposes such as calibration studies using machine learning algorithms, or once the concentration values of the nodes are obtained, they can be used to create tropospheric ozone pollution maps with heterogeneous data sources (reference stations and low-cost sensors).The near infrared spectra of thirty-three freeze-dried and ground organic waste samples of various biochemical composition were collected on four different optical systems, including a laboratory spectrometer, a transportable spectrometer with two measurement configurations (an immersed probe, and a polarized light system) and a micro-spectrometer. The provided data contains one file per spectroscopic system including the reflectance or absorbance spectra with the corresponding sample name and wavelengths. A reference data file containing carbohydrates, lipid and nitrogen content, biochemical methane potential (BMP) and chemical oxygen demand (COD) for each sample is also provided. This data enables the comparison of the optical systems for predictive model calibration based for example on Partial Least Squares Regression (PLS-R) [1], but could be used more broadly to test new chemometrics methods. For example, the data could be used to evaluate different transfer functions between spectroscopic systems [2]. This dataset enabled the research work reported by Mallet et al. 2021 [3].Design smells are recurring patterns of poorly designed (fragments of) software systems that may hinder maintainability. Role-stereotypes indicate generic responsibilities that classes play in system design. Although the concepts of role-stereotypes and design smells are widely divergent, both are significant contributors to the design and maintenance of software systems. To improve software design and maintainability, there is a need to understand the relationship between design smells and role stereotypes. This paper presents a fine-grained dataset of systematically integrated design smells detection and role-stereotypes classification data. The dataset was created from a collection of twelve (12) real-life open-source Java projects mined from GitHub. The dataset consists of 18 design smells columns and 2,513 Java classes (rows) classified into six (6) role-stereotypes taxonomy. We also clustered the dataset into ten (10) different clusters using an unsupervised learning algorithm. Those clusters are useful for understanding the groups of design smells that often co-occur in a particular role-stereotype category. The dataset is significant for understanding the non-innate relationship between design smells and role-stereotypes.Transboundary emissions of smoke-haze from land and forest fires have recurred annually during the dry period (June to October, over the past few decades) in South East Asia. Hazardous air quality has been recorded in Malaysia during these episodes. Agricultural practices such as slash-and-burn of biomass and peat fires particularly in Sumatera and Kalimantan, Indonesia, have been implicated as the major causes of the haze.  https://www.selleckchem.com/products/tucidinostat-chidamide.html  Past findings have shown that a diversity of microbes can thrive in air including in smoke-haze polluted air. In this study, metagenomic data were generated to reveal the diversity of microorganisms in air during days with and without haze. Air samples were collected during non-haze (2013A01) and two haze (2013A04 and 2013A05) periods in the month of June 2013. DNA was extracted from the samples, subjected to Multiple Displacement Amplification and whole genome sequencing (Next Generation Sequencing) using the HiSeq 2000 Platform. Extensive bio-informatic analyses of the raw sequence data then followed. Raw reads from these six air samples were deposited in the NCBI SRA databases under Bioproject PRJNA662021 with accession numbers SRX9087478, SRX9087479 and SRX9087480.Ottawa sand and Angular sand consist of particles with distinct shapes. The x-ray computed tomography (XCT) image stacks of their in-situ confined compressive testings are provided in this paper. For each image stack, a contact network, a thermal network and a network feature - edge betweenness centrality - of each edge in the networks are also provided. The readers can use the image data to construct digital sands with applications of (1) extracting microstructural parameters such as particle size, particle shape, coordination number and more network features; (2) analysing mechanical behaviour and transport processes such as fluid flow, heat transfer and electrical conduction using either traditional simulation tools such as finite element method and discrete element method or newly network models which could be built based on the network files available here.