3 datasets found
  1. d

    Metabolic heat maps of tea and coffee variants

    • search.dataone.org
    • doi.pangaea.de
    Updated Jan 5, 2018
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    Montero-Vargas, Josaphat Miguel; Gonzáles-Gonzáles, Lindbergh Humberto; Galvez-Ponce, Eligio; Ramírez-Chávez, Enrique; Molina-Torres, Jorge; Chagolla, Alicia; Montagnon, Christophe; Winkler, Robert (2018). Metabolic heat maps of tea and coffee variants [Dataset]. http://doi.org/10.1594/PANGAEA.774218
    Explore at:
    Dataset updated
    Jan 5, 2018
    Dataset provided by
    PANGAEA Data Publisher for Earth and Environmental Science
    Authors
    Montero-Vargas, Josaphat Miguel; Gonzáles-Gonzáles, Lindbergh Humberto; Galvez-Ponce, Eligio; Ramírez-Chávez, Enrique; Molina-Torres, Jorge; Chagolla, Alicia; Montagnon, Christophe; Winkler, Robert
    Area covered
    Description

    High-throughput metabolic phenotyping is a challenge, but it provides an alternative and comprehensive access to the rapid and accurate characterization of plants. In addition to the technical issues of obtaining quantitative data of plenty of metabolic traits from numerous samples, a suitable data processing and statistical evaluation strategy must be developed. We present a simple, robust and highly scalable strategy for the comparison of multiple chemical profiles from coffee and tea leaf extracts, based on direct-injection electrospray mass spectrometry (DIESI-MS) and hierarchical cluster analysis (HCA). More than 3500 individual Coffea canephora and Coffea arabica trees from experimental fields in Mexico were sampled and processed using this method. Our strategy permits the classification of trees according to their metabolic fingerprints and the screening for families with desired characteristics, such as extraordinarily high or low caffeine content in their leaves.

  2. green tea analysis

    • kaggle.com
    Updated Mar 30, 2025
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    sarita (2025). green tea analysis [Dataset]. https://www.kaggle.com/datasets/saritas95/green-tea-analysis/versions/1
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 30, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    sarita
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    🌿 Green Tea Sales Analysis Dashboard I’m excited to share my latest Power BI project — a dynamic and interactive dashboard designed to analyze Green Tea sales data. This comprehensive solution offers actionable insights into key metrics such as revenue, product performance, customer behavior, and geographical distribution. With this dashboard, stakeholders can easily monitor sales trends, compare year-over-year performance, and make data-driven decisions.

    🖥️ Key Dashboard Features Net Revenue & Total Bills Generated: Provides a clear view of overall financial performance.

    Salesman Experience Analysis: Visualizes the average experience of sales representatives and its impact on sales.

    Geographical Sales Distribution: An interactive map highlights sales performance across different regions.

    Customer Type Breakdown: A detailed pie chart categorizes customers into Retail, Institutional, and Online segments.

    Product Performance: A combination of treemap and bar chart visualizations showcase top-selling and underperforming products.

    Revenue Trend & Discount Analysis: Year-over-year revenue and discount trends are analyzed to identify patterns and anomalies.

    Date & Quarter Filters: Users can filter data using interactive controls for year, month, or quarter-based analysis.

    📊 Dataset Overview The dataset used for this analysis contains essential information, including:

    Sales Date

    Total Sales Revenue

    Product Category

    Sales Volume (Tons)

    Customer Type

    Region & Country

    Salesman Experience (Years)

    🛠️ Tools Used Power BI – For data visualization and dashboard development

    DAX (Data Analysis Expressions) – For complex calculations and dynamic data representation

  3. f

    Additional file 8: of High-density SNP linkage map construction and QTL...

    • springernature.figshare.com
    • search.datacite.org
    zip
    Updated Feb 12, 2024
    + more versions
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    Li-Yi Xu; Li-Yuan Wang; Kang Wei; Li-Qiang Tan; Jing-Jing Su; Hao Cheng (2024). Additional file 8: of High-density SNP linkage map construction and QTL mapping for flavonoid-related traits in a tea plant (Camellia sinensis) using 2b-RAD sequencing [Dataset]. http://doi.org/10.6084/m9.figshare.7502951.v1
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    zipAvailable download formats
    Dataset updated
    Feb 12, 2024
    Dataset provided by
    figshare
    Authors
    Li-Yi Xu; Li-Yuan Wang; Kang Wei; Li-Qiang Tan; Jing-Jing Su; Hao Cheng
    License

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

    Description

    Figure S2. A comparison of current and previous genetic maps (map 1 is our SNP genetic map, map 2 quoted from previous study). (ZIP 210362 kb)

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

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Montero-Vargas, Josaphat Miguel; Gonzáles-Gonzáles, Lindbergh Humberto; Galvez-Ponce, Eligio; Ramírez-Chávez, Enrique; Molina-Torres, Jorge; Chagolla, Alicia; Montagnon, Christophe; Winkler, Robert (2018). Metabolic heat maps of tea and coffee variants [Dataset]. http://doi.org/10.1594/PANGAEA.774218

Metabolic heat maps of tea and coffee variants

Explore at:
Dataset updated
Jan 5, 2018
Dataset provided by
PANGAEA Data Publisher for Earth and Environmental Science
Authors
Montero-Vargas, Josaphat Miguel; Gonzáles-Gonzáles, Lindbergh Humberto; Galvez-Ponce, Eligio; Ramírez-Chávez, Enrique; Molina-Torres, Jorge; Chagolla, Alicia; Montagnon, Christophe; Winkler, Robert
Area covered
Description

High-throughput metabolic phenotyping is a challenge, but it provides an alternative and comprehensive access to the rapid and accurate characterization of plants. In addition to the technical issues of obtaining quantitative data of plenty of metabolic traits from numerous samples, a suitable data processing and statistical evaluation strategy must be developed. We present a simple, robust and highly scalable strategy for the comparison of multiple chemical profiles from coffee and tea leaf extracts, based on direct-injection electrospray mass spectrometry (DIESI-MS) and hierarchical cluster analysis (HCA). More than 3500 individual Coffea canephora and Coffea arabica trees from experimental fields in Mexico were sampled and processed using this method. Our strategy permits the classification of trees according to their metabolic fingerprints and the screening for families with desired characteristics, such as extraordinarily high or low caffeine content in their leaves.

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