Survey on the Use of Information and Communication Technologies and Electronic Commerce in Companies: Big Data Analysis. National.

Analysis of ‘Marine Phytoplankton Grouped by Size Class’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/532dcd3c-a7e2-45f8-adbc-99f04e8e1e34 on 27 January 2022.

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

This dataset contains phytoplankton samples collected from Puget Sound and analyzed using a particle imaging analyzer (FlowCAM®). For more information see the King County marine phytoplankton webpage.

This specific dataset summarizes phytoplankton abundance and biovolume data summarized by size classes. Additional datasets summarize abundance and biovolume data by:

• Sample By Size Class (each sample size class in one line)

• Sample by Generic Functional Group

• Sample by Specific Functional Group

• Sample by Taxonomic Group

For locator information, see the WLRD Water Quality Collection Sites dataset.

For corresponding water quality data matched by Grab ID see the Water Quality dataset.

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

The global clinical risk grouping solutions market is anticipated to reach USD XXX million by 2033, expanding at a CAGR of XX% from 2025 to 2033. The market's growth is influenced by factors such as the rising healthcare expenditure, growing awareness of patient safety and quality of care, and increasing adoption of health information technology (HIT) systems. The growing need to manage risk in healthcare settings to mitigate financial and legal liabilities is also driving the demand for clinical risk grouping solutions. Key trends within the market include the integration of artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) technologies into clinical risk grouping solutions. These advanced technologies enable more accurate risk identification, prediction, and stratification, leading to improved healthcare outcomes and cost savings. Additionally, the growing adoption of value-based healthcare models is promoting the adoption of clinical risk grouping solutions as they facilitate accurate risk adjustment and reimbursement. Geographic regions with developed healthcare infrastructure and high healthcare spending, such as North America and Europe, are expected to be major markets for clinical risk grouping solutions, while emerging markets in Asia-Pacific and the Middle East and Africa present significant growth opportunities.

About this dataset

Context

The dataset tabulates the population of Marked Tree by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Marked Tree. The dataset can be utilized to understand the population distribution of Marked Tree by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Marked Tree. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Marked Tree.

Key observations

Largest age group (population): Male # 55-59 years (139) | Female # 0-4 years (153). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Marked Tree population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Marked Tree is shown in the following column.
• Population (Female): The female population in the Marked Tree is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Marked Tree for each age group.

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 Marked Tree Population by Gender. You can refer the same here

aPolymorphism information content.

Analysis of ‘Thematic grouping of services to the citizen’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-analisi-transparenciacatalunya-cat-api-views-wajj-p75b on 07 January 2022.

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

Llistat de temes o assumptes en que es poden agrupar els serveis que s'ofereixen al ciutadà.

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

Clinical Risk Grouping Solutions Market Outlook

According to our latest research, the global Clinical Risk Grouping Solutions market size reached USD 1.84 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.8% expected through the forecast period. By 2033, the market is projected to attain a value of USD 5.53 billion, reflecting the increasing adoption of advanced healthcare analytics and risk stratification tools worldwide. The primary growth drivers for this market include the rising need for population health management, the surge in value-based care initiatives, and the escalating demand for efficient claims management and medical risk assessment solutions across healthcare ecosystems.

The growth trajectory of the Clinical Risk Grouping Solutions market is significantly influenced by the healthcare industry’s shift towards data-driven decision-making. As healthcare providers and payers increasingly focus on optimizing resource allocation and improving patient outcomes, the demand for sophisticated risk grouping and stratification solutions has intensified. The proliferation of electronic health records (EHRs) and the integration of big data analytics in healthcare have enabled organizations to identify at-risk populations more accurately, manage chronic conditions proactively, and reduce preventable hospitalizations. Additionally, the mounting pressure to control healthcare costs and adhere to regulatory requirements has further accelerated the adoption of these solutions, particularly in developed economies.

Another major growth catalyst is the ongoing implementation of value-based care models, which emphasize quality and cost-effectiveness in healthcare delivery. Clinical risk grouping solutions play a pivotal role in these models by enabling healthcare providers and payers to stratify patient populations based on risk profiles, predict future healthcare utilization, and design targeted intervention programs. This capability not only improves patient care but also enhances financial performance by reducing unnecessary expenditures and optimizing reimbursement processes. As more governments and private insurers worldwide embrace value-based care, the market for clinical risk grouping solutions is expected to witness sustained expansion.

Technological advancements in machine learning, artificial intelligence, and cloud computing are also contributing to the market’s upward trajectory. Modern clinical risk grouping platforms leverage advanced algorithms to process vast amounts of patient data, uncover hidden patterns, and deliver actionable insights in real-time. The growing adoption of cloud-based solutions, in particular, has democratized access to these sophisticated tools, allowing even small and medium-sized healthcare organizations to benefit from scalable, cost-effective risk management capabilities. As interoperability standards improve and healthcare data becomes more accessible, the potential for innovation and market growth will only increase.

From a regional perspective, North America continues to dominate the Clinical Risk Grouping Solutions market, accounting for the largest share in 2024, driven by a mature healthcare IT infrastructure, supportive regulatory frameworks, and the presence of leading market players. Europe follows closely, benefitting from extensive government initiatives to digitize healthcare and promote integrated care models. The Asia Pacific region is emerging as a key growth frontier, fueled by rapid healthcare modernization, increasing investments in health IT, and a rising burden of chronic diseases. Latin America and the Middle East & Africa, while still nascent markets, are beginning to recognize the value of clinical risk grouping solutions as they strive to enhance healthcare quality and efficiency.

Component Analysis

The Clinical Risk Grouping Solutions market is segmented by component into Software and Services, each playing a dis

About this dataset

Context

The dataset tabulates the population of Orchard by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Orchard. The dataset can be utilized to understand the population distribution of Orchard by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Orchard. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Orchard.

Key observations

Largest age group (population): Male # 40-44 years (9) | Female # 10-14 years (10). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Orchard population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Orchard is shown in the following column.
• Population (Female): The female population in the Orchard is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Orchard for each age group.

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 Orchard Population by Gender. You can refer the same here

The Swatch Group AG Company Profile, Opportunities, Challenges and Risk (SWOT, PESTLE and Value Chain); Corporate and ESG Strategies; Competitive Intelligence; Financial KPI’s; Operational KPI’s; Recent Trends: “ Read More

The Orphanages & Group Homes industry in Idaho is expected to grow an annualized x.x% to $x.x million over the five years to 2025, while the national industry will likely decline at -x% during the same period. Industry establishments increased an annualized x.x% to xx locations. Industry employment has increased an annualized x.x% to xxx workers, while industry wages have increased an annualized x.x% to $x.x million.

Vector Group Ltd Company Profile, Opportunities, Challenges and Risk (SWOT, PESTLE and Value Chain); Corporate and ESG Strategies; Competitive Intelligence; Financial KPI’s; Operational KPI’s; Recent Trends: “ Read More

Only the most enriched terms are shown, while the significantly enriched terms (p-value

Mean confidence (SD) split by sex (N female = 90; N male = 55) and topic with statistical analysis of between-gender differences.

The Cigna Group Company Profile, Opportunities, Challenges and Risk (SWOT, PESTLE and Value Chain); Corporate and ESG Strategies; Competitive Intelligence; Financial KPI’s; Operational KPI’s; Recent Trends: “ Read More

China Resources Gas Group Ltd Company Profile, Opportunities, Challenges and Risk (SWOT, PESTLE and Value Chain); Corporate and ESG Strategies; Competitive Intelligence; Financial KPI’s; Operational KPI’s; Recent Trends: “ Read More

About this dataset

Context

The dataset tabulates the population of Orchard Park town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Orchard Park town. The dataset can be utilized to understand the population distribution of Orchard Park town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Orchard Park town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Orchard Park town.

Key observations

Largest age group (population): Male # 5-9 years (1,154) | Female # 60-64 years (1,253). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Orchard Park town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Orchard Park town is shown in the following column.
• Population (Female): The female population in the Orchard Park town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Orchard Park town for each age group.

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 Orchard Park town Population by Gender. You can refer the same here

Grouping analysis of FIQ document heterogeneity.

About this dataset

Context

The dataset tabulates the population of Pyatt by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Pyatt. The dataset can be utilized to understand the population distribution of Pyatt by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Pyatt. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Pyatt.

Key observations

Largest age group (population): Male # 25-29 years (16) | Female # 0-4 years (10). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Pyatt population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Pyatt is shown in the following column.
• Population (Female): The female population in the Pyatt is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Pyatt for each age group.

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 Pyatt Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Strong by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Strong. The dataset can be utilized to understand the population distribution of Strong by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Strong. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Strong.

Key observations

Largest age group (population): Male # 60-64 years (68) | Female # 80-84 years (32). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Strong population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Strong is shown in the following column.
• Population (Female): The female population in the Strong is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Strong for each age group.

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 Strong Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Mountain View by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Mountain View. The dataset can be utilized to understand the population distribution of Mountain View by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Mountain View. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Mountain View.

Key observations

Largest age group (population): Male # 35-39 years (40) | Female # 30-34 years (32). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Mountain View population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Mountain View is shown in the following column.
• Population (Female): The female population in the Mountain View is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Mountain View for each age group.

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 Mountain View Population by Gender. You can refer the same here