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Agricultural Research
  • Language: en
  • Pages: 256

Agricultural Research

  • Type: Book
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  • Published: 2008
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  • Publisher: Unknown

None

Sugar Content of Selected Foods
  • Language: en
  • Pages: 48

Sugar Content of Selected Foods

  • Type: Book
  • -
  • Published: 1987
  • -
  • Publisher: Unknown

None

Composition of Foods, Beverages
  • Language: en
  • Pages: 192

Composition of Foods, Beverages

  • Type: Book
  • -
  • Published: 1986
  • -
  • Publisher: Unknown

None

Composition of Foods
  • Language: en
  • Pages: 356

Composition of Foods

  • Type: Book
  • -
  • Published: 1991
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  • Publisher: Unknown

None

Dietary Supplements, Botanicals and Herbs at The Interface of Food and Medicine
  • Language: en
  • Pages: 660
Agriculture Handbook
  • Language: en
  • Pages: 476

Agriculture Handbook

  • Type: Book
  • -
  • Published: 1992
  • -
  • Publisher: Unknown

Set includes revised editions of some issues.

Composition of Foods
  • Language: en
  • Pages: 382

Composition of Foods

  • Type: Book
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  • Published: 1978
  • -
  • Publisher: Unknown

None

The Oxford Companion to Sugar and Sweets
  • Language: en
  • Pages: 947

The Oxford Companion to Sugar and Sweets

Not a cookbook, but a encyclopedia collection of entries on all things sweet. The articles explore the ways in which our taste for sweetness have shaped-- and been shaped by-- history. In addition, you'll discover the origins of mud pie; who the Sara Lee company was named after; why Walker Smith, Jr. is better known as "Sugar Ray Robinson"; and how lyricists have immortalized sweets from "Blueberry Hill" to "Tutti Fruiti".

Federal Register
  • Language: en
  • Pages: 324

Federal Register

  • Type: Book
  • -
  • Published: 2004-07-16
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  • Publisher: Unknown

None

Veridical Data Science
  • Language: en
  • Pages: 527

Veridical Data Science

  • Type: Book
  • -
  • Published: 2024-10-15
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  • Publisher: MIT Press

Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to...