JavaScript Object Notation (JSON) and eXtensible Markup Language (XML) are two data serialisation methods that have been compared over many applications including client-server transmission, internet communication, and large-scale data storage. Due to the smaller file size, JSON is faster for transmitting data. However, XML is better for sending complex data structures. This dataport contains C code project to compare performance of XML with JSON, considering factors such as time, memory, and power to identify efficient characteristics of each method. 


This dataset is used for network anomaly detection and is based on the UGR16 dataset network traffic flows. We used June week 2 to 4 tensors generated from raw flow data to train the models. The dataset includes a set of tensors generated from the whole UGR’16 network traffic (general tensor data) and several sets of port tensors (for specific port numbers). It also includes the trained models for each type of tensor. The tensors extracted from network traffic in the period from July week 5 to the end of August can be used for evaluation. The naming convention is as follows:


(Work in progress)

This dataset contains the augmented images and the images & segmentation maps for seven handwashing steps, six of which are prescirbed WHO handwashing steps.

This work is based on a sample handwashing video dataset uploaded by Kaggle user real-timeAR.


This data resource is an outcome of the NSF RAPID project titled "Democratizing Genome Sequence Analysis for COVID-19 Using CloudLab" awarded to University of Missouri-Columbia.

The resource contains the output of variant analysis (along with CADD scores) on human genome sequences obtained from the COVID-19 Data Portal. The variants include single nucleotide polymorphisms (SNPs) and short insert and deletes (indels).


In February 2016, LIGO announced the first observation of gravitational waves from a binary black hole merger, known as GW150914. To establish the confidence of this detection, large-scale scientific workflows were used to measure the event's statistical significance. These workflows used code written by the LIGO Scientific Collaboration and were executed on the LIGO Data Grid.


The following data set is modelled after the implementers’ test data in 3GPP TS 33.501 “Security architecture and procedures for 5G System” with the same terminology. The data set corresponds to SUCI (Subscription Concealed Identifier) computation in the 5G UE (User Equipment) for IMSI (International Mobile Subscriber Identity) based SUPI (Subscription Permanent Identifier) and ECIES Profile A.