Oil and gas organizations have enormous operations which generate terabytes of data. Management and utilization of this big data requires large IT infrastructure comprising of various information systems. Oil and gas organizations are spending much of their budgets on information security and privacy related issues. Most of these organizations put their efforts on technical solutions for information security. Whereas majority of the security incidents occur due to negligence of internal employees’ attitude.

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Data corresponds to quantitative (raw) effort assessments/predictions during maintenance process of a sample of 1000 possible instances of the general selection problem among Visitor and Inheritance Based Implementation over the Composite design patterns (CIBI vs CVP).

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*This datasheet is being updated progressively to provide more details.

This datasheet provides the phasor measurement data in actual power systems.

These PMU data were recorded during a Low Frequency Oscillation incident and a Short Circuit Incident, respectively.

These PMU data are used for the studies in wide-area control systems (WACS) and PMU data compressions.

Please cite this datasheet and the papers in your work if they help.

Instructions: 

Refer to the documentation file for detailed information.

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PRIME-FP20 dataset is established for development and evaluation of retinal vessel segmentation algorithms in ultra-widefield fundus photography. PRIME-FP20 provides 15 high-resolution ultra-widefield fundus photography images acquired using the Optos 200Tx camera (Optos plc, Dunfermline, United Kingdom), the corresponding labeled binary vessel maps, and the corresponding binary masks for the FOV of the images.

Instructions: 

Ultra-widefield fundus photography images and the corresponding labeled vessel maps and binary masks are provided where the file names indicate the correspondence between them.

Currently, only a sample low-resolution image is provided. The full set of high-resolution images will be provided upon the publication of the associated paper, which is currently submitted for review.

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Several pathological phenomena are closely associated with mechanical properties of vessel and interactions of blood flow–wall dynamics. However, conventional techniques cannot easily measure these features. In this study, new deep learning-based simultaneous measurement of flow–wall dynamics (DL-SFW) is proposed by devising integrated neural network for super-resolved localization and vessel wall segmentation and combining with tissue motion measurement technique and flow velocimetry.

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Each voice sample is stored as a .WAV file, which is then pre-processed for acoustic analysis using the specan function from the WarbleR R package. Specan measures 22 acoustic parameters on acoustic signals for which the start and end times are provided.

The output from the pre-processed WAV files were saved into a CSV file, containing 3168 rows and 21 columns (20 columns for each feature and one label column for the classification of male or female).

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This is an example of clustering to replicate OpenNym experiments

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An ontology for the multidisciplinary phenomenon of creating web application ove encrypted data

Instructions: 

The ontology OWL file is generated using protege tool and can be imported for future modifications.

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Instructions: 

The .zip file contains 6 folders when unzipped. We provide the details of each folder below.

 

“Proteins” folder: Contains 20 protein targets organized into two folders (Benchmark and CASP) depending on the family each target belongs to. Data for each protein is provided in a subfolder named with its id. Each such subfolder contains the following 4 files.

  1. A .fasta file containing the amino-acid sequence of the protein.

  2. A .pdb file containing the native tertiary structure coordinates. Detailed format for a .pdb file can be found in http://www.wwpdb.org/documentation/file-format

  3. A .frag3 file containing the fragments of length 3 for the protein sequence generated from http://old.robetta.org/

  4. A .frag9 file containing the fragments of length 9 for the protein sequence generated from http://old.robetta.org/

 

“Generation” folder: Contains the generated ensembles for the protein targets in 20 subfolders, one for each target, named with their ids. Each subfolder contains 5 files, each containing the generated ensemble for one run. Each such file contains 14 columns and each row represents one generated structure. The first column provides the Rosetta score4 energy, the second column provides the lRMSD to the native structure, and each of the rest of the 12 columns provides one USR feature for the structure.

 

“Reduced” folder: Contains the reduced ensembles for each clustering technique in separate folders. Each such folder contains 20 subfolders, one for each target, named with their ids. Each such subfolder contains 5 files, each containing the reduced ensemble for one run. Each such file contains 2 columns and each row represents one structure in the reduced ensemble. The first column provides the Rosetta score4 energy and the second column provides the lRMSD to the native structure.

 

“Truncation” folder: Contains the reduced ensembles via truncation for the protein targets in 20 subfolders, one for each target, named with their ids. Each such subfolder contains 5 files, each containing the reduced ensemble for one run. Each such file contains 2 columns and each row represents one structure in the reduced ensemble. The first column provides the Rosetta score4 energy and the second column provides the lRMSD to the native structure.

 

“Ks” folder: Contains 4 separate files, one for each clustering technique, containing the number of clusters for each run of each protein target. These files can be used to plot the distributions for the number of clusters.

 

“Bars” folder: Contains 3 separate subfolders containing the information needed to plot the bar charts for the minimum, average, and standard deviation of lRMSDs to the native structure for the CASP targets. Each subfolder contains 10 files, one for each target. Each file contains 6 rows that provide the lRMSD value for original ensemble, reduced ensemble for hierarchical clustering, reduced ensemble for k-means clustering, reduced ensemble for GMM clustering, reduced ensemble for gmx-cluster clustering, and reduced ensemble for truncation, respectively.

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Nodes represent personality facets (a description of each facet is provided in Table 3), green lines represent positive connections and red lines represent negative connections. Thicker lines represent stronger connections and thinner lines represent weaker connections. The node placement of all graphs is based on the adaptive LASSO network to facilitate comparison. The width and color are scaled to the strongest edge and are not comparable between graphs; edge strengths in the correlation network are generally stronger than edge strengths in the partial correlation network.

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