This dataset is a copy of data exported from the park's GIS SDE geodatabases that were used to publish AGOL hosted feature services used in the public-facing Park Atlas. The NPS requires AGOL content used in public-facing applications we uploaded to IRMA. The zipped file contains : 1) an ESRI file geodatabase with selected feature layers that are uwsed in Park Atlas, 2) the ArcGIS Pro project that was used to publish the SDE data to AGOL, and 3) an extensive library of ESRI layer files that are used at the park.

The Department of Health Care Services (DHCS) Long-Term Services and Supports (LTSS) Data Dashboard is an initiative of the Home and Community Based Services Spending Plan. The initiative's primary goal is to create a public-facing LTSS data dashboard to track demographic, utilization, quality, and cost data related to LTSS services. This dashboard will link statewide long-term care and home and community-based services (HCBS) data with the goal of increased transparency to make it possible for regulators, policymakers, and the public to be informed while the state continues to expand, enhance, and improve the quality of LTSS in all home, community, and congregate settings. The first iteration of the LTSS Dashboard was released in December 2022 as an Open Data Portal file with 40 measures pertaining to LTSS beneficiaries, which includes ten different demographics, plan-related dimensions, and dual stratification. The December 2023 Data Release includes 16 new measures on the Medi-Cal LTSS Dashboard and Open Data Portal (Select “View Underlying Data”); and additional measures and dimensions, including dual stratification, will be added to the Open Data Portal in 2024. Note: The LTSS Dashboard measures are based on certified eligible beneficiaries who were enrolled in Medi-Cal for one or more months during the reporting interval. Most of the DHCS LTSS dashboard measures report the annual number of certified eligible Medi-Cal beneficiaries who have used LTSS services within a year. Other departments may report on these programs differently. For example, the Department of Social Services (CDSS) reports monthly IHSS recipient/consumer counts. The California Department of Aging (CDA) reports monthly CBAS Medi-Cal participants. DHCS’ annual utilization / enrollment counts of IHSS and CBAS beneficiaries are larger than CDSS/CDA's monthly counts because of data source differences and new enrollment or program attrition over time. Monthly snap-shot measures (average monthly utilization) for IHSS and CBAS have been added to the LTSS Dashboard to align with CDSS and CDA monthly reporting. Refer to the LTSS-Dashboard (ca.gov) program page for: 1) a Fact Sheet with highlights from the initial data release including changes over time in use of Home and Community-Based Services as well as select demographic information; 2) the Measure Specifications document – that describes business rules and inclusion/exclusion criteria related to age groups, plan types, aid code, geographic, or other important program/waiver-specific eligibility criteria; and 3) User guide – that shows how to navigate the Open Data Portal data file with specific examples.

Please note: This dataset has been superceded by dataset: Environment Agency and Natural England Public Facing Area Names v2. This is the archived version 1 of the authoritative controlled list which specifies the shared area names of the Environment Agency and Natural England. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved.

This dataset supports the SWAMP Data Dashboard, a public-facing tool developed by the Surface Water Ambient Monitoring Program (SWAMP) to provide accessible, user-friendly access to water quality monitoring data across California. The dashboard and its associated datasets are designed to help the public, researchers, and decision-makers explore and download monitoring data collected from California’s surface waters.

This dataset includes five distinct resources:

• SWAMP Stations – Geospatial and descriptive information about SWAMP monitoring sites.
• Water Quality Results – Field and lab analysis results for chemical and physical parameters measured in water samples.
• Toxicity Summary Results – Summarized results from aquatic toxicity tests. Summary records are entries in the database that summarize the results from multiple replicate toxicity tests of the same sample water.
• Habitat Results – Data on physical habitat conditions typically collected alongside biological monitoring to provide context for interpreting water quality conditions. Includes scores for the California Stream Condition Index (CSCI) and Algal Stream Condition Index (ASCI).
• Tissue Summary Results – Annual summary statistics of contaminant concentrations in aquatic organism tissue samples. The data are derived from raw individual and composite tissue sample results.

These data are collected by SWAMP and its partners to support water quality assessments, identify trends, and inform water resource management. The SWAMP Data Dashboard provides interactive visualizations and filtering tools to explore this data by region, parameter, and more.

The SWAMP dataset is sourced from the California Environmental Data Exchange Network (CEDEN), which serves as the central repository for water quality data collected by various monitoring programs throughout the state. As such, there is some overlap between this dataset and the broader CEDEN datasets also published on the California Open Data Portal (see Related Resources). This SWAMP dataset represents a curated subset of CEDEN data, specifically tailored for use in the SWAMP Data Dashboard.

Access the SWAMP Data Dashboard: https://gispublic.waterboards.ca.gov/swamp-data/

*This dataset is provisional and subject to revision. It should not be used for regulatory purposes.

This is the controlled list of 14 operational area names, codes and descriptions of the Environment Agency and Natural England. This is the standard list for re-use across the Environment Agency and Natural England. The previous version contained only Environment Agency Area Names, but was updated to merge with Natural England public facing Areas. Attribution statement: © Environment Agency copyright and/or database right 2017. All rights reserved.

Dataset containing information related to Force Incidents involving NYPD Members of Service. The Threat, Resistance, or Injury (TRI) Report is the primary means by which the NYPD records use of force incidents. All reportable instances of force – whether used by a member of the Department, or against the member – are recorded on a TRI Report. Data provided here are a result of the information captured on TRI Reports. Each record corresponds to an incident where a member of service used force. The data can be used to explore the various categories of force incidents and when and in which precinct they occurred. The data is used to populate the public facing Force Dashboard (https://app.powerbigov.us/view?r=eyJrIjoiOGNhMjVhYTctMjk3Ny00MTZjLTliNDAtY2M2ZTQ5YWI3N2ViIiwidCI6IjJiOWY1N2ViLTc4ZDEtNDZmYi1iZTgzLWEyYWZkZDdjNjA0MyJ9).

This data aligns with WWC Certification requirements, and serves as the basis for our data warehouse and open data roadmap. It's a continual work in progress across all departments.Louisville Metro Technology Services builds data and technology platforms to ready our government for our community’s digital future.Data Dictionary:

Field Name

Description

Dataset Name

The official title of the dataset as listed in the inventory.

Brief Description of Data

A short summary explaining the contents and purpose of the dataset.

Data Source

The origin or system from which the data is collected or generated.

Home Department

The primary department responsible for the dataset.

Home Department Division

The specific division within the department that manages the dataset.

Data Steward (Business) Name

The name of person responsible for the dataset’s accuracy and relevance.

Data Custodian (Technical) Name)

The technical contact responsible for maintaining and managing the dataset infrastructure.

Data Classification

The sensitivity level of the data (e.g., Public, Internal, Confidential)

Data Format

The file format(s) in which the dataset is available (e.g., CSV, JSON, Shapefile).

Frequency of Data Change

How often the dataset is updated (e.g., Daily, Weekly, Monthly, Annually).

Time Spam

The overall time period the dataset covers.

Start Date

The beginning date of the data collection period.

End Date

The end date of the data collection period

Geographic Coverage

The geographic area that the dataset pertains to (e.g., Louisville Metro).

Geographic Granularity

The level of geographic detail (e.g., parcel, neighborhood, ZIP code).

Link to Existing Publication

A URL linking to the dataset’s public-facing page or open data portal entry.

This dataset has now been Retired as it has been replaced by "Administrative Boundaries - Environment Agency and Natural England Public Face Areas". This is for Approval for Access product AfA015 Environment Agency Administrative Boundaries set at 1:10,000 scale. These consist of 2 discrete data layers showing: Water Management Areas and Public Face Areas. Water management and Public Face boundaries are attributed with the name and address for each head office. This dataset is for Environment Agency Public Face Areas. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

Attribute-Based Access Control for Government Data Market Outlook

According to our latest research, the Global Attribute-Based Access Control (ABAC) for Government Data market size was valued at $1.2 billion in 2024 and is projected to reach $4.8 billion by 2033, expanding at a robust CAGR of 16.7% during 2024–2033. The primary growth driver for this market is the increasing necessity for dynamic and context-aware security frameworks in government agencies, which are dealing with ever-increasing volumes of sensitive data and facing sophisticated cyber threats. As governments worldwide transition to digital-first operations, the adoption of ABAC solutions is becoming critical for ensuring data privacy, compliance, and secure information sharing across departments and jurisdictions.

Regional Outlook

North America holds the largest share of the Attribute-Based Access Control for Government Data market, accounting for nearly 38% of the global revenue in 2024. The region’s dominance is attributed to its mature cybersecurity infrastructure, widespread adoption of cloud technologies, and stringent data protection regulations such as FedRAMP and FISMA. The presence of major technology vendors and a proactive approach to public sector digitalization have further accelerated ABAC deployment across federal, state, and local agencies. Additionally, ongoing investments in safeguarding critical infrastructure and national security data have led to higher demand for advanced access control solutions, ensuring North America remains at the forefront of this market segment throughout the forecast period.

The Asia Pacific region is anticipated to be the fastest-growing market, with a projected CAGR of 20.3% between 2024 and 2033. Rapid digital transformation initiatives, expanding government digital services, and increasing cybersecurity awareness are key drivers fueling this growth. Countries such as China, India, Japan, and South Korea are investing heavily in public sector IT modernization, leading to significant opportunities for ABAC solution providers. Government mandates for data localization and privacy, coupled with the rising frequency of cyber incidents targeting public data repositories, are compelling agencies to adopt more granular and dynamic access control frameworks. The influx of international technology vendors and robust venture capital activity are further catalyzing market expansion in the region.

Emerging economies in Latin America and the Middle East & Africa are witnessing a gradual uptake of ABAC solutions, primarily driven by increasing digitization of government services and evolving regulatory landscapes. However, adoption is tempered by challenges such as limited IT budgets, lack of skilled cybersecurity professionals, and fragmented policy frameworks. Despite these hurdles, localized demand for secure citizen data management, e-government initiatives, and cross-border data sharing is expected to spur incremental growth. Strategic collaborations with global technology partners and investments in capacity building are likely to help these regions overcome implementation barriers and accelerate ABAC adoption over the coming years.

Report Scope

Background: The analysis of clinical free text from patient records for research has potential to contribute to the medical evidence base but access to clinical free text is frequently denied by data custodians who perceive that the privacy risks of data-sharing are too high. Engagement activities with patients and regulators, where views on the sharing of clinical free text data for research have been discussed, have identified that stakeholders would like to understand the potential clinical benefits that could be achieved if access to free text for clinical research were improved. We aimed to systematically review all UK research studies which used clinical free text and report direct or potential benefits to patients, synthesizing possible benefits into an easy to communicate taxonomy for public engagement and policy discussions.Methods: We conducted a systematic search for articles which reported primary research using clinical free text, drawn from UK health record databases, which reported a benefit or potential benefit for patients, actionable in a clinical environment or health service, and not solely methods development or data quality improvement. We screened eligible papers and thematically analyzed information about clinical benefits reported in the paper to create a taxonomy of benefits.Results: We identified 43 papers and derived five themes of benefits: health-care quality or services improvement, observational risk factor-outcome research, drug prescribing safety, case-finding for clinical trials, and development of clinical decision support. Five papers compared study quality with and without free text and found an improvement of accuracy when free text was included in analytical models.Conclusions: Findings will help stakeholders weigh the potential benefits of free text research against perceived risks to patient privacy. The taxonomy can be used to aid public and policy discussions, and identified studies could form a public-facing repository which will help the health-care text analysis research community better communicate the impact of their work.

This dataset contains the underlying data for the public-facing 'Mental Health Related EMS and ED Events' dashboard. 7/25/2025: We have recently updated the data for more recent years on our public data hub. As a result, you may notice changes in the data for previous years. This change is due to a correction in our data processing methods, which has led to more accurate counts for past years.

Government Cloud Market Outlook

The global Government Cloud market size was valued at approximately USD 26 billion in 2023 and is projected to reach around USD 78 billion by 2032, exhibiting a robust compound annual growth rate (CAGR) of 12.8% during the forecast period. The growth of this market is primarily driven by the increasing demand for cost-efficient and flexible IT solutions, combined with the rising need for enhanced data security and compliance with government regulations. As governments across the globe are increasingly focusing on digital transformation, the adoption of cloud services has become essential for enhancing operational efficiency and enabling better citizen services.

The growth of the Government Cloud market is significantly influenced by the need for regulatory compliance and data security. Governments around the world are under constant pressure to maintain data sovereignty and ensure that sensitive information is stored and managed securely. Cloud solutions offer advanced security features, including data encryption, identity and access management, and threat protection, which are critical for safeguarding governmental data. Moreover, the increasing occurrence of cyber threats and data breaches necessitates the adoption of secure cloud services, driving the market further. Additionally, the COVID-19 pandemic has accelerated the shift towards digitalization, compelling governments to adopt cloud technologies to maintain business continuity and provide uninterrupted public services.

The demand for scalability and flexibility is another crucial growth factor for the Government Cloud market. Cloud solutions offer the ability to easily scale IT resources, allowing government agencies to respond quickly to changing demands and manage workloads efficiently. This flexibility is particularly important for handling large volumes of data and providing seamless public services. Furthermore, cloud computing enables the integration of advanced technologies, such as artificial intelligence and machine learning, into government operations, fostering innovation and improving decision-making processes. As governments strive to enhance their service delivery and optimize resource utilization, the adoption of cloud solutions is expected to rise.

Cost efficiency is also a major driver of the Government Cloud market. Traditional IT infrastructure involves significant capital expenditures and ongoing maintenance costs, which can be a financial burden for government agencies. Cloud services offer a more cost-effective alternative by allowing governments to pay only for the resources they use, thus reducing upfront investments and operational expenses. Additionally, cloud solutions eliminate the need for physical hardware and infrastructure, further lowering costs. This economic advantage is particularly appealing to governments facing budget constraints and seeking to maximize the value of public funds.

Solution Analysis

The Government Cloud market is segmented by solution into Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Each of these solutions offers distinct advantages that cater to the specific needs of government agencies. IaaS provides a virtualized computing infrastructure over the internet, allowing governments to access and manage computing resources without the burden of maintaining physical hardware. This solution is particularly beneficial for agencies looking to quickly scale their IT resources and reduce capital expenditures. It also offers high levels of customization, making it suitable for a wide range of applications within the public sector.

Platform as a Service (PaaS) is another critical component of the Government Cloud market. PaaS offers a platform that allows government agencies to develop, run, and manage applications without the complexity of building and maintaining the underlying infrastructure. This solution is ideal for fostering innovation and accelerating the development of new applications and services. By providing pre-built software components and tools, PaaS enables agencies to streamline their development processes, reduce time-to-market, and focus on delivering value-added services to citizens. Moreover, PaaS supports the integration of cutting-edge technologies, such as AI and IoT, enhancing the capabilities of government applications.

Software as a Service (SaaS) is a pivotal solution in the Government Cloud market, offering ready-to-use applications that can be accessed over the internet. SaaS eliminates the need for local installation and maintenance, allowing go

Public-facing application depicting Office of Land Use Permits from the Land Use Permitting System (LUPS).

🔗 Related Datasets

• Loudoun Land Applications — Loudoun County
• 2024 Permit CUA Review App — Datathon 2025
• Land Disturbing Web App — Bedford County - GIS Portal
• LandMARC — Loudoun County
• LandTrack Public App — Datathon 2025

Attribute-Based Access Control for Gov Data Market Outlook

According to our latest research, the global Attribute-Based Access Control (ABAC) for Government Data market size reached $2.92 billion in 2024, reflecting robust adoption across public sector agencies worldwide. The market is experiencing strong momentum, driven by escalating data security concerns and regulatory mandates, and is projected to achieve a CAGR of 15.4% from 2025 to 2033. By the end of 2033, the market is forecasted to reach $10.67 billion, underscoring the rapid digital transformation and modernization efforts within government infrastructures. The growth is primarily attributed to the increasing need for granular access control solutions that can address emerging cybersecurity threats and evolving compliance requirements.

One of the key growth factors propelling the Attribute-Based Access Control for Government Data market is the exponential increase in the volume and sensitivity of data handled by government bodies. As government agencies rapidly digitize their operations and migrate to interconnected platforms, the risk of unauthorized access and data breaches has grown substantially. ABAC solutions are uniquely positioned to address these risks by allowing dynamic, context-aware access policies based on user attributes, environmental conditions, and resource sensitivity. This flexibility is crucial for supporting modern government use cases, such as secure information sharing between agencies, remote workforce enablement, and citizen-facing digital services, all of which require robust, scalable, and adaptive security frameworks. The proliferation of cyberattacks targeting public sector data repositories further amplifies the need for advanced access control mechanisms, making ABAC an essential component of government cybersecurity strategies.

Another significant driver for market growth is the evolving regulatory landscape, which mandates stringent data protection and privacy controls for government-held information. Legislation such as the General Data Protection Regulation (GDPR) in Europe, the Federal Risk and Authorization Management Program (FedRAMP) in the United States, and similar frameworks in other regions are compelling government agencies to adopt advanced access control solutions that ensure compliance and auditability. ABAC's ability to provide fine-grained policy enforcement and comprehensive logging capabilities makes it an ideal choice for meeting these regulatory requirements. Additionally, the growing emphasis on zero-trust security architectures within government IT environments is accelerating the shift away from traditional role-based models toward attribute-driven access control, further boosting ABAC adoption rates.

The Attribute-Based Access Control for Government Data market is also benefiting from advancements in supporting technologies such as artificial intelligence (AI), machine learning (ML), and cloud computing. AI and ML enable more intelligent policy automation and anomaly detection, enhancing the effectiveness of ABAC systems in real time. Cloud-based ABAC solutions offer scalability, agility, and cost efficiencies, which are particularly attractive for government agencies facing budget constraints and resource limitations. The integration of ABAC with other cybersecurity tools—such as identity and access management (IAM), security information and event management (SIEM), and data loss prevention (DLP) platforms—creates a holistic security posture that addresses both internal and external threats. As a result, governments are increasingly prioritizing investments in ABAC technologies as part of their broader digital transformation and risk management initiatives.

Regionally, North America continues to dominate the ABAC for Government Data market, accounting for the largest share in 2024 due to substantial investments in cybersecurity infrastructure, a mature regulatory environment, and the presence of leading technology vendors. Europe follows closely, driven by strict data privacy laws and active digital government programs. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding e-government initiatives, and increasing awareness of cybersecurity risks. Latin America and the Middle East & Africa are also experiencing steady adoption, supported by international aid programs and partnerships aimed at strengthening public sector cybersecurity. These regional dynamics reflect a global consensus on the critical importance of robust access control mechanisms in sa

This is the Environment Agency controlled list of old (pre-April 2014) Environment Agency Area Names and current Environment Agency and Natural England Area Names. It is for cross-reference purposes. Attribution statement: © Environment Agency copyright and/or database right 2017. All rights reserved.

Reach: unique page views, email delivery and open rates, SMS delivery, flyer distribution counts, media pickups Engagement proxies: click-through rate to action page, time on page, QR scans, hotline calls, 311 tickets Action: registrations, attendance, completed forms, compliance with new rules Equity: language breakdowns, neighborhood coverage, representation of priority populations

This is the Environment Agency controlled list of old (pre-April 2014) Environment Agency Area Names and current Environment Agency and Natural England Area Names. It is for cross-reference purposes. Attribution statement: © Environment Agency copyright and/or database right 2017. All rights reserved.

Dataset Context: This dataset was created from the "Health Bulletin 2019" report, which provides a comprehensive overview of the health status and trends in Bangladesh for the year 2019. It includes various health indicators, disease statistics, healthcare infrastructure, workforce, financing, policies, and health education and training information. The dataset was extracted and cleaned to facilitate further research on public health, disease prevention, healthcare systems, and policy analysis in Bangladesh. This dataset will be valuable for exploring health trends, disparities, and the impact of health programs, as well as for informing the development of new healthcare policies.

Sources: The dataset is sourced from the Health Bulletin 2019, a government publication by the Ministry of Health and Family Welfare, Bangladesh. The report presents a detailed analysis of various aspects of the country's healthcare system, including:

Demographics and Health Indicators Disease Statistics Healthcare Financing and Policy Health Services Utilization Health Workforce Health Education and Training Healthcare Infrastructure The data was extracted from the 263 pages of the Health Bulletin, covering the state of healthcare in Bangladesh, and subsequently cleaned and structured for analysis.

Inspiration Behind the Dataset: The primary motivation behind creating this dataset was to enable in-depth research on several key public health challenges facing Bangladesh. The health indicators and statistics from the bulletin provide crucial insights into the population's health status, the effectiveness of health programs, and the accessibility of healthcare services across different regions.

This dataset serves as a foundation for:

Assessing the health status of the Bangladeshi population: Understanding the current health conditions and disease prevalence in different demographics. Evaluating the effectiveness of health programs: Analyzing the impact of health interventions and public health policies in Bangladesh. Identifying health disparities: Investigating inequalities in healthcare access and outcomes across various regions and population groups. Developing new health policies and programs: Using data-driven insights to inform future healthcare policies, ensuring better health outcomes for the population. The dataset is also intended for use in academic research, policy development, and the creation of evidence-based recommendations for improving healthcare in Bangladesh.

This electronic services monthly MI report contains monthly MI data for most public facing online applications such as iClaim, electronic access, Mobile wage reporting, Online Social Security Statements, Check Your Benefits, Benefit Verification, Change of Address, Direct Deposit, etc. The report is divided into six separate sections: electronic access, my Social Security Suite of Services, my Social Security Help Desk Call Back, Pre-Entitlement Informational Services, Entitlement, and Post-Entitlement.

Dataset Overview

Welcome to the New Hampshire Real Estate dataset! This dataset contains approximately 5,000 active property listings from across the state of New Hampshire, gathered in Q1 2026. It includes a variety of property types—from single-family homes and mobile homes to raw commercial/residential land.

Unlike heavily pre-processed datasets, this data is raw and reflects the true chaotic nature of real-world real estate listings. It features rich textual property descriptions alongside core numerical metrics like list price, square footage, and bed/bath counts.

Data Science Applications

This dataset is an excellent playground for both beginner and advanced data scientists:

• Data Cleaning & Wrangling: I have purposely left raw "land" data in this dataset. Land listings naturally lack bedrooms, bathrooms, and sometimes square footage. This forces learners to practice handling missing values (NaN), filtering sub-groups, and applying conditional imputation logic rather than blindly dropping rows.
• Natural Language Processing (NLP): The text column contains paragraph-length property descriptions written by real estate agents. You can use TF-IDF or transformer models (like BERT) to extract features like "lake access," "newly renovated," or "pool" to see how specific keywords affect property value.
• Regression Modeling: Train XGBoost, Random Forest, or Linear Regression models to predict the listPrice based on square footage, property type, and extracted text features.

Column Descriptors

Ethically Mined Data

• Source: Data was ethically sourced from public-facing real estate listings on Realtor.
• Methodology: Extraction was conducted respectfully using the Apify API framework, ensuring compliance with standard web scraping rate limits and avoiding server overload. No personally identifiable information (PII) of homeowners or agents is included.

Acknowledgements

• Data Source: Data collected and compiled by Kanchana Karunarathna from Realtor. Please use for education only.
• Image credits : Nano Banana 2** for the dataset image!