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How to be a modern scientist (Edisi Bahasa Indonesia)

Buku tentang bagaimana menjadi seorang ilmuwan dengan cara modern, sumber terbuka.

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This book is a translation into Indonesian of How to be a modern scientist which was originally written in English

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About

About

About the Book

Wajah akademis sedang berubah. Tidak lagi cukup untuk hanya menerbitkan atau tenggelam. Kita sekarang berada di era di mana Twitter, Github, Figshare, dan Alt Metrics menjadi bagian reguler dari alur kerja ilmiah. Di sini saya memberikan saran tingkat tinggi tentang alat apa yang harus digunakan, bagaimana menggunakannya, dan hal-hal apa yang harus diwaspadai. Buku ini cocok untuk ilmuwan di semua tingkat yang ingin tetap mengikuti perkembangan teknologi terkini yang mempengaruhi karir ilmiah modern. Buku ini sebagian berdasarkan pada panduan populer penulis termasuk panduan untuk

Buku ini mungkin paling cocok untuk mahasiswa pascasarjana dan postdoc di bidang sains, tetapi mungkin menarik bagi orang lain yang ingin menyesuaikan proses ilmiah mereka untuk menggunakan alat modern. 

Tentang penulis: Jeff Leek adalah Associate Professor Biostatistika di Johns Hopkins Bloomberg School of Public Health. Dia adalah salah satu pendiri dan direktur bersama dari Spesialisasi Data Science Johns Hopkins di Coursera yang telah mendaftarkan lebih dari 3 juta calon ilmuwan data. Penelitiannya telah membantu memperkaya pemahaman kita tentang dasar genomik dari perkembangan otak, trauma tumpul, dan kanker. Dia menulis blog di Simply Statistics dan dapat ditemukan di twitter di @jtleek dan @simplystats

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This book is a translation into Indonesian of How to be a modern scientist which was originally written in English

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Author

About the Authors

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.

Leanpub Podcast

Episode 19

An Interview with Jeffrey Leek

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Contents

Table of Contents

    • Pendahuluan
    • Menulis Makalah
      • Menulis - apa yang harus saya lakukan dan mengapa?
      • Menulis - alat apa yang harus saya gunakan?
      • Menulis - tips dan masalah lebih lanjut
    • Penerbitan
      • Penerbitan - apa yang harus saya lakukan dan mengapa?
      • Penerbitan - alat apa yang harus saya gunakan?
      • Penerbitan - tips dan isu lebih lanjut
    • Tinjauan Sejawat
      • Tinjauan Sejawat - Apa yang harus saya lakukan dan mengapa?
      • Tinjauan Sejawat - alat apa yang harus saya gunakan?
      • Ulasan Peer - tip dan masalah lebih lanjut
    • Berbagi Data
      • Berbagi Data - Apa yang Harus Saya Lakukan dan Mengapa?
      • Berbagi Data - alat apa yang harus saya gunakan?
      • Berbagi Data - tips dan masalah lebih lanjut
    • Blog Ilmiah
      • Blogging - apa yang harus saya lakukan dan mengapa?
      • Blogging - alat apa yang sebaiknya saya gunakan?
      • Blogging - tips dan masalah lebih lanjut
    • Kode Ilmiah
      • Kode Ilmiah - apa yang harus saya lakukan dan mengapa?
      • Kode Ilmiah - alat apa yang harus saya gunakan?
      • Kode ilmiah - tips dan masalah lebih lanjut
    • Media sosial dalam sains
      • Media sosial - apa yang harus saya lakukan dan mengapa?
      • Media sosial - alat apa yang harus saya gunakan?
      • Media sosial - tips dan masalah lebih lanjut
    • Mengajar dalam sains
      • Mengajar - apa yang harus saya lakukan dan mengapa?
      • Pengajaran - alat apa yang harus saya gunakan?
      • Pengajaran - tip dan isu lebih lanjut
    • Buku
      • Buku - apa yang harus saya lakukan dan mengapa?
      • Buku - alat apa yang harus saya gunakan?
      • Buku - tips dan masalah lebih lanjut
    • Komunikasi ilmiah internal
      • Komunikasi internal - apa yang harus saya lakukan dan mengapa?
      • Komunikasi internal - alat apa yang harus saya gunakan?
      • Komunikasi internal - tips dan masalah lebih lanjut
    • Presentasi Ilmiah
      • Presentasi Ilmiah - apa yang harus saya lakukan dan mengapa?
      • Pembicaraan Ilmiah - alat apa yang harus saya gunakan?
      • Pembicaraan ilmiah - tips dan masalah lebih lanjut
    • Membaca makalah ilmiah
      • Membaca makalah ilmiah - apa yang harus saya lakukan dan mengapa?
      • Membaca makalah ilmiah - alat apa yang harus saya gunakan?
      • Membaca makalah ilmiah - tips dan trik lebih lanjut
    • Kredit
      • Kredit - apa yang harus saya lakukan dan mengapa?
      • Kredit - alat apa yang harus saya gunakan?
      • Kredit - tips dan masalah lebih lanjut
    • Perencanaan Karir
      • Perencanaan Karir - apa yang harus saya lakukan dan mengapa?
      • Perencanaan karier - alat apa yang harus saya gunakan?
    • Identitas online Anda
      • Identitas online Anda - apa yang harus saya lakukan dan mengapa?
      • Identitas online Anda - alat apa yang harus Anda gunakan?
      • Identitas Online Anda - tips dan trik tambahan
    • Tentang Penulis
      • Apakah Anda harus mengikuti jejak saya?

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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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