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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.).
Data Types:
  • Software/Code
  • Tabular Data
  • Dataset
  • Document
  • Text
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Results of EMD-based Nonstationary Frequency Analysis over South Korea with Climate Indices for different lags
Data Types:
  • Dataset
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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
Data Types:
  • Other
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  • Text
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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
Data Types:
  • Dataset
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Batch mesophilic 37oC reactors fed with acetic acid (0.5 mL AC/L every 5-6 days), have been amended with different amount of incineration bottom ash/ and ammonium chloride for 120 days. At the start of the experiments, different mass of IBA was added to the reactors which had been amended with IBA. Then the group of the reactors which amended with NH4CL had received 4 g/L NH4Cl every run of 5-6 days. In parallel batch reactors without IBA/ and NH4Cl were also run. Reactor performance (methane production) and stability (pH drop and VFA accumulation) were investigated. On day end of the experiments i.e. on day 120, a representative digestate sample was collected from each reactor, then sequenced for 16S rRNA gene. The sequence files shown in this data set are fastq files from the illumina sequencing analysis.
Data Types:
  • Software/Code
  • Dataset
  • Text
  • File Set
Databases (for SQLite SpatiaLite) were created from publicly available OpenStreetMap data for Poland. The db_small database comprises data for the area of the city of Kraków in the Małopolskie Province. The db_medium database comprises data from the entire Małopolskie Province. The db_large database, in addition to the Małopolskie Province, covers the Podkarpackie and Dolnośląskie Provinces. The db_v_large database covers the entire country. Available tables: - buildings_points - landuse_polygon - pois_polygon - roads_lines - waterways_lines
Data Types:
  • Dataset
  • File Set
This dataset includes the calculation of dialect distance for 278 Chinese prefecture cities used in the paper "Promoting or preventing labor migration? Revisiting the role of language" (https://doi.org/10.1016/j.chieco.2020.101407)
Data Types:
  • Dataset
  • File Set
BarkVN-50 consists of 50 categories of bark texture images. Total number is 5,578 images with 303× 404 pixels. Image classification task can be performed on this dataset.
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Data File
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