This record is for Approval for Access product AfA345. This is a bundle of all AIMS Asset Types into a single download. The Environment Agency's (EA) defence information is the only comprehensive and up-to-date dataset in England that shows flood defences currently owned, managed or inspected by the EA. Flood defences can be structures, buildings or parts of buildings. Typically these are earth banks, stone and concrete walls, or sheet-piling that is used to prevent or control the extent of flooding. A defence is any asset that provides flood defence or coastal protection functions. This includes both man-made and natural defences. Natural defences may include man-made elements to make them more effective or protect them from erosion. Normally a number of assets will be used together to manage the risk in a particular area, working in combination within a risk management system. PLEASE NOTE: This data is updated daily. This is a large dataset and depending on the chosen download format, it may take 7-8 minutes to download the full national dataset. Attribution statement: © Environment Agency copyright and/or database right 2020. All rights reserved.

The AIMS: Address Reference data table contains the relationship between Address data and other related datasets including Roads, Parcels, and Suburbs and is part of the AIMS: Street Address.

Refer to the NZ Addresses Data Dictionary for detailed metadata and information about this dataset.

Background

This dataset provides all allocated addresses as advised to Toitū Te Whenua LINZ by Territorial Authorities (TAs). Under the Local Government Act 1974 (section 319) it is the responsibility of the TAs to advise the Surveyor-General at Toitū Te Whenua LINZ of all allocated addresses in their district.

The dataset is maintained by Toitū Te Whenua LINZ in the Address Information Management System (AIMS) which is centralised database for the management of national addresses, including for electoral purposes. This dataset is updated weekly on the LINZ Data Service.

For a simplified version of the data contained within these tables see NZ Addresses

This record is for Approval for Access product AfA345.

The Environment Agency's (EA) Spatial Flood defences layer is the only comprehensive and up-to-date dataset in England that shows flood defences currently owned, managed or inspected by the EA.

Flood defences can be structures, buildings or parts of buildings. Typically these are earth banks, stone and concrete walls, or sheet-piling that is used to prevent or control the extent of flooding.

A defence is any asset that provides flood defence or coastal protection functions. This includes both man-made and natural defences. Natural defences may include man-made elements to make them more effective or protect them from erosion. Normally a number of assets will be used together to manage the risk in a particular area, working in combination within a risk management system.

PLEASE NOTE: This data is updated daily. This is a large dataset and depending on the chosen download format, it may take 7-8 minutes to download the full national dataset.This is a full extract from the Asset Information Management System (AIMS) where the following apply: • EA has operational powers to maintain or operate (whether or not we exercise those powers) the flood defence asset; • There is a liability on Flood and Coastal Risk Management (FCRM) for the flood defence asset; • Assets are attached to a Flood Risk Management Systems (FRMS).

This product replaces AfA006 and was changed as a result of our asset migration from NFCDD to AIMSClick Here to go straight to the DSP Metadata Page for this Dataset.

An asset used to control the flow of water

Asset Sub-Types include: Control Gate, Draw Off Tower, Fish Pass, Hydrobrake, In Channel Stoplogs, Inspection Chamber, Jetty, Outfall, Screen, Spillway, Stilling Basin, Weir

See the Data Requirements Library for more detail: https://environment.data.gov.uk/asset-management/drl-app/asset-types

PLEASE NOTE: This data is updated daily. Data is generated from the Asset Information Management System which holds records of assets associated with flood defences.Click Here to go straight to the DSP Metadata Page for this Dataset.

An asset that conveys water

Asset Sub-Types include: Complex Culvert, Open Channel, Simple Culvert

See the Data Requirements Library for more detail: https://environment.data.gov.uk/asset-management/drl-app/asset-types

PLEASE NOTE: This data is updated daily. This is a large dataset and depending on the chosen download format, it may take 7-8 minutes to download the full national dataset.Data is generated from the Asset Information Management System which holds records of assets associated with flood defences.Click Here to go straight to the DSP Metadata Page for this Dataset.

An area of land that is involved in water management.

Asset Sub-Types include: Mudflats, Salt Marsh, Water Storage Area

See the Data Requirements Library for more detail: https://environment.data.gov.uk/asset-management/drl-app/asset-types

PLEASE NOTE: This data is updated daily. Depending on the chosen download format, it may take 2-3 minutes to download the full national dataset.

Historical Dataset of Aims College Prep High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2019-2023),Total Classroom Teachers Trends Over Years (2019-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2019-2023),American Indian Student Percentage Comparison Over Years (2019-2020),Asian Student Percentage Comparison Over Years (2019-2023),Hispanic Student Percentage Comparison Over Years (2019-2023),Black Student Percentage Comparison Over Years (2019-2023),White Student Percentage Comparison Over Years (2019-2023),Two or More Races Student Percentage Comparison Over Years (2021-2023),Diversity Score Comparison Over Years (2019-2023),Free Lunch Eligibility Comparison Over Years (2019-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2019-2023),Reading and Language Arts Proficiency Comparison Over Years (2019-2022),Math Proficiency Comparison Over Years (2019-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2019-2023),Graduation Rate Comparison Over Years (2019-2023)

This dataset tracks annual white student percentage from 2019 to 2023 for Aims College Prep High School vs. California and Aims College Prep High School District

An asset that is used to provide shelter for equipment, storage or personnel. Asset Sub Type: Pump House, Office, Workshop, Gauge House, Garage, Residential, Boat Shed

For more information visit the Data Requirements Library: https://environment.data.gov.uk/asset-management/drl-app/asset-types

PLEASE NOTE: This data is updated daily. Depending on the chosen download format, it may take 2-3 minutes to download the full national dataset.

An asset used to measure water level and flow

Asset Sub-Types include: Active Monitoring Instrument, Passive Monitoring Instrument

See the Data Requirements Library for more detail: https://environment.data.gov.uk/asset-management/drl-app/asset-types

PLEASE NOTE: This data is updated daily. Depending on the chosen download format, it may take 2-3 minutes to download the full national dataset.Data is generated from the Asset Information Management System which holds records of assets associated with flood defences.Click Here to go straight to the DSP Metadata Page for this Dataset.

This dataset tracks annual total students amount from 2019 to 2023 for Aims College Prep High School

An asset that allows access across a channel. Asset Sub Types include: Bridge; Utility Services

For more information visit the Data Requirements Library: https://environment.data.gov.uk/asset-management/drl-app/asset-types

PLEASE NOTE: This data is updated daily. Depending on the chosen download format, it may take 2-3 minutes to download the full national dataset.

This dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which has the vision to develop and validate novel Artificial Intelligence (AI) algorithms to unlock the true potential of remote monitoring and enable a faster transition to a climate neutral society and economy: the AI algorithms will leverage the advantages of usual monitoring methodologies of the features of waves and offshore wind, and eventually overcome their intrinsic limitations. The value and resolution of sparse measurements of satellites and unevenly-distributed in-situ instruments will be increased, hence leading to a significant reduction of the cost and execution time of data collection, ultimately making knowledge wider and more accessible.

This dataset in particular ir related to collection of a moored Directional Wave Spectra Drifter (DWSD) buoy network deployed offshore of Livorno as part of the AIMS project. The campaign aimed to create an experimental 3 x 3 buoy grid, positioned at 1.5 km intervals, to study wave dynamics in a confined area and evaluate the accuracy of numerical wave models. Each buoy, equipped with GPS and real-time data transmission via satellite, recorded wave and meteo-marine parameters at three-hour intervals over approximately five months. The data, though partially affected by buoy losses, provide a high-resolution view of wave behaviour and serve as a valuable resource for wave energy studies and forecasting models. This report includes detailed descriptions of the dataset and its structure, offering insights into data quality, operational challenges, and guidelines for future campaigns.

The following standard operating procedure (SOP) was created for the the Aquatic Intermittency effects on Microbiomes in Streams (AIMS), an NSF EPSCoR funded project (OIA 2019603) seeking to explore the impacts of stream drying on downstream water quality across Kansas, Oklahoma, Alabama, Idaho, and Mississippi. AIMS integrates datasets on hydrology, microbiomes, macroinvertebrates, and biogeochemistry in three regions (Mountain West, Great Plains, and Southeast Forests) to test the overarching hypothesis that physical drivers (e.g., climate, hydrology) interact with biological drivers (e.g., microbes, biogeochemistry) to control water quality in intermittent streams. An overview of the AIMS project can be found here: https://youtu.be/HDKIBNEnwdM

This protocol will detail the process for calibrating and launching STIC (Stream Temperature Intermittency & Relative Conductivity) sensors.

The "living" version of this SOP can be found on Google Docs: https://docs.google.com/document/d/17nQj1tIW42W_opQpSKIezl_pxacDicHuLs5GB-OVrjE/edit?tab=t.0

From this SOP, the following data types will be created: Time series of pressure, temperature, water level, water height, water depth, and water elevation at stilling wells and piezometers [AIMS rTypes: PRES]

Tuition, average financial aid awarded, real net cost, and student debt outcomes for Aims Community Collegeundergraduate students in 2025.

Data derived from previous AIMS BRUVS projects was uploaded to GlobalArchive as part of the Australian BRUVS Synthesis. This workshop was to synthesise continental-scale data on the distribution, relative abundance, biomass and habitat use of shark, ray and finfish populations, for the purpose of producing a series of scientific publications with researchers across Australia. Uploaded data is accessible by workshop participants and used for publications developed at the workshop.


Data includes fish counts, habitat and deployment metadata from historical AIMS BRUVS data collected from east and west coasts. Specifically, AIMS BRUVS fish data will include MaxN, benthic habitat, depth and coordinates for each deployment associated with the cruise numbers: 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 21, 22, 24, 26, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 47, 48, 49, 52, 53, 54, 59, 62, 63, 64, 73.

The Automated Information Management System (AIMS) market is experiencing robust growth, driven by the increasing need for efficient data handling and improved patient care within healthcare settings. The market's expansion is fueled by several key factors. The rising adoption of electronic health records (EHRs) and the increasing prevalence of chronic diseases necessitate sophisticated systems capable of managing the vast amounts of patient data generated. Furthermore, government initiatives promoting digital health and interoperability are creating a favorable regulatory environment for AIMS adoption. The shift towards cloud-based solutions offers scalability and cost-effectiveness, further accelerating market growth. While initial investment costs can be a barrier for smaller healthcare providers, the long-term benefits of improved operational efficiency and reduced errors outweigh these concerns. The market segmentation reveals a strong preference for cloud-based systems due to their flexibility and accessibility. Hospitals and nursing homes are the primary adopters, reflecting the high volume of data management requirements in these settings. Competition is intense, with a range of established players and emerging technology providers vying for market share. This competitive landscape is driving innovation and fostering the development of more advanced, user-friendly AIMS solutions. The future trajectory of the AIMS market points to continued expansion. The ongoing development of artificial intelligence (AI) and machine learning (ML) technologies will further enhance the capabilities of AIMS, enabling predictive analytics, personalized medicine, and streamlined workflows. Integration with other healthcare IT systems will become increasingly crucial, facilitating seamless data exchange and improved care coordination. Geographic expansion is also expected, with significant growth potential in emerging markets as healthcare infrastructure develops and digitization efforts intensify. However, challenges remain, including data security concerns, the need for robust cybersecurity measures, and the necessity for comprehensive staff training to ensure effective system utilization. Addressing these challenges will be essential for realizing the full potential of AIMS and driving sustainable market growth throughout the forecast period. We project a healthy CAGR, reflecting this positive outlook, albeit with cautious consideration of potential economic fluctuations impacting healthcare spending.

This dataset contains real time geographic co-ordinates of the AIMS research fleet.

Information for current and previous voyages of the two AIMS research vessels:

RV Solandar 

RV Cape Ferguson

This interactive online tool provides access to the voyage information for the AIMS research vessels for the past 30 days. Vessel positions are updated every 20 minutes. Dates are provided in UTC locale.

Oceanographic conditions from our real time marine weather stations are also available as well as cyclone forecast tracks for any current cyclones issued by the Bureau of Meteorology.

70 selected reefs throughout the Great Barrier Reef (GBR) are sampled in the AIMS Long-term Monitoring Project (LTMP). Underwater visual census is used to survey reef fishes on fixed transects (3 sites per reef, 5 x 50 m transects per site). The abundance and length of all diurnally active, non-cryptic fishes are recorded. A full list of species observed each year can be obtained on request.


The overarching goal of LTMP fish surveys are to detect changes in reef fish communities over time at a regional scale, but also to examine the effectiveness of Marine Protected Areas.


All fish species counted are largely non-cryptic, easily identified underwater and include both commercial and non-commercial taxa. Because surveys span the annual recruitment season, 0+ individuals are excluded from counts: these are distinguished from adults by their small size and often distinctive colouration.


Abundance data for each fish species is subsequently summed over the five transects at each site on each reef to provide reasonable sample sizes for analysis and interpretation.


Updated results of surveys can be found at:


https://apps.aims.gov.au/reef-monitoring/reefs


A subset of the data has been provided to the Ocean Biogeographic Information System (OBIS, http://www.iobis.org/explore/#/dataset/3936).


Data have been used for the e-Atlas:


http://eatlas.org.au/data/uuid/05bde62a-70ec-407b-b999-30cf369498af

Snapshot img

Asset Integrity Management Systems Market Size 2025-2029

The asset integrity management systems market size is forecast to increase by USD 6.02 billion, at a CAGR of 4.8% between 2024 and 2029.

The market is driven by the pressing need to maintain and optimize aging infrastructure, particularly in energy and industrial sectors. This requirement is fueled by increasing regulatory scrutiny and the potential for costly downtime or catastrophic failures. Another key trend is the strategic deployment of partnerships and acquisitions to expand offerings and enhance capabilities. However, the integration of legacy systems poses a significant challenge. Companies must navigate the complexity of merging disparate data sources and technologies to achieve seamless, effective AIMS.
This intricacy necessitates substantial investment in resources and expertise. To capitalize on market opportunities and address these challenges, organizations should prioritize standardization, interoperability, and continuous improvement in their AIMS strategies. By focusing on these areas, they can ensure the reliable operation of their assets, mitigate risks, and maintain a competitive edge.

What will be the Size of the Asset Integrity Management Systems Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

Asset Integrity Management Systems continue to evolve, adapting to the ever-changing market dynamics and expanding their applications across various sectors. Integral to these systems are sensor technologies, which provide real-time data for continuous monitoring and analysis. Visual inspection, risk-based inspection, and acoustic emission monitoring are crucial components, ensuring the early detection of defects and potential failures. Cathodic protection and regulatory compliance are essential elements, safeguarding assets from corrosion and ensuring adherence to industry standards. Cloud computing facilitates data storage and accessibility, enabling data visualization and analytics for enhanced operational efficiency and safety improvement. Anodic protection, pipeline integrity management, and defect detection are vital for maintaining the integrity of critical infrastructure.

API standards and corrosion prevention strategies are integrated into these systems, optimizing maintenance and reducing downtime. The integration of data analytics, machine learning, and remote monitoring further enhances AIMS capabilities. These technologies facilitate predictive maintenance, enabling proactive interventions and reducing costs. Ultrasonic testing, eddy current testing, and non-destructive testing (NDT) are essential techniques for structural health monitoring and material selection. Vibration monitoring, pressure vessel integrity, and process safety management are also crucial components, ensuring the ongoing safety and reliability of assets. The unfolding market activities reveal a continuous focus on cost reduction, safety improvement, and risk mitigation.

Leak detection and downtime reduction are essential aspects, ensuring the optimal performance of assets and minimizing potential hazards. Incorporating technologies such as dye penetrant testing, corrosion monitoring, and big data analytics, AIMS are poised to revolutionize asset management across industries. The integration of these advanced technologies ensures a comprehensive approach to asset management, enabling organizations to make informed decisions and maintain the integrity of their critical infrastructure.

How is this Asset Integrity Management Systems Industry segmented?

The asset integrity management systems (aims) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service Type

  Non-destructive testing
  Risk based inspection
  Corrosion management
  Pipeline integrity management
  Others


End-user

  Oil and gas
  Power generation
  Aerospace and defense
  Manufacturing and processing
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Service Type Insights

The non-destructive testing segment is estimated to witness significant growth during the forecast period.

Asset Integrity Management Systems (AIMS) have gained significant importance in maintaining the reliability, safety, and longevity of industrial assets. Non-destructive testing (NDT), a key component of AIMS, employs various techniques to evaluate asset conditions without disrupting their operation. Ultrasonic testing (UT) uses sound waves