Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.

Big Data Analysis Platform Market Outlook

The global market size for Big Data Analysis Platforms is projected to grow from USD 35.5 billion in 2023 to an impressive USD 110.7 billion by 2032, reflecting a CAGR of 13.5%. This substantial growth can be attributed to the increasing adoption of data-driven decision-making processes across various industries, the rapid proliferation of IoT devices, and the ever-growing volumes of data generated globally.

One of the primary growth factors for the Big Data Analysis Platform market is the escalating need for businesses to derive actionable insights from complex and voluminous datasets. With the advent of technologies such as artificial intelligence and machine learning, organizations are increasingly leveraging big data analytics to enhance their operational efficiency, customer experience, and competitiveness. The ability to process vast amounts of data quickly and accurately is proving to be a game-changer, enabling businesses to make more informed decisions, predict market trends, and optimize their supply chains.

Another significant driver is the rise of digital transformation initiatives across various sectors. Companies are increasingly adopting digital technologies to improve their business processes and meet changing customer expectations. Big Data Analysis Platforms are central to these initiatives, providing the necessary tools to analyze and interpret data from diverse sources, including social media, customer transactions, and sensor data. This trend is particularly pronounced in sectors such as retail, healthcare, and BFSI (banking, financial services, and insurance), where data analytics is crucial for personalizing customer experiences, managing risks, and improving operational efficiencies.

Moreover, the growing adoption of cloud computing is significantly influencing the market. Cloud-based Big Data Analysis Platforms offer several advantages over traditional on-premises solutions, including scalability, flexibility, and cost-effectiveness. Businesses of all sizes are increasingly turning to cloud-based analytics solutions to handle their data processing needs. The ability to scale up or down based on demand, coupled with reduced infrastructure costs, makes cloud-based solutions particularly appealing to small and medium-sized enterprises (SMEs) that may not have the resources to invest in extensive on-premises infrastructure.

Data Science and Machine-Learning Platforms play a pivotal role in the evolution of Big Data Analysis Platforms. These platforms provide the necessary tools and frameworks for processing and analyzing vast datasets, enabling organizations to uncover hidden patterns and insights. By integrating data science techniques with machine learning algorithms, businesses can automate the analysis process, leading to more accurate predictions and efficient decision-making. This integration is particularly beneficial in sectors such as finance and healthcare, where the ability to quickly analyze complex data can lead to significant competitive advantages. As the demand for data-driven insights continues to grow, the role of data science and machine-learning platforms in enhancing big data analytics capabilities is becoming increasingly critical.

From a regional perspective, North America currently holds the largest market share, driven by the presence of major technology companies, high adoption rates of advanced technologies, and substantial investments in data analytics infrastructure. Europe and the Asia Pacific regions are also experiencing significant growth, fueled by increasing digitalization efforts and the rising importance of data analytics in business strategy. The Asia Pacific region, in particular, is expected to witness the highest CAGR during the forecast period, propelled by rapid economic growth, a burgeoning middle class, and increasing internet and smartphone penetration.

Component Analysis

The Big Data Analysis Platform market can be broadly categorized into three components: Software, Hardware, and Services. The software segment includes analytics software, data management software, and visualization tools, which are crucial for analyzing and interpreting large datasets. This segment is expected to dominate the market due to the continuous advancements in analytics software and the increasing need for sophisticated data analysis tools. Analytics software enables organizations to process and analyze data from multiple sources,

Supplementary material and data for Pfohl and Cole-Lewis et al., "A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models" (2024).

We include the sets of adversarial questions for each of the seven EquityMedQA datasets (OMAQ, EHAI, FBRT-Manual, FBRT-LLM, TRINDS, CC-Manual, and CC-LLM), the three other non-EquityMedQA datasets used in this work (HealthSearchQA, Mixed MMQA-OMAQ, and Omiye et al.), as well as the data generated as a part of the empirical study, including the generated model outputs (Med-PaLM 2 [1] primarily, with Med-PaLM [2] answers for pairwise analyses) and ratings from human annotators (physicians, health equity experts, and consumers). See the paper for details on all datasets.

We include other datasets evaluated in this work: HealthSearchQA [2], Mixed MMQA-OMAQ, and Omiye et al [3].

• Mixed MMQA-OMAQ is composed of the 140 question subset of MultiMedQA questions described in [1,2] with an additional 100 questions from OMAQ (described below). The 140 MultiMedQA questions are composed of 100 from HealthSearchQA, 20 from LiveQA [4], and 20 from MedicationQA [5]. In the data presented here, we do not reproduce the text of the questions from LiveQA and MedicationQA. For LiveQA, we instead use identifier that correspond to those presented in the original dataset. For MedicationQA, we designate "MedicationQA_N" to refer to the N-th row of MedicationQA (0-indexed).

A limited number of data elements described in the paper are not included here. The following elements are excluded:

1. The reference answers written by physicians to HealthSearchQA questions, introduced in [2], and the set of corresponding pairwise ratings. This accounts for 2,122 rated instances.

2. The free-text comments written by raters during the ratings process.

3. Demographic information associated with the consumer raters (only age group information is included).

References

1. Singhal, K., et al. Towards expert-level medical question answering with large language models. arXiv preprint arXiv:2305.09617 (2023).

2. Singhal, K., Azizi, S., Tu, T. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). https://doi.org/10.1038/s41586-023-06291-2

3. Omiye, J.A., Lester, J.C., Spichak, S. et al. Large language models propagate race-based medicine. npj Digit. Med. 6, 195 (2023). https://doi.org/10.1038/s41746-023-00939-z

4. Abacha, Asma Ben, et al. "Overview of the medical question answering task at TREC 2017 LiveQA." TREC. 2017.

5. Abacha, Asma Ben, et al. "Bridging the gap between consumers’ medication questions and trusted answers." MEDINFO 2019: Health and Wellbeing e-Networks for All. IOS Press, 2019. 25-29.

Description of files and sheets

1. Independent Ratings [ratings_independent.csv]: Contains ratings of the presence of bias and its dimensions in Med-PaLM 2 outputs using the independent assessment rubric for each of the datasets studied. The primary response regarding the presence of bias is encoded in the column bias_presence with three possible values (No bias, Minor bias, Severe bias). Binary assessments of the dimensions of bias are encoded in separate columns (e.g., inaccuracy_for_some_axes). Instances for the Mixed MMQA-OMAQ dataset are triple-rated for each rater group; other datasets are single-rated. Instances were missing for five instances in MMQA-OMAQ and two instances in CC-Manual. This file contains 7,519 rated instances.

2. Paired Ratings [ratings_pairwise.csv]: Contains comparisons of the presence or degree of bias and its dimensions in Med-PaLM and Med-PaLM 2 outputs for each of the datasets studied. Pairwise responses are encoded in terms of two binary columns corresponding to which of the answers was judged to contain a greater degree of bias (e.g., Med-PaLM-2_answer_more_bias). Dimensions of bias are encoded in the same way as for ratings_independent.csv. Instances for the Mixed MMQA-OMAQ dataset are triple-rated for each rater group; other datasets are single-rated. Four ratings were missing (one for EHAI, two for FRT-Manual, one for FBRT-LLM). This file contains 6,446 rated instances.

3. Counterfactual Paired Ratings [ratings_counterfactual.csv]: Contains ratings under the counterfactual rubric for pairs of questions defined in the CC-Manual and CC-LLM datasets. Contains a binary assessment of the presence of bias (bias_presence), columns for each dimension of bias, and categorical columns corresponding to other elements of the rubric (ideal_answers_diff, how_answers_diff). Instances for the CC-Manual dataset are triple-rated, instances for CC-LLM are single-rated. Due to a data processing error, we removed questions that refer to `Natal'' from the analysis of the counterfactual rubric on the CC-Manual dataset. This affects three questions (corresponding to 21 pairs) derived from one seed question based on the TRINDS dataset. This file contains 1,012 rated instances.

4. Open-ended Medical Adversarial Queries (OMAQ) [equitymedqa_omaq.csv]: Contains questions that compose the OMAQ dataset. The OMAQ dataset was first described in [1].

5. Equity in Health AI (EHAI) [equitymedqa_ehai.csv]: Contains questions that compose the EHAI dataset.

6. Failure-Based Red Teaming - Manual (FBRT-Manual) [equitymedqa_fbrt_manual.csv]: Contains questions that compose the FBRT-Manual dataset.

7. Failure-Based Red Teaming - LLM (FBRT-LLM); full [equitymedqa_fbrt_llm.csv]: Contains questions that compose the extended FBRT-LLM dataset.

8. Failure-Based Red Teaming - LLM (FBRT-LLM) [equitymedqa_fbrt_llm_661_sampled.csv]: Contains questions that compose the sampled FBRT-LLM dataset used in the empirical study.

9. TRopical and INfectious DiseaseS (TRINDS) [equitymedqa_trinds.csv]: Contains questions that compose the TRINDS dataset.

10. Counterfactual Context - Manual (CC-Manual) [equitymedqa_cc_manual.csv]: Contains pairs of questions that compose the CC-Manual dataset.

11. Counterfactual Context - LLM (CC-LLM) [equitymedqa_cc_llm.csv]: Contains pairs of questions that compose the CC-LLM dataset.

12. HealthSearchQA [other_datasets_healthsearchqa.csv]: Contains questions sampled from the HealthSearchQA dataset [1,2].

13. Mixed MMQA-OMAQ [other_datasets_mixed_mmqa_omaq]: Contains questions that compose the Mixed MMQA-OMAQ dataset.

14. Omiye et al. [other datasets_omiye_et_al]: Contains questions proposed in Omiye et al. [3].

Version history

Version 2: Updated to include ratings and generated model outputs. Dataset files were updated to include unique ids associated with each question. Version 1: Contained datasets of questions without ratings. Consistent with v1 available as a preprint on Arxiv (https://arxiv.org/abs/2403.12025)

WARNING: These datasets contain adversarial questions designed specifically to probe biases in AI systems. They can include human-written and model-generated language and content that may be inaccurate, misleading, biased, disturbing, sensitive, or offensive.

NOTE: the content of this research repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.

To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.

For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.

Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.

During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.

With this comprehensive dataset, the project aims to explore various neural network models and achieve optimal results in Alzheimer's disease detection and analysis.

Acknowledgments: “Data were provided 1-12 by OASIS-1: Cross-Sectional: Principal Investigators: D. Marcus, R, Buckner, J, Csernansky J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382”

Citation: OASIS-1: Cross-Sectional: https://doi.org/10.1162/jocn.2007.19.9.1498

If you are looking for processed NifTi image version of this dataset please click here

Data Cleaning Tools Market Outlook

As of 2023, the global market size for data cleaning tools is estimated at $2.5 billion, with projections indicating that it will reach approximately $7.1 billion by 2032, reflecting a robust CAGR of 12.1% during the forecast period. This growth is primarily driven by the increasing importance of data quality in business intelligence and analytics workflows across various industries.

The growth of the data cleaning tools market can be attributed to several critical factors. Firstly, the exponential increase in data generation across industries necessitates efficient tools to manage data quality. Poor data quality can result in significant financial losses, inefficient business processes, and faulty decision-making. Organizations recognize the value of clean, accurate data in driving business insights and operational efficiency, thereby propelling the adoption of data cleaning tools. Additionally, regulatory requirements and compliance standards also push companies to maintain high data quality standards, further driving market growth.

Another significant growth factor is the rising adoption of AI and machine learning technologies. These advanced technologies rely heavily on high-quality data to deliver accurate results. Data cleaning tools play a crucial role in preparing datasets for AI and machine learning models, ensuring that the data is free from errors, inconsistencies, and redundancies. This surge in the use of AI and machine learning across various sectors like healthcare, finance, and retail is driving the demand for efficient data cleaning solutions.

The proliferation of big data analytics is another critical factor contributing to market growth. Big data analytics enables organizations to uncover hidden patterns, correlations, and insights from large datasets. However, the effectiveness of big data analytics is contingent upon the quality of the data being analyzed. Data cleaning tools help in sanitizing large datasets, making them suitable for analysis and thus enhancing the accuracy and reliability of analytics outcomes. This trend is expected to continue, fueling the demand for data cleaning tools.

In terms of regional growth, North America holds a dominant position in the data cleaning tools market. The region's strong technological infrastructure, coupled with the presence of major market players and a high adoption rate of advanced data management solutions, contributes to its leadership. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid digitization of businesses, increasing investments in IT infrastructure, and a growing focus on data-driven decision-making are key factors driving the market in this region.

As organizations strive to maintain high data quality standards, the role of an Email List Cleaning Service becomes increasingly vital. These services ensure that email databases are free from invalid addresses, duplicates, and outdated information, thereby enhancing the effectiveness of marketing campaigns and communications. By leveraging sophisticated algorithms and validation techniques, email list cleaning services help businesses improve their email deliverability rates and reduce the risk of being flagged as spam. This not only optimizes marketing efforts but also protects the reputation of the sender. As a result, the demand for such services is expected to grow alongside the broader data cleaning tools market, as companies recognize the importance of maintaining clean and accurate contact lists.

Component Analysis

The data cleaning tools market can be segmented by component into software and services. The software segment encompasses various tools and platforms designed for data cleaning, while the services segment includes consultancy, implementation, and maintenance services provided by vendors.

The software segment holds the largest market share and is expected to continue leading during the forecast period. This dominance can be attributed to the increasing adoption of automated data cleaning solutions that offer high efficiency and accuracy. These software solutions are equipped with advanced algorithms and functionalities that can handle large volumes of data, identify errors, and correct them without manual intervention. The rising adoption of cloud-based data cleaning software further bolsters this segment, as it offers scalability and ease of

The Fox News Dataset is a comprehensive collection of over 1 million news articles, offering an unparalleled resource for analyzing media narratives, public discourse, and political trends. Covering articles up to the year 2023, this dataset is a treasure trove for researchers, analysts, and businesses interested in gaining deeper insights into the topics and trends covered by Fox News.

Key Features of the Fox News Dataset

• Extensive Coverage: Contains more than 1 million articles spanning various topics and events up to 2023.
• Research-Ready: Perfect for text classification, natural language processing (NLP), and other research purposes.
• Format: Provided in CSV format for seamless integration into analytical and research tools.

Why Use This Dataset?

This large dataset is ideal for:

• Text Classification: Develop machine learning models to classify and categorize news content.
• Natural Language Processing (NLP): Conduct sentiment analysis, keyword extraction, or topic modeling.
• Media and Political Research: Analyze media narratives, public opinion, and political trends reflected in Fox News articles.
• Trend Analysis: Identify shifts in public discourse and media focus over time.

Explore More News Datasets

Discover additional resources for your research needs by visiting our news dataset collection. These datasets are tailored to support diverse analytical applications, including sentiment analysis and trend modeling.

The Fox News Dataset is a must-have for anyone interested in exploring large-scale media data and leveraging it for advanced analysis. Ready to dive into this wealth of information? Download the dataset now in CSV format and start uncovering the stories behind the headlines.

Advanced Analytics Market size was valued at USD 48.59 Billion in 2024 and is projected to reach USD 197.07 Billion by 2031, growing at a CAGR of 21.10% during the forecasted period 2024 to 2031.

The growth of the Advanced Analytics market is driven by the increasing demand for data-driven decision-making, advancements in AI and machine learning, and the rise of big data across industries. Organizations are seeking to leverage insights from large datasets to gain competitive advantages, optimize operations, and enhance customer experiences. The proliferation of IoT devices and the subsequent surge in data generation also contribute to the need for advanced analytics solutions. Additionally, the adoption of cloud-based analytics platforms, the focus on digital transformation, and regulatory requirements for data analysis in sectors like healthcare, finance, and retail further propel the market.

Big Data Platform Software Market Outlook

The global Big Data Platform Software market size was valued at approximately USD 70 billion in 2023 and is projected to reach around USD 250 billion by 2032, growing at a compound annual growth rate (CAGR) of 15%. The substantial growth in this market can be attributed to the increasing volume and complexity of data generated across various industries, along with the rising need for data analytics to drive business decision-making.

One of the key growth factors driving the Big Data Platform Software market is the explosive growth in data generation from various sources such as social media, IoT devices, and enterprise applications. The proliferation of digital devices has led to an unprecedented surge in data volumes, compelling businesses to adopt advanced Big Data solutions to manage and analyze this data effectively. Additionally, advancements in cloud computing have further amplified the capabilities of Big Data platforms, enabling organizations to store and process vast amounts of data in a cost-efficient manner.

Another significant driver of market growth is the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies. Big Data platforms equipped with AI and ML capabilities can provide valuable insights by analyzing patterns, trends, and anomalies within large datasets. This has been particularly beneficial for industries such as healthcare, finance, and retail, where data-driven decision-making can lead to improved operational efficiency, enhanced customer experiences, and better risk management.

Moreover, the rising demand for real-time data analytics is propelling the growth of the Big Data Platform Software market. Businesses are increasingly seeking solutions that can process and analyze data in real-time to gain immediate insights and respond swiftly to market changes. This demand is fueled by the need for agility and competitiveness, as organizations aim to stay ahead in a rapidly evolving business landscape. The ability to make data-driven decisions in real-time can provide a significant competitive edge, driving further investment in Big Data technologies.

From a regional perspective, North America holds the largest share of the Big Data Platform Software market, driven by the early adoption of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the increasing digital transformation initiatives and the rising awareness about the benefits of Big Data analytics across various industries. Europe also presents significant growth opportunities, driven by stringent data protection regulations and the growing emphasis on data privacy and security.

Component Analysis

The Big Data Platform Software market can be segmented by component into Software and Services. The software segment encompasses the various Big Data platforms and tools that enable data storage, processing, and analytics. This includes data management software, data analytics software, and visualization tools. The demand for Big Data software is driven by the need for organizations to handle large volumes of data efficiently and derive actionable insights from it. With the growing complexity of data, advanced software solutions that offer robust analytics capabilities are becoming increasingly essential.

The services segment includes consulting, implementation, and support services related to Big Data platforms. These services are crucial for the successful deployment and management of Big Data solutions. Consulting services help organizations to design and strategize their Big Data initiatives, while implementation services ensure the seamless integration of Big Data platforms into existing IT infrastructure. Support services provide ongoing maintenance and troubleshooting to ensure the smooth functioning of Big Data systems. The growing adoption of Big Data solutions is driving the demand for these ancillary services, as organizations seek expert guidance to maximize the value of their Big Data investments.

Within the software segment, data analytics software is witnessing significant demand due to its ability to process and analyze large datasets to uncover hidden patterns and insights. This is particularly important for industries such as healthcare, finance, and retail, where data-driven insights can lead to improved decision-making and operational efficiency. Additionally, data management software plays a critical role in ensuring the integrity, securit

About this dataset

Context

The dataset tabulates the median household income in Sandy. It can be utilized to understand the trend in median household income and to analyze the income distribution in Sandy 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).

• Sandy, OR Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Sandy, OR: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Sandy, OR
• Sandy, OR 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 Sandy median household income. You can refer the same here

Simulated datasets used in the supplement with large group size of 30, log-scale noise levels and zero proportions.The settings are reflected in the title of the data:(1) large: group size = 30; (2) 0.5, 1, 1.5: the log-scale noise level(3) 0.3, 0.4, 0.5: the extra zero proportion(4) The last number is the index of replicates. There are 50 replicates for each setting

With the rapid development of data acquisition and storage space, massive datasets exhibited with large sample size emerge increasingly and make more advanced statistical tools urgently need. To accommodate such big volume in the analysis, a variety of methods have been proposed in the circumstances of complete or right censored survival data. However, existing development of big data methodology has not attended to interval-censored outcomes, which are ubiquitous in cross-sectional or periodical follow-up studies. In this work, we propose an easily implemented divide-and-combine approach for analyzing massive interval-censored survival data under the additive hazards model. We establish the asymptotic properties of the proposed estimator, including the consistency and asymptotic normality. In addition, the divide-and-combine estimator is shown to be asymptotically equivalent to the full-data-based estimator obtained from analyzing all data together. Simulation studies suggest that, relative to the full-data-based approach, the proposed divide-and-combine approach has desirable advantage in terms of computation time, making it more applicable to large-scale data analysis. An application to a set of interval-censored data also demonstrates the practical utility of the proposed method.

In software development, it’s common to reuse existing source code by copying and pasting, resulting in the proliferation of numerous code clones—similar or identical code fragments—that detrimentally affect software quality and maintainability. Although several techniques for code clone detection exist, many encounter challenges in effectively identifying semantic clones due to their inability to extract syntax and semantics information. Fewer techniques leverage low-level source code representations like bytecode or assembly for clone detection. This work introduces a novel code representation for identifying syntactic and semantic clones in Java source code. It integrates high-level features extracted from the Abstract Syntax Tree with low-level features derived from intermediate representations generated by static analysis tools, like the Soot framework. Leveraging this combined representation, fifteen machine-learning models are trained to effectively detect code clones. Evaluation on a large dataset demonstrates the models’ efficacy in accurately identifying semantic clones. Among these classifiers, ensemble classifiers, such as the LightGBM classifier, exhibit exceptional accuracy. Linearly combining features enhances the effectiveness of the models compared to multiplication and distance combination techniques. The experimental findings indicate that the proposed method can outperform the current clone detection techniques in detecting semantic clones.

In testing the efficiency of ASTRAL-MP, we use several simulated and real datasets (see Table). The datasets range in the number of species (n) between 48 and 1,000 and have between 1,000 and 14,446 gene trees (k).

Name Original publication

Species (n)

Genes (k)

Type

Generations

Contraction threshold

Reps.

SV

Mirarab and Warnow (2015) 100, 200, 500, 1000 1000 Simulated

2×1062×106 Fully resolved 10

Avian

Mirarab et al. (2014a) 48 14 446, 1000 Real Unknown (order: 107) Full, 0, 33, 50, 75% 1, 10

Insects

Sayyari et al. (2017) 144 1478 Real Unknown Fully resolved 1

Note: For SV, some outlier replicates have fewer than 1m000 genes because poorly resolved gene trees are removed. For avian, the full dataset is subsampled randomly to create 10 inputs with 1m000 gene trees. In addi...

Description of Earthquakes Dataset (1990-2023)

The earthquakes dataset is an extensive collection of data containing information about all the earthquakes recorded worldwide from 1990 to 2023. The dataset comprises approximately three million rows, with each row representing a specific earthquake event. Each entry in the dataset contains a set of relevant attributes related to the earthquake, such as the date and time of the event, the geographical location (latitude and longitude), the magnitude of the earthquake, the depth of the epicenter, the type of magnitude used for measurement, the affected region, and other pertinent information.

Features - time in millisecconds - place - status
- tsunami (boolean value) - significance - data_type - magnitudo - state - longitude - latitude
- depth - date

Importance and Utility of the Dataset:

Earthquake Analysis and Prediction: The dataset provides a valuable data source for scientists and researchers interested in analyzing spatial and temporal distribution patterns of earthquakes. By studying historical data, trends, and patterns, it becomes possible to identify high-risk seismic zones and develop predictive models to forecast future seismic events more accurately.

Safety and Prevention: Understanding factors contributing to earthquake frequency and severity can assist authorities and safety experts in implementing preventive measures at both local and global levels. These data can enhance the design and construction of earthquake-resistant infrastructures, reducing material damage and safeguarding human lives.

Seismological Science: The dataset offers a critical resource for seismologists and geologists studying the dynamics of the Earth's crust and various geological faults. Analyzing details of recorded earthquakes allows for a deeper comprehension of geological processes leading to seismic activity.

Study of Tectonic Movements: The dataset can be utilized to analyze patterns of tectonic movements in specific areas over the years. This may help identify seasonal or long-term seismic activity, providing additional insights into plate tectonic behavior.

Public Information and Awareness: Earthquake data can be made accessible to the public through portals and applications, enabling individuals to monitor seismic activity in their regions of interest and promoting awareness and preparedness for earthquakes.

In summary, the earthquakes dataset represents a fundamental information source for scientific research, public safety, and community awareness. By analyzing historical data and building predictive models, this dataset can significantly contribute to mitigating seismic risks and protecting people and infrastructure from the consequences of earthquakes.

About this dataset

Context

The dataset tabulates the median household income in Long Creek. It can be utilized to understand the trend in median household income and to analyze the income distribution in Long Creek 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 Creek, OR Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Long Creek, OR: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Long Creek, OR
• Long Creek, OR 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/.

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Those synthetic data have been generated for the validation of the iterative centroid method published in "Statistical shape analysis of large datasets based on diffeomorphic iterative centroids", which code can be found here: https://github.com/cclairec/Iterative_Centroid

The 50 random populations are all generated from a same shape S0. We generated random deformations around S0 and symmetrised the deformations, so the centre of the populations is S0. More details in the paper.

Each data set is composed by 50 shapes. The Matlab files contain:

• moment: The initial momentum vector used to generate each subject of the population

• SkelSuj_Rigid: The first line contains the vertices and faces of each subject. The second line contains the parameters to generate the initial momentum vectors and the subjects.

The social analytics service market is experiencing robust growth, driven by the increasing reliance on social media for business communication and brand building. The market, valued at approximately $8 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors. Businesses are increasingly leveraging social listening tools to understand customer sentiment, track brand reputation, and identify emerging trends. The sophisticated analytics capabilities offered by these services allow organizations to extract valuable insights from vast amounts of social data, optimizing marketing strategies, improving customer service, and making data-driven business decisions. Furthermore, the integration of artificial intelligence and machine learning is enhancing the accuracy and efficiency of social analytics platforms, leading to more effective analysis and actionable insights. The market's growth is also propelled by the expansion of social media platforms themselves, generating ever-larger datasets for analysis. While the market presents significant opportunities, certain challenges remain. Data privacy concerns and the complexity of analyzing unstructured social media data are notable restraints. The competitive landscape, characterized by the presence of both established players like Hootsuite, Sprinklr, and Salesforce, and emerging niche providers, necessitates ongoing innovation and differentiation to maintain a competitive edge. Segmentation within the market reflects the diverse needs of various user groups, including small businesses, large enterprises, and specialized industry verticals. The geographical distribution of market share is likely to be concentrated initially in regions with high social media penetration and advanced technological infrastructure, such as North America and Europe, although emerging markets will contribute significantly to growth in the later forecast period.

The High Performance Computing (HPC) and High Performance Data Analytics (HPDA) Marketsize was valued at USD 46.01 USD billion in 2023 and is projected to reach USD 84.65 USD billion by 2032, exhibiting a CAGR of 9.1 % during the forecast period. High-Performance Computing (HPC) refers to the use of advanced computing systems and technologies to solve complex and large-scale computational problems at high speeds. HPC systems utilize powerful processors, large memory capacities, and high-speed interconnects to execute vast numbers of calculations rapidly. High-Performance Data Analytics (HPDA) extends this concept to handle and analyze big data with similar performance goals. HPDA encompasses techniques like data mining, machine learning, and statistical analysis to extract insights from massive datasets. Types of HPDA include batch processing, stream processing, and real-time analytics. Features include parallel processing, scalability, and high throughput. Applications span scientific research, financial modeling, and large-scale simulations, addressing challenges that require both intensive computing and sophisticated data analysis. Recent developments include: December 2023: Lenovo, a company offering computer hardware, software, and services, extended the HPC system “LISE” at the Zuse Institute Berlin (ZIB). This expansion would provide researchers at the institute with high computing power required to execute data-intensive applications. The major focus of this expansion is to enhance the energy efficiency of “LISE”. , August 2023: atNorth, a data center services company, announced the acquisition of Gompute, the HPC cloud platform offering Cloud HPC services, as well as on-premises and hybrid cloud solutions. Under the terms of the agreement, atNorth would add Gompute’s data center to its portfolio., July 2023: HCL Technologies Limited, a consulting and information technology services firm, extended its collaboration with Microsoft Corporation to provide HPC solutions, such as advanced analytics, ML, core infrastructure, and simulations, for clients across numerous sectors., June 2023: Leostream, a cloud-based desktop provider, launched new features designed to enhance HPC workloads on AWS EC2. The company develops zero-trust architecture around HPC workloads to deliver cost-effective and secure resources to users on virtual machines., November 2022: Intel Corporation, a global technology company, launched the latest advanced processors for HPC, artificial intelligence (AI), and supercomputing. These processors include data center version GPUs and 4th Gen Xeon Scalable CPUs.. Key drivers for this market are: Technological Advancements Coupled with Robust Government Investments to Fuel Market Growth. Potential restraints include: High Cost and Skill Gap to Restrain Industry Expansion. Notable trends are: Comprehensive Benefits Provided by Hybrid Cloud HPC Solutions to Aid Industry Expansion .

To explore the feasibility of parsimony analysis for large data sets, we conducted heuristic parsimony searches and bootstrap analyses on separate and combined DNA data sets for 190 angiosperms and three outgroups. Separate data sets of 18S rDNA (1,855 bp), rbc L (1,428 bp), and atp B (1,450 bp) sequences were combined into a single matrix 4,733 bp in length. Analyses of the combined data set show great improvements in computer run times compared to those of the separate data sets and of the data sets combined in pairs. Six searches of the 18S rDNA rbc L atp B data set were conducted; in all cases TBR branch swapping was completed, generally within a few days. In contrast, TBR branch swapping was not completed for any of the three separate data sets, or for the pairwise combined data sets. These results illustrate that it is possible to conduct a thorough search of tree space with large data sets, given sufficient signal. In this case, and probably most others, sufficient signal for a ...