According to a survey held in January 2022, over one third of responding U.S. adults said that they watched or streamed TV shows multiple times or once a day. Whilst TV has always been a popular pastime, the growing number of streaming services allows viewers to enjoy content they want to see when they want to see it, and has led to a decline in traditional TV viewing among some audiences as they turn to video-on-demand platforms instead.

The Office for Civil Rights (OCR), U.S. Department of Education developed these materials in response to requests from school districts for a reference tool to assist them through the process of developing a comprehensive English language proficiency or English language learners (ELL) program.

The 2023-24 Budget is officially available at budget.gov.au as the authoritative source of Budget Papers (BPs) and Portfolio Budget Statement (PBS) documents. This dataset is a collection of data …Show full descriptionThe 2023-24 Budget is officially available at budget.gov.au as the authoritative source of Budget Papers (BPs) and Portfolio Budget Statement (PBS) documents. This dataset is a collection of data sources from the 2023-24 Budget, including: PBS Excel spreadsheets (including Table 2.X.1 Budgeted Expenses for Outcome X in machine readable format) – available after PBSs are tabled in the Senate (~8.30pm Budget night); and Selected tables from Budget Paper No. 4 (including in machine readable format) – available after the BPs are published on budget.gov.au (~7.30 pm Budget night). The data has been provided to assist those who wish to analyse and visualise key elements of the 2023-24 Budget. Data users should refer to footnotes and memoranda in the original files as these are not usually captured in machine readable CSVs. We welcome your feedback and comments below. This dataset was prepared by the Department of Finance. Information about the PBS Excel files and CSV: The PBS Excel files published should include the following financial tables with headings and footnotes, which are also are available in CSV. Much of the other data is also available in Budget Papers (No.1) and (No.4) in aggregate form: Table 1.1: Entity Resource Statement Table 1.2: Entity 2023-24 Budget Measures Table 2.X.1: Budgeted Expenses for Outcome X Table 2.X.2: Program Component Expenses Table 3.1 to 3.6: Departmental Budgeted Financial Statements and Tables 3.7 to 3.11: Administered Budgeted Financial Statements. Please note, total expenses reported in the CSV file ‘2023-24 PBS line items dataset’ were prepared from individual entity program expense tables. Totalling these figures does not produce the total expense figure in ‘Table 1: Estimates of General Government Expenses’ (Statement 6, Budget Paper 1). Differences relate to: Intra entity charging for services which are eliminated for the reporting of general government financial statements Entity expenses that involve revaluation of assets and liabilities are reported as other economic flows in general government financial statements and Additional entities’ expenses are included in general government sector expenses (e.g. Australian Strategic Policy Institute Limited and other entities) noting that only entities that receive funding (either directly or via portfolio department through the annual appropriation acts. The original PBS Excel files and published documents include sub-totals and totals by entity and appropriation type which are not included in the line item CSV. These can be calculated programmatically. Where modifications are identified they will be updated as required. The structure of the line item CSV is: Portfolio Department/Entity Outcome Program Expense type Appropriation type Description 2022-23 2023-24 2024-25 2025-26 2026-27 Source document Source table URL The following Portfolios are included in the line item CSV: Agriculture, Fisheries and Forestry Attorney-General's Climate Change, Energy, the Environment and Water Defence Education Employment and Workplace Relations Finance Foreign Affairs and Trade Health and Aged Care Home Affairs Industry, Science and Resources Infrastructure, Transport, Regional Development, Communications and the Arts Prime Minister and Cabinet Social Services Treasury Veterans' Affairs (part of the Defence Portfolio) Department of the House of Representatives Department of the Senate Department of Parliamentary Services Parliamentary Budget Office Tables of interest found in both Budget Paper No.1 Budget Strategy and Outlook and Budget Paper No.4 Agency Resourcing are included for reference as well.

This statistic shows the types of data that organizations protect by using data backups worldwide as of 2019. Around 91 percent of respondents stated that they used backups to protect their business' databases, while only 16 percent stated that they used backups to protect their SaaS data.

These quarterly reports show the number of receipts, dispositions and pending New Court Cases (NCCs) during the defined period. The data shown is by month with quarterly and fiscal year (FY) summaries through the most recently completed quarter. Report for FY 2015.

This dataset provides information about the number of properties, residents, and average property values for Shows Lane cross streets in China Spring, TX.

This data represents the outputs and outcomes of the City funded digital literacy training and public access computer lab contract (Community Technology Access Lab Management & Digital Literacy Skills Training Services contract). This data shows the number of clients served and the percent of digital literacy training clients who increase their digital skill as well as data showing usage and availability of computer labs. Data is reported by contractors quarterly via a grant management system (PartnerGrants) and then transferred to this reporting format. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/muck-3gny

This map service, derived from World Bank data, shows various characteristics of the Health topic. The World Bank Group provides financing, state-of-the-art analysis, and policy advice to help countries expand access to quality, affordable health care; protects people from falling into poverty or worsening poverty due to illness; and promotes investments in all sectors that form the foundation of healthy societies.Age Dependency Ratio: Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population. Data from 1960 – 2012.Age Dependency Ratio Old: Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population. Data from 1960 – 2012.Birth/Death Rate: Crude birth/death rate indicates the number of births/deaths occurring during the year, per
1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Data spans from 1960 – 2008.Total Fertility: Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates. Data shown is for 1960 - 2008.Population Growth: Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage.
Population is based on the de facto definition of population, which
counts all residents regardless of legal status or citizenship--except
for refugees not permanently settled in the country of asylum, who are
generally considered part of the population of the country of origin. Data spans from 1960 – 2009.Life Expectancy: Life expectancy at birth indicates the number of years a newborn infant
would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. Data spans from 1960 – 2008.Population Female: Female population is the percentage of the population that is female. Population is based on the de facto definition of population. Data from 1960 – 2009.For more information, please visit: World Bank Open Data. _Other International User Community content that may interest you World Bank World Bank Age World Bank Health

This data shows the location of Wellington City Council operated Parks and reserves for the Wellington City area. For further information of Wellington's Parks and Reserve areas please follow this link: http://wellington.govt.nz/recreation/enjoy-the-outdoors/parks-and-reserves

This dataset illustrates the fluid dynamics of human coughing and breathing by using schlieren imaging. This dataset was used to help inform the general public about the importance of face coverings during the COVID-19 global pandemic.

First, a caveat: the NFIP data does NOT provide information specific to individual homes or parcels. This information is protected under federal law. All personal identifying information about policy holders has been redacted, and data has been anonymized to census tract, reported ZIP code, and one decimal point digit of latitute and longitude. If mapped, flood insurance policies and claims may appear to be clustered at a particular location due to this anonymization. What all that means: you cannot search for an address to see whether it has flooded. However, among many things, this data shows flooding trends in Norfolk over the last 40+ years. It shows the census tracts that flood most frequently. And it shows where the largest number and highest value of claims occur.

FEMA believes this historic release of NFIP data promotes transparency, reduces complexity related to public data requests, and improves how stakeholders interact with and understand the program. This is the largest, most comprehensive release of NFIP data coordinated by FEMA to date. This dataset allows for customizable searches to create reports, analyze and visualize present and historical NFIP data faster and easier than before. This data will help FEMA build a national culture of preparedness by providing claims and policy information people need to make better choices about their flood risk and the insurance they need to protect the life they've built. Norfolk's Open Data team extracted city-specific information from the FEMA dataset. The dataset included here represents almost 6,000 claims on record from 1977 through 2019, totaling 67 million dollars in damage in the City of Norfolk.

This dataset shows detailed data for sidewalk network segments within the full purpose jurisdiction of the City of Austin. It includes constructed ('Existing') and planned ('Absent') sidewalk segments for the network as described in the Sidewalk Master Plan adopted by the Austin City Council in June 2016. Data in this dataset may change in the future following sidewalk plan updates, which may change the composition of the planned sidewalk network.

This dataset supports two SD23 performance measures: M.C.6a and HE.C.5. Data showing calendar year construction can be found in the dataset Strategic Measure_Aggregated Sidewalk Construction Data.

This dataset provides information about the number of properties, residents, and average property values for 30th Avenue cross streets in Show Low, AZ.

VA is posting regular data updates showing progress on its efforts to accelerate access to quality health care for Veterans who have been waiting for appointments. These access data updates will be posted at the middle and end of each month at VA.gov. The facility-level data show the current status of VA's: 1) New Enrollee Appointment Request (NEAR) List: The total number of newly enrolled Veterans that have asked for an appointment during the enrollment process. Out of an abundance of caution, VA reviewed the NEAR data from the past decade to identify any individual who: has enrolled for care since the inception of enrollment, indicated they desired an appointment on the enrollment form, and has not yet had a scheduled appointment in the VHA health care system. 2) Electronic Wait List (EWL) Count: The total number of all new patients (those who have not been seen in the specific clinic in the previous 24 months) for whom appointments cannot be scheduled in 90 days or less; and 3) Total Appointments Scheduled: Every appointment scheduled at the facility except surgery and medical procedures.

Dataset

This dataset was created by Ankita9Sharma

Contents

It contains the following files:

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Data shows location of BoM …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Data shows location of BoM climate stations in Arckaringa Subregion. Purpose Demonstrates the breadth of rainfall data capture across the subregion. Dataset History Rainfall Monitoring Sites is a spatial dataset that was generated from tabular data provided by the Bureau of Meteorology back in the early 2000s. This dataset was used to generate a range of statewide rainfall datasets around 2002. Dataset Citation SA Department of Environment, Water and Natural Resources (2015) Rainfall Monitoring Sites - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/9fa56cfd-39a0-4771-abbe-944516846c93.

Deep learning (DL) techniques have seen tremendous interest in medical imaging, particularly in the use of convolutional neural networks (CNNs) for the development of automated diagnostic tools. The facility of its non-invasive acquisition makes retinal fundus imaging particularly amenable to such automated approaches. Recent work in the analysis of fundus images using CNNs relies on access to massive datasets for training and validation, composed of hundreds of thousands of images. However, data residency and data privacy restrictions stymie the applicability of this approach in medical settings where patient confidentiality is a mandate. Here, we showcase results for the performance of DL on small datasets to classify patient sex from fundus images—a trait thought not to be present or quantifiable in fundus images until recently. Specifically, we fine-tune a Resnet-152 model whose last layer has been modified to a fully-connected layer for binary classification. We carried out several experiments to assess performance in the small dataset context using one private (DOVS) and one public (ODIR) data source. Our models, developed using approximately 2500 fundus images, achieved test AUC scores of up to 0.72 (95% CI: [0.67, 0.77]). This corresponds to a mere 25% decrease in performance despite a nearly 1000-fold decrease in the dataset size compared to prior results in the literature. Our results show that binary classification, even with a hard task such as sex categorization from retinal fundus images, is possible with very small datasets. Our domain adaptation results show that models trained with one distribution of images may generalize well to an independent external source, as in the case of models trained on DOVS and tested on ODIR. Our results also show that eliminating poor quality images may hamper training of the CNN due to reducing the already small dataset size even further. Nevertheless, using high quality images may be an important factor as evidenced by superior generalizability of results in the domain adaptation experiments. Finally, our work shows that ensembling is an important tool in maximizing performance of deep CNNs in the context of small development datasets.

The website shows data on the plan and implementation of the health services program by individual health activities (VZD) :

• hospital medical activity,
• general outpatient medical activity,
• specialist outpatient medical activity,
• dental practice,
• other health activities,
• activity of accommodation facilities for patient care,
• social care without accommodation for the elderly and disabled,
• production of pharmaceutical preparations,
• retail trade in specialized stores with pharmaceutical products,
• compulsory social security activity.

Within the framework of each activity, the data for each period are shown separately by contractors and together, the activity by regional units of ZZZS and the activity data at the level of Slovenia together.

Data on the plan and implementation of the health services program are shown in the accounting unit (e.g. points, quotients, weights, groups of comparable cases, non-medical care day, care, days...), which are used to calculate the work performed in the field of individual activities.

The publication of information about the plan and implementation of the program on the ZZZS website is primarily intended for the professional public. The displayed program plan for an individual contractor refers to the defined billing period. (example: The plan for the period 1-3 201X is calculated as 3/12 of the annual plan agreed in the contract).

The data on the implementation of the program represents the implementation of the program at an individual provider for insured persons who benefited from medical services from him during the accounting period. Data on the realization of the program do not refer to persons insured in accordance with the European legal order and bilateral agreements on social security. Data for individual contractors are classified by regional units based on the contractor's headquarters. The content of the data on the "number of cases" is defined in the Instruction on recording and accounting for medical services and issued materials.

The institute reserves the right to change the data, in the event of subsequently discovered irregularities after already published on the Internet.

Over the past decade, a growing number of publications have claimed to provide evidence for the existence and function of neonatal imitation in rhesus macaques. Here I show that there is in fact no empirical basis for these claims. Studies of the phenomenon have consistently failed to implement the gold standard cross-target analytical approach, which controls for increases in matching responses that may not be a function of the specific modelled behaviour. Critically, a preregistered re-analysis of the entire set of existing data using this cross-target approach shows that macaque neonates have failed to produce matching tongue protrusion or lipsmacking responses at levels greater than chance. Furthermore, there is no evidence for intra-individual consistency in “imitative” responses across different actions, as imitation scores for the two actions are negatively correlated with each other. Macaque tongue protrusion and lipsmacking responses may vary as a function of general factors that fluctuate over testing sessions, rather than as a function of the specific model or of between-individual variations in imitative tendencies.

The 2009 Federal Campus-Based Programs Data Book provides comprehensive program funding information for these federal student aid programs: Federal Supplemental Educational Opportunity Grants, Federal Work-Study, and Federal Perkins Loans. Program allocation data is presented for award year 2009-2010. Fiscal and Recipient data are presented for award year 2007-2008.