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This Ocean Hazards Database (OHD) contains all relevant geographic information system (GIS) layers, maps, measures, interpretations, and rankings from the Ocean Hazards Classification Scheme (OHCS) assessment of 302 mileposts and points of interest across coastal state routes in Hawaii. As a part of the State of Hawaii Department of Transportation Statewide Highway Shoreline Program Report, the OHCS assesses and ranks shoreline roadway vulnerability to historical sea-level rise rates (in/yr), projected sea-level raise rates by 2050 and 2100 (in/yr), mean tidal ranges (ft), maximum annually recurring peak wave periods (sec) and significant wave heights (ft), mean projected shoreline change rates (ft/yr) and coastal armoring, historical and hypothetical tsunami flow depths (ft), and hypothetical category 1-4 storm surge inundation heights (ft). OHCS ranking criteria and methods are described in chapter 3 of the State of Hawaii Statewide Coastal Highway Program Report (2019). OHD materials are organized into coastal highway digital elevation models, ocean hazard map packages, ocean hazard supplementary maps, and ocean hazard supplementary tables. Digital Elevation Models (DEM) contains GIS layers for nearshore topographic and bathymetric elevations on the islands of Hawaii, Maui, Molokai, Oahu, and Kauai. Map Packages contain GIS layers and mapping layouts for the assessment and projection of sea-level rise inundation, maximum annually recurring wave characteristics, projected shoreline change, storm surge inundation, and offshore bathymetry. Supplementary Tables contain OHCS measure results and rankings for 302 mileposts and points of interest across coastal state routes in Hawaii. Representative dataset references are included in table footnotes. Supplementary Maps contain image files of nearshore and offshore transects, sea-level rise inundation extents, maximum annually recurring wave characteristics, projected shoreline changes, and storm surge inundation extents for 302 mileposts and points of interest across coastal state routes in Hawaii. This project was funded by the Hawaii Department of Transportation, HWY-06-16, entitled "Statewide Highway Shoreline Protection Program Study Update."
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The PICO Statements dataset is a collection of 130 abstracts from Randomized Clinical Trials and Controlled Trials, manually annotated by medical practitioners, to identify sentences that not only contain all four PICO elements but also answer clinically stated questions. These sentences are referred to as PICO Statements. In Evidence-Based Medicine (EBM), the PICO framework is used by medical practitioners to narrow the search space and enable faster decision-making towards treatment procedures. The framework is named after the four elements that comprise it, Population, Intervention, Comparator and Outcome. Previous datasets focus on identifying either whole sentence to a single PICO element or, more recently, the sequence of tokens in the sentence that describe each element. Similar to previous research, we consider Intervention and Comparator as one element in our annotation scheme. For each sentence, we binary annotate the existence of each PICO element individually and if the sentence is a PICO Statement. The dataset is offered, in an abstract per file manner, in two formats: 1) XML format, for sentence classification. The XML format present each abstract, along with its title, annotated on a sentence level, with all four annotations present for each sentence in a binary format. The XML Schema (.xsd) files are also available in the miscellaneous folder. 2) pseudo-IOB format, for PICO entity prediction. The pseudo-IOB format, presents each abstract, along with its title, annotated on a token level, with the same binary annotations repeating for each token in the sentence. The binary annotations in the pseudo-IOB format are corresponding to the PICO elements in the following order: Population, Intervention/Comparator, Outcome, PICO Statement. In both annotation schemes contain the same abstracts and the file names are corresponding to the PubMedIDs of the publications from which the abstracts originate.
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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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This dataset contains cross-sectional and longitudinal tongue images and assessments obtained from 206 ALS patients and 104 age- and sex-matched controls that underwent high-resolution ultrasound (HRUS) and 3T MRI. In each image, the tongue is delineated in an additional region of interest (ROI) file provided for each of the coronal cross-sections acquired via HRUS and the midsagittal slices from the sagittal cerebral 3D-MPRAGE 3T MRI of the head. For these ROIs size and mean intensity markers are calculated. From the MRI images quantitative parameters for the shape and relative position of the tongue are derived. For each individual in the dataset these obtained markers are provided along with demographic and disease specific information in accompanying lists. The dataset can be combined with other data to increase statistical power or to extend the analysis with more advanced algorithms to implement and study additional markers for size, shape or texture of the tongue in ALS patients and controls.
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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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The present work starts from a previous experiment where a culture medium for Dunaliella tertiolecta is developed, aiming its use as biofuel feedstock. The effect of the addition of fertilizer NPK-10: 26: 26, NaCl, NaOH, and the intensity of light incident on algal biomass growth, lipid productivity and CO2 sequestration were analyzed. The experimental data set, is first graphed using the graphical outputs of Engineering Equation Solver (EES), then is adjusted into an Adaptive Neuro Diffuse Inference System (ANFIS), obtaining a simulation of the cultivation process which is an easy to use and very accurate tool for instant evaluation of the process under study. The obtained ANFIS facilitates the analysis of the simultaneous influence of independent variables on the output variables. It is thus shown that the most recent computational facilities are of fundamental interest for the analysis of fermentative processes and in particular to model the cultivation of microalgae to be used as fuel feedstock. The results of the ANFIS model are compared with the experimental data and the effective evaluation of the performed simulation is proved.
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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:
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  • Document
  • 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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Esse modelo foi desenhado no software treeage e foi criado um modelo tipo player onde indivíduos que não possuem o software podem fazer o download no site de uma versão do visualizador e abrir o arquivo. Para tal, é preciso acessar: www.treeage.com, clicar no menu em "Free Trial", preencher o formulário e adquirir uma licença gratuita de visualizador (Viewer license). Foi inserido também uma tabela do Excel com o cálculo dos valores mensais baseado nas posologias de tratamento e nos preços relativos a tabela da CMED de fev/2020 com PMVG de 0%. Ele permite alterar os custos de tratamento a fim de verificar o preço em que cada estrategia de tratamento se tornariam custo-efetiva. É possível realizar análise de custo-efetividade com sensibilidades determinísticas para os custos mensais e probabilística de maneira geral, simulando as distribuições inseridas.
Data Types:
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