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Food intake remains an essential component of human health life and productivity. Poor health inextricably threaten the ability of several developing nations to achieve the Millennium Development Goals by 2015, this stubborn threat is still a major concern to the achievement of the sustainable development goals (SDG. 2030). The economic burdens of poor nutrition and ill health in the development of African continent cannot be overemphasized. Therefore, eating a varied, well-balanced food groups daily, in the recommended amounts is important. Considering the existing malnutrition and ill health situation report in Nigeria, rural farmer's dietary diversity and health record is important for pertinent policy evaluation since these people are the principal operators of the nations' food system but yet one of the most vulnerable category of the countrie's working class. The survey that gave this dataset was conducted through a multi stage sampling technique with a well structured questionnaires with in the months of September 2014 and April 2015 from households selected from 18 randomly sampled villages. The administered questionnaires were divided in seven sections namely; respondent's socio-economic characteristics, health and environmental profile, food utilization and nutrition, requested information about respondent's agricultural labour productivity, agricultural production cost and return, cost implication of health and nutrition and dietary diversity nutrition and other problems. The questionnaires were written in English language but translated in local language during the interview for ease of understanding by the participants, the survey successfully ended with a total of 420 properly filled and captured questionaires which was quite representative. The dataset is hereby made available as it is considered vital for policy recommendations. © 2020 The Author(s).These data are supplied for supporting their interpretations and discussions provided in the research article "Large influence of vacancies on the elastic constants of cubic epitaxial tantalum nitride layers grown by reactive magnetron sputtering" by Abadias et al. (2020) [doi 10.1016/j.actamat.2019.11.041]. The datasheet describes the experimental methods used to measure the longitudinal (VL) and transverse (VT) sound velocities of cubic epitaxial TaN/MgO thin films, and their related cubic elastic constants (c11, c12 and c44), by the picosecond laser ultrasonic (PLU) and the Brillouin light scattering (BLS) techniques, respectively. First-principles numerical simulations provide additional data using specifically designed supercells of TaN structures, generated either by hand or using the alloy theoretical automated toolkit (ATAT) method [A. Zunger et al. (1990)], with different configurations (random, cluster and ordered) of defects (Ta and N vacancies). Phonons calculations support discussion of dynamical mechanical stability of defected TaN cubic structures. The data illustrate the huge role of vacancies in elastic properties and phase stability of TaN films. © 2020 The Author(s).In the recent years, the dominant cementitious materials have been industrial by products such as fly ash. This present data describes some of the cementitious products that are attracting attention in the global research community and the properties and characteristics of these materials that affect their performance such durability, mechanically properties and reduction of carbon dioxid (CO2). The present investigation deals with the chemical synthesis of cementitious material using fly ash of eggs shell rich in calcium(Ca) and sand dune(southern west of Algeria) rich in silica(SiO2).The composition of geopolymers synthesized are the most compressive resistant with a maximum stress of 49.71 MPa, the most flexible (E = 2.63 GPa) and the most ductile (εr = 65.42%).The characteristic properties of the chemically synthesized cementitious materials were analyzed by the chemical composition analysis XRF, XRD and SEM analyses. © 2020 The Author(s).The data in this report are associated with https//doi.org/10.1016/j.scitotenv.2020.137085[4] and include data on water volumes and water quality related to the major unconventional oil and gas plays in the U.S. The data include volumes of water co-produced with oil and gas production, county-level estimates of annual water use volumes by various sectors, including hydraulic fracturing water use, and the quality of produced water. The data on volumes of produced water and hydraulic fracturing water volumes were obtained from the IHS Enerdeq and FracFocus databases. Bcl-2 inhibitor Water use in other sectors was obtained from the U.S. Geological Survey water use database. Data on produced water quality were obtained from the USGS produced waters database. © 2020 Published by Elsevier Inc.In computer security, botnets still represent a significant cyber threat. Concealing techniques such as the dynamic addressing and the domain generation algorithms (DGAs) require an improved and more effective detection process. To this extent, this data descriptor presents a collection of over 30 million manually-labeled algorithmically generated domain names decorated with a feature set ready-to-use for machine learning (ML) analysis. This proposed dataset has been co-submitted with the research article "UMUDGA a dataset for profiling DGA-based botnet" [1], and it aims to enable researchers to move forward the data collection, organization, and pre-processing phases, eventually enabling them to focus on the analysis and the production of ML-powered solutions for network intrusion detection. In this research, we selected 50 among the most notorious malware variants to be as exhaustive as possible. Inhere, each family is available both as a list of domains (generated by executing the malware DGAs in a controlled environment with fixed parameters) and as a collection of features (generated by extracting a combination of statistical and natural language processing metrics). © 2020 The Author(s).This article describes the time series data for optimizing the Non-linear Muskingum flood routing of the Kardeh River, located in Northeastern of Iran for a period of 2 days (from 27 April 1992 to 28 April 1992). The utilized time-series data included river inflow, Storage volume and river outflow. In this data article, a model based on the Grasshopper Optimization Algorithm (GOA) was developed for the optimization of the Non-linear Muskingum flood routing model. The GOA algorithm was compared with other metaheuristic algorithms such as the Genetic Algorithm (GA) and Harmony search (HS). The analysis showed that the best solutions achieved by the GOA, Genetic Algorithm (GA), and Harmony search (HS) were 3.53, 5.29, and 5.69, respectively. The analysis of these datasets revealed that the GOA algorithm was superior to GA and HS algorithms for the optimal flood routing river problem. © 2020 The Author(s).