Identity Data from VisitIQ™ offers a comprehensive suite of tools designed to enhance marketing efforts with unmatched precision and privacy. Our platform utilizes unique generated IDs, and hashed emails to create a robust identity graph, providing businesses with highly accurate and actionable data. This data can be seamlessly applied across various applications, including digital onboarding, personalized direct mail campaigns, audience segmentation, and other targeted marketing initiatives.

With VisitIQ™, you can be confident in a 100% privacy-compliant solution that adheres to all regulatory standards, ensuring the protection and ethical use of consumer data. Whether you are a small business or a large enterprise, our platform empowers you to reach your ideal customers with greater accuracy, optimize your marketing strategies, and drive meaningful engagement, all while respecting user privacy.

Insights from City Supply and Demand Data This data project has been used as a take-home assignment in the recruitment process for the data science positions at Uber.

Assignment Using the provided dataset, answer the following questions:

1. Which date had the most completed trips during the two week period?
2. What was the highest number of completed trips within a 24 hour period?
3. Which hour of the day had the most requests during the two week period?
4. What percentages of all zeroes during the two week period occurred on weekend (Friday at 5 pm to Sunday at 3 am)? Tip: The local time value is the start of the hour (e.g. 15 is the hour from 3:00pm - 4:00pm)
5. What is the weighted average ratio of completed trips per driver during the two week period? Tip: "Weighted average" means your answer should account for the total trip volume in each hour to determine the most accurate number in whole period.
6. In drafting a driver schedule in terms of 8 hours shifts, when are the busiest 8 consecutive hours over the two week period in terms of unique requests? A new shift starts in every 8 hours. Assume that a driver will work same shift each day.
7. True or False: Driver supply always increases when demand increases during the two week period. Tip: Visualize the data to confirm your answer if needed.
8. In which 72 hour period is the ratio of Zeroes to Eyeballs the highest?
9. If you could add 5 drivers to any single hour of every day during the two week period, which hour should you add them to? Hint: Consider both rider eyeballs and driver supply when choosing
10. True or False: There is exactly two weeks of data in this analysis
11. Looking at the data from all two weeks, which time might make the most sense to consider a true "end day" instead of midnight? (i.e when are supply and demand at both their natural minimums) Tip: Visualize the data to confirm your answer if needed.

Data Description To answer the question, use the dataset from the file dataset_1.csv. For example, consider the row 11 from this dataset:

Date Time (Local) Eyeballs Zeroes Completed Trips Requests Unique Drivers

2012-09-10 16 11 2 3 4 6

This means that during the hour beginning at 4pm (hour 16), on September 10th, 2012, 11 people opened the Uber app (Eyeballs). 2 of them did not see any car (Zeroes) and 4 of them requested a car (Requests). Of the 4 requests, only 3 complete trips actually resulted (Completed Trips). During this time, there were a total of 6 drivers who logged in (Unique Drivers)

Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

The market for competitor analysis tools is experiencing robust growth, driven by the increasing importance of competitive intelligence in today's dynamic business landscape. The surge in digital marketing and the need for businesses, both SMEs and large enterprises, to understand their competitive positioning fuels demand for sophisticated tools offering comprehensive data analysis and actionable insights. Cloud-based solutions are dominating the market due to their scalability, accessibility, and cost-effectiveness compared to on-premises deployments. Key players like SEMrush, Ahrefs, and SimilarWeb are establishing strong market presence through continuous innovation, comprehensive feature sets, and targeted marketing strategies. However, the market also faces challenges, including the rising costs of data acquisition and the complexity of integrating various tools into existing workflows. The competitive landscape is characterized by a mix of established players and emerging niche providers. Differentiation is achieved through unique data sources, specialized analytics capabilities, and the ability to integrate seamlessly with other marketing and business intelligence platforms. The North American and European markets currently hold a significant share, owing to high technology adoption and established digital marketing ecosystems. However, growth is expected in Asia-Pacific regions as businesses in developing economies increasingly adopt digital strategies and seek competitive advantages. The forecast period (2025-2033) suggests continued expansion, propelled by technological advancements like AI-powered insights and the expanding use of social media analytics within competitor analysis. The market's segmentation reflects varying needs across different business sizes and deployment preferences. While large enterprises typically opt for comprehensive, feature-rich solutions capable of handling large datasets and integrating with various systems, SMEs often prioritize cost-effective, user-friendly tools providing essential insights. The choice between cloud-based and on-premises solutions depends on factors like IT infrastructure, security considerations, and budget constraints. As the market matures, we anticipate further consolidation through mergers and acquisitions, and the emergence of more specialized tools catering to specific industry needs. The overall trajectory indicates continued strong growth, with a focus on enhanced data analysis, improved user experiences, and seamless integration within broader business intelligence platforms.

WiserBrand's Comprehensive Customer Call Transcription Dataset: Tailored Insights

WiserBrand offers a customizable dataset comprising transcribed customer call records, meticulously tailored to your specific requirements. This extensive dataset includes:

User ID and Firm Name: Identify and categorize calls by unique user IDs and company names. Call Duration: Analyze engagement levels through call lengths. Geographical Information: Detailed data on city, state, and country for regional analysis. Call Timing: Track peak interaction times with precise timestamps. Call Reason and Group: Categorised reasons for calls, helping to identify common customer issues. Device and OS Types: Information on the devices and operating systems used for technical support analysis. Transcriptions: Full-text transcriptions of each call, enabling sentiment analysis, keyword extraction, and detailed interaction reviews.

Our dataset is designed for businesses aiming to enhance customer service strategies, develop targeted marketing campaigns, and improve product support systems. Gain actionable insights into customer needs and behavior patterns with this comprehensive collection, particularly useful for Consumer Data, Consumer Behavior Data, Consumer Sentiment Data, Consumer Review Data, AI Training Data, Textual Data, and Transcription Data applications.

WiserBrand's dataset is essential for companies looking to leverage Consumer Data and B2B Marketing Data to drive their strategic initiatives in the English-speaking markets of the USA, UK, and Australia. By accessing this rich dataset, businesses can uncover trends and insights critical for improving customer engagement and satisfaction.

Cases:

1. Training Speech Recognition (Speech-to-Text) and Speech Synthesis (Text-to-Speech) Models WiserBrand's Comprehensive Customer Call Transcription Dataset is an excellent resource for training and improving speech recognition models (Speech-to-Text, STT) and speech synthesis systems (Text-to-Speech, TTS). Here’s how this dataset can contribute to these tasks:

Enriching STT Models: The dataset includes a wide variety of real-world customer service calls with diverse accents, tones, and terminologies. This makes it highly valuable for training speech-to-text models to better recognize different dialects, regional speech patterns, and industry-specific jargon. It could help improve accuracy in transcribing conversations in customer service, sales, or technical support.

Contextualized Speech Recognition: Given the contextual information (e.g., reasons for calls, call categories, etc.), it can help models differentiate between various types of conversations (technical support vs. sales queries), which would improve the model’s ability to transcribe in a more contextually relevant manner.

Improving TTS Systems: The transcriptions, along with their associated metadata (such as call duration, timing, and call reason), can aid in training Text-to-Speech models that mimic natural conversation patterns, including pauses, tone variation, and proper intonation. This is especially beneficial for developing conversational agents that sound more natural and human-like in their responses.

Noise and Speech Quality Handling: Real-world customer service calls often contain background noise, overlapping speech, and interruptions, which are crucial elements for training speech models to handle real-life scenarios more effectively.

1. Training AI Agents for Replacing Customer Service Representatives WiserBrand’s dataset can be incredibly valuable for businesses looking to develop AI-powered customer support agents that can replace or augment human customer service representatives. Here’s how this dataset supports AI agent training:

Customer Interaction Simulation: The transcriptions provide a comprehensive view of real customer interactions, including common queries, complaints, and support requests. By training AI models on this data, businesses can equip their virtual agents with the ability to understand customer concerns, follow up on issues, and provide meaningful solutions, all while mimicking human-like conversational flow.

Sentiment Analysis and Emotional Intelligence: The full-text transcriptions, along with associated call metadata (e.g., reason for the call, call duration, and geographical data), allow for sentiment analysis, enabling AI agents to gauge the emotional tone of customers. This helps the agents respond appropriately, whether it’s providing reassurance during frustrating technical issues or offering solutions in a polite, empathetic manner. Such capabilities are essential for improving customer satisfaction in automated systems.

Customizable Dialogue Systems: The dataset allows for categorizing and identifying recurring call patterns and issues. This means AI agents can be trained to recognize the types of queries that come up frequently, allowing them to automate routine tasks such as ...

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Big Data As A Service Market Size 2025-2029

The big data as a service market size is forecast to increase by USD 75.71 billion, at a CAGR of 20.5% between 2024 and 2029.

The Big Data as a Service (BDaaS) market is experiencing significant growth, driven by the increasing volume of data being generated daily. This trend is further fueled by the rising popularity of big data in emerging technologies, such as blockchain, which requires massive amounts of data for optimal functionality. However, this market is not without challenges. Data privacy and security risks pose a significant obstacle, as the handling of large volumes of data increases the potential for breaches and cyberattacks. Edge computing solutions and on-premise data centers facilitate real-time data processing and analysis, while alerting systems and data validation rules maintain data quality.
Companies must navigate these challenges to effectively capitalize on the opportunities presented by the BDaaS market. By implementing robust data security measures and adhering to data privacy regulations, organizations can mitigate risks and build trust with their customers, ensuring long-term success in this dynamic market.

What will be the Size of the Big Data As A Service Market during the forecast period?

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The market continues to evolve, offering a range of solutions that address various data management needs across industries. Hadoop ecosystem services play a crucial role in handling large volumes of data, while ETL process optimization ensures data quality metrics are met. Data transformation services and data pipeline automation streamline data workflows, enabling businesses to derive valuable insights from their data. Nosql database solutions and custom data solutions cater to unique data requirements, with Spark cluster management optimizing performance. Data security protocols, metadata management tools, and data encryption methods protect sensitive information. Cloud data storage, predictive modeling APIs, and real-time data ingestion facilitate agile data processing.
Data anonymization techniques and data governance frameworks ensure compliance with regulations. Machine learning algorithms, access control mechanisms, and data processing pipelines drive automation and efficiency. API integration services, scalable data infrastructure, and distributed computing platforms enable seamless data integration and processing. Data lineage tracking, high-velocity data streams, data visualization dashboards, and data lake formation provide actionable insights for informed decision-making.
For instance, a leading retailer leveraged data warehousing services and predictive modeling APIs to analyze customer buying patterns, resulting in a 15% increase in sales. This success story highlights the potential of big data solutions to drive business growth and innovation.

How is this Big Data As A Service Industry segmented?

The big data as a service industry research 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.

Type

  Data Analytics-as-a-service (DAaaS)
  Hadoop-as-a-service (HaaS)
  Data-as-a-service (DaaS)


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


End-user

  Large enterprises
  SMEs


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The Data analytics-as-a-service (DAaas) segment is estimated to witness significant growth during the forecast period. The data analytics-as-a-service (DAaaS) segment experiences significant growth within the market. Currently, over 30% of businesses adopt cloud-based data analytics solutions, reflecting the increasing demand for flexible, cost-effective alternatives to traditional on-premises infrastructure. Furthermore, industry experts anticipate that the DAaaS market will expand by approximately 25% in the upcoming years. This market segment offers organizations of all sizes the opportunity to access advanced analytical tools without the need for substantial capital investment and operational overhead. DAaaS solutions encompass the entire data analytics process, from data ingestion and preparation to advanced modeling and visualization, on a subscription or pay-per-use basis. Data integration tools, data cataloging systems, self-service data discovery, and data version control enhance data accessibility and usability.

The continuous evolution of this market is driven by the increasing volume, variety, and velocity of data, as well as the growing recognition of the business value that can be derived from data insights. Organizations across var

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Customer Data Platform Market Size 2024-2028

The customer data platform market size is forecast to increase by USD 19.02 billion at a CAGR of 32.12% between 2023 and 2028.

The customer data platform (CDP) market is experiencing significant growth due to several key trends. The increasing demand for personalized customer services in various industries, particularly e-commerce retail, is driving market growth. This trend is being fueled by the rising preference for omnichannel platforms that enable seamless customer interactions across multiple touchpoints. Additionally, the need to address customer data privacy concerns is another major factor contributing to the market's growth.
As businesses strive to provide more personalized experiences to their customers while ensuring data security, CDPs and workforce analytics are becoming an essential tool for managing and activating customer data in real time. This CDP market analysis report provides a comprehensive examination of these trends and other growth factors, offering valuable insights for businesses looking to leverage CDPs to enhance their customer engagement strategies.

What will be the Size of the Customer Data Platform Market During the Forecast Period?

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The customer data platform (CDP) market is experiencing significant growth due to the increasing importance of customer intelligence for delivering omnichannel experiences. Businesses seek to understand their customers across multiple channels and touchpoints, requiring the ability to handle large volumes of complex data. CDP solutions enable data unification and identity resolution, ensuring accurate and consistent customer profiles. Data governance and privacy laws are driving the need for robust data protection and security measures, including data breach prevention and compliance with regulations such as GDPR and CCPA.
Additionally, AI and machine learning are being integrated into CDPs to enhance data analytics capabilities, providing valuable insights for industries like healthcare, telecom, travel and hospitality, and advertising.
The customer data platform market is evolving with AI-powered CDP solutions enhancing real-time data processing, customer data integration, and omnichannel marketing. Businesses focus on data privacy compliance and first-party data management to drive predictive analytics, customer segmentation, and personalized marketing. Cloud-based CDP adoption supports customer journey analytics, CDP for e-commerce, and cross-channel data activation. Data monetization strategies, identity resolution, and enterprise CDP solutions fuel CDP market growth, enabling data-driven customer insights and customer retention strategies.
Big data and real-time data processing are essential features, enabling businesses to make informed decisions and respond quickly to customer needs.

How is this Customer Data Platform Industry segmented and which is the largest segment?

The customer data platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud based


End-user

  Large enterprises
  Small and medium size enterprises


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

The on-premises the market is experiencing substantial growth due to its ability to process and personalize customer data while maintaining data security within an organization's data centers or servers. On-premises CDPs offer customizable solutions tailored to specific business needs and unique data processing workflows, which may not be available in cloud-based alternatives. However, the need to upgrade hardware for data scalability is a consideration for on-premises CDPs. Key features of on-premises CDPs include data unification, identity resolution, data governance, data privacy, and data security. These platforms enable organizations to comply with data privacy laws, protect against data breaches, and address consumer concerns.

On-premises CDPs are particularly valuable for industries with large data volumes and complexities, such as advertising, healthcare services, telecom, media and entertainment, retail, and travel and hospitality. Integration with mobile devices, Short Message Service, and communication channels is essential for providing a seamless omnichannel experience. Machine learning and natural language processing technologies enhance data analysis and personalization capabilities. Cloud-based technology offers flexibility and cost savings, but on-premises CDP

The size of the Financial Analytics Industry market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 12.25% during the forecast period.Financial analytics refers to the application of analytical techniques on financial data in order to derive insights, predict future occurrences, and make decisions. Financial analytics involves various activities such as financial modeling, risk management, portfolio management, and performance analysis. It applies tools and technologies such as statistical analysis, machine learning, and data visualization in discovering trends, patterns, and financial health of individuals, businesses, and markets. This knowledge is a tool for stakeholders in making decisions about investments, risk adjustment, and general financial performance. Recent developments include: July 2023 - Dobin, an AI-powered FinTech solution, announced its launch. Dobin is the first Southeast Asian company to use open finance and advanced data analytics to give users a single view of their finances, create unique anonymized customer insights, and empower users to get value from their financial data.. Key drivers for this market are: Advancement in BI and Business Analytics Tools, Growing Focus on Data Driven Financial Decisions in End Users. Potential restraints include: Lack of Awareness Regarding Fraud Detection Solutions. Notable trends are: Cloud Based Solutions are Expected to Gain Significant Traction.

Many single-species freshwater phylogeographic studies have been carried out in south-east Queensland; however comparative phylogeography requires multiple lines of evidence to infer deep, significant relationships between landscape and biota. The present study aimed to test conclusions resulting from single taxon studies in a multispecies comparative framework: (1) how influential are river basins in the genetic structure of freshwater species; (2) are there biogeographic frontiers between groups of basins; and (3) could deep intraspecific lineages be explained by a single event? New and existing data from 33 freshwater species (23 fishes and 10 crustaceans) were combined, and both standard single-species analyses (haplotype networks, genetic distances, ΦST) and multispecies methods (hierarchical ABC) were carried out for 1814 sequences from eight basins. More than half of the species displayed a high phylogeographic structure and contained at least two distinct lineages. Almost all of the lineage divergences displayed an element of north/south geographic breaks, with the most influential boundary being between the Mary and Brisbane rivers. Of the 11 basin-pair multispecies coalescent analyses, four implied a single divergence as being most likely. A regional analysis of deep lineages within 16 taxon-pairs resulted in a strongly supported inference of a single divergence, probably dating to the Pleistocene. Basin boundaries are a key determinant of phylogeographic patterns for most of these freshwater species, although the specific biogeographic relationship between basins often varies depending on the species. There are a number of influential biogeographic frontiers, with the Brisbane-Mary being the most important. The finding that a single event may be responsible for multiple deep lineages across the region implies that a highly influential climate change event may have been detected.

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Big Data Services Market Size 2025-2029

The big data services market size is forecast to increase by USD 604.2 billion, at a CAGR of 54.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of big data in various industries, particularly in blockchain technology. The ability to process and analyze vast amounts of data in real-time is revolutionizing business operations and decision-making processes. However, this market is not without challenges. One of the most pressing issues is the need to cater to diverse client requirements, each with unique data needs and expectations. This necessitates customized solutions and a deep understanding of various industries and their data requirements. Additionally, ensuring data security and privacy in an increasingly interconnected world poses a significant challenge. Companies must navigate these obstacles while maintaining compliance with regulations and adhering to ethical data handling practices. To capitalize on the opportunities presented by the market, organizations must focus on developing innovative solutions that address these challenges while delivering value to their clients. By staying abreast of industry trends and investing in advanced technologies, they can effectively meet client demands and differentiate themselves in a competitive landscape.

What will be the Size of the Big Data Services Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the ever-increasing volume, velocity, and variety of data being generated across various sectors. Data extraction is a crucial component of this dynamic landscape, enabling entities to derive valuable insights from their data. Human resource management, for instance, benefits from data-driven decision making, operational efficiency, and data enrichment. Batch processing and data integration are essential for data warehousing and data pipeline management. Data governance and data federation ensure data accessibility, quality, and security. Data lineage and data monetization facilitate data sharing and collaboration, while data discovery and data mining uncover hidden patterns and trends. Real-time analytics and risk management provide operational agility and help mitigate potential threats. Machine learning and deep learning algorithms enable predictive analytics, enhancing business intelligence and customer insights. Data visualization and data transformation facilitate data usability and data loading into NoSQL databases. Government analytics, financial services analytics, supply chain optimization, and manufacturing analytics are just a few applications of big data services. Cloud computing and data streaming further expand the market's reach and capabilities. Data literacy and data collaboration are essential for effective data usage and collaboration. Data security and data cleansing are ongoing concerns, with the market continuously evolving to address these challenges. The integration of natural language processing, computer vision, and fraud detection further enhances the value proposition of big data services. The market's continuous dynamism underscores the importance of data cataloging, metadata management, and data modeling for effective data management and optimization.

How is this Big Data Services Industry segmented?

The big data services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentSolutionServicesEnd-userBFSITelecomRetailOthersTypeData storage and managementData analytics and visualizationConsulting servicesImplementation and integration servicesSupport and maintenance servicesSectorLarge enterprisesSmall and medium enterprises (SMEs)GeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW).

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.Big data services have become indispensable for businesses seeking operational efficiency and customer insight. The vast expanse of structured and unstructured data presents an opportunity for organizations to analyze consumer behaviors across multiple channels. Big data solutions facilitate the integration and processing of data from various sources, enabling businesses to gain a deeper understanding of customer sentiment towards their products or services. Data governance ensures data quality and security, while data federation and data lineage provide transparency and traceability. Artificial intelligenc

Dataset description:The data Organization of files:1) Features: features.tar.gzsinglecell_features_CellProfiler.parquet: This file contains the single-cell profiles extracted with CellProfiler used for the analysis in this publication. Features are normalised and filtered according to Fig. 1B, C in the paper.singlecell_features_DeepProfiler.parquet: This file contains the single-cell profiles extracted with DeepProfiler used for the analysis in this publication. Features are normalised and filtered according to Fig. 1B, C in the paper.singlecell_features_DINO.parquet: This file contains the single-cell profiles extracted with DINO used for the analysis in this publication. Features are normalised and filtered according to Fig. 1B, C in the paper (number of profiles here is smaller than in the other two approaches as outlined in the paper).aggregate_profiles_DINO_adjusted.parquet, aggregate_profiles_CP_aggregated.parquet, aggregated_profiles_DP_adjusted.parquet: Aggregated profile parquets for the three feature extractors.2) Metadata: metadata_celldeath_paper.csv. This file contains the metadata used in the orgiinal cell painting experiment. It contains, plate, well, site (field-of-view), compounds, moa as well used concentrations and treatment conditions.3). Grit scores: grit_scores.tar.gz. This zipped folder contains the grit scores for the compound concentrations for all three feature extractors. This info is provided for all the compounds concentrations for which grit could be computed. One file each for CellProfiler, DeepProfiler and DINO.4.) E-distance: edistance.tar.gz. This zipped folder contains the edistances and etest results for the compound concentrations for all three feature extractors. This info is provided for all the compounds concentrations for which they could be computed. One file each for CellProfiler, DeepProfiler, and DINO. The file names indicate the number of samples and permutations used in the permutation test.5.) Splits: splits.tar.gz. This zipped folder contains the splits used in the supervised model training. One file for each CellProfiler, DeepProfiler, and DINO. As described in the paper, splits were performed based on the wells of plates. Each file contains moa, compound, plate, well as well as split and the fraction of cells in each well.6.) map: map.tar.gz. This zipped folder contains map results for the compound concentrations for all three feature extractors. This info is provided for all the compounds concentrations for which they could be computed. One file each for CellProfiler, DeepProfiler, and DINO. The file names indicate the number of samples and permutations used in the permutation test.7) Embeddings: embeddings.tar.gz contains .h5ad with anndata files of calculated single-cell embeddings for CellProfiler, DeepProfiler, and DINO features. Each anndata file contains features, metadata as well as calculated UMAP, and PCA embeddings used in the figures. Additionally contains one anndata file with the DINO apoptosis embedding used to generate Fig. 3.8) QC: qc_df.csv, File containing quality control flags used to filter out images and calculated viability values from cell counts.9) Classification splits: classification_splits.tar.gz contains specific datasets used in training of supervised models for DINO, CellProfiler, and DeepProfiler on both aggregated and single-cell level.Publication:The data in this repository supports the following publication:"Single-cell morphological profiling reveals insights into programmed cell death" by Frey et al.Abstract:Analysis at the single-cell level is a powerful approach to study biological processes and responses to perturbations. However, its application in morphological profiling with phenomics remains underexplored. Here, we use the Cell Painting assay to investigate morphological effects of 53 small molecule compounds, associated with six distinct cell death mechanisms, across six concentrations in MCF7 cells. To compare single-cell and aggregated analysis strategies, we conduct both supervised and unsupervised evaluations aimed at identifying features linked to programmed cell death. We apply an energy distance as a metric to quantify morphological perturbation strength, enabling efficient filtering. Among three tested feature extraction methods, self-supervised DINO embeddings applied to single-cell data captured high-resolution morphological patterns. Focused analyses of apoptosis-inducing compounds revealed biological heterogeneity attributable to specific molecular targets and concentration-dependent effects, which were not apparent in aggregated profiles. In contrast, multi-class classification models for the six programmed cell death mechanisms trained on single-cell features achieved F1 scores of 79.86\%, while models trained on aggregated features reached F1 scores of up to 89.97\%.Our results highlight the advantages of single-cell data for unsupervised exploration and show that aggregated representations yield more robust and accurate performance in supervised models.

Sampling localities, sample sizes and genetic diversity at mtDNA and microsatellite markers (PWD, pair wise differences).

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. We also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

The intracellular pathogen Legionella pneumophila hijacks the endoplasmic reticulum (ER)-derived vesicles to create an organelle designated Legionella-containing vacuole (LCV) required for bacterial replication. Maturation of the LCV involved acquisition of Rab1, which is mediated by the bacterial effector protein SidM/DrrA. SidM/DrrA is a bifunctional enzyme having the activity of both Rab1-specific GDP dissociation inhibitor (GDI) displacement factor (GDF) and guanine nucleotide exchange factor (GEF). LidA, another Rab1-interacting bacterial effector protein, was reported to promote SidM/DrrA-mediated recruitment of Rab1 to the LCV as well. Here we report the crystal structures of LidA complexes with GDP- and GTP-bound Rab1 respectively. Structural comparison revealed that GDP-Rab1 bound by LidA exhibits an active and nearly identical conformation with that of GTP-Rab1, suggesting that LidA can disrupt the switch function of Rab1 and render it persistently active. As with GTP, LidA maintains GDP-Rab1 in the active conformation through interaction with its two conserved switch regions. Consistent with the structural observations, biochemical assays showed that LidA binds to GDP- and GTP-Rab1 equally well with an affinity approximately 7.5 nM. We propose that the tight interaction with Rab1 allows LidA to facilitate SidM/DrrA-catalyzed release of Rab1 from GDIs. Taken together, our results support a unique mechanism by which a bacterial effector protein regulates Rab1 recycling.

OutreachGenius's Intent data offers a comprehensive solution for businesses aiming to enhance their marketing strategies through precise, real-time intent data. By delivering over 3 billion new data points daily across more than 21,000 unique B2B and B2C topic categories. OutreachGenius provides unparalleled insights into online search trends and user behaviors.

Key Features:

Real-Time Data Acquisition: OutreachGenius captures and processes billions of user interactions every 24 hours, ensuring access to the most current and relevant intent data.

Extensive Topic Coverage: With tracking across 21,000+ unique topic categories, businesses can delve into specific interests and niches, facilitating highly targeted marketing efforts.

30-Day Data Repository: OutreachGenius maintains a rolling 30-day archive of intent data, enabling trend analysis and behavioral predictions to inform strategic decision-making.

Person-Level Insights: OutreachGenius goes beyond aggregate data, offering granular insights into individual user preferences and behaviors for precise audience targeting.

AI-Driven Outreach Automation: Leveraging artificial intelligence, OutreachGenius automates personalized outreach, streamlining communication processes and enhancing engagement and lead generation.

Data Sourcing and Uniqueness:

OutreachGenius's data is sourced from a vast array of online user interactions, including search queries, website visits, and content engagement. This extensive data collection is processed in real-time, ensuring that businesses receive the most up-to-date insights.

OutreachGenius's ability to deliver person-level intent data across a wide spectrum of topics sets it apart, providing a depth of insight that is both unique and actionable.

Primary Use Cases:

Targeted Marketing/Lead Generation Campaigns: Utilize detailed intent data to craft marketing messages that resonate with specific audience segments, improving conversion rates.

Sales Prospecting: Identify potential leads exhibiting interest in relevant topics, enabling sales teams to prioritize outreach efforts effectively.

Product Development: Gain insights into emerging trends and consumer interests to guide product innovation and development strategies.

Competitive Analysis: Monitor shifts in market interest and competitor activities to maintain a competitive edge.

Integration and Accessibility:

OutreachGenius's intent data is designed for seamless integration into existing systems, offering API and webhook access for efficient data utilization.

This flexibility ensures that businesses can incorporate intent data into their workflows without disruption, enhancing the effectiveness of their marketing and sales operations.

In summary, OutreachGenius's intent data provides a robust platform for businesses seeking to leverage real-time intent data to drive marketing success. Its unique combination of extensive data coverage, real-time processing, and person-level insights makes it an invaluable tool for informed decision-making and strategic planning.

Protein-Protein, Genetic, and Chemical Interactions for Cheng L (2019):Structural insights into a unique preference for 3' terminal guanine of mirtron in Drosophila TUTase tailor. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Terminal uridylyl transferase (TUTase) is one type of enzyme that modifies RNA molecules by facilitating the post-transcriptional addition of uridyl ribonucleotides to their 3' ends. Recent researches have reported that Drosophila TUTase, Tailor, exhibits an intrinsic preference for RNA substrates ending in 3'G, distinguishing it from any other known TUTases. Through this unique feature, Tailor plays a crucial role as the repressor in the biogenesis pathway of splicing-derived mirtron pre-miRNAs. Here we describe crystal structures of core catalytic domain of Tailor and its complexes with RNA stretches 5'-AGU-3' and 5'-AGUU-3'. We demonstrate that R327 and N347 are two key residues contributing cooperatively to Tailor's preference for 3'G, and R327 may play an extra role in facilitating the extension of polyuridylation chain. We also demonstrate that conformational stability of the exit of RNA-binding groove also contributes significantly to Tailor's activity. Overall, our work reveals useful insights to explain why Drosophila Tailor can preferentially select RNA substrates ending in 3'G and provides important values for further understanding the biological significances of biogenesis pathway of mirtron in flies.

Single-cell proteomics has emerged as a powerful technology for unraveling the complexities of cellular heterogeneity, enabling insights into individual cell functions and pathologies. One of the primary challenges in single-cell proteomics is data generation, where low mass spectral signals often preclude the triggering of MS2 events. This challenge is addressed by Data Independent Acquisition (DIA), a data acquisition strategy that does not depend on peptide ion isotopic signatures to generate an MS2 event. In this study, we present data generated from the integration of DIA single-cell proteomics with a version of the DiagnoMass Proteomic Hub that was adapted to handle DIA data. DiagnoMass employs a hierarchical clustering methodology that enables the identification of tandem mass spectral clusters that are discriminative of biological conditions, thereby reducing the reliance on search engine biases for identifications. Nevertheless, a search engine (in this work, DIA-NN) can be integrated with DiagnoMass for spectral annotation. We used single-cell proteomic data from iPSC-derived neuroprogenitor cell cultures as a test study of this integrated approach. We were able to differentiate between control and Rett Syndrome patient cells to discern the proteomic variances potentially contributing to the disease’s pathology. Our research confirms that the DiagnoMass-DIA synergy significantly enhances the identification of discriminative proteomic signatures, highlighting critical biological variations such as the presence of unique spectra that could be related to Rett Syndrome pathology.

Email Thread Summary Dataset

Overview:

The Email Thread Dataset consists of two main files: email_thread_details and email_thread_summaries. These files collectively offer a comprehensive compilation of email thread information alongside human-generated summaries.

Email Thread Details:

Description:

The email_thread_details file provides a detailed perspective on individual email threads, encompassing crucial information such as subject, timestamp, sender, recipients, and the content of the email.

Columns:

• thread_id: A unique identifier for each email thread.
• subject: Subject of the email thread.
• timestamp: Timestamp indicating when the message was sent.
• from: Sender of the email.
• to: List of recipients of the email.
• body: Content of the email message.

Additional Information:

The "to" column is available in both CSV and Pickle (pkl) formats, facilitating convenient access to recipient information as a column of lists of strings.

Email Thread Summaries:

Description:

The email_thread_summaries file contains concise summaries crafted by human annotators for each email thread, offering a high-level overview of the content.

Columns:

• thread_id: A unique identifier for each email thread.
• summary: A concise summary of the email thread.

Dataset Structure:

The dataset is organized into threads and emails. There are a total of 4,167 threads and 21,684 emails, providing a rich source of information for analysis and research purposes.

• Threads: 4,167 threads
• Emails: 21,684 emails

Language:

• Languages: English (en)

Use Cases:

1. Natural Language Processing (NLP) Research:
   • Analyze email thread contents and human-generated summaries for advancements in NLP tasks.
2. Text Summarization Models:
   • Train and evaluate text summarization models using the provided email threads and summaries.
3. Email Analytics:
   • Gain insights into communication patterns, sender-receiver relationships, and content analysis.

File Formats:

• CSV Files:
  • Easily importable into various data analysis tools.
• Pickle (pkl) Files:
  • Facilitates direct reading of the "to" column as a column of lists of strings.

• JSON Files:

  • Offers compatibility with JSON data structures, providing an additional option for users who prefer or require this widely-used format in their analytical workflows.

  • ****JSON File Features Description****

    [
      {
        "thread_id": [unique identifier],
        "subject": "[email thread subject]",
        "timestamp": [timestamp in milliseconds],
        "from": "[sender's name and identifier]",
        "to": [
          "[recipient 1]",
          "[recipient 2]",
          "[recipient 3]",
          ...
        ],
        "body": "[email content]"
      },
      ...
    ]
    

    [
      {
        "thread_id": [unique identifier],
        "summary": "[summary content]"
      },
      ...
    ]
    

****Files Structure:****

- Dataset
 ├── CSV
 │  ├── email_thread_details.csv
 │  └── email_thread_summaries.csv
 ├── Pickle
 │  ├── email_thread_details.pkl
 │  └── email_thread_summaries.pkl
 └── JSON
   ├── email_thread_details.json
   └── email_thread_summaries.json

License:

This dataset is provided under the MIT License.

Disclaimer:

The dataset has been anonymized and sanitized to ensure privacy and confidentiality.

Data Analysis Software Market Outlook

The global data analysis software market size was estimated at USD 45.6 billion in 2023 and is projected to reach approximately USD 123.8 billion by 2032, exhibiting a CAGR of 11.6% during the forecast period from 2024 to 2032. This impressive growth is fuelled by the increasing necessity of data-driven decision-making across various industries. The ability to derive actionable insights from vast amounts of data is making data analysis software indispensable for modern businesses.

One of the primary factors driving the growth of the data analysis software market is the exponential increase in data generation. With the proliferation of digital technologies, the amount of data generated by businesses, consumers, and devices has grown exponentially. This data explosion has created a pressing need for sophisticated tools that can analyze and interpret vast datasets. Consequently, organizations are increasingly turning to data analysis software to gain a competitive edge by uncovering hidden patterns and trends.

Another significant growth factor is the adoption of artificial intelligence (AI) and machine learning (ML) technologies. Modern data analysis software increasingly incorporates AI and ML algorithms to enhance the accuracy and speed of data processing. These advanced technologies enable predictive analytics, which helps organizations forecast future trends and make proactive decisions. The integration of AI and ML in data analysis tools is expected to drive further market growth as businesses seek more intelligent and automated solutions.

The rising demand for real-time analytics is also a crucial driver of market expansion. In today's fast-paced business environment, timely insights are critical for staying ahead of the competition. Real-time data analysis allows organizations to respond swiftly to changing market conditions, customer preferences, and operational challenges. This need for immediate data-driven decision-making is propelling the adoption of data analysis software that can provide real-time analytics capabilities.

Regionally, North America dominates the data analysis software market due to the presence of major technology companies and a high level of digital adoption across various industries. However, Asia Pacific is expected to witness the highest growth rate during the forecast period. The rapid economic development in countries like China and India, coupled with increasing investments in digital infrastructure, is driving the demand for data analysis solutions in the region. Additionally, the European market is also significant, with stringent data protection regulations fostering the need for advanced data analysis tools.

Component Analysis

The data analysis software market by component is segmented into software and services. The software segment is the largest, driven by the increasing need for sophisticated tools that can handle large volumes of data and provide deep insights. Modern data analysis software offers a wide range of functionalities, including data mining, predictive analytics, statistical analysis, and machine learning. These capabilities make software solutions indispensable for organizations looking to leverage data for strategic decision-making.

The services segment, although smaller than the software segment, is also experiencing significant growth. Services include consulting, implementation, and maintenance of data analysis software. As businesses adopt these technologies, they often require expert guidance to integrate them into their existing systems and workflows. Consulting services help organizations identify the best tools for their needs, while implementation services ensure smooth deployment and integration. Ongoing maintenance services are also crucial for ensuring that the software operates efficiently and stays up-to-date with the latest features and security protocols.

Customization services are another vital part of the services segment. Many organizations have unique data analysis requirements that off-the-shelf software may not fully meet. Customized solutions tailored to an organization's specific needs are becoming increasingly common. These bespoke solutions help companies maximize the value they derive from their data, making customization an important growth driver for the services segment.

The growing complexity of data and the increasing use of hybrid and multi-cloud environments are also contributing to the demand for professional services. As organizations store and

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Cool. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Cool, the median income for all workers aged 15 years and older, regardless of work hours, was $21,250 for males and $26,250 for females.

Contrary to expectations, women in Cool, women, regardless of work hours, earn a higher income than men, earning 1.24 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Cool was not feasible.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Cool median household income by race. You can refer the same here