Data on coffee leaves of the CATIMOR, CATURRA, and BORBON types, from the plantations in San Miguel de las Naranjas and La Palma Central, Jaen Province, Cajamarca, Peru, is presented in this article. Leaves exhibiting nutritional deficiencies were identified using a controlled environment, the design of its physical structure by agronomists, and the use of a digital camera to capture the images. Within the dataset, 1006 leaf images are sorted according to the particular nutritional deficiencies they display, including Boron, Iron, Potassium, Calcium, Magnesium, Manganese, Nitrogen, and other nutritional deficiencies. The CoLeaf dataset's images serve as a foundation for deep learning algorithms to train and validate their ability to identify and classify nutritional deficiencies in coffee plant leaves. At the URL http://dx.doi.org/10.17632/brfgw46wzb.1, the dataset is freely and publicly accessible.
Successful adult optic nerve regeneration is a characteristic of zebrafish, specifically Danio rerio. Mammals are deficient in this intrinsic capability, leading to the irreversible neurodegeneration seen in glaucoma and other similar optic neuropathies. Clinical immunoassays Optic nerve regeneration studies often employ the optic nerve crush, a mechanical model of neurodegeneration. The efficacy of untargeted metabolomic analyses in successful regenerative models is, at present, insufficient. Zebrafish optic nerve regeneration, when assessed metabolomically, offers a window into prioritized metabolic pathways that can be pursued for therapeutic interventions in mammals. Wild-type zebrafish (6 months to 1 year old) optic nerves, both male and female, were collected three days after they were crushed. In order to establish a control, uninjured contralateral optic nerves were collected. Euthanized fish tissue was dissected and preserved by freezing on dry ice. Pooling samples from each group (female crush, female control, male crush, and male control) to reach n = 31 samples ensured sufficient metabolite concentrations were available for analysis. Fluorescence microscopy of Tg(gap43GFP) transgenic fish, 3 days after a crush injury, revealed regeneration in the optic nerve. A Precellys Homogenizer, coupled with a serial extraction technique, was used to extract the metabolites. First, a 11 Methanol/Water solution was employed; second, a 811 Acetonitrile/Methanol/Acetone solution was used. An untargeted liquid chromatography-mass spectrometry (LC-MS-MS) profiling of metabolites was executed by utilizing the Vanquish Horizon Binary UHPLC LC-MS system, interconnected with the Q-Exactive Orbitrap instrument. Using Compound Discoverer 33 and isotopic internal metabolite standards, metabolites were both identified and quantified.
The ability of dimethyl sulfoxide (DMSO) to inhibit methane hydrate formation thermodynamically was determined by measuring the pressures and temperatures at the monovariant equilibrium involving the three phases: gaseous methane, aqueous DMSO solution, and methane hydrate. From the data, a total of 54 equilibrium points were extrapolated. Hydrate equilibrium conditions were quantified at various dimethyl sulfoxide concentrations (0 to 55% by mass) at temperatures (242-289 K) and pressures (3-13 MPa). Maraviroc in vivo Using a 600 cm3 isochoric autoclave (inside diameter of 85 cm), measurements were made at a rate of 0.1 K/h, with vigorous fluid agitation (600 rpm), employing a four-blade impeller (diameter 61 cm, height 2 cm). Aqueous DMSO solutions stirred at temperatures between 273 and 293 Kelvin exhibit Reynolds numbers falling within the range of 53103 to 37104. At the specified temperature and pressure, the conclusion of methane hydrate dissociation marked the equilibrium point. Measurements of DMSO's anti-hydrate activity were conducted on a scale incorporating both mass percentage and mole percentage. Precise mathematical connections were established between the thermodynamic inhibition effect of dimethyl sulfoxide (DMSO) and its controlling parameters of concentration and pressure. The phase composition of the samples at 153 Kelvin was assessed through the use of powder X-ray diffractometry techniques.
Vibration-based condition monitoring hinges on vibration analysis, a process that scrutinizes vibration signals to identify faults, anomalies, and assess the operational state of belt drive systems. This article's data includes vibration measurements from a belt drive system, varying parameters such as speed, pretension, and operational settings. medical mobile apps Three levels of belt pretension are accompanied by corresponding low, medium, and high operating speeds in the dataset. This article categorizes three operating conditions of a belt system: healthy operation with a good belt, unbalanced operation with an added unbalanced weight, and abnormal operation with a damaged belt. Analysis of the accumulated data sheds light on the belt drive system's operational performance, enabling the identification of the underlying cause of any detected anomalies.
A lab-in-field experiment and an exit questionnaire, undertaken in Denmark, Spain, and Ghana, produced the 716 individual decisions and responses found in the data. Individuals were first engaged in a minor effort of counting ones and zeros on a page for monetary reward. Thereafter, they were inquired about their willingness to donate a proportion of their earnings to BirdLife International, supporting the conservation of the Montagu's Harrier's habitats in Denmark, Spain, and Ghana. The data provides a crucial understanding of individual willingness-to-pay for conserving the Montagu's Harrier's habitats along its flyway, offering potential assistance to policymakers in achieving a clearer and more complete picture of support for international conservation initiatives. The data, among other uses, can illuminate the effect of individual social and demographic traits, perspectives on the environment, and donation preferences on real-world philanthropic actions.
Resolving the challenge of limited geological datasets for image classification and object detection on 2D geological outcrop images, Geo Fossils-I serves as a practical synthetic image dataset. To engineer a custom image classification model for geological fossil identification, the Geo Fossils-I dataset was meticulously compiled, subsequently motivating the generation of further research endeavors surrounding synthetic geological data with the implementation of Stable Diffusion models. A custom training process, incorporating the fine-tuning of a pre-trained Stable Diffusion model, was instrumental in generating the Geo Fossils-I dataset. Textual input fuels Stable Diffusion, an advanced text-to-image model, producing highly lifelike images. To instruct Stable Diffusion on novel concepts, the specialized fine-tuning technique of Dreambooth is applied effectively. The textual description served as a guide for Dreambooth to produce fresh fossil images or to modify pre-existing ones. Geological outcrops hosting the Geo Fossils-I dataset contain six various fossil types, each one indicative of a particular depositional environment. A total of 1200 fossil images, evenly distributed among various fossil types, are included in the dataset, encompassing ammonites, belemnites, corals, crinoids, leaf fossils, and trilobites. Within this series' first dataset compilation, the aim is to enhance the availability of 2D outcrop images, ultimately supporting the field of automated depositional environment interpretation for geoscientists.
A substantial portion of health concerns are attributable to functional disorders, imposing a burden on both patients and the medical system. This dataset, spanning multiple disciplines, seeks to deepen our understanding of the intricate connections between different factors influencing functional somatic syndromes. This dataset comprises information gathered from randomly selected, seemingly healthy adults, aged between 18 and 65, in Isfahan, Iran, during a four-year monitoring period. Seven distinct data sets constitute the research data, comprising (a) functional symptom evaluations across numerous body parts, (b) psychological tests, (c) lifestyle habits, (d) demographics and socioeconomic information, (e) laboratory readings, (f) clinical observations, and (g) historical context. A total of 1930 individuals joined the study's ranks in its inception year of 2017. The first annual follow-up round in 2018 had 1697 participants; the subsequent round in 2019 had 1616 participants; and the final round, in 2020, attracted 1176 participants. Healthcare policymakers, clinicians, and researchers with varied backgrounds can utilize this dataset for further analysis.
The objective, design, and methodology of accelerated tests used for battery State of Health (SOH) estimations are discussed in this article. To achieve this, 25 unused cylindrical cells were subjected to accelerated aging through continuous electrical cycling, employing a 0.5C charge and a 1C discharge, targeting five distinct state-of-health (SOH) breakpoints (80%, 85%, 90%, 95%, and 100%). The process of cell aging, corresponding to varying SOH values, was performed at a temperature of 25 degrees Celsius. An electrochemical impedance spectroscopy (EIS) evaluation was conducted on each cell across varying states of charge (5%, 20%, 50%, 70%, and 95%) and temperatures (15°C, 25°C, and 35°C). The provided data includes the raw data files from the reference test, and the determined values of energy capacity and state of health (SOH) for every cell. The package contains the 360 EIS data files and a file presenting a tabular overview of the essential features of each EIS plot per test case. To rapidly estimate battery SOH, a machine-learning model was trained using the data reported in the co-submitted manuscript (MF Niri et al., 2022). The creation of battery performance and aging models, and their validation, are enabled by the reported data, providing the basis for multiple application studies and the development of control algorithms integral to battery management systems (BMS).
Metagenomic sequencing of maize rhizosphere microbiomes, specifically those infested with Striga hermonthica in Mbuzini, South Africa, and Eruwa, Nigeria, constitutes this dataset.