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Mechanical properties such as the Young modulus, shear modulus and Poisson’s coefficient are very important to define different materials applications, for basic research and for quality control procedures. Impulse excitation technique (IET) is a non-destructive, easy and fast method for characterization of elastic and acoustic properties of materials. The technique consists in sending a mechanical impulse in a sample and measuring the output sound wave. Commercial instruments are widely spread in metal industry, but they are not diffused in academic research centres. Such instruments can be easily self-built at low cost, allowing a much wider diffusion and exploitation in many fields involving materials characterization, since they guarantee high precision and high data reproducibility. For a proper acoustic characterization, necessary to obtain reliable mechanical data, a calibration of the instrument must be performed, for a proper association of the acoustic response to the features of each specific material. In this data article, a data set of impulses, collected on different materials by a self-built instrument for IET, named IETeasy, is provided for mechanical properties characterization by a self-built IET tool, and multivariate statistical analysis purposes. The aim is double in the short term: on one hand, providing a verified data set useful to develop, test and verify methods of analysis and tailor the IETeasy instrument on the needs of each specific user; on the other hand, giving a benchmark for any one designing, building and testing his IET home-made instrument. In the long term, since the data base is open, any contribution consisting in data collected by similar self-made or commercial instruments can be added to the data base, with the aim of building a large collection of data, useful for automatic recognition of sound outputs by machine learning or other multivariate or monovariate data analysis approaches, and for instrument performance comparison and alignment.

In the present paper we present a dataset that provides data of two experiments in which we investigated the presence of Inhibition of Return (IOR) during and after a visual search. Participants either had to saccade (Experiment 1 and 2) or make a manual response (Experiment 2) to a probe during a visual search task (searching for a target letter among a set of distractors) or immediately after its completion. The data consist of the unprocessed raw data and one csv-file of the processed eye tracking data on eight (Experiment 1) and 18 (Experiment 2) participants, respectively. In total, we obtained 5,116 trials in Experiment 1 and 18,424 in Experiment 2. The data set is stored at the repository DOOR hosted by the University of Krems ( https://door.donau-uni.ac.at/view/o:1014 ). Detailed information about the experiments and the interpretation of the data can be found in the paper “Post-search IOR: Searching for inhibition of return after search” (Hofler et al., 2019) [1] .

It is known that solar flares can affect the near-Earth space, incurring in consequences for radio communications. Therefore, there is a need to research systems for monitoring solar events. This article presents a data set which can be used in the analysis of such events. This data set originated from a set of records from magnetic attributes and solar flare data. In order to create this data set, authors used the SunPy library which provided access to data from the Joint Science Operations Center (JSOC) and Space Weather Prediction Center (SWPC). By integrating data from those two sources, 8,874 samples were obtained comprehending the period between May, 2010 and December, 2019. The collected data were stored as a CSV data set. This data set can be used to support the research of solar flare forecasting, as well as to be compared to other data sets or expanded with new attributes.

This document compiles the data related to a high temperature heat injection test, which was carried out at an injection temperature of 74 °C in a shallow aquifer and is presented by Heldt et al. [1] . The data set contains transient measurements of temperatures at 18 wells in 10 depths and measurements of the experimental boundary conditions (injection temperature and flow rate) at a temporal resolution of up to 1 min. The spatial configuration and the technical details about where and how the data have been measured are provided. In addition, data of a multilevel multi well pumping test are shown. The presented data is useful to gain insights into the thermohydraulic processes induced by a high temperature heat injection test and can furthermore be used for the development and verification of numerical models of the presented experiment and similar applications like high temperature aquifer thermal energy storage.

This article describes a mass spectrometric data set from rat retinae spiked with indexed Retention Time (iRT) peptides. The provided data set can be used as a spectral library to investigate for instance eye disorders as well as ocular function by data-independent acquisition (DIA) based mass spectrometry. Consequently, there is no urgent need to create an own spectral library, which requires money, time, effort as well as tissue. Besides the use as a spectral library, this data set can improve our knowledge about proteins present in the rat retina and thus the protein pathways within this tissue. The data set may also help to determine optimal parameters for peptide identification by mass spectrometry. To generate the presented data set, six rat retinae were homogenized with glass beads and pooled. The pooled sample was fractionated by SDS-PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis) followed by tryptic in-gel digestion. The fractionation of the pooled sample was repeated for further 4 times, to end up with in total 5 technical replicates. Peptide extracts were spiked with iRT peptides and analyzed by data-dependent (DDA) nanoHPLC-ESI-MS/MS resulting in 60 files. All resulting data files are hosted in the public repository ProteomeXchange under the identifier PXD021937.

Dataset from a large-scale air quality citizen science campaign is presented (xAire, 725 measurements, see Ref. [1] ). A broad partnership with 1650 citizens from communities around 18 primary schools across Barcelona (Spain) provided the capacity to obtain unprecedented high-resolution NO2 levels which had in turn the capacity to provide an updated asthma Health Impact Assessment. Nitrogen dioxide levels being obtained in a 4-week period during February and March 2018 with Palmes’ diffusion samplers are herein provided. Dataset includes NO2 levels from outdoor locations n=671, playgrounds n=31, and inside school buildings (mostly classrooms) n=23. Data was calibrated and annualized with concentration levels from automatic reference stations. It is shown that NO2 levels vary considerably with at some cases very high levels. Strong differences might also however be explained by the fact that ambient air pollution is reduced exponentially with distance from an emission source like traffic meaning that two samplers located about 100 m away can measure a tenfold difference concentration level.

This academic research explicit the data-set of academic difficulties among different age groups of students studying in various schools, colleges or Universities during the COVID-19 induced lockdown. The western part of Uganda comprises 26 districts and the survey was conducted in those regions employing a simple random sampling technique. The dataset is descriptive and an aggregate of 405 students participated in this survey. Among that, 253 students are from rural regions, 59 students are from semi-urban regions and 93 students are from urban regions. This survey was started in April 2020 and data were collected till June 2020. A statistical run was made with the aid of SPSS version 20 software to evaluate the significance level (P-Value

The paper presents a collection of electroencephalography (EEG) data from a portable Steady State Visual Evoked Potentials (SSVEP)-based Brain Computer Interface (BCI). The collection of data was acquired by means of experiments based on repetitive visual stimuli with four different flickering frequencies. The main novelty of the proposed data set is related to the usage of a single-channel dry-sensor acquisition device. Different from conventional BCI helmets, this kind of device strongly improves the users’ comfort and, therefore, there is a strong interest in using it to pave the way towards the future generation of Internet of Things (IoT) applications. Consequently, the dataset proposed in this paper aims to act as a key tool to support the research activities in this emerging topic of human-computer interaction.

Facial and other human recognition techniques are being used for a growing number of applications, ranging from device security to surveillance video identification to forensics. Data sets are required to test recognitions algorithms. This data set facilitates the evaluation of the impact of multiple factors on algorithm performance. The data set includes images taken under five different lighting levels (which vary in light brightness and temperature), seven different lighting positions and five different subject positions. The data set includes data collected for all combinations of the foregoing three collection variables, for a total of 175 images per subject. In addition, sets of data under three different occlusion conditions (no occlusion, glasses and hat) have been collected. Each data set includes images taken under all lighting level, lighting position and subject position combinations, for a total of 525 images of each subject. The images are all taken in the same location with the same background and camera equipment.

This article provides data for five different neuropsychiatric disorders—Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome–along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters. Keywords: Functional magnetic resonance imaging, Functional connectivity, Effective connectivity, Unsupervised learning, Clustering, Genetic algorithm, Psychiatric disorders

This article presents the raw and analyzed data from a set of experiments performed to study the role of junctions on the electrostatic gating of carbon nanotube (CNT) network field effect transistor (FET) aptasensors. It consists of the raw data used for the calculation of junction and bundle densities and describes the calculation of metallic content of the bundles. In addition, the data set consists of the electrical measurement data in a liquid gated environment for 119 different devices with four different CNT densities and summarizes their electrical properties. The data presented in this article are related to research article titled “Metallic-semiconducting junctions create sensing hot-spots in carbon nanotube FET aptasensors near percolation” (doi:10.1016/j.bios.2018.09.021) [1].

This is a data set of blood spatter patterns scanned at high resolution, generated in controlled experiments. The spatter patterns were generated with a rifle or a handgun with varying ammunition. The resulting atomized blood droplets travelled opposite to the bullet direction, generating a gunshot backspatter on a poster board target sheet. Fresh blood with anticoagulants was used; its hematocrit and temperature were measured. The main parameters of the study were the bullet shape, size and speed, and the distance between the blood source and target sheet. Several other parameters were explored in a less systematic way. This new and original data set is suitable for training or research purposes in the forensic discipline of bloodstain pattern analysis.

The development of future generations of Ni-base superalloys will depend on a systematic understanding of how each alloying element affects the fundamental properties of Ni-base superalloys, particularly with respect to their creep behavior. First, this article presents the temperature-dependent data of all factors entering into dilute impurity diffusion for 26 Ni-X alloy systems, including atomic jump frequencies, thermodynamic parameters, and diffusivity plots. Second, this article presents the data used to calculate the relative creep rate ratios showing the effect of each of the 26 alloying elements, X, on the dilute Ni-X alloy. The dataset refers to "A comprehensive first-principles study of solute elements in dilute Ni alloys: Diffusion coefficients and their implications to tailor creep rate" by Hargather et al. [1].

Ambient air pollution is a major risk to the human health and to the environment. The data presented quantifies the (PM10) contribution of the Zouk Mikael power plant to the ambient air pollution in Lebanon for the year 2014. The data is the outcome of a computer simulation using The Air Pollution Model (TAPM), taking into account the emission source data, the spatio-temporal meteorological conditions, the terrain height, and the land cover characteristics. The data set presents the annual, seasonal and monthly averages of the spatial distribution of the ground-level particulate (PM10) concentrations in the ambient air. The data set also includes spatial distribution of the maximum concentrations, which revealed two zones of elevated concentrations. Monthly averages and maximum concentrations in these two zones are also reported. Analysis of the data can provide information on the health risk the residents in the affected areas are subjected to. The data can also provide insight on the impact of the meteorological conditions (temperature and velocity) and the topography on pollutant dispersion in regions bounded by the sea and by a mountain range. Keywords: Ambient air pollution, PM10, Zouk Mikael power plant, TAPM

High-entropy alloys (HEA), a new class of engineering alloy, are characterized by high concentrations of multiple main elements. These alloys have revealed a vast and largely unexplored compositional space that gives substantial promise for the discovery of new and interesting alloys and properties. In this data article, calculated data and applied inferential statistics are given for six structures related to the calculation of stacking fault energy in a refractory AlNbTaTiV BCC high-entropy alloy (HEA). Global populations of 120 atomic permutations of a special quasirandom structure are calculated for four of the six structures, and a complete statistical inference analysis is performed. Partial sample distributions are created for two of the six structures, and the trends and statistical parameters of the unknown global populations are predicted. The dataset refers to the research article “Stacking fault energies on the {112} planes of an AlNbTaTiV BCC high-entropy alloy from first-principles calculations, analyzed with inferential statistics” by Strother and Hargather [1].

The data set includes hourly time series for a period of one year of electricity demand of three different types of industries. The industries include a small food processing company, one small paper industry and one medium-large paper producing company. The data have been synthetized from two years of measured data from industries in Chile using a comprehensive clustering analysis. The synthetic data possess the same statistical characteristics as the measured data but are provided normalized to one kWh and anonymized in order to be used without confidentiality issues. The data were originally used in the associated paper [1] to assess the demand side management potential of the industries but these can be used for further energy system modelling exercises including these types of industries.

A data set was generated comprising currently available competitive and allosteric human protein kinase inhibitors confirmed by X-ray crystallography. This data set has been used to systematically explore structural relationships between these types of inhibitors with different mechanisms of action. A major finding of this study has been that these different inhibitor types frequently displayed structural relationships and essentially represented a structural continuum [1]. Use of the data set is not limited to the inhibitor-centric exploration of structural relationships. The collection of kinase inhibitors with structurally confirmed distinct mechanisms of action can also be used, for example, to aid in structure-based drug design or the search for new allosteric kinase inhibitors.

This article presents a test data set combining data relevant for power system reliability analysis, including network data, reliability data, basic interruption cost data, and exemplary operating state data. The data set originated as a data set for testing power market models with network constraints and was later extended for use in integrated power market and power system reliability analyses. The network model consists of 25 buses and four price (market) areas representing small regions of the Nordic power system. Three of the areas are meshed but with relatively weak connections between them, and a fourth area is represented by a single bus connected by an HVDC cable. Reliability data (failure rates and outage times) are based on statistics from the Norwegian standardised system FASIT for collection, calculation and reporting of disturbance and reliability data. Data set for power system reliability analysis using a four-area test network

Spectral libraries generated by data dependent acquisition (DDA) are a useful tool for the analysis of data created by data independent acquisition (DIA) in mass spectrometry. The quality of DIA analysis is dependent on the quality of the spectral library. We used cerebrospinal fluid (CSF) of patients with Parkinson's disease and healthy controls to create a spectral library of human CSF proteome. To this date, there is no validated CSF biomarker for Parkinson's disease. This data set may therefore be valuable for the future analysis of CSF proteins. Part of the samples consisted of fractions that were separated by gel electrophoresis. After tryptic digestion, all samples were spiked with indexed retention time (iRT) peptides and were measured using a DDA mass spectrometry approach. The here provided data set can be used as a CSF-specific spectral library. Data files generated from the described workflow are hosted in the public repository ProteomeXchange under the identifier PXD013487.

The data of COVID-19 disease in China and then in South Korea were collected daily from several different official websites. The collected data included 33 death cases in Wuhan city of Hubei province during early outbreak as well as confirmed cases and death toll in some specific regions, which were chosen as representatives from the perspective of the coronavirus outbreak in China. Data were copied and pasted onto Excel spreadsheets to perform data analysis. A new methodology, Patient Information Based Algorithm (PIBA) [1], has been adapted to process the data and used to estimate the death rate of COVID-19 in real-time. Assumption is that the number of days from inpatients to death fall into a pattern of normal distribution and the scores in normal distribution can be obtained by observing 33 death cases and analysing the data [2]. We selected 5 scores in normal distribution of these durations as lagging days, which will be used in the following estimation of death rate. We calculated each death rate on accumulative confirmed cases with each lagging day from the current data and then weighted every death rate with its corresponding possibility to obtain the total death rate on each day. While the trendline of these death rate curves meet the curve of current ratio between accumulative death cases and confirmed cases at some points in the near future, we considered that these intersections are within the range of real death rates. Six tables were presented to illustrate the PIBA method using data from China and South Korea. One figure on estimated rate of infection and patients in serious condition and retrospective estimation of initially occurring time of CORID-19 based on PIBA.

The data presented in this data paper describe the early growth of cover crop cultivated in growth chamber under non-limiting conditions. Seventeen species of four botanical groups were described after one month of growth. Traits related to plant growth and leaf area development were measured (five traits) and calculated (eight traits). This data set is made available to enable comparisons between dataset, extended analysis and meta-analysis on cover crop traits. The data presented in this article were used on the research article entitled “Leaf area development strategies of cover plants used in banana plantations identified from a set of plant traits’ [1]. Keywords: Cover crops, Plant growth, Leaf area, Leaf traits, Plant traits

This dataset includes data obtained at the Atmospheric Microphysics and Radiation Laboratory (LAMAR) of the Huancayo Observatory (12.04° S, 75.32° W, 3313 m ASL). Two Parsivel2 and two tipping bucket rain gauges are used in this dataset which are operating together since 2018. Data is given in NetCDF format, including two types of files, one NetCDF for precipitation totals and another which contains Parsivel2 data. This data set was collected in the complex topography conditions of the tropical Andes, and its potential use is to study the microphysics of orographic rainfall, atmospheric models and rainfall estimation algorithms. Keywords: DSD, Disdrometer, In situ atmospheric observations, Huancayo observatory

In this article, we introduce a data set concerning electric-power consumption-related features registered in seven main municipalities of Nariño, Colombia, from December 2010 to May 2016. The data set consists of 4427 socio-demographic characteristics, and 7 power-consumption-referred measured values. Data were fully collected by the company Centrales Eléctricas de Nariño (CEDENAR) according to the client consumption records. Power consumption data collection was carried following a manual procedure wherein company workers are in charge of manually registering the readings (measured in kWh) reported by the electric energy meters installed at each housing/building. Released data set is aimed at providing researchers a suitable input for designing and assessing the performance of forecasting, modelling, simulation and optimization approaches applied to electric power consumption prediction and characterization problems. The data set, so-named in shorthand PCSTCOL, is freely and publicly available at https://doi.org/10.17632/xbt7scz5ny.3. Keywords: Electric power consumption, Forecasting, Machine learning, Socio-demographic data, Smart grid

Workers movements and their body mechanics during work, design of tools as well as work layout is important to fit the task to match differences between human capabilities. The data set examined 40 physically disabled workers from different areas and background in Jordan. It consists of anthropometric measurements that are categorized into 7 key measures namely: weight, stature, hip height, knee height, elbow height, hand length and Elbow-fingertip length. Also, it includes information on the most parts that cause pain for the same participants using the discomfort questionnaire. The dataset supports the article '' An integration of a QFD model with Fuzzy-ANP approach for determining the importance weights for engineering characteristics of the proposed wheelchair design''[1]. The obtained dataset can also be used to support an ergonomic and bio-mechanical evaluation performance of physically disabled workers as well as using it in conjunction with ISO standards for equipment design and safety. Moreover, This dataset is useful for optimizing the dimensions’ design for the physically disabled workplace as well as preparing the House of Quality to prioritize the final design requirements.

In this paper, operational data of a natural gas-fired generation engine at 2 MW of power is presented. This engine is used as part of the power supply system of a flexible packaging transformation and conversion plant. This plant, besides having the power supply generated by the engine, receives electrical energy from the network. The data collected from this engine corresponds to measurements taken before, during and after engine stops, whether due to engine maintenance stops, engine failures or external power grid failures. The measurement was made every 10 seconds, and for the storage of these data a data acquisition software was used, which, besides allowing to take these data, shows in real time the electrical behavior of the electrical supply system, as well as the mechanical behavior of the engine.

The current paper shows a data set of load-displacement output from axial tensile loading of CFRP-SPCC hybrid laminates. The specimen geometries are cut based on standard procedure from ASTM D-3039. At least 3 positions in each specimen, we measured its width and thickness. Data of the load and displacement were repeated at least 3 samples in each combination of hybrid laminates. Tensile test was conducted with a 1 mm/min of loading rate. The data were recorded from unloading until failure of specimens. The data gives information about the highest load and the behavior of load-displacement in axial tensile loading. By using width and thickness, normalized data can be obtained, the load can be calculated into stress (MPa) unit. The data are useful for researchers and structural engineers that deals with CFRP, SPCC, and hybrid CFRP-SPCC laminates. Keywords: Tensile loading, Hybrid laminate, CFRP, SPCC, Load-displacement

The repository is composed of 1224 images divided into two sets of images with two different resolutions. First set consists of 89 histopathological images with the normal epithelium of the oral cavity and 439 images of Oral Squamous Cell Carcinoma (OSCC) in 100x magnification. The second set consists of 201 images with the normal epithelium of the oral cavity and 495 histopathological images of OSCC in 400x magnification. The images were captured using a Leica ICC50 HD microscope from Hematoxyline and Eosin (H&E) stained tissue slides collected, prepared and catalogued by medical experts from 230 patients. A subset of 269 images from the second data set was used to detect OSCC based on textural features [1]. Histopathology plays a very important role in diagnosing a disease. It is the investigation of biological tissues to detect the presence of diseased cells in microscopic detail. It usually involves a biopsy. Till date biopsy is the gold-standard test to diagnose cancer. The biopsy slides are examined based on various cytological criteria under a microscope. Therefore, there is a high possibility of not retaining uniformity and ensuring reproducibility in outcomes [2, 3]. Computational diagnostic tools, on the other hand, facilitate objective judgments by making the use of the quantitative measure. This dataset can be utilized in establishing automated diagnostic tool using Artificial Intelligence approaches. Keywords: Oral cancer, Histopathology, OSCC, Biopsy slides, 100x, 400x

This paper presents the data set of Optic coherence tomography (OCT) and fundus Images of human eye. The OCT machine TOPCON'S 3D OCT-1000 camera is employed to acquire the images. The dataset is comprised of 50 images which includes control and glaucomatous images. For each OCT Image there is a corresponding fundus Image with annotation. Cup to disc ratio (CDR) values annotated by glaucoma specialists through fundus Images are provided in excel file. OCT images are optic nerve head (ONH) centred. Manually annotation is performed for the delineation of the Inner Limiting Membrane (ILM) Layer and Retinal pigmented epithelium (RPE) layer with the help of ophthalmologist. The data is valuable for the development of automated algorithm for glaucoma diagnosis. Keywords: Fundus images, OCT images, Optic nerve head (ONH), Cup to disc ratio (CDR) and glaucoma

This paper presents wind speed and direction data measured with a weather station located in Puerto Bolivar, department of La Guajira, situated in the extreme north of Colombia, whose geographic coordinates are 12°11′N 71°55′W. A wind speed and direction sensor, a barometric pressure sensor, and a temperature sensor were used to obtain the presented data. These data were taken at the height of 10 m, which is the highest point of the weather station. The data taken by the meteorological station correspond to a period of 20 years (1993–2013), with hourly frequency. For the missing data, a mathematical model to estimate the Julian averages was developed, allowing to calculate the frequency histograms and four types of probability distributions for these data. Also, the representative wind roses were generated, taking into account the averages in each of the 12 months of the year. Keywords: Wind speed, Wind probability distribution, Wind direction

This article consists of data sets of concentrations, dose and deposition of some labile metals bound to inhale ambient particulate matter collected at human breathing height of 1.5–2 m in Akure, South Western Nigeria. Ten (10) data points, of different air quality, were selected for study using active sampling method; during the dry season months of November, 2016 to March, 2017. At each data point, the dust particles were collected four times, sorted into inhalable and respirable fractions. The metal concentrations in each fraction were determined using Perkin-Elmer 6000 Inductively Coupled Plasma - Atomic Emission Spectrometry (ICP-AES) analysis. The data set were processed and analyzed via descriptive statistics (averages and standard deviations), and numerical analyses. The data were explored further to estimate the exposure dose of metal particles and deposition in various regions of lung (alveolar, trachea-bronchial and extra thoracic) in adults (male and female) dwelling in the area. The data revealed that the highest dose and deposition of metals (Pb, Cd and Cr) occur in the alveolar region of the lung of adults. Keywords: Ambient particulate matter, Inhalable and respirable, Metals, Health risk, Exposure dose

The performance test on a helical Savonius style VAWT are carried out with end plates and without end plates for low wind velocities from 3 m/s to 6 m/s. The raw data measured using instruments are recorded using digital acquisition system. These data are processed and presented as dimensionless parameters namely, coefficient of power, coefficient of torque and tip speed ratio in order to compare it with other VAWTs. Keywords: Savonius style, Helical shaped blade, Vertical axis wind turbine, Performance, Low rated wind speeds

This paper provides data on the interactive quality of the educational services rendered in south west, Nigeria. Data were gathered based on conclusive research design. Stratified and convenience sampling techniques were adopted. Responses were elicited from the alumni as regards to their perception towards the interactive quality; learning, group discussion, breadth, assignment, examination as well as social relationships. Interactive quality component of the Student Evaluation of Educational Quality (SEEQ) developed by previous scholars was adapted. The research instrument was confirmed to have all the necessary psychometric values considered appropriate for the study. Some descriptive statistical analyses were carried out to further clarify the data and provide the necessary platform for further analyses. Keywords: Interactive quality, Service marketing, University, Education marketing

This article consists of data sets for thirty (30) vertical electrical sounding (VES) and four (4) traverses of 2D electrical resistivity imaging (ERI) collected within, Ado-Ekiti, southwestern Nigeria using an ABEM Terrameter (SAS 1000/4000) system. Win-Resist computer program was used to process the apparent resistivity data sets for the VES to determine the geoelectric layers and their respective parameters (resistivity and thickness). The observed data sets for the 2D ERI were processed using RES2DINV software to obtain 2D inverse model resistivity distribution of the subsurface. The resistivity soundings and the 2D ERI were combined to delineate and characterise the crystalline basement features associated with basement aquifers.

This article is essentially a dataset necessary for analysing the taxation. The data analysis has allowed to determine the optimal taxation model, when the criteria of economic efficiency and equity are incompatible. The dataset has allowed the use of the method of successive concessions in tax optimization. The practical significance of the dataset lies in the ability to simultaneously improve the efficiency and equity in taxation.The dataset was obtained by using the method of expert estimates. A group of experts was asked to rank the taxes established by the Tax Code of the Russian Federation, in descending order of importance. Only strict rankings were allowed. The consistency of expert opinion was evaluated using the Kendall coefficient of concordance.The data set was supplemented with the expert ranking data of the basic principles of taxation, such as the principle of equity; the principle of certainty and accuracy of taxes; the principle of ease of tax collection for taxpayers; the principle of efficiency; the principle of commitment.The dataset can be used in the future to determine a rational amount of taxation depending on the established criteria. Keywords: Data set, Analysis of taxation, Optimization of taxation, Criterion of economic efficiency, Justice criterion, Successive assignment method, Simultaneous increase of efficiency and equity of taxation, Kendall coefficient of concordance

This data informs about the profile of volatile compound of coffee flower (Coffee arabica) from different locations with different annual rainfall by using gas chromatography - mass spectrometry (GC-MS). The volatile compounds were captured by solid phase micro extraction (SPME) methods. The extract then subjected to GC-MS for separation and identification of compounds. The profile of volatile compound was provided in, Table 1, Table 2, Table 3 and Table 4. Keywords: Altitude, GC-MS, Metabolite profiling, Microclimate, SPME

1541 Sentinel-1 SAR images, acquired over Fram Strait between 2014 and 2016, were considered for sea ice drift retrieval using an open-source feature tracking algorithm. Based on this SAR image data set, 2026 and 3120 image pairs in HH and HV polarisation were used to calculate monthly mean sea ice velocities at 79 N.

This article presents data on the effect of professional attraction to entrepreneurship and the development of entrepreneurial intentions by university students using Covenant University in Nigeria as the case study. The study employed a descriptive quantitative research design by means of survey method. The population of the study comprised all students in the selected university with a total of 7988 students. A sample size of 400 students was selected. Reliability and validity measures were established. Data was analyzed employing Statistical Package for Social Sciences (SPSS). Regression analysis was used as statistical tool of analysis. The analyzed field data set is presented in this article. Keywords: Professional attraction, Entrepreneurial intention, University students Nigeria

Designers of location algorithms share test data sets (benchmarks) to be able to compare performance of newly developed algorithms. In previous decades, the availability of locational data was limited. Big data has revolutionised the amount and detail of information available about human activities and the environment. It is expected that integration of big data into location analysis will increase the resolution and precision of input data. Consequently, the size of solved problems will significantly increase the demand on the development of algorithms that will be able to solve such problems. Accessibility of realistic large scale test data sets, with the number of demands points above 100,000, is very limited. The presented data set covers entire area of Slovakia and consists of the graph of the road network and almost 700,000 connected demand points. The population of 5.5 million inhabitants is allocated to the locations of demand points considering the residential population grid to estimate the size of the demand. The resolution of demand point locations is 100 m. With this article the test data is made publicly available to enable other researches to investigate their algorithms. The second area of its utilisation is the design of methods to eliminate aggregation errors that are usually present when considering location problems of such size. The data set is related to two research articles: “A Versatile Adaptive Aggregation Framework for Spatially Large Discrete Location-Allocation Problem” (Cebecauer and Buzna, 2017) [1] and “Effects of demand estimates on the evaluation and optimality of service centre locations” (Cebecauer et al., 2016) [2].

The data presented in this article are related to the research article entitled ''Magnetic field and its experimental measurement in teaching in high schools'' (J. Beňuška, D. Polčin, 2016) [1].The article describes the possibilities of relatively accurate experimental measurement of magnetic field in any geographical conditions, in both exteriors and interiors, without the need for special instrumentation, with minimal financial costs.The data set is publicly available to allow the comparison of magnetic induction values in given latitudes with the other latitudes by the given methods and a critical analysis of the applicability of these methods in areas and locations without more demanding instrumentation. Keywords: Magnetic field, Magnetic induction, Measurement, Measuring instruments, Faraday's law, Monitoring

We describe the publicly available data created by the NIA funded Early Indicators program project, often referred to as the Union Army data, and the subset of these data used in “Persistent Social Networks: Civil War Veterans Who Fought Together Co-Locate in Later Life” (Costa et al., Forthcoming) [1]. This data subset can be used for reproducibility and extensions and also illustrates how the original complex data derived from archival administrative records can be used. Keywords: Social networks, Migration, Mortality

Guanine-rich DNA/RNA fragments can fold into G-quadruplexes (G4s) – non-canonical four-strand secondary structures. The article contains data on quadruplex interaction with human proteins. Binding of three topologically different G4 structures to more than 9000 human proteins was analyzed. Physicochemical methods were used to verify the results.The dataset was generated to identify the protein targets for DNA quadruplex structures for the purpose of better understanding the role of the structures in gene expression regulation. Presented data include functional interpretation of obtained gene lists, visualized with Cytoscape.

This is a data set of 61 blood spatter patterns scanned at high resolution, generated by controlled impact events corresponding to forensic beating situations. The spatter patterns were realized with two test rigs, to vary the geometry and speed of the impact of a solid object on a blood source - a pool of blood. The resulting atomized blood droplets travelled a set distance towards a poster board sheet, creating a blood spatter. Fresh swine blood was used; its hematocrit and temperature were measured. Main parameters of the study were the impact velocity and the distance between blood source and target sheet, and several other parameters were explored in a less systematic way. This new and original data set is suitable for training or research purposes in the forensic discipline of bloodstain pattern analysis.

This article presented data on the effect of training and development on academic staff creativity using Covenant University in Nigeria as the case study. The article was a based on a descriptive quantitative research design using Survey method. The population of the study included all academic staff in the selected university with a total of 535 faculties. A sample size of 226 faculties was selected. Reliability and validity procedures were confirmed. Data was analyzed with the use of Statistical Package for Social Sciences (SPSS).Regression analysis was employed as statistical tool of analysis. The field data set is made publicly available to enable critical or extended analysis. Keywords: Training and development, Creativity, Academic staff, Nigeria

The data set presented is related to the milling process of AA6061-4.5%Cu-5%SiCp composite. The data primarily concentrates on predicting values of some machining responses, such as cutting force, surface finish and power utilization utilizing using forward back propagation neural network based approach, i.e. ANN based on three process parameters, such as spindle speed, feed rate and depth of cut.The comparing reverse model is likewise created to prescribe the ideal settings of processing parameters for accomplishing the desired responses as indicated by the necessities of the end clients. These modelling approaches are very proficient to foresee the benefits of machining responses and also process parameter settings in light of the experimental technique. Keywords: ANN, Forward mapping, Reverse mapping, Milling process

The dataset contains thermal properties of soil such as thermal conductivity, thermal diffusivity, temperature and specific heat capacity in an agricultural farm within the University of Ibadan, Ibadan, Nigeria. The data were acquired in forty (40) sampling points using thermal analyzer called KD-2 Pro. Soil samples taken at these sampling points were analyzed in the laboratory for their moisture content following the standard reference of American Association of State Highway and Transport Officials (AASHTO) T265. The data were acquired within the first and second weeks in the month of April, 2012. Statistical analyses were performed on the data set to understand the data. The data is made available publicly because thermal properties of soils have significant role in understanding the water retention capacity of soil and could be helpful for proper irrigation water management.

This article provides Abaqus input files and user subroutines for performing finite element simulations of the tricuspid heart valve with an idealized geometry. Additional post-processing steps to obtain a ParaView visualization file (*.vtk) of the deformed geometry are also provided to allow the readers to use the included ParaView state file (*.pvsm) for customizable visualization and evaluation of the simulation results. We expect this first-of-its-kind in-silico benchmark dataset will facilitate user-friendly simulations considering material nonlinearity, leaflet-to-leaflet contact, and large deformations. Additionally, the information included herein can be used to rapidly evaluate other novel in-silico approaches developed for simulating cardiac valve function. The benchmark can be expanded to consider more complex features of the tricuspid valve function, such as the dynamic annulus motion or the time-varying transvalvular pressure. Interested readers are referred to the companion article (Johnson et al., 2021) for an example application of this in-silico tool for isogeometric analysis of tricuspid valves.