This is the data analysis done for the paper titled "The Effects of Human Aspects on the Requirements Engineering Process: A Systematic Literature Review" (submitted to IEEE Transcations on Software Engineering). 


This repository includes the data collected from the aocl profile and the Jupyter notebooks with the memory model equations proposed. Application folder contains a set of benchmarks to validate the proposed model.

The model was developed using the Quartus aocl version 18.1 for Stratix10 GX and 19.4 for Stratix10MX


This dataset is the recorded data for a Dissipative Particle Dynamics simulator which is designed to run on an event-driven high performance computing platform, POETS ( 

This is in support of an article submitted to IEEE Transactions on Parallel and Distributed Systems, entitled "An event-driven approach to Dissipative Particle Dynamics".


The Python Packaging Index is an invaluable resource that is used by developers to improve their projects; however, there are glaring issues in its implementation that will hinder development until resolved. The Python Packaging Index (PyPI) is the official third-party software repository for Python where the majority of open-source Python packages are published. Each package has wheel and egg files accessible from the Python package management system PIP, as well as queryable metadata that contains important package information.


The dataset is a product of LowEndInsight - details here:


This paper introduces a low profile wideband Planar Inverted-F antenna (PIFA) for vehicular applications in the 5G systems (below 6GHz) and Vehicle-to-Everything (V2X) communications. The antenna covers a wide range of bandwidth that operates from 617MHz to 6GHz while having an acceptable filtering on the GNSS bands. This design’s physical dimensions and electrical performance makes it suitable for low profile wireless applications in the automotive field.


These datasets contain bulk BTE simulation results for GaAs, InP, GaSb and InAs as a function of electric field at 300 K.


To read the data we suggest to

1. un-zip the data

2. read it with e.g. the pandas library for Python or any *csv reader


This is a large Chinese taxonomic knowledge base, which is translated from Probase by the neural network.

It has 11,292,493 IsA pairs with an accuracy of 86.6%.



This is a large Chinese commonsense knowledge base, which is translated from ConceptNet 5.6, with around 2 million triples and an accuracy of 89.6%.


This dataset presents the results obtained for Ingestion and Reporting layers of a Big Data architecture for processing performance management (PM) files in a mobile network. Flume was used in the Ingestion layer. Flume collected PM files from a virtual machine that replicates PM files from a 5G network element (gNodeB). Flume transferred PM files to High Distributed File System (HDFS) in XML format. Hive was used in the Reporting layer. Hive queries the raw data from HDFS. Hive queries a view from HDFS.



The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at-sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, we present a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks.


This repository contains the AURORA dataset, a multi sensor dataset for robotic ocean exploration.

It is accompanied by the report "AURORA, A multi sensor dataset for robotic ocean exploration", by Marco Bernardi, Brett Hosking, Chiara Petrioli, Brian J. Bett, Daniel Jones, Veerle Huvenne, Rachel Marlow, Maaten Furlong, Steve McPhail and Andrea Munafo.

Exemplar python code is provided at


The dataset provided in this repository includes data collected during cruise James Cook 125 (JC125) of the National Oceanography Centre, using the Autonomous Underwater Vehicle Autosub 6000. It is composed of two AUV missions: M86 and M86.

  • M86 contains a sample of multi-beam echosounder data in .all format. It also contains CTD and navigation data in .csv format.

  • M87 contains a sample of the camera and side-scan sonar data. The camera data contains 8 of 45320 images of the original dataset. The camera data are provided in .raw format (pixels are ordered in Bayer format). The size of each image is of size 2448x2048. The side-scan sonar folder contains a one ping sample of side-scan data provided in .xtf format.

  • The AUV navigation file is provided as part of the data available in each mission in .csv form.


The dataset is approximately 200GB in size. A smaller sample is provided at and contains a sample of about 200MB.

Each individual group of data (CTD, multibeam, side scan sonar, vertical camera) for each mission (M86, M87) is also available to be downloaded as a separate file.