This chart counts the number of unique children in DFPS custody who lived in an adoptive placement at some point during the fiscal year and the total number of adoptive placements during the year. Children can have more than one adoptive placement. This chart includes children in DFPS custody for whom a court has appointed DFPS legal responsibility through Permanent Managing Conservatorship. An adoptive placement occurs when the child's caseworker, the family's case manager, and the adoptive family sign paperwork officially placing the child in the home for adoption. Before the paperwork can be signed, a child must be free for adoption (meaning a court has terminated parental rights), have a permanency goal of adoption and the family must have been approved for adoption through a licensed child placing agency. Visit dfps.state.tx.us for information on adoption and all DFPS programs.

Users can download data regarding characteristics and adoptive experiences of families of adoptive children. Topics include type of adoption, birth family contact, developmental problems, and adoption support. BackgroundThe National Survey of Adoptive Parents (NSAP) is a SLAITS survey operated by the Centers for Disease Control and Prevention (CDC) and National Center for Health Statistics (HCHS) and is sponsored by the Department of Health and Human Services (DHHS). NSAP provides information on characteristics, pre-adoption experiences and post-adoption support experiences of families of adoptive children. Topics include, but are not limited to: type of adoption, birth family contact, developmental problems, and adoption support. User FunctionalityUsers can download the survey instrument and frequency counts as PDFs, the codebook into Microsoft Excel and the public-access dataset into SAS statistical software. Data NotesThe NSAP telephone interview was administered to parents (n=2,089) who adopted a child 0 to 17 years of age. Children were identified as being adopted through the US foster care system, domestic private adoption agencies, or in ternational adoption agencies. Parents were identified through the National Survey of Children’s Health (NSCH). Data were collected between April 2007 and July 2008 and are available on a national level.

This chart counts children who exited DFPS custody to adoption during the fiscal year. To be adopted, a court must have terminated parental rights, the child must have lived with the adoptive family for at least 6 months, the family must have been approved for adoption through a licensed child placing agency and a court must have ordered legal custody to the adoptive parents. Visit dfps.state.tx.us for information on all DFPS programs

This chart counts the number of unique children in DFPS custody who lived in an adoptive placement at some point during the fiscal year. Children in DFPS custody are those for whom a court has appointed DFPS legal responsibility through temporary or permanent managing conservatorship or other court ordered legal basis. An adoptive placement occurs when the child's caseworker, the family's case manager, and the adoptive family sign paperwork officially placing the child in the home for adoption. Before the paperwork can be signed, a child must be free for adoption (meaning a court has terminated parental rights), have a permanency goal of adoption and the family must have been approved for adoption through a licensed child placing agency. Children may have more than one disabling condition. Drug/Alcohol disabling condition can either be due to self-abuse or exposure to an individual with the condition. Other includes teen parent or pregnant teen. Please visit dfps.state.tx.us for more information about DFPS Adoptions and all our programs.

description: This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census 1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey. 2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons. 3. description: Provides a concise description of the variable. 4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS. 5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (CountSE). DEMOGRAPHIC CATEGORIES 1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable. 2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314 columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use). 3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest. 4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data. 5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest. 6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals. 7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives. 8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest. 9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group. 10. scChldHome: 11.

Adoptions by Breed and Date

Animals, Breeds, and Outcomes

By data.world's Admin [source]

About this dataset

This dataset contains information about adopted animals from the Bloomington Animal Shelter. The migration from their former software, AnimalShelterNet, to the newer system Shelter Manager allowed us to preserve as much of this information as possible. It records data on intakedate, intakereason, istransfer, sheltercode, animalname and more. Each field contains unique information such as breed name, color and age of the animal which can be used to gain a better understanding of adoption trends in this area. Additionally it has fields on movement type/date and return/death records can help track changes in animal welfare technology over time. This dataset is incredibly valuable when it comes to further researching not only animal adoption trends but also progression in humane shelters and humane legislative systems alike!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides information on animals that were adopted from the Bloomington Animal Shelter between 2017 and 2020. It includes data on intakes, breeds, colors, species, ages, sexes, locations of the animal at time of adoption and other related movement periods to determine outcomes. This dataset can be used to analyze trends in animal adoptions at the shelter over time to better understand what type of animals are being adopted and why.

To get started using this dataset first familiarize yourself with all of its features by exploring it further such as using a visualization tool or reviewing its columns/attributes. Then you can begin to analyze any trends over time by looking into variables such as location of adoption (where was it), breedtype (what type was it), age (how old was it) etc. Once you decide on variables to focus on you can visualize them in a variety of ways such as line graphs or heatmaps so that patterns become easier to discern and explain any insights gained from your analysis. Utilizing methods such as correlation analysis or machine learning algorithms may also be applied depending on which analytical approaches will suit your needs best for uncovering deeper meaning from this data set. You could even involve external sources for more pertinent information about each variable or dive deeper into textual descriptions for a richer analysis if required.

Once you have determined any areas that stand out based off your analytics then making an informed decision is next step which you will only now be able begin with real actionable conclusions based off yours research - allowing us all take part in creating better understanding about how adoptions result from our beloved animal shelters!

Research Ideas

• Tracking popular breed trends to better inform spay and neuter resources within the shelter.
• Correlating movement type, return date and return reason to identify common reasons for animals returning or being transferred out of the shelter.
• Analysing intake reason and intake date with species name to understand seasonal animal intake patterns at the shelter, as well as health issues which arise due to overpopulation or climate change in Bloomington

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: animal-data-1.csv | Column name | Description | |:-------------------|:-------------------------------------------------------------------| | intakedate | Date of admission to the shelter. (Date) | | intakereason | Reason for admission to the shelter. (String) | | istransfer | Whether the animal was transferred from another shelter. (Boolean) | | sheltercode | Unique code assigned to the animal. (String) | | animalname | Name of the animal. (String) | | breedname | Breed of the animal. (String) | | basecolour | Color of the animal. (String) | | speciesname | Species of the animal. (String) | | animalage | Age of the animal. (Integer) | | sexname | Sex of the animal. (String) | | location | Location of the an...

Key indicators of broadband adoption, service and infrastructure in New York City. Data Limitations: Data accuracy is limited as of the date of publication and by the methodology and accuracy of the original sources. The City shall not be liable for any costs related to, or in reliance of, the data contained in these datasets.

Analysis of ‘Broadband Adoption and Computer Use by year, state, demographic characteristics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/720f8c4b-7a1c-415c-9297-55904ba24840 on 26 January 2022.

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

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad,

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

Key indicators of broadband adoption, service and infrastructure in New York City by Council District Data Limitations: Data accuracy is limited as of the date of publication and by the methodology and accuracy of the original sources. The City shall not be liable for any costs related to, or in reliance of, the data contained in these datasets.

Electronic prescribing (eRx) is a key component of the meaningful use of health IT to improve health care quality and lower costs. This dataset includes national and state eRx and health information exchange activity by community pharmacies and office-based health care providers active through the Surescripts Network. Surescripts is a health information network, and ONC procured electronic prescribing activity data conducted within its network from December 2008 through April 2014. The Surescripts network is used by the majority of all U.S. community pharmacies to rout prescriptions, excluding closed systems such as Kaiser Permanente. These include chain, franchise, and independently owned pharmacies. Data for annual percentages of new and renewal prescriptions routed through the Surescripts network exclude controlled substances. You may view more information about Surescripts, contact the company, and access more network data through the company's official site.

This survey provides nationally representative estimates on the characteristics, living arrangements, and service accessibility of noninstitutionalized children who were living apart from their parents (in foster care, grandparent care or other nonparental care) and who were aged 0 to 16 years in 2011-2012. Data on the well-being of the children and of their caregivers are also available. The children’s nonparental care status was identified in a previous SLAITS survey, the 2011-2012 National Survey of Children’s Health.

Units of Response: Caregiver

Type of Data: Survey

Tribal Data: No

COVID-19 Data: No

Periodicity: One-time

SORN: https://www.federalregister.gov/documents/2022/09/19/2022-20139/privacy-act-of-1974-system-of-records

Data Use Agreement: No

Data Use Agreement Location: Unavailable

Equity Indicators: Disability;Ethnicity;Household Income;Household Size;Housing Status;Race;Sex

Granularity: Household

Spatial: United States

Geocoding: Unavailable

Scenario data from the Electrification Futures Study Scenarios of Electric Technology Adoption and Power Consumption for the United States report. Annual projections from 2017 to 2050 of electric technology adoption and energy consumption for five scenarios reference electrification medium electrification high electrification electrification potential and low electricity growth. Each scenario assumes moderate technology advancement as described by Jadun et al. 2017 https//www.nrel.gov/docs/fy18osti/70485.pdf.

The dataset for this research project was meticulously constructed to investigate the adoption of ChatGPT among students in the United States. The primary objective was to gain insights into the technological barriers and resistances faced by students in integrating ChatGPT into their information systems. The dataset was designed to capture the diverse adoption patterns among students in various public and private schools and universities across the United States. By examining adoption rates, frequency of usage, and the contexts in which ChatGPT is employed, the research sought to provide a comprehensive understanding of how students are incorporating this technology into their information systems. Moreover, by including participants from diverse educational institutions, the research sought to ensure a comprehensive representation of the student population in the United States. This approach aimed to provide nuanced insights into how factors such as educational background, institution type, and technological familiarity influence ChatGPT adoption.

The percentage of households that have both Home Broadband and Mobile Broadband subscriptions for each of New York City Public Use Microdata Areas. Data Limitations: Data accuracy is limited as of the date of publication and by the methodology and accuracy of the original sources. The City shall not be liable for any costs related to, or in reliance of, the data contained in these datasets.

The table Counties is part of the dataset Electric School Bus (ESB) Adoption in the United States - May, 2022 ***, available at https://redivis.com/datasets/y29n-14cwxamcw. It contains 25410 rows across 6 variables.

Technological innovation market diffusion data.Principal dataset sources.----The Cross-country Historical Adoption of Technology (CHAT) Dataset. No. w15319. National Bureau of Economic Research, 2009. [67]Discovered via Horace Dediu, Clayton Christensen Institute. [68]Note: Only U.S. data was extracted and used.----Comin, D.A., & Hobijn, B. (2004). Cross-country technology adoption: making the theories face the facts. Journal of Monetary Economics 51.1 (2004): 39-83. [69]Discovered via Ritchie, H., & Roser, M. (2017). Technology Diffusion & Adoption. [70]Note: Only U.S. data was extracted and used.----Cox, W. M., & Alm, R. (1997). Time Well Spent: The Declining Real Cost of Living in America. Annual Report Federal Reserve Bank of Dallas, pages 2-24 [71]Derived and built from American Association of Home Appliance Manufacturers; Cellular Telephone Industry Association; Electrical Merchandising, various issues; Information Please Almanac; Public Roads Administration; Television Bureau of Advertising; U.S. Bureau of the Census (Census of Housing; Current Population Reports; Historical Statistics of the United States, Colonial Times to 1970; Statistical Abstract of the United States); U.S. Department of Energy; U.S. Department of Transportation.

The table State School Bus Fleets is part of the dataset Electric School Bus (ESB) Adoption in the United States - May, 2022 ***, available at https://redivis.com/datasets/y29n-14cwxamcw. It contains 52 rows across 5 variables.

The AI training dataset market is experiencing robust growth, driven by the increasing adoption of artificial intelligence across diverse sectors. The market's expansion is fueled by the urgent need for high-quality data to train sophisticated AI models capable of handling complex tasks. Key application areas, such as autonomous vehicles in the automotive industry, advanced medical diagnosis in healthcare, and personalized experiences in retail and e-commerce, are significantly contributing to this market's upward trajectory. The prevalence of text, image/video, and audio data types further diversifies the market, offering opportunities for specialized dataset providers. While the market faces challenges like data privacy concerns and the high cost of data annotation, the overall trajectory remains positive, with a projected Compound Annual Growth Rate (CAGR) exceeding 20% for the forecast period (2025-2033). This growth is further supported by advancements in deep learning techniques that demand increasingly larger and more diverse datasets for optimal performance. Leading companies like Google, Amazon, and Microsoft are actively investing in this space, expanding their dataset offerings and fostering competition within the market. Furthermore, the emergence of specialized data annotation providers caters to the specific needs of various industries, ensuring accurate and reliable data for AI model development. The geographic distribution of the market reveals strong presence in North America and Europe, driven by early adoption of AI technologies and the presence of major technology players. However, Asia Pacific is projected to witness significant growth in the coming years, propelled by increasing digitalization and a burgeoning AI ecosystem in countries like China and India. Government initiatives promoting AI development in various regions are also expected to stimulate demand for high-quality training datasets. While challenges related to data security and ethical considerations remain, the long-term outlook for the AI training dataset market is exceptionally promising, fueled by the continued evolution of artificial intelligence and its increasing integration into various aspects of modern life. The market segmentation by application and data type allows for granular analysis and targeted investments for businesses operating in this rapidly expanding sector.

NoSQL Database Market Outlook 2032

The global NoSQL database market size was USD 5.9 Billion in 2023 and is likely to reach USD 36.6 Billion by 2032, expanding at a CAGR of 30% during 2024–2032. The market growth is attributed to the rising adoption of NoSQL databases by industries to manage large amounts of data efficiently.



Increasing adoption of digital solutions by businesses is augmenting the NoSQL database industry. Businesses continue using the unique capabilities that NoSQL databases bring to their data management strategies. The NoSQL solutions work without any predefined schemas, thus, offering more flexibility to businesses that need to handle and manage ever-evolving data types and formats.

The factors behind the accelerating growth of the NoSQL database market include the omnipresence of internet-related activities, a surge in big data, and others. NoSQL database solutions present exceptional scalability and offer superior performance while managing extensive datasets. Moreover, the shift from conventional SQL databases to NoSQL databases to handle big-data and real-time web application data augmented the market.

Impact of Artificial Intelligence (AI) on the NoSQL Database Market

Artificial Intelligence (AI) has a significant impact on the NoSQL databases market by creating a surge in data volume and variety. AI technologies, including machine learning and deep learning, generate and process vast amounts of data, necessitating efficient data management solutions. The integration of AI with NoSQL databases further enhances data analysis capabilities and enables businesses to acquire valuable insights and make informed decisions. Therefore, the rise of AI technologies is propelling the market.

Non-Relational Databases, commonly referred to as NoSQL databases, have gained significant traction in recent years due to their ability to handle diverse data types and structures. Unlike traditional relational databases, non-relational databases do not rely on a fixed schema, which allows for greater flexibility and scalability. This adaptability is particularly beneficial for businesses dealing with large volumes of unstructured data, such as social media content, customer reviews, and multimedia files. As organizations continue to embrace digital transformation, the demand for non-relational databases is expected to rise, further driving the growth of the NoSQL database market.

<li style="margin-left: 8px; text-align: justi

Cover crops have critical significance for agroecosystem sustainability and have long been promoted in the U.S. Midwest. Knowledge of the variations of cover cropping and the impacts of government policies remains very limited. We developed an accurate and cost-effective approach utilizing multi-source satellite fusion data, environmental variables, and machine learning to quantify cover cropping in corn and soybean fields from 2000 to 2021 in the U.S. Midwest. We found that cover crop adoption in most counties has significantly increased in the recent 11 years from 2011 to 2021. The adoption percentage of 2021 is 3.3 times that of 2011, which was highly correlated to the increased funding for federal and state conservation programs. However, the percentage of cover crop adoption is still low (7.2%). The averaged county-level cover crop adoption rates in 2000-2010 and 2011-2021 are publicly available on Dryad.