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Data for Analysis of Nano-Silica and Xanthan Gum as a High-Temperature Thixotropic Agent for Oil-Well Cement
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Supplementary materials corresponding to the identically named paper including R scripts, derived data sets, and the full statistical test results.
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The raw sequencing data obtained from hamsters treated with different interventions including 1) standard diet (control); (2) standard diet and monosodium glutamate (MSG) in drinking water (MSG); (3) high-fat and high-fructose diets (HFF), and (4) MSG+HFF.
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Geometric and energetic features of halogenated rotamers of the following backbone structures, C-C, N-N, P-P, O-O, S-S, N-P, O-S, C-N, C-P, C-O, C-S, N-O, N-S, P-O and P-S from quantum chemical calculations are presented. The data set is considered to be comprehensive combinations of non-metal elements in the form abcx-ydef whereby a,b,c,d,e,f are halogen (fluorine to iodine), hydrogen or a lone pair and x,y are carbon, nitrogen, phosphorus, oxygen and sulfur. Preliminary work on all possible halogenation of methane, ammonia, phosphine, water and hydrogen sulfide are also included.
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Data accompanied with the paper "Reliability and Validity of the Turkish Version of the Health Professionals Communication Skills Scale (HP-CSS)". The sample consisted of 394 health professionals in Turkey.
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resources from the w.p. 'Uncertainty and stochastic theories on derivatives and risk valuation', by C. Alexander Grajales, Santiago Medina, 2020 * Matlab code * output data * paper figures
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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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  • Software/Code
  • Tabular Data
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  • Text
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Results of EMD-based Nonstationary Frequency Analysis over South Korea with Climate Indices for different lags
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Associated research in : Gordon, B. L., Paige, G. B., Miller, S. N., Claes, N., & Parsekian, A. D. (2020). Field scale quantification indicates potential for variability in return flows from flood irrigation in the high altitude western US. Agricultural Water Management, 232, 106062. Readme: The included files are: Calculated Flow, Calculated_Losses, Calculated_Return_Flows, ET_Not_Interpolated, Precipitation, and GIS Database. All the data (except GIS) are in tab delimited ASCII files. GIS data are in standard formats, most site specific information including soils, meadow delineation, instrumentation, etc. can be found in the site_information file. Flow data (Calculated_Flow, Calculated_Losses, Calculated_Return_Flows) were obtained using developed rating curves at each site, where each stilling well was instrumented with a pressure transducer (Level TROLL 500 Data Logger, In-Situ, USA) and manual flow measurements consisting of 25+ individual points for each measurement were made using an electromagnetic current meter (MF Pro, OTT Hydromet, USA). ET data include both measurements from a Large Aperture Scintillometer (LAS MKII, Kipp & Zonen, NLD) and from Penman-Monteith Calculations performed on raw meteorological data collected on site. For Penman-Monteith, we include both raw values and values modified using a crop coefficient from Pochop et al. (1992). Precipitation data were collected using a tipping bucket rain gauge (Rain Collector II, Davis Instruments, USA). All data (except the ET data for the scintillometer) are from May 2015 to October 2015; the ET data from the scintillometer are from June 2015 to October 2015. If you have any questions, or would like raw flow data or unprocessed meterological data, please contact me via email at: beatrice.gordon1@gmail.com
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Data and code for: Time-Varying Causality between Bond and Oil Markets of the United States: Evidence from Over One and Half Centuries of Data
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