Crash Statistics are summarized crash statistics for large trucks and buses involved in fatal and non-fatal Crashes that occurred in the United States. These statistics are derived from two sources: the Fatality Analysis Reporting System (FARS) and the Motor Carrier Management Information System (MCMIS). Crash Statistics contain information that can be used to identify safety problems in specific geographical areas or to compare state statistics to the national crash figures.

Provides a reference for the comparison of key figures between the constituent countries, and between the UK as a whole and other nation states. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: United Kingdom Health Statistics

VHS-22 is a heterogeneous, flow-level dataset which combines ISOT, CICIDS-17, Booters and CTU-13 datasets, as well as traffic from Malware Traffic Analysis (MTA) site, to increase variety of malicious and legitimate traffic flows. It contains 27.7 million flows (20.3 million legitimate and 7.4 million of attacks). The flows are represented in the form of 45 features; apart from classical NetFlow features, VHS-22 contains statistical parameters and network-level features. Their detailed description and the results of initial detection experiments are presented in the paper:

Paweł Szumełda, Natan Orzechowski, Mariusz Rawski, and Artur Janicki. 2022. VHS-22 – A Very Heterogeneous Set of Network Traffic Data for Threat Detection. In Proc. European Interdisciplinary Cybersecurity Conference (EICC 2022), June 15–16, 2022, Barcelona, Spain. ACM, New York, NY, USA, https://doi.org/10.1145/3528580.3532843

Every day contains different attacks mixed with legitimate traffic. 01-01-2022 Botnet attacks from ISOT dataset. 02-01-2022 Various attacks from MTA dataset. 03-01-2022 Web attacks from CICIDS-17 dataset. 04-01-2022 Bruteforce attacks from CICIDS-17 dataset. 05-01-2022 Botnet attacks from CICIDS-17 dataset. 06-01-2022 DDoS attacks from CICIDS-17 dataset 07-01-2022 to 11-01-2022 DDoS attacks from Booters dataset. 12-01-2022 to 23-01-2022 Botnet traffic from CTU-13 dataset.

The VHS-22 dataset consists of labeled network flows and all data is publicly available for researchers in .csv format. When using VHS-22, please cite our paper which describes the VHS-22 dataset in detail, as well as the publications describing the source datasets:

Paweł Szumełda, Natan Orzechowski, Mariusz Rawski, and Artur Janicki. 2022. VHS-22 – A Very Heterogeneous Set of Network Traffic Data for Threat Detection. In Proc. European Interdisciplinary Cybersecurity Conference (EICC 2022), June 15–16, 2022, Barcelona, Spain. ACM, New York, NY, USA, https://doi.org/10.1145/3528580.3532843

Sherif Saad, Issa Traore, Ali Ghorbani, Bassam Sayed, David Zhao, Wei Lu, John Felix, and Payman Hakimian. 2011. Detecting P2P botnets through network behavior analysis and machine learning. In Proc. International Conference on Privacy, Security and Trust. IEEE, Montreal, Canada, 174–1

Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani. 2018. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization, In Proc. 4th International Conference on Information Systems Security and Privacy (ICISSP 2018), Funchal, Portugal

José Jair Santanna, Romain Durban, Anna Sperotto, and Aiko Pras. 2015. Inside booters: An analysis on operational databases. In Proc. International Symposium on Integrated Network Management (INM 2015). IFIP/IEEE, Ottawa, Canada, 432–440. https://doi.org/10.1109/INM.2015.71403

Riaz Khan, Xiaosong Zhang, Rajesh Kumar, Abubakar Sharif, Noorbakhsh Amiri Golilarz, and Mamoun Alazab. 2019. An Adaptive Multi-Layer Botnet Detection Technique Using Machine Learning Classifiers. Applied Sciences 9 (06 2019), 2375. https://doi.org/10.3390/app91123

The Malware Traffic Analysis data originate from https://www.malware-traffic-analysis.net, authored by Brad.

The work has been funded by the SIMARGL Project -- Secure Intelligent Methods for Advanced RecoGnition of malware and stegomalware, with the support of the European Commission and the Horizon 2020 Program, under Grant Agreement No. 833042.

This is the official website for downloading the CA sub-dataset of the LargeST benchmark dataset. There are a total of 7 files in this page. Among them, 5 files in .h5 format contain the traffic flow raw data from 2017 to 2021, 1 file in .csv format provides the metadata for sensors, and 1 file in .npy format represents the adjacency matrix constructed based on road network distances. Please refer to https://github.com/liuxu77/LargeST for more information.

Annual descriptive price statistics for each calendar year 2005 – 2024 for 11 Local Government Districts in Northern Ireland. The statistics include: • Minimum sale price • Lower quartile sale price • Median sale price • Simple Mean sale price • Upper Quartile sale price • Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for all MSOAs and compare this with Leicester overall statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsProficiency in EnglishThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by their proficiency in English. The estimates are as at Census Day, 21 March 2021.Definition: How well people whose main language is not English (English or Welsh in Wales) speak English.This dataset provides details for the MSOAs of Leicester city.

This table shows the international developments in the capital stock and the investments. Beside the picture of the total economy, a category has been made for ICT (information and communication technology). The table is related both to the physical capital stock and its renewal or extension by means of (foreign) capital investments, and to the money that is necessary to finance the investments, in particular the venture capital. The scope of the capital and the investments in a country are mainly defined by the propensity of entrepreneurs to invest. Investment behaviour is partly defined by the investment climate.

Note: Comparable definitions are used to compare the figures presented internationally. The definitions sometimes differ from definitions used by Statistics Netherlands. The figures in this table could differ from Dutch figures presented elsewhere on the website of Statistics Netherlands.

Data available from 1990 up to 2012.

Status of the figures: The external source of these data frequently supplies adjusted figures on preceding periods. For example, it often happens that countries still provide figures on older years. The reverse, older figures being withdrawn, also happens now and then. These adjusted data are not mentioned as such in the table.

Changes as of 22 December 2017: No, table is stopped.

When will new figures be published? Not.

This dataset contains traffic violation information from all electronic traffic violations issued in the County. Any information that can be used to uniquely identify the vehicle, the vehicle owner or the officer issuing the violation will not be published. Update Frequency: Daily

This dataset contains raw Signal Phasing and Timing (SPaT), MAP, and Basic Safety Messages (BSM) data from the "Feasibility Study and Assessment of Communications Approaches for Real-Time Traffic Signal Applications" project in the hexadecimal string and pcap formats. The project characterizes and assesses the feasibility of SPaT messages for infrastructure-based safety applications by comparing messages received through cellular networks with those received through Dedicated Short Range Communication (DSRC). This dataset contains the raw research data collected for the project. The project's final report and supporting dataset can be found at the National Transportation Library and is linked in the references section of this dataset.

This dataset provides information on motor vehicle operators (drivers) involved in traffic collisions occurring on county and local roadways. The dataset reports details of all traffic collisions occurring on county and local roadways within Montgomery County, as collected via the Automated Crash Reporting System (ACRS) of the Maryland State Police, and reported by the Montgomery County Police, Gaithersburg Police, Rockville Police, or the Maryland-National Capital Park Police. This dataset shows each collision data recorded and the drivers involved. Please note that these collision reports are based on preliminary information supplied to the Police Department by the reporting parties. Therefore, the collision data available on this web page may reflect: -Information not yet verified by further investigation -Information that may include verified and unverified collision data -Preliminary collision classifications may be changed at a later date based upon further investigation -Information may include mechanical or human error This dataset can be joined with the other 2 Crash Reporting datasets (see URLs below) by the State Report Number. * Crash Reporting - Incidents Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Incidents-Data/bhju-22kf * Crash Reporting - Non-Motorists Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Non-Motorists-Data/n7fk-dce5 Update Frequency : Weekly