5 datasets found
  1. RGB Image Pine-seedling Dataset: Three Population with half-sib structure,...

    • figshare.com
    txt
    Updated Jan 22, 2025
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    Jiri Chuchlík; Jaroslav Čepl; Eva Neuwirthová; Jan Stejskal; Jiří Korecký (2025). RGB Image Pine-seedling Dataset: Three Population with half-sib structure, dataset for segmentation model training and data of mean seedlings' color [Dataset]. http://doi.org/10.6084/m9.figshare.28239326.v1
    Explore at:
    txtAvailable download formats
    Dataset updated
    Jan 22, 2025
    Dataset provided by
    figshare
    Authors
    Jiri Chuchlík; Jaroslav Čepl; Eva Neuwirthová; Jan Stejskal; Jiří Korecký
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The datasets contain RGB photos of Scots pine seedlings of three populations from two different ecotypes originating in the Czech Republic:Plasy - lowland ecotype,Trebon - lowland ecotype,Decin - upland ecotype.These photos were taken in three different periods (September 10th 2021, October 23rd 2021, January 22nd 2022).File dataset_for_YOLOv7_training.zip contains image data with annotations for training YOLOv7 segmentation model (training and validation sets)The dataset also contains a table with information on individual Scots pine seedlings:affiliation to parent tree (mum)affiliation to population (site)row and column in which the seedling was grown (row, col)affiliation to the planter in which the seedling was grown (box)mean RGB values of pine seedling in three different periods (B_september, G_september, R_september B_october, G_october, R_october, B_january, G_january, R_january)mean HSV values of pine seedling in three different periods (H_september, S_september, V_september, H_october, S_october, V_october, H_january, S_january, V_january)

  2. o

    Synthetic Customer Feedback and Satisfaction Dataset

    • opendatabay.com
    .csv
    Updated May 6, 2025
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    Opendatabay Labs (2025). Synthetic Customer Feedback and Satisfaction Dataset [Dataset]. https://www.opendatabay.com/data/synthetic/6c96919d-2a30-4bb5-8a0d-4fb084964167
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    .csvAvailable download formats
    Dataset updated
    May 6, 2025
    Dataset authored and provided by
    Opendatabay Labs
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Reviews & Ratings
    Description

    This dataset is a synthetic representation of customer feedback and satisfaction data. It includes demographic, economic, and behavioural attributes alongside customer satisfaction metrics, providing insights into customer experience and loyalty.

    Dataset Features:

    • CustomerID: Unique identifier for each customer.
    • Age: Age of the customer in years.
    • Gender: Gender of the customer (e.g., "Male," "Female").
    • Country: Country of residence (e.g., "USA," "Canada").
    • Income: Annual income of the customer in USD.
    • ProductQuality: Rating of product quality on a scale from 1 (lowest) to 10 (highest).
    • ServiceQuality: Rating of service quality on a scale from 1 (lowest) to 10 (highest).
    • PurchaseFrequency: Number of purchases made by the customer within a specified period.
    • FeedbackScore: Categorization of customer feedback (e.g., "Low," "Medium," "High").
    • LoyaltyLevel: Loyalty tier assigned to the customer based on feedback and behaviour (e.g., "Bronze," "Silver," "Gold").

    - SatisfactionScore: Overall satisfaction score calculated based on various factors, ranging from 0 to 100.

    Distribution:

    https://storage.googleapis.com/opendatabay_public/images/download_(11)_copy_7a10f7d8-f3d7-465c-9700-d6905b0ded52.jpg" alt="Synthetic Customer Feedback and Satisfaction Data Distribution">

    Usage:

    This dataset can be used for various purposes, such as:

    • Customer Segmentation: Analyzing demographic and behavioral patterns to group customers by loyalty or satisfaction levels.
    • Predictive Modeling: Building models to predict customer satisfaction or loyalty based on quality ratings and demographic information.
    • Sentiment Analysis: Understanding customer sentiment through feedback scores and satisfaction ratings.

    - Service Improvement: Identifying gaps in product and service quality to enhance customer experience.

    Coverage:

    This synthetic dataset includes anonymized and fictionalized data, allowing for safe experimentation and analysis without violating real-world privacy constraints.

    License:

    CC0 (Public Domain)

    Who Can Use It:

    • Businesses: To simulate and analyze customer satisfaction and feedback trends.
    • Students and Educators: For practising data analysis, visualization, and predictive modelling.
    • Data Scientists: To develop and test machine learning models related to customer satisfaction and loyalty prediction.
  3. Consumer Sentiment Data | Global Audience Insights | Psychographic Profiles...

    • datarade.ai
    Updated Oct 27, 2021
    + more versions
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    Success.ai (2021). Consumer Sentiment Data | Global Audience Insights | Psychographic Profiles & Trends | Best Price Guaranteed [Dataset]. https://datarade.ai/data-products/consumer-sentiment-data-global-audience-insights-psychogr-success-ai
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Oct 27, 2021
    Dataset provided by
    Area covered
    Curaçao, Nigeria, Hong Kong, Hungary, Barbados, Italy, Ecuador, Uganda, South Africa, Macedonia (the former Yugoslav Republic of)
    Description

    Success.ai’s Consumer Sentiment Data offers businesses unparalleled insights into global audience attitudes, preferences, and emotional triggers. Sourced from continuous analysis of consumer behaviors, conversations, and feedback, this dataset includes psychographic profiles, interest data, and sentiment trends that help marketers, product teams, and strategists better understand their target customers. Whether you’re exploring a new market, refining your brand message, or enhancing product offerings, Success.ai ensures your consumer intelligence efforts are guided by timely, accurate, and context-rich data.

    Why Choose Success.ai’s Consumer Sentiment Data?

    1. Comprehensive Audience Insights

      • Access psychographic and interest-based profiles that reveal what motivates and influences your audience’s decisions.
      • Continuous updates ensure you stay aligned with shifting consumer sentiments, seasonal preferences, and emerging trends.
    2. Global Reach Across Industries and Demographics

      • Includes insights from various markets, age groups, cultural backgrounds, and income levels.
      • Identify consumer attitudes in different regions, helping you tailor campaigns, products, and messaging to diverse audiences.
    3. Continuously Updated Datasets

      • Real-time data analysis ensures that your consumer sentiment insights remain fresh, relevant, and actionable.
      • Adapt quickly to consumer feedback, market changes, and competitive pressures.
    4. Ethical and Compliant

      • Adheres to global data privacy regulations, ensuring your usage of consumer sentiment data is both legal and respectful of personal boundaries.

    Data Highlights:

    • Psychographic Profiles: Understand lifestyle preferences, values, and interests that shape consumer choices.
    • Sentiment Trends: Track evolving emotional responses to brands, products, and categories.
    • Global Audience Insights: Evaluate consumer sentiments across multiple regions, languages, and cultural contexts.
    • Continuous Updates: Receive current data that reflects the latest shifts in mood, opinion, and interest.

    Key Features of the Dataset:

    1. Granular Segmentation

      • Segment audiences by demographic, interest, buying behavior, and sentiment scores for targeted marketing efforts.
      • Focus on the attributes that matter most, from eco-conscious consumers to luxury shoppers or value seekers.
    2. Contextual Sentiment Analysis

      • Go beyond basic positive/negative sentiment to understand nuanced emotional responses.
      • Identify triggers that inspire loyalty, dissatisfaction, trust, or skepticism.
    3. AI-Driven Enrichment

      • Profiles enriched with actionable data provide deeper insights into consumer lifestyles, brand perceptions, and product affinities.
      • Leverage advanced analytics to develop personalized campaigns and product strategies.

    Strategic Use Cases:

    1. Marketing and Campaign Optimization

      • Craft campaigns that resonate emotionally by understanding what drives consumer engagement.
      • Adjust messaging, timing, and channels to align with evolving sentiment trends and seasonal shifts in consumer mood.
    2. Product Development and Innovation

      • Identify unmet consumer needs and preferences before launching new products.
      • Refine features, packaging, and pricing strategies based on real-time consumer responses.
    3. Brand Management and Positioning

      • Monitor brand perceptions to detect early signs of brand fatigue, trust erosion, or negative publicity.
      • Strengthen brand loyalty by addressing concerns, highlighting strengths, and adapting to changing market contexts.
    4. Competitive Analysis and Market Entry

      • Benchmark consumer sentiment towards competitors, industry leaders, and emerging disruptors.
      • Assess market readiness and optimize entry strategies for new regions or segments.

    Why Choose Success.ai?

    1. Best Price Guarantee

      • Access high-quality, verified data at competitive prices, ensuring efficient allocation of your marketing and research budgets.
    2. Seamless Integration

      • Integrate enriched sentiment data into your analytics, CRM, or marketing platforms via APIs or downloadable formats.
      • Simplify data management and accelerate decision-making processes.
    3. Data Accuracy with AI Validation

      • Benefit from AI-driven validation for reliable insights into consumer attitudes, leading to more confident data-driven strategies.
    4. Customizable and Scalable Solutions

      • Tailor datasets to focus on specific segments, regions, or interests, and scale as your business grows and evolves.

    APIs for Enhanced Functionality:

    1. Data Enrichment API

      • Enhance your existing consumer records with psychographic and sentiment insights, deepening your understanding of audience motivations.
    2. Lead Generation API

      • Identify audience segments receptive to your messaging, streamlini...
  4. f

    Demographic data: Gender and race distribution, and mean values with...

    • plos.figshare.com
    xls
    Updated Jun 5, 2023
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    Stefan Maetschke; Bhavna Antony; Hiroshi Ishikawa; Gadi Wollstein; Joel Schuman; Rahil Garnavi (2023). Demographic data: Gender and race distribution, and mean values with standard deviations and ranges for age, IOP, MD and GHT. [Dataset]. http://doi.org/10.1371/journal.pone.0219126.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Stefan Maetschke; Bhavna Antony; Hiroshi Ishikawa; Gadi Wollstein; Joel Schuman; Rahil Garnavi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Demographic data: Gender and race distribution, and mean values with standard deviations and ranges for age, IOP, MD and GHT.

  5. P

    DIHARD II Dataset

    • paperswithcode.com
    • opendatalab.com
    + more versions
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    Neville Ryant; Kenneth Church; Christopher Cieri; Alejandrina Cristia; Jun Du; Sriram Ganapathy; Mark Liberman, DIHARD II Dataset [Dataset]. https://paperswithcode.com/dataset/dihard-ii
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    Authors
    Neville Ryant; Kenneth Church; Christopher Cieri; Alejandrina Cristia; Jun Du; Sriram Ganapathy; Mark Liberman
    Description

    The DIHARD II development and evaluation sets draw from a diverse set of sources exhibiting wide variation in recording equipment, recording environment, ambient noise, number of speakers, and speaker demographics. The development set includes reference diarization and speech segmentation and may be used for any purpose including system development or training.

  6. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Jiri Chuchlík; Jaroslav Čepl; Eva Neuwirthová; Jan Stejskal; Jiří Korecký (2025). RGB Image Pine-seedling Dataset: Three Population with half-sib structure, dataset for segmentation model training and data of mean seedlings' color [Dataset]. http://doi.org/10.6084/m9.figshare.28239326.v1
Organization logo

RGB Image Pine-seedling Dataset: Three Population with half-sib structure, dataset for segmentation model training and data of mean seedlings' color

Explore at:
txtAvailable download formats
Dataset updated
Jan 22, 2025
Dataset provided by
figshare
Authors
Jiri Chuchlík; Jaroslav Čepl; Eva Neuwirthová; Jan Stejskal; Jiří Korecký
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

The datasets contain RGB photos of Scots pine seedlings of three populations from two different ecotypes originating in the Czech Republic:Plasy - lowland ecotype,Trebon - lowland ecotype,Decin - upland ecotype.These photos were taken in three different periods (September 10th 2021, October 23rd 2021, January 22nd 2022).File dataset_for_YOLOv7_training.zip contains image data with annotations for training YOLOv7 segmentation model (training and validation sets)The dataset also contains a table with information on individual Scots pine seedlings:affiliation to parent tree (mum)affiliation to population (site)row and column in which the seedling was grown (row, col)affiliation to the planter in which the seedling was grown (box)mean RGB values of pine seedling in three different periods (B_september, G_september, R_september B_october, G_october, R_october, B_january, G_january, R_january)mean HSV values of pine seedling in three different periods (H_september, S_september, V_september, H_october, S_october, V_october, H_january, S_january, V_january)

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