How to be a modern scientist (Українське Видання)
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How to be a modern scientist (Українське Видання)

About the Book

Обличчя академічного середовища змінюється. Недостатньо просто публікувати чи зазнавати невдачі. Ми зараз перебуваємо в епосі, де Twitter, Github, Figshare та Alt Metrics є регулярними частинами наукового робочого процесу. Тут я даю поради високого рівня щодо того, які інструменти використовувати, як їх використовувати та на що звертати увагу. Ця книга підходить для науковців усіх рівнів, хто хоче залишатися на хвилі поточних технологічних розвитків, що впливають на сучасні наукові кар'єри. Книга частково базується на популярних посібниках автора, включаючи посібники для

Ця книга, напевно, найбільше підходить аспірантам та постдокам у галузі наук, але може бути цікавою і для інших, хто хоче адаптувати свій науковий процес для використання сучасних інструментів.

Про автора: Jeff Leek є асоційованим професором біостатистики у Школі громадської охорони здоров'я Bloomberg при Джонс Гопкінс. Він є співзасновником та співдиректором Спеціалізації з наукових даних Джонса Гопкінса на Coursera, яка залучила понад 3 мільйони аспірантів, які прагнуть стати науковцями з даних. Його дослідження сприяли розумінню геномної основи розвитку мозку, тупої травми та раку. Він веде блог на Simply Statistics і можна знайти в twitter за адресою @jtleek і @simplystats

This book is a translation into Українська of How to be a modern scientist which was originally written in Англійська.

About the Authors

Jeffrey Leek
Jeffrey Leek

Jeff is Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics at the Fred Hutchinson Cancer Center. Previously, he was a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. His group develops statistical methods, software, data resources, and data analyses that help people make sense of massive-scale genomic and biomedical data. As the co-director of the Johns Hopkins Data Science Lab he helped to develop massive online open programs that have enrolled more than 8 million individuals and partnered with community-based non-profits to use data science education for economic and public health development. He is a Fellow of the American Statistical Association and a recipient of the Mortimer Spiegelman Award and Committee of Presidents of Statistical Societies Presidential Award.


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Table of Contents

    • Вступ
    • Написання наукової роботи
      • Письмо - що мені робити і чому?
      • Письмо - які інструменти я повинен використовувати?
      • Письмо - додаткові поради та питання
    • Публікація
      • Публікація - що мені робити і чому?
      • Публікація - які інструменти мені використовувати?
      • Публікація - додаткові поради та проблеми
    • Рецензування колег
      • Рецензування колег - що я повинен робити і чому?
      • Рецензування - які інструменти я повинен використовувати?
      • Рецензування - додаткові поради та питання
    • Обмін даними
      • Обмін даними - що мені робити і чому?
      • Поділ даних - які інструменти мені використовувати?
      • Обмін даними - додаткові поради та проблеми
    • Науковий блогінг
      • Блогінг - що мені робити і чому?
      • Блогінг - які інструменти слід використовувати?
      • Блогінг - додаткові поради та проблеми
    • Науковий код
      • Науковий код - що мені робити і чому?
      • Науковий код - які інструменти мені використовувати?
      • Науковий код - додаткові поради та питання
    • Соціальні мережі в науці
      • Соціальні мережі - що мені робити і чому?
      • Соціальні мережі - які інструменти мені використовувати?
      • Соціальні мережі - додаткові поради та питання
    • Викладання в науці
      • Викладання - що я повинен робити та чому?
      • Викладання - які інструменти слід використовувати?
      • Викладання - додаткові поради та питання
    • Книги
      • Книги - що мені робити і чому?
      • Книги - які інструменти мені слід використовувати?
      • Книги - додаткові поради та проблеми
    • Внутрішнє наукове спілкування
      • Внутрішнє спілкування - що мені робити і чому?
      • Внутрішнє спілкування - які інструменти мені використовувати?
      • Внутрішнє спілкування - додаткові поради та проблеми
    • Наукові доповіді
      • Наукові доповіді - що мені робити і чому?
      • Наукові доповіді - які інструменти мені слід використовувати?
      • Наукові доповіді - додаткові поради та питання
    • Читання наукових статей
      • Читання наукових статей - що мені робити і чому?
      • Читання наукових статей - які інструменти я повинен використовувати?
      • Читання наукових статей - додаткові поради та хитрощі
    • Кредит
      • Кредит - що мені робити і чому?
      • Кредит - які інструменти слід використовувати?
      • Заслуги - додаткові поради та питання
    • Планування кар’єри
      • Планування кар’єри - що мені робити і чому?
      • Планування кар’єри - які інструменти слід використовувати?
    • Ваша онлайн-ідентичність
      • Ваша онлайн-ідентичність - що робити та чому?
      • Ваша онлайн-ідентичність - які інструменти вам слід використовувати?
      • Ваша онлайн-ідентичність - додаткові поради та хитрощі
    • Про автора
      • Чи варто слідувати моєму прикладу?

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From astronomy to molecular biology and digital text, our increasing capacity to collect data is changing research. It allows us to ask questions that previously could not have been answered, and it changes the impact that research has on society. Although petabytes of data are now available, most disciplines are struggling to translate this sea of data into knowledge. The missing step between data collection and research progress is a lack of training for scientists in crucial skills for effectively managing and analyzing large amounts of data. Data Carpentry addresses this gap by teaching researchers the fundamental data skills they need to conduct their research. Our goal is to provide researchers high-quality, domain-specific training covering the full lifecycle of data-driven research. We teach hands-on workshops in data organization, management, and analysis to increase data literacy and improve research efficiency and reproducibility.

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