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These datasets involve 1) ambient air quality testing, 2) spontaneous combustion fire frequency record, 3) temperature anomalies detected by Landsat, 4) photos of mine waste heap as well as affected environment, 5) estimation of remedial cost, 6) VDO of gas emission from a crack on top of the mine waste heap, and 7) XRD analysis of coal-mine waste
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A sample dataset of a 3-element SoundTrap array, as recorded on the bench is available here. This is supplementary research data to accompany the following article: Malinka CE, Atkins J, Johnson MP, Tonnesen PH, Dunn CA, Claridge DE, Aguilar de Soto N, Madsen PT. "An autonomous hydrophone array to study the acoustic ecology of deep-water toothed whales." Deep Sea Research I. https://doi.org/10.​1016/​j.​dsr.​2020.​103233 This data is included to allow for testing of the MATLAB library (github.com/cemalinka/SoundTrapArray) for the time synchronisation of all channels on the SoundTrap array. 1678024710 is the transmitter, and the other two SoundTraps are receivers. Upon using this software and data, taps of both receivers against the transmitter at the end of the ~2.5 h long deployment will be accurately time-aligned in the resulting multi-channel WAV files.
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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.
Data Types:
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  • Software/Code
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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.
Data Types:
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  • Tabular Data
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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
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  • Document
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Smoke Test 17Feb2020 rdmmibtest1 (Dataset-1)
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Dual numbers are used to develop methods for computing velocities and accelerations
Data Types:
  • Software/Code
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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.
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  • Tabular Data
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Tank Detection and Count Dataset contains 760 satellite image tiles of size 512*512 pixels and one-pixel cover 30cm*30cm at ground level. Each tile is associated with .xml and .txt files. Both .xml and .txt file contains the same annotations of oil/gas tanks but in a different format. .xml contains the pascal VOC format and in .txt file, every line contains the class of the tank and four coordinates of the bounding box: xmin, ymin, xmax, ymax.
Data Types:
  • Image
  • Dataset
  • Document
  • Text