Unemployment Insurance Weekly Claims Data - 2020 year to dateThis map contain Unemployment Insurance Weekly Claims data, from the United State Department of Labor, Employment & Training Administration, starting on 01/01/2020 and updated weekly. These data are used in current economic analysis of unemployment trends in the nation, and in each state. Initial claims is a measure of emerging unemployment. It counts the number of new persons claiming unemployment benefits and it is released after one week.Continued claims is a measure of the total number of persons claiming unemployment benefits, and it is released one week later than the initial claims.The data is organized by state, with the following attributes (as defined by the United State Department of Labor) repeated for each weekWeek/date when claims were filedNumber of initial claimsWeek/date reflected in the data weekNumber of continued claims Total covered employment Insured unemployment rateThe latest information on unemployment insurance claims can be found here.TECHNICAL NOTESThese data represent the weekly unemployment insurance (UI) claims reported by each state's unemployment insurance program offices. These claims may be used for monitoring workload volume, assessing state program operations and for assessing labor market conditions. States initially report claims directly taken by the state liable for the benefit payments, regardless of where the claimant who filed the claim resided. These are the basis for the advance initial claims and continued claims reported each week. These data come from ETA 538, Advance Weekly Initial and Continued Claims Report. The following week initial claims and continued claims are revised based on a second reporting by states that reflect the claimants by state of residence. These data come from the ETA 539, Weekly Claims and Extended Benefits Trigger Data Report.A. Initial ClaimsAn initial claim is a claim filed by an unemployed individual after a separation from an employer. The claimant requests a determination of basic eligibility for the UI program. When an initial claim is filed with a state, certain programmatic activities take place and these result in activity counts including the count of initial claims. The count of U.S. initial claims for unemployment insurance is a leading economic indicator because it is an indication of emerging labor market conditions in the country. However, these are weekly administrative data which are difficult to seasonally adjust, making the series subject to some volatility.B. Continued Weeks ClaimedA person who has already filed an initial claim and who has experienced a week of unemployment then files a continued claim to claim benefits for that week of unemployment. Continued claims are also referred to as insured unemployment. The count of U.S. continued weeks claimed is also a good indicator of labor market conditions. Continued claims reflect the current number of insured unemployed workers filing for UI benefits in the nation. While continued claims are not a leading indicator (they roughly coincide with economic cycles at their peaks and lag at cycle troughs), they provide confirming evidence of the direction of the U.S. economyC. Seasonal Adjustments and Annual RevisionsOver the course of a year, the weekly changes in the levels of initial claims and continued claims undergo regularly occurring fluctuations. These fluctuations may result from seasonal changes in weather, major holidays, the opening and closing of schools, or other similar events. Because these seasonal events follow a more or less regular pattern each year, their influence on the level of a series can be tempered by adjusting for regular seasonal variation. These adjustments make trend and cycle developments easier to spot. At the beginning of each calendar year, the Bureau of Labor Statistics provides the Employment and Training Administration (ETA) with a set of seasonal factors to apply to the unadjusted data during that year. Concurrent with the implementation and release of the new seasonal factors, ETA incorporates revisions to the UI claims historical series caused by updates to the unadjusted data.

The Mass Layoff Statistics program is a Federal-State cooperative statistical effort which uses a standardized, automated approach to identify, describe, and track the effects of major job cutbacks, using data from each State's unemployment insurance database. Establishments which have at least 50 initial claims for unemployment insurance (UI) filed against them during a consecutive 5-week period are contacted by State agencies to determine whether those separations are of at least 31 days duration, and, if so, information is obtained on the total number of persons separated, the reasons for these separations, and recall expectations. Establishments are identified according to industry classification and location, and unemployment insurance claimants are identified by such demographic characteristics as age, race, sex, ethnic group, and place of residence. The program yields information on an individual's entire spell of unemployment, to the point when regular unemployment insurance benefits are exhausted. It provides databases of establishments and claimants, both of which are used for further research and analysis. Data available Monthly data report summary information on all establishments which have at least 50 initial claims for unemployment insurance (UI) filed against them during a 5-week period. Data are available for 50 States, the District of Columbia, and Puerto Rico, as well as by industry. Quarterly data report on private sector nonfarm establishments which have at least 50 initial claims filed against them during a 5-week period and where the employer indicates that 50 or more people were separated from their jobs for at least 31 days. Information is obtained on the total number of persons separated; the reasons for separation; worksite closures; recall expectations; and socioeconomic characteristics on UI claimants such as gender, age, race, and residency. These characteristics are collected at two points in time when an initial claim is filed and when the claimant exhausts regular UI benefits. In between these points, the unemployment status of claimants is tracked through the monitoring of certifications for unemployment (continued claims) filed under the regular State UI program. Data are available for 50 States, the District of Columbia, and Puerto Rico, as well as by industry. Coverage Monthly, quarterly, and annual data for 50 States, the District of Columbia, and Puerto Rico. Monthly data are available since April 1995; quarterly data since second quarter 1995.

This data set contains information about the security configuration and vulnerability status of DOT information technology assets. The information identifies individual hardware assets, their security configuration settings, their status with respect to known cyber security vulnerabilities, the software products, version numbers and service packs installed thereon, and the individual users who access those information systems.

As part of the Accelerated Value Chain Development (AVCD) Program, the root crops value chain aims to transform Potato and Sweetpotato Food Security, Nutrition and Incomes in Kenya. At least 93,500 smallholder households (HH) will be reached over three years. We anticipate that by the end of the project, approximately $2M in profit will be generated annually through sales of planting material and increased yields of both crops, and the dietary quality of least 54,000 children under five and 65,000 women will be improved by 20% as measured through diet diversity scores.

Problem Statement

👉 Download the case studies here

Hospitals and healthcare providers faced challenges in ensuring continuous monitoring of patient vitals, especially for high-risk patients. Traditional monitoring methods often lacked real-time data processing and timely alerts, leading to delayed responses and increased hospital readmissions. The healthcare provider needed a solution to monitor patient health continuously and deliver actionable insights for improved care.

Challenge

Implementing an advanced patient monitoring system involved overcoming several challenges:

Collecting and analyzing real-time data from multiple IoT-enabled medical devices.

Ensuring accurate health insights while minimizing false alarms.

Integrating the system seamlessly with hospital workflows and electronic health records (EHR).

Solution Provided

A comprehensive patient monitoring system was developed using IoT-enabled medical devices and AI-based monitoring systems. The solution was designed to:

Continuously collect patient vital data such as heart rate, blood pressure, oxygen levels, and temperature.

Analyze data in real-time to detect anomalies and provide early warnings for potential health issues.

Send alerts to healthcare professionals and caregivers for timely interventions.

Development Steps

Data Collection

Deployed IoT-enabled devices such as wearable monitors, smart sensors, and bedside equipment to collect patient data continuously.

Preprocessing

Cleaned and standardized data streams to ensure accurate analysis and integration with hospital systems.

AI Model Development

Built machine learning models to analyze vital trends and detect abnormalities in real-time

Validation

Tested the system in controlled environments to ensure accuracy and reliability in detecting health issues.

Deployment

Implemented the solution in hospitals and care facilities, integrating it with EHR systems and alert mechanisms for seamless operation.

Continuous Monitoring & Improvement

Established a feedback loop to refine models and algorithms based on real-world data and healthcare provider feedback.

Results

Enhanced Patient Care

Real-time monitoring and proactive alerts enabled healthcare professionals to provide timely interventions, improving patient outcomes.

Early Detection of Health Issues

The system detected potential health complications early, reducing the severity of conditions and preventing critical events.

Reduced Hospital Readmissions

Continuous monitoring helped manage patient health effectively, leading to a significant decrease in readmission rates.

Improved Operational Efficiency

Automation and real-time insights reduced the burden on healthcare staff, allowing them to focus on critical cases.

Scalable Solution

The system adapted seamlessly to various healthcare settings, including hospitals, clinics, and home care environments.

Tillamook Estuary, OR continuous water quality monitoring dataset: 2017-2023

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Hydstra continuous database management system used for collection of high frequency continuous timeseries data for groundwater, surface water, water quality and tidal station types. The QA/QC data timeseries data associated with these stations is published through the Departments Water Data Library web application. This dataset is comprised of a “Stations Table” and a related “Period of Record Table”. Stations table is the primary feature class and contains basic information about each station including Station Name, Latitude, Longitude and Description. The Period of Record Table is a related feature class that contains a list of parameters (i.e. stage, flow, depth to groundwater, water temperature, turbidity, pH, etc.) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected.

The U.S. Department of Energy's Enhanced Geothermal System (EGS) Collab project aims to improve our understanding of hydraulic stimulations in crystalline rock for enhanced geothermal energy production through execution of intensely monitored meso-scale experiments. The first experiment was performed at the 4850 ft level of the Sanford Underground Research Facility (SURF), approximately 1.5 km below the surface at Lead, South Dakota. The data reported here were collected by the continuous active-source seismic monitoring (CASSM) system (Ajo-Franklin et al., 2011). This system was permanently installed in the testbed and consisted of 17 piezoelectric sources that were recorded by 2-12 channel hydrophone arrays, 18 3-C accelerometers, and 4 3-C geophones at a Nyquist frequency of 24kHz. The source array was activated in a repeated sequence of shots (each source fired 16 times and stacked into resultant waveforms) for the duration of the experiment (April 25, 2018 - March 7, 2019) with few exceptions. Please see the attached documents describing the source / receiver geometry. The data are available in both seg2 (.dat extension) and segy (.sgy extension) format. Each segy file contains multiple seg2 files.

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Hydstra continuous database management system used for collection of high frequency continuous timeseries data for groundwater, surface water, water quality and tidal station types. The QA/QC data timeseries data associated with these stations is published through the Departments Water Data Library web application. This dataset is comprised of a “Stations Table” and a related “Period of Record Table”. Stations table is the primary feature class and contains basic information about each station including Station Name, Latitude, Longitude and Description. The Period of Record Table is a related feature class that contains a list of parameters (i.e. stage, flow, depth to groundwater, water temperature, turbidity, pH, etc.) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected.

The dataset contains continuous active-source seismic monitoring (CASSM) data collected during EGS Collab Experiment 2, conducted from February to September 2022 at the Sanford Underground Research Facility in Lead, South Dakota. This experiment aimed to investigate enhanced geothermal systems through high-pressure fluid injections at depths of 1200-1500 meters. The seismic monitoring system included 16 three-component piezoelectric accelerometers and 24 hydrophones installed in boreholes around the injection zones, recording signals from piezoelectric seismic sources. Data were acquired using both continuous and triggered recording systems, with sampling rates of up to 100 kHz. The raw data are organized by timestamps and stored in .dat format, with accompanying log files. Calibration certificates for selected accelerometers are provided to aid in correcting sensor responses, though users are advised to consider possible effects of enclosures and installation on sensor performance. Users are strongly advised to consult the accompanying report, which outlines the experimental setup, data acquisition, sensor specifications, and recording systems.

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Hydstra continuous database management system used for collection of high frequency continuous timeseries data for groundwater, surface water, water quality and tidal station types. The QA/QC data timeseries data associated with these stations is published through the Departments Water Data Library web application. This dataset is comprised of a “Stations Table” and a related “Period of Record Table”. Stations table is the primary feature class and contains basic information about each station including Station Name, Latitude, Longitude and Description. The Period of Record Table is a related feature class that contains a list of parameters (i.e. stage, flow, depth to groundwater, water temperature, turbidity, pH, etc.) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected.

In cooperation with the San Antonio Water System, continuous and discrete water-quality data were collected from groundwater wells completed in the Edwards aquifer, Texas, 2014-2015. Discrete measurements of nitrate were made by using a nitrate sensor. Precipitation data from two sites in the National Oceanic and Atmospheric Administration Global Historical Climatology Network are included in the dataset. The continuous monitoring data were collected using water quality sensors and include hourly measurements of nitrate, specific conductance, and water level in two wells. Discrete measurements of nitrate, specific conductance, and vertical flow rate were collected from one well site at different depths throughout the well bore.

Continuous Water Quality Monitoring (CWQM) refers to the application of in situ instrumentation coupled with operational and analytical methodologies that enable acquisition of temporally and (or) spatially dense datasets without the need for routine collection and analysis of physical samples other than for periodic calibration purposes.

This data collection contains the results of an evaluation of commercially available CWM instruments for deployment in Queensland estuarine and marine settings. The instruments were evaluated on criteria which included (i) utility of analyte to both modelling and monitoring requirements, (ii) ability to measure analyte concentrations accurately and capture relevant temporal and spatial variability, (iii) cost, both capital and ongoing operational, (iv) logistics, including maintenance requirements, mounting, telemetry infrastructure and site access, and (v) ease of instrument operation and the required effort for information to be understood and applied.

This information was used to inform the strategy for implementing a CWQM program to support the eReefs research platform (please refer to the linked publication for details)

Important Disclaimer The information contained in this datasets includes general statements based on scientific research and on evaluation of instruments as they were at a particular point in time for a specific set of purposes. This information may be obsolete, incomplete or unable to be used in any specific situation. Reuse it at your own risk. Lineage: The CWQM Instruments Database was originally published as a subsection of the TERN Australian Coastal Ecosystems Facility (ACEF) website at a URL of http://acef.tern.org.au/wqtech/. The database records were create on September 24th, 2014 and so the instrument information is only current for that date.

This website was based on the Drupal 7 Content Management System (CMS) and the database was implemented using a mix of Drupal entity types, including taxonomies, fieldsets and custom content types. The files in this data collection have been created by exporting data from that website in tabular formats, editing some column names for readability, and updating the CWQM Instrument Image filenames to be readable. The exports were performed on 2024-12-03, using a backup of the ACEF website database created at the time the website was decommissioned in July 2024.

The data files are: CWQM-Instruments-Database.xlsx - contains multiple worksheets, one for each Drupal entity which made up the database. Each worksheet has a single header-row.

CWQM-Instrument-Manufacturers.csv, CWQM-Instrument-Parameters.csv, CWQM-Instruments.csv, CWQM-Interfaces.csv and CWQM-Parameters.csv are all UTF-8-encoded exports from the CWQM-Instruments-Database.xlsx spreadsheet in comma-separated-variables (CSV) format, one file per worksheet. All CSV files have a single header-row.

The CWQM-Instruments-Images image gallery contains an image of each of the instruments in the database (see the CWQM Instruments worksheet/csv file for which filename goes with which instrument). Copyright and credit for these images should be attributed to the manufacturer of the instrument in question.

The overall purpose of the USAID-funded Feed the Future AVCD DTC Value Chain ------------*-

This dataset provides the monitoring data of the continuous automatic monitoring facility CEMS in Tainan City.For real-time data, please visit http://60.248.54.71:88

LUQS, the air quality monitoring system of the state of North Rhine-Westphalia, records and examines the concentrations of various pollutants in the air. The measurement system integrates continuous and discontinuous measurements and provides a comprehensive representation of air quality data. The data offered here are continuously measured and hourly calculated arithmetic (AM1H) mean values ​​of the 5-second individual data of an hour. The reference time is the end of the measurement hour (range 01:00-24:00). This data record contains all data files for individual stations with all components, validated values ​​up to the end of the previous year, the values ​​listed as current have not yet been finally validated.

This dataset show continuous emissions data from the Dickerson waste-to-energy plant. The composition of the gases generated in each combustion unit are sampled every ten seconds. This is referred to as "continuous emissions monitoring," or CEM. The numbers shown on the dataset are hourly averages. An air pollution control retrofit at this facility in 2009 reduced nitrogen oxide emissions up to 500 tons a year. The Resource Recovery Facility must comply with the requirements of Maryland Department of the Environment (MDE) Permit # 24-031-01718, as well as Federal standards. Under this air quality permit, the Federal standard limits are posted on https://www.montgomerycountymd.gov/sws/facilities/rrf/cem.html Update Frequency : Daily

Offsite Medical Case Management Services Market Outlook

The global market size for Offsite Medical Case Management Services was valued at approximately USD 4.5 billion in 2023 and is projected to reach USD 8.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.5% during the forecast period. The growth of this market is primarily driven by the increasing demand for efficient healthcare management, the rising prevalence of chronic diseases, and the continuous advancements in telecommunication technologies.

One of the significant growth factors for the Offsite Medical Case Management Services market is the surge in chronic diseases such as diabetes, cardiovascular diseases, and cancer. These conditions require ongoing monitoring and management, making offsite case management services a critical component in patient care. The need for specialized management, especially for catastrophic cases, has led to increased adoption of these services by healthcare providers and insurance companies. Moreover, the aging population globally is creating a demand for more efficient and accessible healthcare management solutions, further propelling the market growth.

Advancements in telecommunications and digital health technologies are also major drivers of this market. The integration of telehealth platforms with medical case management services has significantly improved the accessibility and efficiency of patient care. Telephonic and virtual consultations have made it easier for healthcare providers to manage patients remotely, thereby reducing the need for frequent hospital visits. This not only enhances patient satisfaction but also reduces healthcare costs, making it a win-win for both providers and patients.

Furthermore, the increasing focus on personalized patient care and the shift towards value-based healthcare are contributing to the market's growth. Offsite medical case management services offer a tailored approach to managing patient health, ensuring that patients receive the most appropriate and effective care. This is particularly important in managing complex cases that require coordinated efforts among multiple healthcare providers. The increased emphasis on patient outcomes and the need for cost-effective healthcare solutions are driving the adoption of these services across various healthcare settings.

Claims Management Solutions play a pivotal role in the efficient functioning of offsite medical case management services. These solutions streamline the process of handling insurance claims, ensuring that healthcare providers receive timely reimbursements for the services rendered. By integrating claims management solutions with offsite case management services, healthcare organizations can reduce administrative burdens and focus more on patient care. This integration not only enhances operational efficiency but also improves the accuracy and speed of claims processing, leading to better financial outcomes for healthcare providers. As the demand for offsite medical case management services continues to grow, the role of claims management solutions becomes increasingly important in supporting the financial health of healthcare institutions.

Regionally, North America holds the largest share of the offsite medical case management services market, followed by Europe and Asia Pacific. The high adoption rate of advanced healthcare technologies, the presence of established healthcare infrastructure, and the increasing prevalence of chronic diseases are some of the factors contributing to the market's dominance in these regions. However, the market in the Asia Pacific is expected to grow at the highest CAGR during the forecast period, driven by the rapidly growing healthcare sector and increasing investments in telehealth services.

Service Type Analysis

The Offsite Medical Case Management Services market is segmented by service type, which includes Telephonic Case Management, Field Case Management, Catastrophic Case Management, and Others. Telephonic case management is one of the most widely adopted service types due to its convenience and cost-effectiveness. This service allows healthcare providers to manage patients' health remotely through phone consultations, making it easier to monitor and follow up with patients regularly. The demand for telephonic case management is particularly high among patients with chronic conditions who require continuous monitoring.

Field Case M

Data will be in the form of a report, which will be a secured (printable, non-editable), searchable, Portable Document Format (PDF) showing the results of the analyses. The objective of WAMSI DREDGING SCIENCE NODE Project 4.4 is to examine the suitability of automated in situ sediment deposition sensors based essentially on an upward facing nephelometer (see Ridd et al. 2001) to assess sediment deposition. Prototype versions of this device developed by Peter Ridd at James Cook University - have been deployed in situ and although the sensor currently gives useful information, the shape of the sensor needs to be optimized and the sensor readings need to be calibrated against settled sediment surface density as measured in units of mg cm2. The objective of Project 4.4, which is being led by Peter Ridd at James Cook University, is to re-design, optimise and calibrate the deposition sensor head to more closely mimic the trapping of sediments on a coral's surface. The project will determine how the new sensor geometry responds to turbidity events and deposition events in situ and in the laboratory caused by different particle size distributions under different turbulence regimes. Once working, the deposition sensor will be used in the laboratory experiments (WAMSI DREDGING SCIENCE NODE Project Project 4.6).

LUQS, the air quality monitoring system of the state of North Rhine-Westphalia, records and studies the concentrations of various pollutants in the air. The measurement system integrates continuous and discontinuous measurements and provides a comprehensive representation of air quality data. The data offered here are continuously measured and hourly calculated arithmetic (AM1H) averages of the 5sec individual data of one hour. Reference time is the end of the measurement hour (range 01:00-24:00). This dataset contains all data files for all stations and all measured components in 10-year files: 1980-1989, 1990-2000, 2001-2010, 2011-previous year, by component. The data for the current year (last 365 days) are available in a non-finally validated form.