This is list of data elements and their attributes that are used by data assets at the Federal Highway Administration.

Dataset contains basic changed data of RÚIAN, e.g. basic descriptive data of territorial elements and units of territorial registration, for which at least one of their attributes has changed on the selected day. Dataset contains no spatial location (polygons and definition lines) and centroids of RÚIAN elements. The file contains following elements (in case they have been changed): state, cohesion region, higher territorial self-governing entity (VÚSC), municipality with extended competence(ORP), authorized municipal office (POU), region (old ones – defined in 1960), county, municipality, municipality part, town district (MOMC), Prague city district (MOP), town district of Prague (SOP), cadastral units and basic urban units (ZSJ), streets, building objects and address point. Up-to-date data is specified for each element: code, centroid (if exists) and all available descriptive attributes including the code of superior element. Dataset is provided as Open Data (licence CC-BY 4.0). Data is based on RÚIAN (Register of Territorial Identification, Addresses and Real Estates). Files are created every day (in case any change of any element occurred). Data is provided in RÚIAN exchange format (VFR), which is based on XML language and fulfils the GML 3.2.1 standard (according to ISO 19136:2007). Dataset is compressed (ZIP) for downloading. More in the Act No. 111/2009 Coll., on the Basic Registers, in Decree No. 359/2011 Coll., on the Basic Register of Territorial Identification, Addresses and Real Estates.

The National Bridge Inventory Elements dataset is as of June 20, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data describes more than 620,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible bridges on Federal and Tribal lands. The element data present a breakdown of the condition of each structural and bridge management element for each bridge on the National Highway System (NHS). The Specification for the National Bridge Inventory Bridge Elements contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Guide is available at: https://doi.org/10.21949/1519106. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1519106

The dataset contains relevant and irrelevant image tags of Web pages of 125 different domains. The image dataset contains the web domain, file number, the text of image HTML element, attributes of image elements, the size attributes, the parent HTML element of the image, and relevancy of the image. Each Web domain contains 100 Web pages with varying number of image elements.

1- The Zieni Dataset (2024): This is a recent, balanced dataset comprising 10,000 websites, with 5,000 phishing and 5,000 legitimate samples. The phishing URLs were sourced from PhishTank and Tranco, while legitimate URLs came from Alexa. Each of the 10,000 instances is characterized by 74 features, with 70 being numerical and 4 binary. These features comprehensively describe various components of a URL, including the domain, path, filename, and parameters.

2- The UCI Phishing Websites Dataset: This dataset contains 11,055 website instances, each labeled as either phishing (1) or legitimate (-1). It provides 30 diverse features that capture address bar characteristics, domain-based attributes, and other HTML and JavaScript elements (e.g., prefix-suffix, google_index, iframe, https_token). The data was aggregated from several reputable sources, including the PhishTank and MillerSmiles archives.

3- The Mendeley Phishing Dataset: This dataset includes 10,000 webpages, evenly split between phishing and legitimate categories. It describes each sample using 48 features. The data was collected in two periods: from January to May 2015 and from May to June 2017.

References [1] R. Zieni, “Zieni dataset for Phishing detection,” vol. 1, 2024. doi: 10.17632/8MCZ8JSGNB.1. [2] R. Mohammad et al., “An assessment of features related to phishing websites using an automated technique,” in International Conference for Internet Technology and Secured Transactions, 2012. [3] C. L. Tan, “Phishing Dataset for Machine Learning: Feature Evaluation,” vol. 1, 2018. doi: 10.17632/H3CGNJ8HFT.1.

The dataset contains extracted attributes from websites that can be used for Classification of webpages as malicious or benign. The dataset also includes raw page content including JavaScript code that can be used as unstructured data in Deep Learning or for extracting further attributes. The data has been collected by crawling the Internet using MalCrawler [1]. The labels have been verified using the Google Safe Browsing API [2]. Attributes have been selected based on their relevance [3]. The details of dataset attributes is as given below: 'url' - The URL of the webpage. 'ip_add' - IP Address of the webpage. 'geo_loc' - The geographic location where the webpage is hosted. 'url_len' - The length of URL. 'js_len' - Length of JavaScript code on the webpage. 'js_obf_len - Length of obfuscated JavaScript code. 'tld' - The Top Level Domain of the webpage. 'who_is' - Whether the WHO IS domain information is compete or not. 'https' - Whether the site uses https or http. 'content' - The raw webpage content including JavaScript code. 'label' - The class label for benign or malicious webpage.

Python code for extraction of the above listed dataset attributes is attached. The Visualisation of this dataset and it python code is also attached. This visualisation can be seen online on Kaggle [5].

The description of the attributes from the Dimension class in version 1.0 of the CSD model.

The data set contains basic descriptive current RÚIAN data, i.e. descriptive data on territorial elements and territorial registration units either for the whole state or for the chosen municipality. The data set does not contain spatial delimitation of RÚIAN elements. The state-wide file (ST_UZSZ) contains the following elements: state, cohesion regions, higher territorial self-governing units (VÚSC), municipalities with extended competence (ORP), municipalities with a mandated municipal authority (POU), regions (from 1960), districts, municipalities, parts of the municipality, municipal districts/urban districts (MOMC), municipal districts of Prague (ILO), administrative districts of Prague (SOP), cadastral territory and basic settlement units (ZSJ). The files for each municipality (OB_UZSZ) contain the following elements: municipality, parts of the municipality, MOMC (for territorially divided statutory cities, ILO (for Prague), SOP (for Prague), cadastral territory, ZSJ, streets, plots, building buildings and address points. For each element, its code, definition point (if any) and all available descriptive attributes, including the parent element code, are given. The data set is provided as open data (CC-BY 4.0 license). Data is based on RÚIAN (registry of territorial identification, addresses and real estate). Data are generated once a month in RÚIAN (VFR) exchange format, which is based on XML language and conforms to GML 3.2.1 (according to ISO 19136:2007). For download, each file is compressed as a ZIP. More in Act No. 111/2009 Coll., on basic registers, in Decree No. 359/2011 Coll., on the basic register of territorial identification, addresses and real estate. The data set contains basic descriptive current RÚIAN data, i.e. descriptive data on territorial elements and territorial registration units either for the whole state or for the chosen municipality. The data set does not contain spatial delimitation of RÚIAN elements. The state-wide file (ST_UZSZ) contains the following elements: state, cohesion regions, higher territorial self-governing units (VÚSC), municipalities with extended competence (ORP), municipalities with a mandated municipal authority (POU), regions (from 1960), districts, municipalities, parts of the municipality, municipal districts/urban districts (MOMC), municipal districts of Prague (ILO), administrative districts of Prague (SOP), cadastral territory and basic settlement units (ZSJ). The files for each municipality (OB_UZSZ) contain the following elements: municipality, parts of the municipality, MOMC (for territorially divided statutory cities, ILO (for Prague), SOP (for Prague), cadastral territory, ZSJ, streets, plots, building buildings and address points. For each element, its code, definition point (if any) and all available descriptive attributes, including the parent element code, are given. The data set is provided as open data (CC-BY 4.0 license). Data is based on RÚIAN (registry of territorial identification, addresses and real estate). Data are generated once a month in RÚIAN (VFR) exchange format, which is based on XML language and conforms to GML 3.2.1 (according to ISO 19136:2007). For download, each file is compressed as a ZIP. More in Act No. 111/2009 Coll., on basic registers, in Decree No. 359/2011 Coll., on the basic register of territorial identification, addresses and real estate.

Context

This dataset has been prepared to carryout classification of webpages as malicious or benign.

Content

The dataset contains extracted attributes from websites that can be used for Classification of webpages as malicious or benign. The dataset also includes raw page content including JavaScript code that can be used as unstructured data in Deep Learning or for extracting further attributes. The data has been collected by crawling the Internet using MalCrawler [1]. The labels have been verified using the Google Safe Browsing API [2]. Attributes have been selected based on their relevance [3].

References

[1] Singh, A. K., and Navneet Goyal. "MalCrawler: A crawler for seeking and crawling malicious websites." In International Conference on Distributed Computing and Internet Technology, pp. 210-223. Springer, Cham, 2017. [2] https://developers.google.com/safe-browsing [3] Singh, A. K., and Navneet Goyal. "A Comparison of Machine Learning Attributes for Detecting Malicious Websites." In 2019 11th International Conference on Communication Systems & Networks (COMSNETS), pp. 352-358. IEEE, 2019.

Inspiration

The dataset seeks to address classification of webpages using machine learning techniques.

This data release contains input data for hydrologic simulations of the Alaska Domain application of the U.S. Geological Survey (USGS) Precipitation Runoff Modelling System (PRMS) as implemented in the National Hydrologic Model (NHM) infrastructure (Regan and others, 2018). The NHM Alaska Domain parameter database consists of 114 parameter files in ASCII format (CSV), two files needed to run the Alaska Domain PRMS (control.fy19deliverable and fy19_deliv.param), two xml files (dimensions.xml and parameters.xml) containing descriptive information about the parameters, and a table that defines each parameter (AK_paramDB_datadictionary.csv). The Entity and Attribute element of this metadata record describe the data dictionary (AK_paramDB_datadictionary.csv). Please refer to the Supplemental Information element of this metadata record for references cited.

Every Known Element Dataset: Hydrogen to Oganesson (Periodic Table Data)

This is a comprehensive, tabular collection of all chemical elements from Hydrogen to Oganesson, which could be ideal for chemistry enthusiasts, researchers, educators, and data scientists alike. Each row represents an element, while each column describes a scientifically relevant property, thereby making the table useful for analysis, comparison, and visualization.

Key attributes (columns) include:

Element Symbol and Name: Standard chemical symbol and full name of each element.

Discovery Year - The year the element was discovered or first isolated.

Atomic Number & Atomic Mass (amu): Unique identifier and relative atomic mass.

Electron Configuration: Electron arrangement for each element.

Common Isotopes: Notable isotopes and their properties.

State at 25°C: Physical state at room temperature -solid, liquid, gas.

Melting & Boiling Points (°C, °F, K): Temperature at which an elements changes state.

Density, Color/Appearance: Descriptive and quantitative physical characteristics.

Conductivity, hardness, and malleability are some of the most important features in material science studies.

Reactivity, Electronegativity, Ionization Energy-eV: Key chemical behavior indicators.

Valency, Oxidation States, Atomic Radius: This defines bonding and size.

Metallic Character, Radioactivity, Nuclear Stability, Half-Life Provides nuclear and metallic property data.

Magnetism Type, Magnetic Susceptibility: Details magnetic behavior.

Emission/Absorption Spectra, Spectral Lines: Key for spectroscopy and physics.

Biologischer Funktion, Umweltverhalten: Bedeutung der Elemente in der Biologie und im Umfeld

Allotropes: Different structural forms an element can exist in.

Format:

The data is tabulated in a flat table (columns = properties, rows = elements) and can be comfortably read from Excel, CSV readers, Python Pandas, etc.

Supports filtering, sorting, and export for advanced analysis or machine learning.

Some sheets will have additional information included; for example, heavy element conductivity or special notes.

This is a one-stop resource for those interested in the periodic table, chemistry fundamentals, or data-driven exploration of the properties of elements.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3305472%2F9b6db5199f19afd866a33bc89e56ef07%2F1706183889560.jpeg?generation=1712978213724236&alt=media" alt="">Understanding JSON Data Extraction:

Have you ever wondered how datasets are prepared from JSON after calling their APIs? This repository aims to demystify this process by providing five JSON files for exploration. Each file represents a snapshot of data obtained from different API endpoints.

Dataset Overview:

Data Source: API endpoints providing JSON data. File Formats: JSON (JavaScript Object Notation). Number of Files: 5 Total Records: Varies across files. Data Exploration:

Each JSON file contains structured data representing various aspects of the dataset. Explore different attributes and nested structures within the JSON files. Understand how to navigate and extract relevant information using programming languages like Python.

Included Files:

file1.json file2.json file3.json file4.json file5.json

Final Dataset: Zomato_Final_Data.csv

After extracting and preprocessing data from the five JSON files, a consolidated data frame has been created. The data frame provides a unified view of the data, facilitating analysis and modeling tasks.

Contribute Your Version: Feel free to contribute your code snippets for data extraction. Share your insights and techniques with the community to foster learning and collaboration.

Acknowledgements: Special thanks to Krish Naik and Zomato for providing the data used in this repository.

Feedback and Support: For any questions, feedback, or assistance, please reach out via [contact information]. Feel free to adjust any sections or add more details according to your specific dataset and preferences!

The description of the attributes from the DependentVariable class in version 1.0 of the CSD model.

Mapping of CSD model attribute values to JSON serialized values.

The raw file (RData binary format, use R's load() function to open) contains the properties of the elements, scraped from periodictable.com. The element-data.rda contains cleaned-up database of elemental properties, easily accessible for anyone with a working R install. The blog post in the link below describes how to use this file (see the MWE section). The csv files are ascii-formatted versions of the rda file's content.