This document, Innovating the Data Ecosystem: An Update of The Federal Big Data Research and Development Strategic Plan, updates the 2016 Federal Big Data Research and Development Strategic Plan. This plan updates the vision and strategies on the research and development needs for big data laid out in the 2016 Strategic Plan through the six strategies areas (enhance the reusability and integrity of data; enable innovative, user-driven data science; develop and enhance the robustness of the federated ecosystem; prioritize privacy, ethics, and security; develop necessary expertise and diverse talent; and enhance U.S. leadership in the international context) to enhance data value and reusability and responsiveness to federal policies on data sharing and management.

The Data Mining Tools Market size was valued at USD 1.01 USD billion in 2023 and is projected to reach USD 1.99 USD billion by 2032, exhibiting a CAGR of 10.2 % during the forecast period. The growing adoption of data-driven decision-making and the increasing need for business intelligence are major factors driving market growth. Data mining refers to filtering, sorting, and classifying data from larger datasets to reveal subtle patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. Data mining software tools and techniques allow organizations to foresee future market trends and make business-critical decisions at crucial times. Data mining is an essential component of data science that employs advanced data analytics to derive insightful information from large volumes of data. Businesses rely heavily on data mining to undertake analytics initiatives in the organizational setup. The analyzed data sourced from data mining is used for varied analytics and business intelligence (BI) applications, which consider real-time data analysis along with some historical pieces of information. Recent developments include: May 2023 – WiMi Hologram Cloud Inc. introduced a new data interaction system developed by combining neural network technology and data mining. Using real-time interaction, the system can offer reliable and safe information transmission., May 2023 – U.S. Data Mining Group, Inc., operating in bitcoin mining site, announced a hosting contract to deploy 150,000 bitcoins in partnership with major companies such as TeslaWatt, Sphere 3D, Marathon Digital, and more. The company is offering industry turn-key solutions for curtailment, accounting, and customer relations., April 2023 – Artificial intelligence and single-cell biotech analytics firm, One Biosciences, launched a single cell data mining algorithm called ‘MAYA’. The algorithm is for cancer patients to detect therapeutic vulnerabilities., May 2022 – Europe-based Solarisbank, a banking-as-a-service provider, announced its partnership with Snowflake to boost its cloud data strategy. Using the advanced cloud infrastructure, the company can enhance data mining efficiency and strengthen its banking position.. Key drivers for this market are: Increasing Focus on Customer Satisfaction to Drive Market Growth. Potential restraints include: Requirement of Skilled Technical Resources Likely to Hamper Market Growth. Notable trends are: Incorporation of Data Mining and Machine Learning Solutions to Propel Market Growth.

DECOVID, a multi-centre research consortium, was founded in March 2020 by two United Kingdom (UK) National Health Service (NHS) Foundation Trusts (comprising three acute care hospitals) and three research institutes/universities: University Hospitals Birmingham (UHB), University College London Hospitals (UCLH), University of Birmingham, University College London and The Alan Turing Institute. The original aim of DECOVID was to share harmonised electronic health record (EHR) data from UCLH and UHB to enable researchers affiliated with the DECOVID consortium to answer clinical questions to support the COVID-19 response.   ​​   ​​The DECOVID database has now been placed within the infrastructure of PIONEER, a Health Data Research (HDR) UK funded data hub that contains data from acute care providers, to make the DECOVID database accessible to external researchers not affiliated with the DECOVID consortium.  

This highly granular dataset contains 256,804 spells and 165,414 hospitalised patients. The data includes demographics, serial physiological measurements, laboratory test results, medications, procedures, drugs, mortality and readmission.

Geography: UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UCLH provides first-class acute and specialist services in six hospitals in central London, seeing more than 1 million outpatient and 100,000 admissions per year. Both UHB and UCLH have fully electronic health records. Data has been harmonised using the OMOP data model. Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

The Check-In Dataset is the second public-use dataset in the Dunham's Data series, a unique data collection created by Kate Elswit (Royal Central School of Speech and Drama, University of London) and Harmony Bench (The Ohio State University) to explore questions and problems that make the analysis and visualization of data meaningful for dance history through the case study of choreographer Katherine Dunham. The Check-In Dataset accounts for the comings and goings of Dunham's nearly 200 dancers, drummers, and singers and discerns who among them were working in the studio and theatre together over the years from 1937 to 1962. As with the Everyday Itinerary Dataset, the first public-use dataset from Dunham's Data, data on check-ins come from scattered sources. Due to information available, it has a greater level of ambiguity as many dates are approximated in order to achieve accurate chronological sequence. By showing who shared time and space together, the Check-In Dataset can be used to trace potential lines of transmission of embodied knowledge within and beyond the Dunham Company. Dunham's Data: Digital Methods for Dance Historical Inquiry is funded by the United Kingdom Arts and Humanities Research Council (AHRC AH/R012989/1, 2018-2022) and is part of a larger suite of ongoing digital collaborations by Bench and Elswit, Movement on the Move. The Dunham's Data team also includes digital humanities postdoctoral research assistant Antonio Jiménez-Mavillard and dance history postdoctoral research assistants Takiyah Nur Amin and Tia-Monique Uzor. For more information about Dunham's Data, please see the Dunham's Data website. Also, visit the Dunham's Data research blog to view the interactive visualizations based on the Dunham's Data.

About this dataset

Context

The dataset tabulates the New Market population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for New Market. The dataset can be utilized to understand the population distribution of New Market by age. For example, using this dataset, we can identify the largest age group in New Market.

Key observations

The largest age group in New Market, IN was for the group of age 70 to 74 years years with a population of 80 (14.71%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in New Market, IN was the Under 5 years years with a population of 7 (1.29%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the New Market is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of New Market total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Market Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Reeds Spring population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Reeds Spring. The dataset can be utilized to understand the population distribution of Reeds Spring by age. For example, using this dataset, we can identify the largest age group in Reeds Spring.

Key observations

The largest age group in Reeds Spring, MO was for the group of age 10-14 years with a population of 115 (9.85%), according to the 2021 American Community Survey. At the same time, the smallest age group in Reeds Spring, MO was the 80-84 years with a population of 8 (0.68%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Reeds Spring is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Reeds Spring total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Reeds Spring Population by Age. You can refer the same here

Collection of samples and data across the following diseases: Fit and well

IPLoRD – Intellectual Property Longitudinal Research Dataset – is a publicly available dataset that provides researchers with an applicant activity-centric view of Australian IP right filings. IPLoRD leverages IPGOD data to provide a history of an applicant’s filings over time broken down by financial year. Where available,\r the ABN of the applicant is provided to allow linkage with other government research datasets.IPLoRD makes it possible for economists and other researchers to examine relationships between the filer’s economic circumstances and their intellectual property portfolio. IPLoRD is a biennial release. This year’s version of IPLoRD provides coverage of filing activities from 1904 to the end of 2020.
\r \r To increase accessibility, IP Australia has provided IPLORD as a full-size single file, and as smaller files partitioned as the below time periods:
\r \r * 1903 to 1990\r * 1991 to 1995\r * 1996 to 2001\r * 2001 to 2005\r * 2006 to 2010\r * 2011 to 2016\r * 2016 to 2020\r

Financial Data Services Market size was valued at USD 23.3 Billion in 2023 and is projected to reach USD 42.6 Billion by 2031, growing at a CAGR of 8.1% during the forecast period 2024-2031.

Global Financial Data Services Market Drivers

The market drivers for the Financial Data Services Market can be influenced by various factors. These may include:

The need for real-time analytics is growing: Real-time analytics are becoming more and more necessary in the financial sector due to the acceleration of data consumption. To reduce risks, make wise decisions, and enhance customer service, organizations need quick insights. Stakeholders are giving priority to solutions that enable quick data processing and analysis due to the increase in market volatility and complexity. The need for sophisticated analytical skills is driving providers of financial data services to modernize their products. As companies come to realize that using real-time data is crucial for keeping a competitive edge in a fast-paced financial climate, the competition among them to provide timely insights also boosts market growth.

Growing Machine Learning and AI Adoption: Data analysis has been profoundly changed by the incorporation of AI and machine learning technology into financial data services. By enabling predictive analytics, these technologies help financial organizations make better decisions and reduce risk. Businesses can find trends that were previously invisible by automating data processing operations. This leads to more precise forecasts and improved investment plans. Furthermore, sophisticated algorithms are flexible enough to adjust to shifting circumstances, keeping organizations flexible. The increasing intricacy of financial markets necessitates the use of AI and machine learning, which in turn drives demand for sophisticated financial data services and promotes innovation in the sector.

Global Financial Data Services Market Restraints

Several factors can act as restraints or challenges for the Financial Data Services Market. These may include:

Difficulties in Regulatory Compliance: Regulations controlling data management, privacy, and financial transactions place heavy restrictions on the financial data services market. Regulations like the GDPR, CCPA, and banking industry standards like Basel III and SOX must all be complied with by organizations. Complying with these requirements frequently necessitates a significant investment in staff and compliance systems, which can be taxing, especially for smaller businesses. Regulations are dynamic, and different locations have different needs, which adds to the complexity and expense. Noncompliance not only results in monetary fines but also has the potential to harm an entity's image, so impeding market expansion.

Dangers to Data Security: Threats to data security are a major impediment to the financial data services market. Because they manage sensitive data, financial institutions are often the targets of cyberattacks. Breach can lead to significant monetary losses, legal repercussions, and long-term harm to one's image. Although they can greatly increase operating expenses, investments in strong security measures like encryption, safe access protocols, and continual monitoring are crucial. Moreover, the dynamic strategies employed by cybercriminals need continuous adjustment, placing a burden on resources and detracting from the main operations of businesses. The evolution of security threats poses a challenge to preserving consumer trust, hence impeding industry expansion.

Themes and clusters that emerged from the analysis.

Overview. The Post-Acute Care Supplement (PACS) files were developed from two administrative data sets that capture assessments at multiple points in post-acute care: the Outcome and Assessment Information Set (OASIS) Summary File (OASF): 2000-2010, which covers home health care, and the Long-Term Care Annual Summary File (LTCASF): 2000-2010, which covers nursing homes. Annual files were developed for the period 2000 to 2010 that summarize health conditions, functional status, and care patterns into one observation per beneficiary per year. Data Access. These data are not available from ICPSR. Because the data contain confidential CMS assessment data, researchers will need to secure a data use agreement from CMS. For researchers who secure a DUA, the files will be distributed to them through the Medicare/Medicaid Research Information Center (MedRIC) (medric@acumenllc.com).

2011 Census data for England and Wales, linked to Mortality Data, Hospital Episode Statistics (HES) data, and GP Extraction Service (GPES) data for Pandemic Planning and Research Data.

About this dataset

Context

The dataset tabulates the data for the Des Arc, AR population pyramid, which represents the Des Arc population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Des Arc, AR, is 15.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Des Arc, AR, is 39.2.
• Total dependency ratio for Des Arc, AR is 54.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Des Arc, AR is 2.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Des Arc population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Des Arc for the selected age group is shown in the following column.
• Population (Female): The female population in the Des Arc for the selected age group is shown in the following column.
• Total Population: The total population of the Des Arc for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Des Arc Population by Age. You can refer the same here

PLS-SEM path coefficients–study 1 (survey data, n = 198).

About this dataset

Context

The dataset tabulates the Standard population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Standard. The dataset can be utilized to understand the population distribution of Standard by age. For example, using this dataset, we can identify the largest age group in Standard.

Key observations

The largest age group in Standard, IL was for the group of age 85+ years with a population of 27 (9.68%), according to the 2021 American Community Survey. At the same time, the smallest age group in Standard, IL was the 70-74 years with a population of 1 (0.36%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Standard is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Standard total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Standard Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Red Cross population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Red Cross. The dataset can be utilized to understand the population distribution of Red Cross by age. For example, using this dataset, we can identify the largest age group in Red Cross.

Key observations

The largest age group in Red Cross, NC was for the group of age 70-74 years with a population of 72 (10.50%), according to the 2021 American Community Survey. At the same time, the smallest age group in Red Cross, NC was the 5-9 years with a population of 10 (1.46%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Red Cross is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Red Cross total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Red Cross Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Duncan population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Duncan. The dataset can be utilized to understand the population distribution of Duncan by age. For example, using this dataset, we can identify the largest age group in Duncan.

Key observations

The largest age group in Duncan, MS was for the group of age 30-34 years with a population of 56 (14.97%), according to the 2021 American Community Survey. At the same time, the smallest age group in Duncan, MS was the 85+ years with a population of 0 (0.00%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Duncan is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Duncan total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Duncan Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Fargo population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Fargo. The dataset can be utilized to understand the population distribution of Fargo by age. For example, using this dataset, we can identify the largest age group in Fargo.

Key observations

The largest age group in Fargo, ND was for the group of age 20-24 years with a population of 15,788 (12.63%), according to the 2021 American Community Survey. At the same time, the smallest age group in Fargo, ND was the 80-84 years with a population of 1,910 (1.53%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Fargo is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Fargo total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Fargo Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Garfield Heights population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Garfield Heights. The dataset can be utilized to understand the population distribution of Garfield Heights by age. For example, using this dataset, we can identify the largest age group in Garfield Heights.

Key observations

The largest age group in Garfield Heights, OH was for the group of age 60 to 64 years years with a population of 2,534 (8.63%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Garfield Heights, OH was the 80 to 84 years years with a population of 508 (1.73%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Garfield Heights is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Garfield Heights total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Garfield Heights Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Mill Creek population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Mill Creek. The dataset can be utilized to understand the population distribution of Mill Creek by age. For example, using this dataset, we can identify the largest age group in Mill Creek.

Key observations

The largest age group in Mill Creek, PA was for the group of age 60 to 64 years years with a population of 32 (11.03%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Mill Creek, PA was the 75 to 79 years years with a population of 5 (1.72%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Mill Creek is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Mill Creek total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mill Creek Population by Age. You can refer the same here