The statistic shows the distribution of users potential preference for a voice assistants over a website or application worldwide, as of November 2017. According to the survey, 52 percent of respondents indicated that they would prefer a voice assistant to a website or app due to its convenience and 48 percent of respondents reported that voice assistants could allow them to multi-task or work hands free.

This statistic shows the results of a survey conducted in the first quarter of 2016 among 3,000 adult Americans on their preferred cloud data storage brand. The results were sorted by income tiers. During the survey, 31.2 percent of respondents earning less than 50,000 U.S.dollars per year stated their preferred cloud data storage brand is Amazon Cloud Drive. The same percentage of respondents earning more than 50,000 U.S. dollars per year agreed.

Open Image Preferences

 Prompt: Anime-style concept art of a Mayan Quetzalcoatl biomutant, dystopian world, vibrant colors, 4K.



     Image 1



     Image 2







 Prompt: 8-bit pixel art of a blue knight, green car, and glacier landscape in Norway, fantasy style, colorful and detailed.



     Image 1… See the full description on the dataset page: https://huggingface.co/datasets/data-is-better-together/open-image-preferences-v1-binarized.

Dataset Summary

The wise-data-preferences dataset is a synthetically created collection of values-laden conversations with preferred and rejected responses, designed to train language models to provide more nuanced and helpful responses to harmful, heavy, or exploratory questions. This dataset was specifically created to train the WiseLLama-8B model, a LLaMa-3.1-8B-Instruct model fine-tuned using DPO (Direct Preference Optimization).

  Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/meaningalignment/wise-data-preferences.

As of September 2024, 75 percent of the 100 most visited websites in the United States shared personal data with advertising 3rd parties, even when users opted out. Moreover, 70 percent of them drop advertising 3rd party cookies even when users opt out.

Funk, Patricia, (2016) "How Accurate are Surveyed Preferences for Public Policies? Evidence from a Unique Institutional Setup." Review of Economics and Statistics 98:3, 442-454.

The dataset consists of one large excel spreadsheet, which may be opened with Microsoft Excel or Google Sheets. Each row represents the data collected for one individual tasting by a panelist; all personal identifiers have been removed. The first row contains the 48 variable names. The variables are defined as follows:

Judge: numerical identifier of an individual person. Ranges from 1 to 118. Cluster: numerical identifier for which consumer preference segmentation cluster the individual judge was identified as belonging to. Either 1 or 2. (See Cotter et al. 2021). Week: identifies which week the tasting of that coffee occurred. Either 1, 2, or 3 for first, second, or third weeks respectively. Session Number: identifies which of the 18 unique tasting sessions this tasting occurred at. Ranges from 1 to 18 (with six tastings per week over three weeks). Position: identifies the order in which this specific coffee was sampled within a session....

Descriptive statistics for samples with and without empty cells.

The majority of 18 to 44-year-old U.S. consumers preferred to binge-watch a whole series, according to 40 percent of respondents to a 2023 survey. In comparison, the share of viewers aged 45 years and older stating to do so was 29 percent, while most of the people interviewed in this age group said that this depends on the TV show.

This dataset provides quarterly volumes for spoken language preferences at the national level of individuals filing claims for Supplemental Security Income (SSI) Aged benefits for fiscal years (FY) 2023 on.

This data set provides annual volumes for spoken language preferences at the national level of individuals filing claims for Supplemental Security Income (SSI) Blind and Disabled benefits for federal fiscal years (FY) 2023 on.

This file contains SSA client's language preference for written and spoken communication only if the preference for written or spoken is other than English.

This statistic shows the preferences of customers who interact with customer services online, between a chatbot or virtual assistant and a live customer service representative, as of 2017. At the time of the survey, 80 percent of customers indicated they would prefer to interact with a real person rather than an intelligent robot or chatbot.

Success.ai’s Consumer Behavior Data for Consumer Goods & Electronics Industry Leaders in Asia, the US, and Europe offers a robust dataset designed to empower businesses with actionable insights into global consumer trends and professional profiles. Covering executives, product managers, marketers, and other professionals in the consumer goods and electronics sectors, this dataset includes verified contact information, professional histories, and geographic business data.

With access to over 700 million verified global profiles and firmographic data from leading companies, Success.ai ensures your outreach, market analysis, and strategic planning efforts are powered by accurate, continuously updated, and GDPR-compliant data. Backed by our Best Price Guarantee, this solution is ideal for businesses aiming to navigate and lead in these fast-paced industries.

Why Choose Success.ai’s Consumer Behavior Data?

1. Verified Contact Data for Precision Engagement

   • Access verified email addresses, phone numbers, and LinkedIn profiles of professionals in the consumer goods and electronics industries.
   • AI-driven validation ensures 99% accuracy, optimizing communication efficiency and minimizing data gaps.

2. Comprehensive Global Coverage

   • Includes profiles from key markets in Asia, the US, and Europe, covering regions such as China, India, Germany, and the United States.
   • Gain insights into region-specific consumer trends, product preferences, and purchasing behaviors.

3. Continuously Updated Datasets

   • Real-time updates capture career progressions, company expansions, market shifts, and consumer trend data.
   • Stay aligned with evolving market dynamics and seize emerging opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible use and legal compliance for all data-driven campaigns.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with industry leaders, marketers, and decision-makers in consumer goods and electronics industries worldwide.
• Consumer Trend Insights: Gain detailed insights into product preferences, purchasing patterns, and demographic influences.
• Business Locations: Access geographic data to identify regional markets, operational hubs, and emerging consumer bases.
• Professional Histories: Understand career trajectories, skills, and expertise of professionals driving innovation and strategy.

Key Features of the Dataset:

1. Decision-Maker Profiles in Consumer Goods and Electronics

   • Identify and engage with professionals responsible for product development, marketing strategy, and supply chain optimization.
   • Target individuals making decisions on consumer engagement, distribution, and market entry strategies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (consumer electronics, FMCG, luxury goods), geographic location, or job function.
   • Tailor campaigns to align with specific industry trends, market demands, and regional preferences.

3. Consumer Trend Data and Insights

   • Access data on regional product preferences, spending behaviors, and purchasing influences across key global markets.
   • Leverage these insights to shape product development, marketing campaigns, and customer engagement strategies.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing and Demand Generation

   • Design campaigns tailored to consumer preferences, regional trends, and target demographics in the consumer goods and electronics industries.
   • Leverage verified contact data for multi-channel outreach, including email, social media, and direct marketing.

2. Market Research and Competitive Analysis

   • Analyze global consumer trends, spending patterns, and product preferences to refine your product portfolio and market positioning.
   • Benchmark against competitors to identify gaps, emerging needs, and growth opportunities in target regions.

3. Sales and Partnership Development

   • Build relationships with key decision-makers at companies specializing in consumer goods or electronics manufacturing and distribution.
   • Present innovative solutions, supply chain partnerships, or co-marketing opportunities to grow your market share.

4. Product Development and Innovation

   • Utilize consumer trend insights to inform product design, pricing strategies, and feature prioritization.
   • Develop offerings that align with regional preferences and purchasing behaviors to maximize market impact.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality consumer behavior data at competitive prices, ensuring maximum ROI for your outreach, research, and ma...

According to a 2024 survey on digital usage in China, over a third of Chinese internet users said they used ad blockers when surfing the internet. One in five respondents expressed concerns about internet companies misusing their digital data.

SYNTHETIC-1: Two Million Crowdsourced Reasoning Traces from Deepseek-R1

SYNTHETIC-1 is a reasoning dataset obtained from Deepseek-R1, generated with crowdsourced compute and annotated with diverse verifiers such as LLM judges or symbolic mathematics verifiers. This is the SFT version of the dataset - the raw data and SFT dataset can be found in our 🤗 SYNTHETIC-1 Collection. The dataset consists of the following tasks and verifiers that were implemented in our library genesys:… See the full description on the dataset page: https://huggingface.co/datasets/PrimeIntellect/SYNTHETIC-1-Preference-Data.

Estimation of mating preferences is a prerequisite for understanding how sexual selection through mate choice shapes both mating systems and sexual dimorphisms. Most studies of mating preferences assay mate choice using either a no choice or a binary choice design. Binary choice trials typically employ either an artificial signal or some fixed difference (e.g. colour or size) between the signalling individuals. Although statistically more powerful than no choice designs, such experiments cannot be used to detect stabilizing preference. Further, the use of artificial signals is problematic because signal components tend to be varied in isolation, and hence do not reflect natural variation. Here, we present a new method that uses natural variation among individuals in choice trials to determine if mating preference is absent, directional, and/or stabilizing. The protocol is tested using simulation and shown to be robust to the preference function, to have the required statistical power, to be unbiased in almost all cases, and to give confidence regions that modestly overestimate the desired 95% criterion. We demonstrate the use of the method with data from mate choice trials of the sand cricket, Gryllus firmus. Software to apply this new approach is provided in Dryad.

Children express preferences for a wide range of options, such as objects, and frequently observe the preferences that others express towards these things. However, little is know about how these initial preferences develop. The present research investigated whether one particular type of social information – other children’s preferences – influences children’s own preferences. Four-year-old children observed, via video, two boys and two girls display the same preference for one of two stickers. Each child (peer) expressed liking for one sticker and dislike for the other. Then children completed two rounds of the Dictator Game, a classic resource distribution task. In each round, children distributed either 10 liked stickers or 10 disliked stickers (counterbalanced) between themselves and another child who was not present. If the preferences expressed by their peers influenced children’s own preferences, children should keep more of the liked than disliked stickers for themselves. In line with this prediction, more children kept more liked than disliked stickers, indicating their distribution patterns were influenced by their peers’ preferences. This finding suggests that children extracted informational content about the value of the stickers from their peers and used that information to guide their own preferences. Children might also have aligned their preferences with those of their peers to facilitate social bonding and group membership. This research demonstrates the strong influence of peers on children’s developing preferences, and reveals the effect of peer influence via video – a medium that young children are frequently exposed to but often struggle to learn from in other contexts.

Background The nature of primary care provision is changing. GPs and other staff providing primary care are no longer based solely in general practices but may work in a range of other “settings”, for example providing extended hours, GP streaming in Accident and Emergency (A&E) departments, and Out-of-Hours services. There is an increasing need to understand the different ways in which GPs and their colleagues are providing primary care services. This is a complicated and ever-changing area. Most GPs work in general practices. Information about GPs and other practice-based staff is provided directly to NHS Digital on a quarterly basis by the GP practices which submit record-level data via the National Workforce Reporting System (NWRS). Information about these individuals and their associated workforce are published in General Practice Workforce statistics (https://digital.nhs.uk/data-and-information/publications/statistical/general-and-personal-medical-services). Similarly, information about GPs and other healthcare professionals directly employed by hospital trusts should be captured and included in NHS Workforce Statistics (https://digital.nhs.uk/data-and-information/publications/statistical/nhs-workforce-statistics). Additional work is required to identify which parts, if any, of this activity can reasonably be classified as primary care provision. In addition, where available, details of individuals providing NHS funded care in the independent sector are captured and reported in NHS Digital’s Independent Healthcare Provider Workforce Statistics (https://digital.nhs.uk/data-and-information/publications/statistical/independent-healthcare-provider-workforce-statistics). However, there remains an uncertain number of GPs and other healthcare professionals that are providing patient care in these alternative settings and whose information, including details of their working hours, is not collected. As understanding the entirety of the healthcare workforce, both NHS and independent sector, is crucial to meeting the needs of patients and vital for workforce planning, we have been working to better understand the nature of healthcare provision, and in particular, the scale and extent of GP provision outside the more traditional settings. The number of service providers in these alternative settings – which are not necessarily NHS organisations – is large and services are commissioned differently in each CCG making it difficult to identify GPs and to collect accurate and complete workforce data. We are working closely with key stakeholders including Department of Health and Social Care, NHS England and NHS Improvement and Health Education England to explore the best way to collect more accurate and complete data for this part of the GP workforce. This is likely to include reviewing whether sufficient improvements could be made to this quarterly collection to enhance the data quality, as well as considering whether it would be feasible, affordable or preferable to collect record-level data directly from providers. These are new and experimental statistics which are under development. We welcome feedback from users to help us evaluate their suitability and quality. Please send any comments to PrimaryCareWorkforce@nhs.net including “GPs in Alternative Settings” in the subject line. Your feedback about these experimental statistics will help us evaluate their usefulness and inform our future plans. While the experimental statistics designation should not be taken to indicate that the statistics are of poor quality, there are nonetheless a number of data quality considerations that affect the levels of confidence that may be bestowed upon the figures and users are advised to consult the Data Quality section of this release.

FreedomIntelligence/Arabic-preference-data-RLHF dataset hosted on Hugging Face and contributed by the HF Datasets community