withpi/analyze-paper-data-v01-formatted dataset hosted on Hugging Face and contributed by the HF Datasets community

The Data Analytics Market size was valued at USD 41.05 USD billion in 2023 and is projected to reach USD 222.39 USD billion by 2032, exhibiting a CAGR of 27.3 % during the forecast period. Data Analytics can be defined as the rigorous process of using tools and techniques within a computational framework to analyze various forms of data for the purpose of decision-making by the concerned organization. This is used in almost all fields such as health, money matters, product promotion, and transportation in order to manage businesses, foresee upcoming events, and improve customers’ satisfaction. Some of the principal forms of data analytics include descriptive, diagnostic, prognostic, as well as prescriptive analytics. Data gathering, data manipulation, analysis, and data representation are the major subtopics under this area. There are a lot of advantages of data analytics, and some of the most prominent include better decision making, productivity, and saving costs, as well as the identification of relationships and trends that people could be unaware of. The recent trends identified in the market include the use of AI and ML technologies and their applications, the use of big data, increased focus on real-time data processing, and concerns for data privacy. These developments are shaping and propelling the advancement and proliferation of data analysis functions and uses. Key drivers for this market are: Rising Demand for Edge Computing Likely to Boost Market Growth. Potential restraints include: Data Security Concerns to Impede the Market Progress . Notable trends are: Metadata-Driven Data Fabric Solutions to Expand Market Growth.

This graph presents the results of a survey, conducted by BARC in 2014/15, into the current and planned use of technology for the analysis of big data. At the beginning of 2015, 13 percent of respondents indicated that their company was already using a big data analytical appliance for big data.

We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).

Data Analytics Market Valuation – 2024-2031

Data Analytics Market was valued at USD 68.83 Billion in 2024 and is projected to reach USD 482.73 Billion by 2031, growing at a CAGR of 30.41% from 2024 to 2031.

Data Analytics Market Drivers

Data Explosion: The proliferation of digital devices and the internet has led to an exponential increase in data generation. Businesses are increasingly recognizing the value of harnessing this data to gain competitive insights.

Advancements in Technology: Advancements in data storage, processing power, and analytics tools have made it easier and more cost-effective for organizations to analyze large datasets.

Increased Business Demand: Businesses across various industries are seeking data-driven insights to improve decision-making, optimize operations, and enhance customer experiences.

Data Analytics Market Restraints

Data Quality and Integrity: Ensuring the accuracy, completeness, and consistency of data is crucial for effective analytics. Poor data quality can hinder insights and lead to erroneous conclusions.

Data Privacy and Security Concerns: As organizations collect and analyze sensitive data, concerns about data privacy and security are becoming increasingly important. Breaches can have significant financial and reputational consequences.

This contains the South American portion of the Hydrologic Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

withpi/analyze-paper-data-v01-formatted_preference dataset hosted on Hugging Face and contributed by the HF Datasets community

A suspect screening analysis method is presented to rapidly characterize chemicals in 100 consumer products -- whether they be formulations (shampoos, paints), articles (upholsteries, shower curtains), or foods (cereals) – and therefore supports broader efforts to prioritize chemicals based on potential human health risks. A two-dimensional gas chromatography-time of flight/mass spectrometry method was used to screen for chemicals in selected products. Analysis yielded 4270 unique chemical signatures across the products, with 1602 signatures tentatively identified using the National Institute of Standards and Technology 2008 spectral database. Chemical standards confirmed the presence of 119 compounds. Of the 1602 chemicals, 1404 were not present in a public database of known consumer product chemicals. This dataset is associated with the following publication: Phillips, K., A. Yau, K. Favela, K. Isaacs, A. McEachran, C. Grulke, A. Richard, A. Williams, J. Sobus, R. Thomas, and J. Wambaugh. Suspect Screening Analysis of Chemicals in Consumer Products. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 52(5): 3125-3135, (2018).

About this dataset

Context

The dataset tabulates the median household income in Long Beach. It can be utilized to understand the trend in median household income and to analyze the income distribution in Long Beach by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Long Beach, WA Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Long Beach, WA: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Long Beach, WA
• Long Beach, WA households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Long Beach median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Portsmouth. It can be utilized to understand the trend in median household income and to analyze the income distribution in Portsmouth by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Portsmouth, IA Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Portsmouth, IA: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Portsmouth, IA
• Portsmouth, IA households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Portsmouth median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Ashley. It can be utilized to understand the trend in median household income and to analyze the income distribution in Ashley by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Ashley, ND Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Ashley, ND: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Ashley, ND
• Ashley, ND households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Ashley median household income. You can refer the same here

Through application of a nearest-neighbor imputation approach, mapped estimates of forest carbon density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 8 raster maps: total forest carbon in all stocks, live tree aboveground forest carbon, live tree belowground forest carbon, forest down dead carbon, forest litter carbon, forest standing dead carbon, forest soil organic carbon, and forest understory carbon.�The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004�Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004

The Gene Expression Analysis Market is segmented by Technology (Polymerase Chain Reaction (PCR), Next Generation Sequencing (NGS), Microarrays, and Others), Product (Instruments, Reagents and Consumables, and Services), End-user (Drug Discovery, Diagnostic Laboratories, and Academic Research Centers), and Geography (North America, Europe, Asia-Pacific, Middle East & Africa, and South America). The report offers the value (in USD million) for the above segments.

Snapshot img

What is the Sentiment Analytics Software Market Size?

The sentiment analytics software market size is forecast to increase by USD 2.34 billion, at a CAGR of 16.6% between 2024 and 2029. The market is experiencing significant growth due to the increasing use of social media and the rising internet penetration in North America. Businesses are leveraging sentiment analysis to gain insights into customer opinions and feedback. A key trend in the market is the integration of generative AI to improve the accuracy and context-dependence of sentiment analysis. However, challenges such as context-dependent errors and the need for large amounts of data to train AI models persist. To stay competitive, market participants must focus on addressing these challenges and continuously improving the accuracy and reliability of their sentiment analysis solutions. This market analysis report provides an in-depth examination of the growth drivers, trends, and challenges shaping the sentiment analytics software market.

What will be the size of Market during the forecast period?

Request Free Sentiment Analytics Software Market Sample

Market Segmentation

The market report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments.

Deployment

  On-premises
  Cloud-based


End-user

  Retail
  BFSI
  Healthcare
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  South America



  Middle East and Africa

Which is the largest segment driving market growth?

The on-premises segment is estimated to witness significant growth during the forecast period. In the realm of data analysis, sentiment analytics software plays a pivotal role in understanding public perception toward brands, services, and entities. For organizations in the healthcare sector, reputation management is of utmost importance. Sentiment analytics software deployed on-premises offers several benefits. With on-premises deployment, organizations retain complete control over their data, ensuring privacy and compliance with healthcare regulations. This setup allows for customization to meet specific business needs and seamless integration with existing systems.

Get a glance at the market share of various regions. Download the PDF Sample

The on-premises segment was valued at USD 788.40 million in 2019. Furthermore, the use of dedicated infrastructure results in superior performance and faster processing times. Government institutions, media, telecom, and other industries also reap the benefits of on-premises sentiment analytics software. Data from surveys, social media, and other sources undergoes text analysis to uncover valuable insights. By staying informed of public sentiment, organizations can make data-driven decisions, respond to crises, and improve their offerings. Sentiment analysis is not limited to text data from surveys and social media. Media mentions and customer interactions through phone and email are also valuable sources of data. By harnessing the power of on-premises sentiment analytics software, organizations can gain a competitive edge and maintain a strong reputation.

Which region is leading the market?

For more insights on the market share of various regions, Request Free Sample

North America is estimated to contribute 38% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. In North America, sentiment analytics software has gained significant traction due to the region's high internet penetration and prioritization of enhancing customer experiences. By 2024, internet usage in North America reached nearly 97%, creating a solid base for the implementation of sentiment analysis tools. Companies in the US and Canada are investing heavily in advanced technologies to personalize customer interactions and improve overall satisfaction.

Further, Natural Language Processing (NLP) plays a crucial role in sentiment analysis, enabling businesses to understand and respond effectively to customer opinions. By staying attuned to customer sentiments, North American businesses can foster brand reputation, enhance customer satisfaction, and make data-driven decisions.

How do company ranking index and market positioning come to your aid?

Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.

Alphabet Inc.: The company offers sentiment analytics software that supports multiple languages and can be integrated into various applications for real-time analysis.

A file geodatabase of the Displacement Risk Index (raster) in support of the One Seattle Plan update Anti-Displacement Framework. See the data in action - click here for a web map.The One Seattle Plan, a major update of the City’s Comprehensive Plan, presents a vision for how Seattle will grow, and support community needs over the next 20 years and beyond. In this vision, Seattle welcomes newcomers, supports current residents and businesses to remain and thrive in place, and creates pathways for people who have been displaced to return to their communities.In support of the One Seattle Plan update, an Anti-Displacement Framework has been developed that provides context to help community members engage with the topic of displacement during our outreach for the draft Plan. It also responds to House Bill 1220, adopted by the Washington Legislature in 2021, requiring cities to evaluate displacement risk, identify its causes, and implement policies and strategies to address racial disparities and exclusion. As part of that evaluation, the Displacement Risk Index has been updated from the original 2016 index to a 2022 index which includes updated input data and methodological improvements. See the companion Appendix for more information.The original 2016 indices are described in the first Growth and Equity Analysis, which examined demographic, economic, and physical factors to evaluate the risk of displacement and access to opportunity for marginalized populations across Seattle neighborhoods.Displacement Risk IndexThe City’s Displacement Risk Index identifies areas of Seattle where displacement of people of color, low-income people, renters, and other populations susceptible to displacement may be more likely. It combines demographic, place-based, and market data to provide a longer-term view of displacement risk based on neighborhood characteristics like the presence of vulnerable populations and amenities that tend to increase real estate demand.The higher the pixel value, the higher displacement risk the pixel has.Versions: Compiled in 2016 and 2022For more information contact Nick Welch at the Office of Planning and Community Development, Nicolas.Welch@seattle.gov.

When studying the impacts of climate change, there is a tendency to select climate data from a small set of arbitrary time periods or climate windows (e.g., spring temperature). However, these arbitrary windows may not encompass the strongest periods of climatic sensitivity and may lead to erroneous biological interpretations. Therefore, there is a need to consider a wider range of climate windows to better predict the impacts of future climate change. We introduce the R package climwin that provides a number of methods to test the effect of different climate windows on a chosen response variable and compare these windows to identify potential climate signals. climwin extracts the relevant data for each possible climate window and uses this data to fit a statistical model, the structure of which is chosen by the user. Models are then compared using an information criteria approach. This allows users to determine how well each window explains variation in the response variable and compare model support between windows. climwin also contains methods to detect type I and II errors, which are often a problem with this type of exploratory analysis. This article presents the statistical framework and technical details behind the climwin package and demonstrates the applicability of the method with a number of worked examples.

The current worldwide refugee crisis is often referred to as the worst humanitarian crisis since World War II. Using Insights for ArcGIS, you'll look at data from 1951 to 2017 and find patterns in the global movement of refugees and asylum seekers.

First, you'll use link analysis to map the movement of refugees from their country of origin to their country of residence. Then, you'll create supplemental charts and tables and dig deeper into the data and the patterns that emerge over time.

In this lesson you will build skills in the these areas:

• Creating a link map
• Filtering data cards, tables, and charts
• Using link analysis to find patterns

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

CEOS Analysis Ready Data for Land (CARD4L) are satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets [1]. In this paper, key input data (e.g. aerosol optical depth, precipitable water, BRDF parameters) needed for atmospheric and BRDF corrections of Landsat data are identified and a sensitivity analysis is conducted using outputs of a physics based atmospheric and BRDF model. The results show that aerosol impacts more on the visible bands where the average variation of reflectance could reach 0.05 of reflectance unit. The variation over dark targets can be much higher so that it is a critical parameter for aquatic applications. By contrast, precipitable water (water vapor in the rest of the paper) only impacts the near-infrared (NIR) and shortwave (SWIR) bands and the extent of change is much smaller. BRDF parameters impact time series most on winter and summer images of highly anisotropic areas and when they are normalized to 45º solar angle. Different BRDF levels for different spectrum ranges not only impact the magnitude of reflectance, but also the signature for these areas. It seems that it is necessary to normalize surface BRDF to ensure time series consistency of the Landsat ARD product. Abstract presented at 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

View the web application here to explore the data.