About Datasets: - Domain : Marketing - Project: User Profiling and Segmentation - Datasets: user_profile_for_ads.csv - Dataset Type: Excel Data - Dataset Size: 16k+ records

KPI's: 1. Distribution of Key Demographic Variables like: a. Count of Age b. Count of Gender c. Count of Education Level d. Count of Income Level e. Count of Device Usage

1. Understanding Online Behavior like: a. Count of Time Spent Online (hrs/Weekday) b. Count of Time Spent Online (hrs/Weekend)

2. Ad Interaction Metrics: a. Count of likes and Reactions b. Count of click through rates (CTR) c. Count of Conversion Rate d. Count of Ad Interaction Time (secs) e. Count of Ad Interaction Time by Top Interests

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains pandas, matplotlib, seaborn, isnull, set_style, suptitle, countplot, palette, tight_layout, figsize, histplot, barplot, sklearn, standardscaler, OneHotEncoder, ColumnTransformer, Pipeline, KMeans, cluster_means, groupby, numpy, radar_df

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

The dataset refers to the measurement of axes of Ti-6Al-4V cylindrical surfaces obtained by lathe turning. The machined surfaces were measured using a Coordinate Measuring Machine (CMM) and the axis of each cylinder was derived from the CMM measures.

The dataset consists of a MAT-file including the CMM measurements and a Matlab function “LoadData.m” to extract and convert the data into Cartesian coordinates.

All the details about the dataset can be found in:

Colosimo, B.M., Pacella, M. Analyzing the effect of process parameters on the shape of 3D profiles (2011) Journal of Quality Technology, 43 (3), pp. 169-195.DOI: 10.1080/00224065.2011.11917856 Pacella, M., Colosimo, B.M. Multilinear principal component analysis for statistical modeling of cylindrical surfaces: a case study (2018) Quality Technology and Quantitative Management, 15 (4), pp. 507-525.DOI: 10.1080/16843703.2016.1226710

About Dataset:

Domain : Marketing Project: User Profiling and Segmentation Datasets: user_profile_for_ads Dataset Type: Excel Data Dataset Size: 16k+ record

KPI's: 1. Distribution of Key Demographic Variables like: a. Count of Age b. Count of Gender c. Count of Education Level d. Count of Income Level e. Count of Device Usage

1. Understanding Online Behavior like: a. Count of Time Spent Online (hrs/Weekday) b. Count of Time Spent Online (hrs/Weekend)

2. Ad Interaction Metrics: a. Count of likes and Reactions b. Count of click through rates (CTR) c. Count of Conversion Rate d. Count of Ad Interaction Time (secs) e. Count of Ad Interaction Time by Top Interests

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains bar chart, horizontal bars, circle, treemap, area chart, square, line chart, dashboard, slicers, navigation button.

Profiling as a Service Market Outlook

According to our latest research, the Profiling as a Service market size reached USD 5.8 billion in 2024 at a robust growth momentum, driven by increasing digital transformation initiatives and the rising need for advanced analytics across industries. The market is projected to expand at a CAGR of 17.2% over the forecast period, reaching an estimated USD 27.6 billion by 2033. This strong growth trajectory is underpinned by the surging demand for real-time data-driven decision-making, enhanced risk mitigation, and compliance management in a rapidly evolving threat landscape.

One of the primary growth drivers for the Profiling as a Service market is the exponential increase in data generation across enterprises. With the proliferation of digital channels, IoT devices, and online transactions, organizations are inundated with massive volumes of structured and unstructured data. Profiling as a Service solutions enable businesses to harness this data, extracting actionable insights for fraud detection, customer analytics, and risk management. The ability to integrate disparate data sources and deliver real-time profiling has become a critical differentiator, especially for organizations operating in highly regulated sectors such as BFSI, healthcare, and government. As regulatory requirements become more stringent and the cost of non-compliance rises, demand for sophisticated profiling capabilities continues to accelerate.

Another significant factor fueling the market's expansion is the rapid adoption of cloud-based solutions. Enterprises are increasingly migrating their data infrastructure to the cloud to achieve scalability, flexibility, and cost optimization. Profiling as a Service offerings, delivered via cloud platforms, provide seamless integration, ease of deployment, and on-demand scalability. This shift is particularly advantageous for small and medium enterprises (SMEs), which often lack the resources to invest in complex on-premises infrastructure. Additionally, the rise of hybrid and multi-cloud environments is further boosting the adoption of cloud-based profiling services, enabling organizations to maintain business continuity and enhance operational agility.

Technological advancements in artificial intelligence (AI), machine learning (ML), and big data analytics are also transforming the Profiling as a Service market. Modern profiling solutions leverage AI and ML algorithms to automate data analysis, detect anomalies, and predict potential threats with unprecedented accuracy. These technologies empower organizations to proactively identify fraudulent activities, assess risk, and personalize customer experiences at scale. The integration of AI-driven profiling tools with existing enterprise systems is enabling real-time monitoring and decision-making, thereby improving overall business performance. As AI and ML technologies continue to mature, their role in shaping the future of profiling services will become even more pronounced.

From a regional perspective, North America currently dominates the Profiling as a Service market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is at the forefront due to its advanced technological infrastructure, high adoption rates of cloud services, and stringent regulatory landscape. Meanwhile, Asia Pacific is expected to witness the fastest growth rate over the forecast period, driven by rapid digitalization, expanding e-commerce, and increasing cybersecurity concerns in countries such as China, India, and Japan. The region’s burgeoning SME sector and government initiatives aimed at promoting digital transformation are further propelling market growth. Europe remains a key market, benefiting from robust data privacy regulations and a strong emphasis on compliance management.

Component Analysis

The Profiling as a Service market by component is broadly segmented into software and services. The software segment encompasses a wide array of analytics platforms, data integration tools, and AI-powered profiling engines. These solutions are designed to automate the data profiling process, enabling organizations to identify patterns, detect anomalies, and generate actionable insights in real time. With the increasing complexity of data environments and the growing need for interoperability, software providers are focusing on developing modular, scalable, and customizable solutions. Integration with existing enterprise syste

The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications.

A set of process data from the facultative zone from an activated sludge process in a saline wastewater treatment plant

The Institute for Municipal and Regional Policy (IMRP) at Central Connecticut State University, in consultation with the Office of Policy and Management (OPM), has established a Racial Profiling Prohibition Advisory Board to help oversee the design, evaluation, and management of the racial profiling study mandated by PA 12-74, “An Act Concerning Traffic Stop Information.” The IMRP is working with the advisory board and all appropriate parties to enhance the collection and analysis of traffic stop data in Connecticut. Resources for the project are being made available through the National Highway Traffic and Safety Administration (NHTSA) grant, as administered through the Connecticut Department of Transportation. The primary purpose of the project is to monitor and prohibit racial profiling in Connecticut and to comply with NHTSA grant requirements and are outlined below. Analyze current racial profiling law and make recommendations to the Connecticut General Assembly to better align the statute to legislative intent and current best practices. Ensure compliance with the racial profiling law in as efficient, effective, transparent and inclusive a manner possible. Ensure compliance with NHTSA requirements of Section 1906 funding to include: Fund activities to prohibit racial profiling in the enforcement of State laws regulating the use of Federal-aid highways Collect, maintain and provide public access to traffic stop data Evaluate the results of such data; and develop and implement programs to reduce the occurrence of racial profiling, including programs to train law enforcement officers. The Racial Profiling Prohibition Project Advisory Board and the project staff have been meeting since May 2012 in an effort to outline a plan to successfully implement PA 12-74. The focus of this early phase of the project has been to better understand traffic stop data collection in other states. Four working groups were established to advise on various aspects of the process including; the standardized method for collecting, recording, reporting, and analyzing racial profiling data required by PA 12-74 and to accomplish tasks required to complete the Racial Profiling Prohibition Project. For more information contact: Ken Barrone Policy & Research Specialist Institute for Municipal & Regional Policy, Central Connecticut State University Tel: 860.832.1872 or Email: baroneket@ccsu.edu

About Dataset:

Domain : Marketing Project: User Profiling and Segmentation Datasets: user_profile_for_ads Dataset Type: Excel Data Dataset Size: 16k+ record

KPI's:

1. Distribution of Key Demographic Variables like: a. Count of Age b. Count of Gender c. Count of Education Level d. Count of Income Level e. Count of Device Usage

2. Understanding Online Behavior like: a. Count of Time Spent Online (hrs/Weekday) b. Count of Time Spent Online (hrs/Weekend)

3. Ad Interaction Metrics: a. Count of likes and Reactions b. Count of click through rates (CTR) c. Count of Conversion Rate d. Count of Ad Interaction Time (secs) e. Count of Ad Interaction Time by Top Interests

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains stacked column chart, stacked bar chart, pie chart, dashboard, slicers, page navigation button.

Morphological profiling with the Cell Painting assay has emerged as a promising method in drug discovery research. The assay captures morphological changes across various cellular compartments enabling the rapid prediction of compound bioactivity. We present a comprehensive morphological profiling resource using the carefully curated and well-annotated EU-OPENSCREEN Bioactive Compounds. The data was generated across four imaging sites with high-throughput confocal microscopes using the Hep G2 as well as the U2 OS cell line. We employed an extensive assay optimization process to achieve high data quality across the different sites. An analysis of the extracted profiles validates the robustness of the generated data. We used this resource to compare the morphological features of the different cell lines. By correlating the profiles with overall activity, cellular toxicity, several specific mechanisms of action (MOAs), and protein targets, we demonstrate the dataset's potential for facilitating more extensive exploration of mechanisms of action.

Instrumental-variable (IV) estimation is an essential method for applied researchers across the social and behavioral sciences who analyze randomized control trials marred by non-compliance or leverage partially exogenous treatment variation in observational studies. The potential outcomes framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE), and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far paid little attention to the characteristics of compliers and non-compliers. Yet profiling compliers and non-compliers is necessary to understand what subpopulation the researcher is making inferences about, and an important first step in evaluating the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always-takers and never-takers in terms of their covariates, and easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and non-compliers as standard practice accompanying any IV analysis.

We used label-free proteomics approach to analyze the protein expression dynamics of the antler tip in 6 developmental periods (15, 25, 45, 65, 100 and 130 days after the previous antler cast) and costal cartilage. the stages special proteins and differentially expressed proteins (DEPs) in different development stages were analyzed.

Source data of the revised manuscript.

Optimization of chemical reactions requires a thorough analysis of reaction products and intermediates over a given time course. Chemical reactions are often analyzed by liquid chromatography-mass spectrometry (LC-MS), but generating LC-MS samples and data analysis is time-consuming and produces a significant amount of waste. We sought to remove the sample preparation and data analysis steps by implementing an iChemExplorer/Agilent LC-MS instrument as our reactor and analysis tool, coupled with an automated report generator of reaction progress over time. Herein, we show that our easy-to-use walk-up automated reaction profiling (WARP) system can sample chemical reactions multiple times to produce a data-rich report of reaction progress over time.

The global Data Preparation Platform market is poised for substantial growth, estimated to reach $15,600 million by the study's end in 2033, up from $6,000 million in the base year of 2025. This trajectory is fueled by a Compound Annual Growth Rate (CAGR) of approximately 12.5% over the forecast period. The proliferation of big data and the increasing need for clean, usable data across all business functions are primary drivers. Organizations are recognizing that effective data preparation is foundational to accurate analytics, informed decision-making, and successful AI/ML initiatives. This has led to a surge in demand for platforms that can automate and streamline the complex, time-consuming process of data cleansing, transformation, and enrichment. The market's expansion is further propelled by the growing adoption of cloud-based solutions, offering scalability, flexibility, and cost-efficiency, particularly for Small & Medium Enterprises (SMEs). Key trends shaping the Data Preparation Platform market include the integration of AI and machine learning for automated data profiling and anomaly detection, enhanced collaboration features to facilitate teamwork among data professionals, and a growing focus on data governance and compliance. While the market exhibits robust growth, certain restraints may temper its pace. These include the complexity of integrating data preparation tools with existing IT infrastructures, the shortage of skilled data professionals capable of leveraging advanced platform features, and concerns around data security and privacy. Despite these challenges, the market is expected to witness continuous innovation and strategic partnerships among leading companies like Microsoft, Tableau, and Alteryx, aiming to provide more comprehensive and user-friendly solutions to meet the evolving demands of a data-driven world. Here's a comprehensive report description on Data Preparation Platforms, incorporating the requested information, values, and structure:

This dataset contains the benchmark and profiling results of WaterLily v1.4.0 which have been used to generate Figures 1,2,3 of the linked article. The dataset allows to analyze the computational performance of the solver. The benchmarks data is a JSON file which includes the solver's execution time of 100 time steps for different test cases, backends, and simulation sizes. The profiling data is the NVIDIA NVTX measured time range of the main routines of the solver (.nsys-rep files), and the performance of the main kernels (pressure solver 327, and convection/diffusion 355) measured by NVIDIA NCU (.nsys-ncu files).

Hudson River Estuary Shallow Water Surveys. Subbottom data was collected November 5 to December 15, 2009, in the estuary north from Saugerties to Troy. Data Collection and Processing: Subbottom Data - Fugro utilized the EdgeTech SB216 Chirp subbottom profiler system for seismic data collection. This system was operated using a swept frequency range of 2-16 KHz, maximizing subsurface resolution within the very shallow near-surface material (1- 5 m beneath seafloor). Subbottom data was processed and interpreted using Discover and SMT Kingdom software. The intent of the processing was to provide the NYSDEC with SEG-Y files that were properly filtered and spatially oriented to allow for near-surface interpretation of sediments in the Hudson River. Processing steps for the subbottom data included swell filtering to compensate for sea conditions during survey operations, compiling correct shotpoint navigation, and adjusting data gains for optimal interpretation. An isopach (sediment thickness) of the unconsolidated surficial sediments was created from the seafloor and mapped sediment horizon base using an acoustic two-way travel time of 1500 meters/second. Subbottom data was used to assist in selecting sediment sampling locations. Graphical sub-bottom profiles for areas of interest were produced and descriptive results will be included in the final report. Points were created every 300th shot (approximately 100 meters). Original contact information: Contact Name: John Ladd Contact Org: Hudson River National Estuarine Research Reserve, NYS DEC Phone: 845-889-4745 Email: jxLadd@gw.dec.state.ny.us

Content

Data profiling information about the published fields on the DataSF open data portal

The source code that generates this dataset can be found at: https://github.com/DataSF/datasf-profiler

Context

This is a dataset hosted by the city of San Francisco. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore San Francisco's Data using Kaggle and all of the data sources available through the San Francisco organization page!

• Update Frequency: This dataset is updated quarterly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Zac Ong on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Sake is a traditional Japanese alcoholic beverage prepared by multiple parallel fermentation of rice. The fermentation process of “yamahai-ginjo-shikomi” sake is mainly performed by three microbes, Aspergillus oryzae, Saccharomyces cerevisiae, and Lactobacilli; the levels of various metabolites fluctuate during the fermentation of sake. For evaluation of the fermentation process, we monitored the concentration of moderate-sized molecules (m/z: 200–1000) dynamically changed during the fermentation process of “yamahai-ginjo-shikomi” Japanese sake. This analysis revealed that six compounds were the main factors with characteristic differences in the fermentation process. Among the six compounds, four were leucine- or isoleucine-containing peptides and the remaining two were predicted to be small molecules. Quantification of these compounds revealed that their quantities changed during the month of fermentation process. Our metabolomic approach revealed the dynamic changes observed in moderate-sized molecules during the fermentation process of sake, and the factors found in this analysis will be candidate molecules that indicate the progress of “yamahai-ginjo-shikomi” sake fermentation.

The testis is a highly specialized tissue that plays dual roles in ensuring fertility by producing spermatozoa and hormones. Spermatogenesis is a complex process, resulting in the production of mature sperm from primordial germ cells. Significant structural and biochemical changes take place in the seminiferous epithelium of the adult testis during spermatogenesis. The gene expression pattern of testis in Chinese mitten crab (Eriocheir sinensis) has not been extensively studied, and limited genetic research has been performed on this species. The advent of high-throughput sequencing technologies enables the generation of genomic resources within a short period of time and at minimal cost. In the present study, we performed de novo transcriptome sequencing to produce a comprehensive transcript dataset for testis of E. sinensis. In two runs, we produced 25,698,778 sequencing reads corresponding with 2.31 Gb total nucleotides. These reads were assembled into 342,753 contigs or 141,861 scaffold sequences, which identified 96,311 unigenes. Based on similarity searches with known proteins, 39,995 unigenes were annotated based on having a Blast hit in the non-redundant database or ESTscan results with a cut-off E-value above 10−5. This is the first report of a mitten crab transcriptome using high-throughput sequencing technology, and all these testes transcripts can help us understand the molecular mechanisms involved in spermatogenesis and testis maturation.