Environmental radioactivity data from the Radioactivity Environmental Monitoring (REM) data bank, referred to year 2009.

Analysis of ‘REM data bank - Year 2011’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/c621045f-5569-471e-8d1a-fe4b5fabd418 on 10 January 2022.

--- Dataset description provided by original source is as follows ---

Environmental radioactivity data from the Radioactivity Environmental Monitoring (REM) data bank, referred to year 2009.

--- Original source retains full ownership of the source dataset ---

Bank erosion pins (2-foot lengths of 0.38-inch steel rebar) were placed at 12 locations throughout the Caulks Creek study area in Wildwood, Missouri. Most of the bank erosion pins were located outside of the six study reaches, though one was located within study reach 4 and two were located within study reach 5. The bank erosion pin locations were largely determined by site access and the feasibility of inserting the pin into the bank face and are not intended to be a statistically representative sampling of the channel. The tip of the pin represents a datum from which a change in the bank position can be measured. The distance from the tip of the pin to the bank face was measured on the top, bottom, upstream side, and downstream side of the pin, and these measurements were averaged to obtain a final measurement value. The bank pins were measured six times between February 2022 and July 2023 including during installation and removal. The data are provided in comma-separated value ...

Analysis of ‘REM data bank - Year 1988’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/jrc-10117-10005 on 10 January 2022.

--- Dataset description provided by original source is as follows ---

Environmental radioactivity data from the Radioactivity Environmental Monitoring (REM) data bank, referred to year 1988.

--- Original source retains full ownership of the source dataset ---

DNV is a risk and classification company with roots dating back to the founding of Det Norske Veritas (DNV) in 1864. DNV operates in the oil, gas, and renewable energy sectors. The data produced by DNV is stored in their own Environmental Monitoring database (MOD). It comprises approximately 2.8 million species occurrence records, as well as chemical and geology records. This information comes from grab sampling conducted in areas around oil drilling stations. GBIF Norway is working with DNV to publish the species abundance data in the MOD database. The grab sampling process is done on a yearly basis around the months of May and June, but not all stations are sampled each year. In general sampling is done around each station every third year, and in some areas samples have been repeated since the 1990s.

Environmental radioactivity data from the Radioactivity Environmental Monitoring (REM) data bank, referred to year 2009.

This dataset contains species data extracted from Natural England's Environmental Monitoring Database (EMD) in January 2016. The EMD was developed to hold vegetation, bird and other species data gathered by a wide range of surveys. Most (but not all) of these Surveys were designed to monitor habitats and species being targeted for management by agri-environment schemes. The data has almost all been gathered since 1987 and the main schemes involved comprise the Environmentally Sensitive Areas, Countryside Stewardship schemes and Environmental Stewardship. The data comprise species records from a wide range of moorland, grassland, wetland and coastal habitats. As the dataset comprises records from many surveys, designed with specific individual purposes, the distribution of sampling points are a function of those individual surveys rather than representing any systematic coverage within the dataset as a whole. There are no sensitive records in this dataset. The EMD is no longer used, and this dataset will no longer be updated.

Environmental radioactivity data from the REM data bank, referred to years 1984-2006.

The Radioactivity Environmental Monitoring (REM) data bank was set-up in 1988 to bring together and store in a harmonised way environmental radioactivity data produced in the aftermath of the Chernobyl accident. The database contains a unique collection of environmental radioactivity measurements from a wide number of different sources, media and countries, mainly the 28 EU Member States.

Datasets for "A harmonized chemical monitoring database for support of exposure assessments". This dataset is associated with the following publication: Isaacs, K., J. Wall, A. Williams, K. Hobbie, J. Sobus, E. Ulrich, D. Lyons, K. Dionisio, A. Williams, C. Grulke, C. Foster, J. McCoy, and C. Bevington. A harmonized chemical monitoring database for support of exposure assessments. Scientific Data. Springer Nature, New York, NY, 9: 314, (2022).

Topographic data were collected along six reaches (study reach 1, study reach 2, study reach 3, study reach 4, study reach 5, and study reach 6) along Caulks Creek in Wildwood, Missouri, on multiple dates, using terrestrial light detection and ranging (t-lidar), Global Navigation Satellite System (GNSS), and conventional surveying techniques (Rydlund and Densmore, 2012).These data are high-resolution topography in laser scan format (LAS), collected using a tripod mounted t-lidar at multiple scan setups. Data collection software was used to integrate and store the range and angular measurements from the t-lidar equipment. Computer software was used to process the raw data, align the various scans in reference to one another, classify the data, and extract the topography data in a useable format. The total station data were collected for study reach 3 using a tripod mounted Trimble M3 Total Station are stored in comma-separated value (csv) format. The collected data points represent the channel, bank, and near overbank surface at select locations in the study reach.The t-lidar and total station topographic data are available for each study reach within the data release Child Items. Bank erosion pins (2-foot lengths of 0.38-inch steel rebar) were placed at twelve locations throughout the Caulks Creek study area. Most of the bank erosion pins were located outside of the six study reaches, though one was located within study reach 4 and two were located within study reach 5. The tip of the pin represents a datum from which a change in the bank position can be measured. The distance from the tip of the pin to the bank face was measured on the top, bottom, upstream side, and downstream side of the pin, and these measurements were averaged to obtain a final measurement value. The bank pins were measured six times between February 2022 and July 2023 including installation and removal. The data are provided in (csv) format in the Bank Erosion Pin Child Item. References Cited: Rydlund, P.H., Jr., and Densmore, B.K., 2012, Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey: U.S. Geological Survey Techniques and Methods, book 11, chap. D1, 102 p. with appendixes, https://doi.org/10.3133/tm11D1.

• Reporting units of sample results [where 1 picoCurie (pCi) = 1 trillionth (1E-12) Curie (Ci)]: • Other samples are reported in pCi/g.

• Data Quality Disclaimer: This database is for informational use and is not a controlled quality database. Efforts have been made to ensure accuracy of data in the database; however, errors and omissions may occur.

Examples of potential errors include: • Data entry errors. • Lab results not reported for entry into the database. • Missing results due to equipment failure or unable to retrieve samples due to lost or environmental hazards. • Translation errors – the data has been migrated to newer data platforms numerous times, and each time there have been errors and data losses.

• Error Results are the calculated uncertainty for the sample measurement results and are reported as (+/-).

• Environmental Sample Records are from the year 1998 until present. Prior to 1998 results were stored in hardcopy, in a non-database format.

Requests for results from samples taken prior to 1998 or results subject to quality assurance are available from archived records and can be made through the DEEP Freedom of Information Act (FOIA) administrator at deep.foia@ct.gov. Information on FOIA requests can be found on the DEEP website.

FOIA Administrator Office of the Commissioner Department of Energy and Environmental Protection 79 Elm Street, 3rd Floor Hartford, CT 06106

The global database monitoring software market size reached USD 5.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 16.0 Billion by 2033, exhibiting a growth rate (CAGR) of 13.3% during 2025-2033. The rising prevalence of data breaches and cyberattacks worldwide, increasing digitization, rapid growth in data volumes across diverse industry verticals, and surging penetration of cloud-based solutions are some of the major factors propelling the market.

Report Attribute	Key Statistics
Base Year	2024
Forecast Years	2025-2033
Historical Years	2019-2024
Market Size in 2024	USD 5.2 Billion
Market Forecast in 2033	USD 16.0 Billion
Market Growth Rate (2025-2033)	13.3%

IMARC Group provides an analysis of the key trends in each segment of the global database monitoring software market report, along with forecasts at the global, regional, and country levels from 2025-2033. Our report has categorized the market based on database model, deployment model, organization size, and end use vertical.

This database contains water monitoring data collected by NETN in the CVDT format used by data visualization and other systems. This version of the data is not intended to be an archive.

The TOOCAN database is a level-2 product of Deep Convective Cloud Systems (DCS) parameters derived from Meteosat measurements. Fundamental Climate Data Records of Meteosat First and Second Generation instruments MVIRI and SEVIRI (John et al., 2019) are used as input for the TOOCAN (Fiolleau and Roca 2013) which is a cloud tracking algorithm to detect and track DCS from the geostationary infrared observations. TOOCAN data provides integrated morphological parameters of tropical DCS such as location and time of initiation and dissipation, lifetime duration, propagated distance, and maximum extent. The data are available for region with boundaries 40° S to 40° N and 55° W to 55° E for the 1981-2023 period. The data has been used to reveal changes in convective organisation over tropical Africa and Atlantic Ocean (Rocal et al., 2024).

The TOOCAN database is composed by two types of files: 1. Regional segmented images at a 0.04° spatial resolution and a 30-minute temporal frequency (in NETCDF). 2. Regional and monthly tracking files (in ASCII and in NETCDF) documenting the DCS morphological parameters at each 30 minute-step of their life cycles.

Each DCS is then described by a unique label, which is useful for making the link between a given DCS described in the tracking files and the same DCS identified in the segmented images. Thus, using both the tracking file and the segmented images provides access to all the pixels of a given DCS identified in the segmented images, as well as to its morphological parameters in the monthly tracking files.

Database Performance Monitoring Tools Market Outlook

In 2023, the global market size for Database Performance Monitoring Tools is projected to reach approximately $2.5 billion. With a compound annual growth rate (CAGR) of 11.2%, the market is expected to expand significantly, reaching a forecasted value of $6.5 billion by 2032. This notable growth can be attributed to the increasing demand for efficient database management solutions driven by the exponential rise in data generation across various industries. As organizations strive to optimize their database performance, the need for advanced monitoring tools continues to surge, underpinning the robust growth trajectory of this market.

The primary growth factor driving the Database Performance Monitoring Tools market is the burgeoning volume of data generated by enterprises globally. With the digital transformation wave sweeping across industries, the amount of data being processed, stored, and analyzed is growing exponentially. This necessitates the deployment of sophisticated monitoring tools that can ensure optimal database performance and reduce downtime. Additionally, the rise of big data analytics and cloud computing has further amplified the need for efficient database monitoring solutions. Companies are increasingly recognizing the importance of maintaining high-performance databases to gain insights from large datasets, thereby fueling the demand for performance monitoring tools.

Another significant factor contributing to market growth is the increasing complexity and diversity of database environments. Modern enterprises often operate multiple databases across various platforms, including cloud, on-premises, and hybrid models. This complex landscape requires robust monitoring tools capable of providing comprehensive insights into database performance, detecting anomalies, and predicting potential issues. As a result, organizations are investing in advanced performance monitoring solutions to achieve seamless database operations, enhance productivity, and reduce operational costs. The evolution of artificial intelligence and machine learning technologies is also playing a crucial role in enhancing the capabilities of these monitoring tools, enabling them to offer predictive analytics and automated issue resolution.

Furthermore, the rising emphasis on regulatory compliance and data security is a critical growth driver for the Database Performance Monitoring Tools market. Industries such as BFSI, healthcare, and retail are subject to stringent data protection regulations that necessitate meticulous database monitoring to prevent data breaches and ensure compliance. Monitoring tools equipped with advanced security features are increasingly being adopted to provide real-time alerts, detect suspicious activities, and safeguard sensitive information. The growing awareness of the importance of data security and compliance is encouraging organizations to invest in reliable database performance monitoring solutions, further bolstering market expansion.

In addition to the complexities of database environments, the integration of an Event Monitoring Tool can significantly enhance the capabilities of database performance monitoring solutions. These tools provide real-time insights into database activities, allowing organizations to track and analyze events that may impact performance. By capturing detailed event logs, businesses can identify patterns and anomalies that could lead to potential issues, enabling proactive management of database systems. Event Monitoring Tools are particularly beneficial in environments with high transaction volumes, where even minor disruptions can have significant consequences. As enterprises continue to expand their digital operations, the demand for comprehensive monitoring solutions that include event monitoring capabilities is expected to rise, further driving market growth.

Regionally, North America is anticipated to hold the largest share of the Database Performance Monitoring Tools market, primarily due to the early adoption of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the fastest growth during the forecast period. This growth is driven by the rapid digitization of economies, increasing adoption of cloud services, and the expanding IT sector in countries such as China and India. Meanwhile, Europe, Latin America, and the Middle East & Africa are also expected to experience steady growth, supported by the increasing awareness of database management solutions and the rising n

The LTMM database contains 3-day 3D accelerometer recordings of 71 elder community residents, used to study gait, stability, and fall risk.

Database Performance Monitoring System Market Outlook

The global database performance monitoring system market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 8.2 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 9.5% during the forecast period from 2024 to 2032. The growth of this market is primarily driven by the increasing reliance on data-driven decision-making across industries. As organizations continue to accumulate vast amounts of data, the need for efficient database performance monitoring systems becomes crucial to ensure optimal database performance and prevent potential downtime or performance issues. The proliferation of cloud computing and advancements in database technologies are further accelerating the adoption of these systems.

One of the major growth factors fueling the expansion of the database performance monitoring system market is the exponential growth in data volume. Organizations, regardless of their size, are generating and storing more data than ever before. This surge in data volume necessitates sophisticated monitoring systems to maintain efficiency and performance of databases. Additionally, the increasing complexities and integration of modern database systems have heightened the demand for performance monitoring solutions that can offer real-time insights and predictive analytics. Businesses are recognizing the strategic importance of leveraging high-performing databases to gain competitive advantages, which is further propelling the market growth.

Another significant factor contributing to the growth of this market is the rise of cloud-based database solutions. As companies continue to migrate their data and applications to the cloud, there is an increasing need for performance monitoring systems that can offer comprehensive solutions tailored to cloud environments. The flexibility, scalability, and cost-effectiveness of cloud deployments make them an attractive choice, thus driving the demand for cloud-compatible performance monitoring systems. The ease of integration with existing IT infrastructure and the provision of continuous monitoring and automated tuning are critical features that are enhancing the desirability of these systems in a cloud-based ecosystem.

The market is also witnessing a surge in demand due to the growing emphasis on regulatory compliance and data security. Industries such as BFSI and healthcare, which handle sensitive data, are particularly pressured to adhere to stringent regulatory standards. Database performance monitoring systems play a pivotal role in ensuring data integrity, security, and compliance, thereby mitigating risks associated with data breaches or non-compliance penalties. As regulatory frameworks become more complex, companies are investing in these monitoring systems to safeguard their data assets and maintain compliance with industry standards, further fueling market growth.

In the manufacturing sector, Transaction Monitoring for Manufacturing has become increasingly vital as industries strive to optimize their supply chain and production processes. With the rise of Industry 4.0, manufacturers are leveraging data-driven insights to enhance operational efficiency and reduce costs. Transaction monitoring systems play a crucial role in ensuring the seamless flow of information across various stages of production, from raw material procurement to finished goods delivery. By monitoring transactions in real-time, manufacturers can quickly identify and address any discrepancies or bottlenecks, thereby minimizing downtime and improving overall productivity. As the manufacturing industry continues to embrace digital transformation, the demand for robust transaction monitoring solutions is expected to grow, enabling manufacturers to maintain a competitive edge in the global market.

Regionally, North America currently dominates the database performance monitoring system market, owing to its advanced IT infrastructure and the presence of major technology companies. The region's focus on innovation and early adoption of new technologies contributes significantly to market growth. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is driven by the rapid digital transformation occurring in countries like China and India, coupled with increased IT spending and the emergence of numerous SMEs adopting database solutions to enhance their competitive edge. As these trends continue, the Asia Pacific region is poised to

The world has digitized rapidly, especially since the advent of the internet. Banks, financial institutions, hospitals, insurance companies, and e-commerce platforms rely heavily on databases to manage customer accounts, transactions, and sensitive financial data. With the advancements in the technology sector, the database monitoring software market is poised to be valued at a staggering US$ 2.40 billion in 2024.

Attributes	Details
Market Value for 2024	US$ 2.40 billion
Projected Market Value for 2034	US$ 10.10 billion
Value-based CAGR of the Market for 2024 to 2034	15.20%

Category-wise Insights

Attributes	Details
Component	Software
Market Share (2024)	63%

Attributes	Details
End User	BFSI
Market Share (2024)	29.30%

Country-wise Insights

Countries	CAGR (2024 to 2034)
South Korea	18.00%
Japan	17.20%
The United Kingdom	16.70%
China	16.20%
The United States	15.60%

Section 2

Description and codebook for subset of harmonized variables:

Section 3

Survey:

Section 10

Surveys:

Section 14

Survey:

Section 5

Survey:

Section 6

Survey:

Section 7

Survey:

Section 9

Survey:

Documentation

Guide to datasets:

Full Project Name: Providing Free Bank Accounts in Chile, Malawi, and Uganda

PIs: Pascaline Dupas, Dean Karlan, Jonathan Robinson, Diego Ubfal

JPAL ID: 383

Location: Chile, Malawi, Uganda

Sample: 6,242 households

Timeline: 2010 to 2013

More information:https://www.povertyactionlab.org/evaluation/providing-free-bank-accounts-chile-malawi-and-uganda

Associated Publications:https://www.povertyactionlab.org/sites/default/files/publications/383-477_Banking-the-Unbanked_Duaps-et-al.April2018.pdf

Section 13

Survey:

Section 12

Survey:

Section 11

Survey:

Section 16

Survey:

Section 15

Survey:

Section 8

Survey:

Section 4

Survey:

Section 18

This dataset was created on 2021-10-06 18:56:03.614 by merging multiple datasets together. The source datasets for this version were:

Banking the Unbanked: Malawi Household Census: "glasem_census" : Malawi household data

Banking the Unbanked: Malawi Monitoring 1, Part 1: "glasem_monitoring1" : Malawi individual data from first monitoring survey, first half of variables

Banking the Unbanked: Malawi Monitoring 2, Part 1: "glasem_monitoring2" : Malawi individual data from second monitoring survey, first half of variables

Banking the Unbanked: Malawi Adult Baseline: "glasem_baseline" : Malawi individual adult baseline data

Banking the Unbanked: Uganda Adult Baseline: "glaseu_baseline" : Uganda individual adult baseline data

Banking the Unbanked: Malawi Monitoring 1, Part 2: "glasem_monitoring1" : Malawi individual data from first monitoring survey, second half of variables

Banking the Unbanked: Malawi Monitoring 2, Part 2: "glasem_monitoring2" : Malawi individual data from second monitoring survey, second half of variables

Banking the Unbanked: Uganda Household Census: "glaseu_census" : Uganda household and village data

Banking the Unbanked: Uganda Monitoring 1, Part 1: "glaseu_monitoring1" : Uganda individual data from first monitoring survey, first half of variables

Banking the Unbanked: Uganda Monitoring 1, Part 2: "glaseu_monitoring1": Uganda individual data from first monitoring survey, second half of variables

Banking the Unbanked: Uganda Monitoring 2, Part 1: "glaseu_monitoring2" : Uganda individual data from second monitoring survey, first third of variables

Banking the Unbanked: Uganda Monitoring 2, Part 2: "glaseu_monitoring2" : Uganda individual data from second monitoring survey, second third of variables

Banking the Unbanked: Uganda Endline: "glaseu_endline" : Uganda endline data

Banking the Unbanked: Uganda Monitoring 2, Part 3: "glaseu_monitoring2" : Uganda individual data from second monitoring survey, final third of variables

Banking the Unbanked: Malawi Endline: "glasem_endline" : Malawi endline data

Section 17

Full Project Name: Providing Free Bank Accounts in Chile, Malawi, and Uganda

PIs: Pascaline Dupas, Dean Karlan, Jonathan Robinson, Diego Ubfal

JPAL ID: 383

Location: Chile, Malawi, Uganda

Sample: 6,242 households

Timeline: 2010 to 2013

More information: https://www.povertyactionlab.org/evaluation/providing-free-bank-accounts-chile-malawi-and-uganda

Published paper: https://www.povertyactionlab.org/sites/default/files/publications/383-477_Banking-the-Unbanked_Duaps-et-al.April2018.pdf

Guide to datasets:

• Reporting units of sample results [where 1 picoCurie (pCi) = 1 trillionth (1E-12) Curie (Ci)]: • Water Samples are reported in pCi/L.

• Data Quality Disclaimer: This database is for informational use and is not a controlled quality database. Efforts have been made to ensure accuracy of data in the database; however, errors and omissions may occur.

Examples of potential errors include: • Data entry errors. • Lab results not reported for entry into the database. • Missing results due to equipment failure or unable to retrieve samples due to lost or environmental hazards. • Translation errors – the data has been migrated to newer data platforms numerous times, and each time there have been errors and data losses.

• Error Results are the calculated uncertainty for the sample measurement results and are reported as (+/-).

• Environmental Sample Records are from the year 1998 until present. Prior to 1998 results were stored in hardcopy, in a non-database format.

Requests for results from samples taken prior to 1998 or results subject to quality assurance are available from archived records and can be made through the DEEP Freedom of Information Act (FOIA) administrator at deep.foia@ct.gov. Information on FOIA requests can be found on the DEEP website.

FOIA Administrator Office of the Commissioner Department of Energy and Environmental Protection 79 Elm Street, 3rd Floor Hartford, CT 06106