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Raw data of the unproved treatment study.
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In order to investigate the dietary pattern of gestational diabetes mellitus (GDM) patients, and assess whether maternal pre-gestational body mass index (BMI) and the results of an oral glucose tolerance test administered at weeks 24~28 of gestation (OGTT1) have a relationship with postpartum glucose tolerance (ppOGTT) in patients with gestational diabetes mellitus (GDM). We collected the basic data of 156 patients with GDM for statistical analysis. The original information is shown below.
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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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Detailed costs per sector according to Twitter messages.
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  • Tabular Data
  • Dataset
The present study had four objectives. First, we aimed at replicating the effects of provocation on reactive aggression in a monetary modified Taylor Aggression Paradigm (mTAP). Moreover, we examined the moderating role of gender expecting higher gender differences under conditions of low provocation and smaller gender differences under conditions of high provocation. In terms of convergent validity, we hypothesized a significant relationship between self-reported trait aggression and behavioral aggression outcomes in the laboratory paradigm. Finally, to explore the role of provocation sequence, the monetary stimuli (0 - 90 cents) were presented either randomly or in a fixed sequence. In contrast to the random sequence, the fixed sequence was generated as triplets of the same provocation category. Because of the more homogeneous provocation sequence in the fixed condition, we expected higher aggression levels after higher provocation and lower aggression levels after lower provocation in this experimental condition. In this experiment, 209 young healthy participants (104 males, 105 females) completed a mock competitive reaction time task with a fictional opponent with 40% preprogrammed win and 60% lose trials. In lose trials, participants were provoked by subtracting a low (0 - 20 euro cents), medium (30 - 60 cents) or high (70 - 90 cents) amount of money from their account.
Data Types:
  • Software/Code
  • Dataset
To address the issues in question, the empirical analysis explores annual panel data for 23 developed economies vis-à-vis 21 developing ones, over the period 2002-2017. The time span of the sample and country selection are constrained by data availability.
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  • Tabular Data
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Dataset_Effects of action type and performance type on young children's performance in a causal task
Data Types:
  • Tabular Data
  • Dataset
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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Data for the paper, for the citation count regrssion
Data Types:
  • Tabular Data
  • Dataset
Texture Profile Analysis, viscosity analysis, yield stress analysis and FTIR raw data.
Data Types:
  • Tabular Data
  • Dataset
  • Document
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