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RRDE results from KOH activated char
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Geocentric radiants and heliocentric orbit of 2018 Draconid meteors
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Coccolithophores are important contributors to global calcium carbonate through their species-specific production of calcite coccoliths. Nannofossil coccolith calcite remains an important tool for paleoreconstructions through geochemical analysis of isotopic and trace element incorporation, including Sr, which is a potential indicator of past surface ocean temperature and productivity. Scyphosphaera apsteinii exhibits an unusually high Sr/Ca ratio and correspondingly high partitioning coefficient (DSr) in their two morphologically distinct types of coccoliths. Whether or not this reflects mechanistic differences in calcification compared to other coccolithophores is unknown. We therefore examined the possible role of Sr in S. apsteinii calcification by growing cells in deplete, ambient, and higher than ambient Sr conditions (between 0.33 - 140 mmol/mol Sr/Ca). The effects on growth, quantum efficiency of photosystem II (Fv/Fm), coccolith morphology, and calcite DSr were evaluated. Reducing the Sr/Ca from ambient (9 mmol/mol) did not significantly alter the frequency of malformed and aberrant muroliths and lopadoliths, but at higher than ambient Sr/Ca conditions coccolith morphology was significantly disrupted. This implies that Sr is not a critical determining factor in normal coccolith calcite morphology in this dimorphic species. Interestingly, muroliths had significantly lower Sr/Ca than lopadoliths at ambient and elevated [Sr], and lopadolith tips had lower Sr than bases in ambient conditions. In summary, the Sr fractionation behavior of S. apsteinii is unusual because of an overall high DSr, and an inter- and intra-coccolith variability in Sr/Ca. We hypothesize that differential Sr-and Ca-binding capacity of coccolith associated polysaccharides may account for the unusual Sr fractionation of this species which can explain all observations made in this study.
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Monotonic dataset for 3D printed composites samples reinforced with long continuous fibers. Mechanical properties under monotonic loads were studied for different kinds of printing configurations. Tensile monotonic tests under controlled displacement were performed until sample´s rupture. The data is presented as plain text files without any analysis. A preliminary data analysis has been published already somewhere else [doi:10.1016/j.ijfatigue.2019.105275]. The text files contain, time, displacement, and force. The data is useful for design engineers and researchers involved with AM when new models specifically tailored for CFRTPC are available.
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The files provide the exact location of each sand and hydrate particles in the DEM analysis, together with additional information about running the DEM simulation.
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Total RNA was purified from E. pacifica using with an RNeasy Lipid tissue mini kit. The library of E. pacifica for next generation sequencing was made using with a TruSeq RNA library prep kit v2 (Illumina). RNA purification and library preparation were performed according to the manufacturers’ instructions. The library was analyzed by Miseq using a Miseq reagent kit v3 (600 cycle) (Illumina). The fastaq data was assembled by Trinity.
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The repository contains the ERP data for self-face, friend's face and other's face perception. Raw Data folder contain the EEG data in Brain Products format. Epoched Data folder contain processed EEG data in EEGLAB format. sLORETA files folder contain data of source mean amplitude within-cluster of significant correlations between ERP and heartbeat perception scores. Also, repository include subject description file with the antropometric and psychometric data.
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Data contains all measured striae on slickensides in volcanic rocks within the Lutynia and Ladek Zdrój area catogorized to individual paleostress patterns. Dataset for SW Faultkin7 contains data for iwhole paleostress pattern, datasets for SW Rock2014 contains data for individual paleostress states.
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This dataset is about a systematic review of unsupervised learning techniques for software defect prediction (our related paper: "A Systematic Review of Unsupervised Learning Techniques for Software Defect Prediction" in Information and Software Technology [accepted in Feb, 2020] ). We conducted this systematic literature review that identified 49 studies which satisfied our inclusion criteria containing 2456 individual experimental results. In order to compare prediction performance across these studies in a consistent way, we recomputed the confusion matrices and employed MCC as our main performance measure. From each paper we extracted: Title, Year, Journal/conference, 'Predatory' publisher? (Y | N), Count of results reported in paper, Count of inconsistent results reported in paper, Parameter tuning in SDP? (Yes | Default | ?) and SDP references(SDPRefs OrigResults | SDPRefs |SDPNoRefs | OnlyUnSDP). Then from within each paper, we extracted for each experimental result including: Prediction method name (e.g., DTJ48), Project name trained on (e.g., PC4), Project name tested on (e.g., PC4), Prediction type (within-project | cross-project), No. of input metrics (count | NA), Dataset family (e.g., NASA), Dateset fault rate (%), Was cross validation used? (Y | N | ?), Was error checking possible? (Y | N), Inconsistent results? (Y | N | ?), Error reason description (text), Learning type (Supervised | Unsupervised), Clustering method? (Y | N | NA), Machine learning family (e.g., Un-NN), Machine learning technique (e.g., KM), Prediction results (including TP, TN, FP, FN, etc.).
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Dual numbers are used to develop methods for computing velocities and accelerations
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