A complete list of live websites using the Structured Data technology, compiled through global website indexing conducted by WebTechSurvey.

The structured data management software market is experiencing robust growth, driven by the increasing need for organizations to efficiently manage and analyze ever-expanding data volumes. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% through 2033, reaching approximately $150 billion by the end of the forecast period. This expansion is fueled by several key factors. The rise of big data analytics, cloud computing adoption, and the stringent regulatory requirements for data governance are all compelling businesses to invest in sophisticated structured data management solutions. Furthermore, the growing demand for real-time data processing and improved data security contribute to the market's dynamism. Major players like Google, Salesforce, and IBM are actively shaping the market landscape through continuous innovation and strategic acquisitions. The market is segmented by deployment (cloud, on-premise), organization size (small, medium, large), and industry vertical (finance, healthcare, retail, etc.), presenting diverse growth opportunities across various niches. Competition is fierce, with both established tech giants and specialized vendors vying for market share. Despite the positive outlook, challenges remain, including the complexity of integrating these solutions with existing systems and the need for skilled professionals to manage these complex technologies. The competitive landscape is characterized by a mix of established players and emerging vendors. While giants like Google, Salesforce, and IBM leverage their extensive resources and existing customer bases to maintain market dominance, agile smaller companies are focusing on niche solutions and innovative technologies to capture market share. The global distribution of the market is expected to show strong growth across North America and Europe, driven by high levels of technology adoption and established digital infrastructure. However, growth opportunities also exist in rapidly developing economies in Asia-Pacific and Latin America as businesses in these regions accelerate their digital transformation initiatives. The ongoing development of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), integrated into structured data management software, is a significant catalyst for future market growth, enabling more sophisticated data analysis and improved decision-making.

This dataset is a restructured version of the data from the MABe Challenge competition's train_tracking directory. It has been reformatted to improve human readability and to facilitate easier use with machine learning models.

Disclaimer: I do not own the original data. All credit belongs to the MABe Challenge, and a link to the competition is provided. You are welcome to use this modified dataset in your models.

Note: The 2022 data is not yet included but is scheduled for a future update.

Competition Link :- https://www.kaggle.com/competitions/MABe-mouse-behavior-detection/overview Notebook Link for Dataset Creation :- https://www.kaggle.com/code/kushubhai/mabe-new-dataset-creation

Feel free to check out my another notebook for the visualization of parquet files inside the data of the competition :-

Notebook Link for Visualization :- https://www.kaggle.com/code/kushubhai/mabe-visualization

The size of the Structured Data Management Softwares market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

Structured data vectors utilized in machine learning algorithms.

The size of the Structured Data Archiving (SDA) Software market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Microformat, Microdata and RDFa data from the October 2016 Common Crawl web corpus. We found structured data within 1.24 billion HTML pages out of the 3.2 billion pages contained in the crawl (38%). These pages originate from 5.63 million different pay-level-domains out of the 34 million pay-level-domains covered by the crawl (16.5%). Altogether, the extracted data sets consist of 44.2 billion RDF quads.

Data includes a structured dataset of the forum and marketplace of the dark web cryptomarket Evolution as well as a longitudinal network snapshot dataset of a temporal weighted communication network extracted from the forum data. The datasets were extracted from Dark Net Market archive data while resolving many of the data quality issues inherent to raw data scraped from online sources.

Dark web cryptomarkets facilitate the online trade of illicit goods. Evolution was active from January 2014 until March 2015; and formed a combination of a carding forum and an underground drug market. This dataset includes the vast majority of data that could be obtained from the webpages of the forum and marketplace of Evolution. This includes, for example, information on the number of sales each individual vendor has completed, forum post contents, and product categories.

The extracted communication network models users' co-posting in the same forum topics, in essence modelling communication between them.

Additionally included is the code used to produce the dataset from the Dark Net Market archive data.

Structured Data Archiving And Application Retirement Market size was valued at USD 6.43 Billion in 2024 and is projected to reach USD 14.413 Billion by 2032, growing at a CAGR of 9.5% from 2026 to 2032.

Structured Data Archiving And Application Retirement Market Drivers

Regulatory Compliance Requirements: Organizations in a variety of sectors must adhere to legal requirements pertaining to data archiving and preservation. Structured data must be kept on file for legal, auditing, and compliance reasons, according to regulations. Data from defunct or decommissioned applications must be archived by organizations in order to comply with laws like Sarbanes-Oxley (SOX), GDPR, HIPAA, and others. The demand for application retirement and structured data archiving solutions is driven by the necessity to comply with regulations.

Cost Optimization and Efficiency: By retiring old programs that are no longer in active use, businesses aim to reduce IT expenses and streamline processes. Updating out-of-date apps requires resources for infrastructure, upkeep, and license. Organizations can enhance operational efficiency, save storage costs, and decommission outdated applications by using structured data archiving and application retirement solutions. These services also free up resources for more strategic projects.

Data Governance and Risk Management: Organizations must manage data at every stage of its lifespan, including the archiving and retirement procedures, in order to implement effective data governance standards. Solutions for structured data archiving make it easier to manage structured data assets by offering features like data classification, audit trails, retention policies, and access controls. Through the implementation of application retirement and organized data archiving methods, organizations can reduce the risks associated with data loss, security breaches, and unauthorized access.

The structured social observation data set is composed of four key measures of neighborhood structural and social characteristics. The four measures are social disorder, social order, institutional disorder, and institutional order. Social order captures assets in a community that promote positive socialization (e.g., bus stops, bike racks, neighborhood watch signs, and/or children playing). Social disorder captures potential liabilities of social aspects of a community (e.g., selling drugs, prostitution, and/or fights). Institutional disorder measures structural or physical liabilities of a community (e.g., broken windows, liquor and tobacco sales, and/or commercial property that is burned out, boarded up, or abandoned). Institutional order measures structural or physical assets in a community (e.g., schools, banks, churches, cultural arts, and/or green space). The primary data were collected by raters (research assistants) who walked predetermined segments in the communities that were observed. The predetermined segments were organized in closed-loop routes that strategically reflected census tracts and zip code boundaries to allow for appropriate data aggregation. Two or three routes were within a census tract and zip code boundary. Census tract and zip code-level values represent means of route values within the boundary. Two raters independently observed the same segments until agreement was reached. Inter-rater reliability was assessed using Cohen’s Kappa. An exploratory factor analysis and Cronbach’s alpha (item reliability) was conducted on the data to develop the four constructs presented in the data set.

Benchmark for End-User Structured Data User Interfaces (BESDUI) based on the Berlin SPARQL Benchmark (BSBM) but intended for benchmarking the user experience while exploring a structured dataset, not the performance of the query engine. BSBM is just used to provide the data to be explored. This is a cheap User Interface benchmark as it does not involve users but experts, who measure how many interaction steps are required to complete each of the benchmark tasks, if possible. This also facilitates comparing different tools without the bias that different end-user profiles might introduce. The way to measure this interaction steps and convert them to an estimate of the required time to complete a task is based on the Keystroke-Level Model (KLM)

Information about abstracts with distinct, labeled sections (e.g., Introduction, Methods, Results, discussion) that appear in MEDLINE.

Sample Dataset. The manually annotated dataset used the following color scheme for the seven root elements: Metadata – Grey, Asset – Yellow, Vulnerability – Blue, Impact & Risk – Red, Mitigation – Green, References – Purple, Reporting & Contact – Indigo.

This dataset contains information on customer demographics, account details, and service usage patterns to analyze and predict customer churn. It is commonly used in churn modeling projects to develop machine learning models that classify whether a customer is likely to leave (churn) or stay. The dataset is suitable for tasks such as Exploratory Data Analysis (EDA), feature engineering, model training, and evaluation.

Key Features May Include:

CustomerID: Unique identifier for each customer

Gender, Age: Demographic details

Tenure: Number of months the customer has stayed

Balance, EstimatedSalary: Financial features

IsActiveMember, HasCrCard: Behavioral indicators

Exited: Target variable indicating churn (1 = churned, 0 = retained)

The size of the Structured Data Archiving and Application Retirement Software market was valued at USD 71 million in 2024 and is projected to reach USD 134.02 million by 2033, with an expected CAGR of 9.5 % during the forecast period.

List of Structured Products under registered under Lodge and Launch Framework

Suicide remains a leading cause of preventable death worldwide, despite advances in research and decreases in mental health stigma through government health campaigns. Machine learning (ML), a type of artificial intelligence (AI), is the use of algorithms to simulate and imitate human cognition. Given the lack of improvement in clinician-based suicide prediction over time, advancements in technology have allowed for novel approaches to predicting suicide risk. This systematic review and meta-analysis aimed to synthesize current research regarding data sources in ML prediction of suicide risk, incorporating and comparing outcomes between structured data (human interpretable such as psychometric instruments) and unstructured data (only machine interpretable such as electronic health records). Online databases and gray literature were searched for studies relating to ML and suicide risk prediction. There were 31 eligible studies. The outcome for all studies combined was AUC = 0.860, structured data showed AUC = 0.873, and unstructured data was calculated at AUC = 0.866. There was substantial heterogeneity between the studies, the sources of which were unable to be defined. The studies showed good accuracy levels in the prediction of suicide risk behavior overall. Structured data and unstructured data also showed similar outcome accuracy according to meta-analysis, despite different volumes and types of input data.

Electronic Health Records (EHRs) are integral for storing comprehensive patient medical records, combining structured data (e.g., medications) with detailed clinical notes (e.g., physician notes). These elements are essential for straightforward data retrieval and provide deep, contextual insights into patient care. However, they often suffer from discrepancies due to unintuitive EHR system designs and human errors, posing serious risks to patient safety. To address this, we developed EHRCon, a new dataset and task specifically designed to ensure data consistency between structured tables and unstructured notes in EHRs. EHRCon was crafted in collaboration with healthcare professionals using the MIMIC-III EHR dataset, and includes manual annotations of 4,101 entities across 105 clinical notes checked against database entries for consistency. EHRCon has two versions, one using the original MIMIC-III schema, and another using the OMOP CDM schema, in order to increase its applicability and generalizability.

found primarily via Google Scholar, searching by mentions in the methods sections. Citing EOL is not required when using EOL-hosted records; only the primary source must be cited. Thus, these lists may not be exhaustive.

The Structured Product Labeling dataset contains the most recent drug labeling information submitted to the Food and Drug Administration (FDA) and currently in use. All labels information are published by DailyMed the official provider of FDA label information.