https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy
Discover the Walmart Products Free Dataset, featuring 2,000 records in CSV format. This dataset includes detailed information about various Walmart products, such as names, prices, categories, and descriptions.
It’s perfect for data analysis, e-commerce research, and machine learning projects. Download now and kickstart your insights with accurate, real-world data.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.
Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.
In an effort to help combat COVID-19, we created a COVID-19 Public Datasets program to make data more accessible to researchers, data scientists and analysts. The program will host a repository of public datasets that relate to the COVID-19 crisis and make them free to access and analyze. These include datasets from the New York Times, European Centre for Disease Prevention and Control, Google, Global Health Data from the World Bank, and OpenStreetMap. Free hosting and queries of COVID datasets As with all data in the Google Cloud Public Datasets Program , Google pays for storage of datasets in the program. BigQuery also provides free queries over certain COVID-related datasets to support the response to COVID-19. Queries on COVID datasets will not count against the BigQuery sandbox free tier , where you can query up to 1TB free each month. Limitations and duration Queries of COVID data are free. If, during your analysis, you join COVID datasets with non-COVID datasets, the bytes processed in the non-COVID datasets will be counted against the free tier, then charged accordingly, to prevent abuse. Queries of COVID datasets will remain free until Sept 15, 2021. The contents of these datasets are provided to the public strictly for educational and research purposes only. We are not onboarding or managing PHI or PII data as part of the COVID-19 Public Dataset Program. Google has practices & policies in place to ensure that data is handled in accordance with widely recognized patient privacy and data security policies. See the list of all datasets included in the program
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Gratis by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Gratis. The dataset can be utilized to understand the population distribution of Gratis by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Gratis. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Gratis.
Key observations
Largest age group (population): Male # 30-34 years (79) | Female # 20-24 years (48). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Gratis Population by Gender. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.
Our NFL Data product offers extensive access to historic and current National Football League statistics and results, available in multiple formats. Whether you're a sports analyst, data scientist, fantasy football enthusiast, or a developer building sports-related apps, this dataset provides everything you need to dive deep into NFL performance insights.
Key Benefits:
Comprehensive Coverage: Includes historic and real-time data on NFL stats, game results, team performance, player metrics, and more.
Multiple Formats: Datasets are available in various formats (CSV, JSON, XML) for easy integration into your tools and applications.
User-Friendly Access: Whether you are an advanced analyst or a beginner, you can easily access and manipulate data to suit your needs.
Free Trial: Explore the full range of data with our free trial before committing, ensuring the product meets your expectations.
Customizable: Filter and download only the data you need, tailored to specific seasons, teams, or players.
API Access: Developers can integrate real-time NFL data into their apps with API support, allowing seamless updates and user engagement.
Use Cases:
Fantasy Football Players: Use the data to analyze player performance, helping to draft winning teams and make better game-day decisions.
Sports Analysts: Dive deep into historical and current NFL stats for research, articles, and game predictions.
Developers: Build custom sports apps and dashboards by integrating NFL data directly through API access.
Betting & Prediction Models: Use data to create accurate predictions for NFL games, helping sportsbooks and bettors alike.
Media Outlets: Enhance game previews, post-game analysis, and highlight reels with accurate, detailed NFL stats.
Our NFL Data product ensures you have the most reliable, up-to-date information to drive your projects, whether it's enhancing user experiences, creating predictive models, or simply enjoying in-depth football analysis.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
The Financial Risk Assessment Dataset provides detailed information on individual financial profiles. It includes demographic, financial, and behavioral data to assess financial risk. The dataset features various columns such as income, credit score, and risk rating, with intentional imbalances and missing values to simulate real-world scenarios.
Lucror Analytics: Fundamental Fixed Income Data and Financial Models for High-Yield Bond Issuers
At Lucror Analytics, we deliver expertly curated data solutions focused on corporate credit and high-yield bond issuers across Europe, Asia, and Latin America. Our data offerings integrate comprehensive fundamental analysis, financial models, and analyst-adjusted insights tailored to support professionals in the credit and fixed-income sectors. Covering 400+ bond issuers, our datasets provide a high level of granularity, empowering asset managers, institutional investors, and financial analysts to make informed decisions with confidence.
By combining proprietary financial models with expert analysis, we ensure our Fixed Income Data is actionable, precise, and relevant. Whether you're conducting credit risk assessments, building portfolios, or identifying investment opportunities, Lucror Analytics offers the tools you need to navigate the complexities of high-yield markets.
What Makes Lucror’s Fixed Income Data Unique?
Comprehensive Fundamental Analysis Our datasets focus on issuer-level credit data for complex high-yield bond issuers. Through rigorous fundamental analysis, we provide deep insights into financial performance, credit quality, and key operational metrics. This approach equips users with the critical information needed to assess risk and uncover opportunities in volatile markets.
Analyst-Adjusted Insights Our data isn’t just raw numbers—it’s refined through the expertise of seasoned credit analysts with 14 years average fixed income experience. Each dataset is carefully reviewed and adjusted to reflect real-world conditions, providing clients with actionable intelligence that goes beyond automated outputs.
Focus on High-Yield Markets Lucror’s specialization in high-yield markets across Europe, Asia, and Latin America allows us to offer a targeted and detailed dataset. This focus ensures that our clients gain unparalleled insights into some of the most dynamic and complex credit markets globally.
How Is the Data Sourced? Lucror Analytics employs a robust and transparent methodology to source, refine, and deliver high-quality data:
This rigorous process ensures that our data is both reliable and actionable, enabling clients to base their decisions on solid foundations.
Primary Use Cases 1. Fundamental Research Institutional investors and analysts rely on our data to conduct deep-dive research into specific issuers and sectors. The combination of raw data, adjusted insights, and financial models provides a comprehensive foundation for decision-making.
Credit Risk Assessment Lucror’s financial models provide detailed credit risk evaluations, enabling investors to identify potential vulnerabilities and mitigate exposure. Analyst-adjusted insights offer a nuanced understanding of creditworthiness, making it easier to distinguish between similar issuers.
Portfolio Management Lucror’s datasets support the development of diversified, high-performing portfolios. By combining issuer-level data with robust financial models, asset managers can balance risk and return while staying aligned with investment mandates.
Strategic Decision-Making From assessing market trends to evaluating individual issuers, Lucror’s data empowers organizations to make informed, strategic decisions. The regional focus on Europe, Asia, and Latin America offers unique insights into high-growth and high-risk markets.
Key Features of Lucror’s Data - 400+ High-Yield Bond Issuers: Coverage across Europe, Asia, and Latin America ensures relevance in key regions. - Proprietary Financial Models: Created by one of the best independent analyst teams on the street. - Analyst-Adjusted Data: Insights refined by experts to reflect off-balance sheet items and idiosyncrasies. - Customizable Delivery: Data is provided in formats and frequencies tailored to the needs of individual clients.
Why Choose Lucror Analytics? Lucror Analytics and independent provider free from conflicts of interest. We are committed to delivering high-quality financial models for credit and fixed-income professionals. Our proprietary approach combines proprietary models with expert insights, ensuring accuracy, relevance, and utility.
By partnering with Lucror Analytics, you can: - Safe costs and create internal efficiencies by outsourcing a highly involved and time-consuming processes, including financial analysis and modelling. - Enhance your credit risk ...
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the median household income in Gratis. It can be utilized to understand the trend in median household income and to analyze the income distribution in Gratis by household type, size, and across various income brackets.
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).
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.
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/.
Explore our comprehensive data analysis and visual representations for a deeper understanding of Gratis median household income. You can refer the same here
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
This dataset is a synthetic version inspired by the original Credit Risk dataset on Kaggle and enriched with additional variables based on Financial Risk for Loan Approval data. SMOTENC was used to simulate new data points to enlarge the instances. The dataset is structured for both categorical and continuous features.
The dataset contains 45,000 records and 14 variables, each described below:
Column | Description | Type |
---|---|---|
person_age | Age of the person | Float |
person_gender | Gender of the person | Categorical |
person_education | Highest education level | Categorical |
person_income | Annual income | Float |
person_emp_exp | Years of employment experience | Integer |
person_home_ownership | Home ownership status (e.g., rent, own, mortgage) | Categorical |
loan_amnt | Loan amount requested | Float |
loan_intent | Purpose of the loan | Categorical |
loan_int_rate | Loan interest rate | Float |
loan_percent_income | Loan amount as a percentage of annual income | Float |
cb_person_cred_hist_length | Length of credit history in years | Float |
credit_score | Credit score of the person | Integer |
previous_loan_defaults_on_file | Indicator of previous loan defaults | Categorical |
loan_status (target variable) | Loan approval status: 1 = approved; 0 = rejected | Integer |
The dataset can be used for multiple purposes:
loan_status
variable (approved/not approved) for potential applicants.credit_score
variable based on individual and loan-related attributes. Mind the data issue from the original data, such as the instance > 100-year-old as age.
This dataset provides a rich basis for understanding financial risk factors and simulating predictive modeling processes for loan approval and credit scoring.
https://brightdata.com/licensehttps://brightdata.com/license
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.
https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy
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.
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
Introducing Job Posting Datasets: Uncover labor market insights!
Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.
Job Posting Datasets Source:
Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.
Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.
StackShare: Access StackShare datasets to make data-driven technology decisions.
Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.
Choose your preferred dataset delivery options for convenience:
Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.
Why Choose Oxylabs Job Posting Datasets:
Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.
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Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.
Pricing Options:
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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:
Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Free Soil household income by age. The dataset can be utilized to understand the age-based income distribution of Free Soil income.
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).
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.
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/.
Explore our comprehensive data analysis and visual representations for a deeper understanding of Free Soil income distribution by age. You can refer the same here
https://brightdata.com/licensehttps://brightdata.com/license
We'll tailor a bespoke airline dataset to meet your unique needs, encompassing flight details, destinations, pricing, passenger reviews, on-time performance, and other pertinent metrics.
Leverage our airline datasets for diverse applications to bolster strategic planning and market analysis. Scrutinizing these datasets enables organizations to grasp traveler preferences and industry trends, facilitating nuanced operational adaptations and marketing initiatives. Customize your access to the entire dataset or specific subsets as per your business requisites.
Popular use cases involve optimizing route profitability, improving passenger satisfaction, and conducting competitor analysis.
******CONTEXT******: The data is about hospital patient data, a collection of data from the patient entering the hospital until his exit.
******CONTENT******: Date : The day patient visited Medication Revenue : the revenue of the medication Lab Cost : Lab cost paid by the patient Consultation Revenue : Revenue of the consultation Doctor Type : The type of doctor who treats the patient Financial Class : Patient financial Class Patient Type : (OUTPATIENT) Entry Time : Entered the (OUTPATIENT) & Hospital Post-Consultation Time : when the doctor tells the patients to enter the clinic room Completion Time : when the patients exit the clinic room or the building Patient ID : The unique Identity document
******Requirements******: Dose the patient type affect the waiting time? Is there a specific type of patient waiting a long time? Are we too busy? Do we have staffing issues? How much patients wait before the doctor can see them? What type of staff do we need or where do we need them? What days of the week are affected? How can we fix it?
Please up-vote if you find this dataset helpful!🖤!
https://data.4tu.nl/info/fileadmin/user_upload/Documenten/4TU.ResearchData_Restricted_Data_2022.pdfhttps://data.4tu.nl/info/fileadmin/user_upload/Documenten/4TU.ResearchData_Restricted_Data_2022.pdf
This folder contains processed data (video and wearable sensors) for 16 mins
of interaction that were annotated for the Conflab dataset.
./cameras
./video_segments contains the overhead video recordings for 5 cameras
in MP4 files. These are split into 2 min segments, down-scaled to 960x540px,
and denoised. The resulting files are the ones that were used to annotate poses
and actions.
For applications that require higher resolutions, the original video files in the
"data_raw" folder are at 1920x1080 resolution and a script is provided to extract
the same 2min segments.
./camera-calibration contains the camera instrinsic files obtained from:
https://github.com/idiap/multicamera-calibration. Camera extrinsic parameters
can be calculated using the existing intrinsic parameters and the instructions
in the multicamera-calibration repo. The coordinates in the image are provided
by the crosses marked on the floor, which are visible in the video recordings.
The crosses are 1m apart (=100cm).
./wearables
contains one single dataframe with aggregated sensor information
(accelerometer, gyroscope, magnetometer, rotation, proximity) per each person.
The data has been interpolated and imputed at 50Hz (fixed). The code used to obtain
these files from the raw data can be found at:
https://github.com/TUDelft-SPC-Lab/conflab/blob/master/preprocessing/midge/preprocess.ipynb
This code can be used to preprocess larger segments of the werable data
from the "data_raw" folder.
Audio files are not part of this folder, but are provided in the "data_raw" file.
https://data.4tu.nl/info/fileadmin/user_upload/Documenten/4TU.ResearchData_Restricted_Data_2022.pdfhttps://data.4tu.nl/info/fileadmin/user_upload/Documenten/4TU.ResearchData_Restricted_Data_2022.pdf
This dataset comprises five sets of data collected throughout of Alev Sönmez’s PhD Thesis project: Sönmez, A. (2024). Dancing the Vibe: Designerly Exploration of Group Mood in Work Settings. (Doctoral dissertation in review). Delft University of Technology, Delft, the Netherlands.
This thesis aims to contribute to the granular understanding of group mood by achieving three objectives,each representing a key research question in the project: (1) to develop a descriptive overview of nuanced group moods, (2) to develop knowledge and tools to effectively communicate nuanced group moods, and (3) to develop knowledge and insights to facilitate reflection on group mood. The research was guided by the following research questions: (1) What types of group moods are experienced in small work groups? (2) How can nuanced group moods be effectively communicated? (3) How can group mood reflection be facilitated?
This research was supported by VICI grant number 453-16-009 from The Netherlands Organization for Scientific Research (NWO), Division for the Social and Behavioral Sciences, awarded to Pieter M. A. Desmet.
The data is organized into folders corresponding to the chapters of the thesis. Each folder contains a README file with specific information about the dataset.
Capter_2_PhenomenologicalStudy: This dataset conssists of anonymized transcriptions of co-inquiry sessions where 5 small project groups described the group moods they experienced in their eight most recent meetings. Additonaly, we share the observation notes wwe collected in those meetings, the maps filled in during the co-inquiry sessions, the materials used to collect data, and the coding scheme used to analyze the group mood descriptions.
Chapter_3_ImageEvaluationStudy: This dataset consists of anonymized scores from 38 participants indicating the strength of the association between eight group mood–expressing images and 36 group mood qualities, along with their free descriptions of the group moods perceived in those images. Addtioanlly we share the questionnaire design, the eight images, and the data processing files (t-test, correspondence analysis outputs, free description coding, heat map).
Chapter_4_VideoEvaluationStudy: This dataset consists of anonymized scores from 40 participants indicating the strength of the association between eight group mood–expressing videos and 36 group mood qualities, along with their free descriptions of the group moods perceived in those videos. Addtioanlly we share the questionnaire design, and the data processing files (t-test, correspondence analysis outputs, free description coding, heat map) and data processing files to compare the image and video set (PCA output, and image-video HIT rate comparison table).
Chapter_5_CardsetInterventionStudy: This dataset consists of anonymized written responses from each of the 12 project teams, along with notes taken during a plenary session with these teams, evaluating the efficacy of the intervention on their group mood management.
Chapter_6_WorkshopEvaluationStudy: This dataset consists of Anonymized transcriptions of five small work teams reflecting on their lived group mood experiences following the steps of an embodiment workshop we designed, including their takeaways from the workshop and discussions evaluating the workshop's efficacy in stimulating reflection and the overall experience of the workshop.
All the data is anonymized by removing the names of individuals and institutions. However, the interviews contain details where participants shared personal information about themselves, colleagues, and company dynamics. Therefore, the data should be handled with extra care to ensure that participant privacy is not put in danger. Contact N.A.Romero@tudelft.nl (Natalia Romero Herrera) to request access to the dataset.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The dataset has three sentiments namely, negative, neutral, and positive. It contains two fields for the tweet and label.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Free Soil population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Free Soil across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.
Key observations
In 2022, the population of Free Soil was 153, a 0.66% increase year-by-year from 2021. Previously, in 2021, Free Soil population was 152, an increase of 0.66% compared to a population of 151 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Free Soil decreased by 23. In this period, the peak population was 179 in the year 2004. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Free Soil Population by Year. You can refer the same here
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