Cms Data Download |BEST|
Yves Pottenburgh
In computing, data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary digital form. It is acceptable for data to be used as a singular subject or a plural subject. Raw data is a term used to describe data in its most basic digital format.
The concept of data in the context of computing has its roots in the work of Claude Shannon, an American mathematician known as the father of information theory. He ushered in binary digital concepts based on applying two-value Boolean logic to electronic circuits. Binary digit formats underlie the CPUs, semiconductor memories and disk drives, as well as many of the peripheral devices common in computing today. Early computer input for both control and data took the form of punch cards, followed by magnetic tape and the hard disk.
Early on, data's importance in business computing became apparent by the popularity of the terms "data processing" and "electronic data processing," which, for a time, came to encompass the full gamut of what is now known as information technology. Over the history of corporate computing, specialization occurred, and a distinct data profession emerged along with growth of corporate data processing.
Computers represent data, including video, images, sounds and text, as binary values using patterns of just two numbers: 1 and 0. A bit is the smallest unit of data, and represents just a single value. A byte is eight binary digits long. Storage and memory is measured in megabytes and gigabytes.
The units of data measurement continue to grow as the amount of data collected and stored grows. The relatively new term "brontobyte," for example, is data storage that is equal to 10 to the 27th power of bytes.
Data can be stored in file formats, as in mainframe systems using ISAM and VSAM. Other file formats for data storage, conversion and processing include comma-separated values. These formats continued to find uses across a variety of machine types, even as more structured-data-oriented approaches gained footing in corporate computing.
Growth of the web and smartphones over the past decade led to a surge in digital data creation. Data now includes text, audio and video information, as well as log and web activity records. Much of that is unstructured data.
The term big data has been used to describe data in the petabyte range or larger. A shorthand take depicts big data with 3Vs -- volume, variety and velocity. As web-based e-commerce has spread, big data-driven business models have evolved which treat data as an asset in itself. Such trends have also spawned greater preoccupation with the social uses of data and data privacy.
Data has meaning beyond its use in computing applications oriented toward data processing. For example, in electronic component interconnection and network communication, the term data is often distinguished from "control information," "control bits," and similar terms to identify the main content of a transmission unit. Moreover, in science, the term data is used to describe a gathered body of facts. That is also the case in fields such as finance, marketing, demographics and health.
With the proliferation of data in organizations, added emphasis has been placed on ensuring data quality by reducing duplication and guaranteeing the most accurate, current records are used. The many steps involved with modern data management include data cleansing, as well as extract, transform and load (ETL) processes for integrating data. Data for processing has come to be complemented by metadata, sometimes referred to as "data about data," that helps administrators and users understand database and other data.
Analytics that combine structured and unstructured data have become useful, as organizations seek to capitalize on such information. Systems for such analytics increasingly strive for real-time performance, so they are built to handle incoming data consumed at high ingestion rates, and to process data streams for immediate use in operations.
Over time, the idea of the database for operations and transactions has been extended to the database for reporting and predictive data analytics. A chief example is the data warehouse, which is optimized to process questions about operations for business analysts and business leaders. Increasing emphasis on finding patterns and predicting business outcomes has led to the development of data mining techniques.
The data profession took firm root as the relational database management system (RDBMS) gained wide use in corporations, beginning in the 1980s. The relational database's rise was enabled in part by the Structured Query Language (SQL). Later, non-SQL databases, known as NoSQL databases, arose as an alternative to established RDBMSes.
Today, companies employ data management professionals or assign workers the role of data stewardship, which involves carrying out data usage and security policies as outlined in data governance initiatives.
A distinct title -- the data scientist -- has appeared to describe professionals focused on data mining and analysis. The benefit of presenting data science in an evocative manner has even given rise to the data artist; that is, an individual adept at graphing and visualizing data in creative ways.
The official Google tool for testing your structured data to see which Google rich results can be generated by the structured data on your page. You can also preview how rich results can look in Google Search.
Encounter data includes U.S. Border Patrol (USBP) Title 8 Apprehensions, Office of Field Operations (OFO) Title 8 Inadmissibles, and Title 42 Expulsions* for fiscal years (FY) 2020, 2021, 2022, and 2023. Data is available for the Northern Land Border, Southwest Land Border, and Nationwide (i.e., air, land, and sea modes of transportation) encounters.
Data is extracted from live CBP systems and data sources. Statistical information is subject to change due to corrections, systems changes, change in data definition, additional information, or encounters pending final review. Final statistics are available at the conclusion of each fiscal year.
Note: Nationwide encounters are the sum of CBP encounters across all areas of responsibility including Northern Land Border, Southwest Land Border, OFO non-land border ports of entry (e.g., airports, seaports), and USBP sectors that do not share a land border with Canada or Mexico (e.g., Miami Sector). This data is available for further review and download on the Nationwide Encounters Public Data Portal page.
DHS and its components provide access to statistical reports and machine readable data sets. These datasets are available in the DHS section of Data.gov and follow the guiding principles set in the DHS Digital Government strategy.
One of the main goals of DHS Open Data is to facilitate the release of DHS high-value datasets whenever possible. High-value datasets include data that: were previously not provided but increase accountability and responsiveness, increase public knowledge, further the core mission of DHS, create economic opportunity or respond to identified needs or demands. DHS will continue to make more datasets available as we follow our process for identifying and approving the release of high-value datasets. If you have ideas or suggestions for new datasets or datasets you have seen on one of the DHS web sites you can email your ideas to CDO...@hq.dhs.gov.
The COVID-19 vaccine is now available to all New York residents 6 months and older.
The Health Department is closely monitoring the status of vaccinations in NYC, including the demographics and locations of people who have received the vaccine. The data below show how vaccinations in NYC have progressed since December 2020.
These data show the percent of NYC residents who received at least one dose of COVID-19 vaccine and the percent who completed the primary series, by Modified ZIP Code Tabulation Areas (MODZCTAs). MODZCTAs can provide better estimates of population size than ZIP codes because they combine census blocks that have smaller populations.
These data show the percent of NYC residents who received at least one dose of COVID-19 vaccine and the percent who completed the primary series, by borough of residence and demographic group. The percentages reflect the number of people vaccinated within that specific demographic group.
These data show the total number of new cases per 100,000 people over the past seven days, with the table showing the most recent value. More information about the CDC's levels of community transmission.
Open Data is free public data published by New York City agencies and other partners. Help us build Open Data Week 2024, or sign up for the NYC Open Data mailing list to find training opportunities and upcoming events.
As part of our research studies, we put out publicly available datasets that can be used by other researchers and practitioners to support their own work. These datasets allow you to analyze social mobility and a variety of other outcomes from life expectancy to patent rates by neighborhood, college, parental income level, and racial background. You can search for datasets by geographic level (e.g., Census tracts), by topic (e.g., education), or by the title of the paper. Need technical assistance with these data? Contact us at [email protected]
Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Our mission is to provide researchers high-quality, domain-specific training covering the full lifecycle of data-driven research.
Data Carpentry is now a lesson program within The Carpentries, having merged with Software Carpentry in January, 2018. Data Carpentry's focus is on the introductory computational skills needed for data management and analysis in all domains of research. Our lessons are domain-specific, and build on the existing knowledge of learners to enable them to quickly apply skills learned to their own research. Our initial target audience is learners who have little to no prior computational experience. We create a friendly environment for learning to empower researchers and enable data driven discovery.
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