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How to be a modern scientist (Hrvatsko Izdanje)

Knjiga o tome kako biti znanstvenik na suvremen, otvoreni kod način.

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

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Lice akademije se mijenja. Više nije dovoljno samo objavljivati ili propasti. Sada smo u eri gdje su Twitter, Github, Figshare i Alt Metrics redovni dijelovi znanstvenog toka rada. Ovdje dajem savjete visoke razine o tome koje alate koristiti, kako ih koristiti i na što obratiti pozornost. Ova knjiga je prikladna za znanstvenike na svim razinama koji žele biti u tijeku s trenutnim tehnološkim razvojem koji utječe na moderne znanstvene karijere. Knjiga je djelomično zasnovana na popularnim vodičima autora, uključujući vodiče za

Knjiga je vjerojatno najprikladnija za diplomirane studente i poslijedoktorande u znanostima, ali može biti od interesa i drugima koji žele prilagoditi svoj znanstveni proces korištenju modernih alata.

O autoru: Jeff Leek je izvanredni profesor biostatistike na Bloomberg školi javnog zdravstva Johns Hopkins. On je suosnivač i suvoditelj Specijalizacije iz podatkovne znanosti Johns Hopkins na Courseri koja je upisala preko 3 milijuna težitelja podatkovnih znanstvenika. Njegovo istraživanje je pomoglo doprinijeti našem razumijevanju genomske osnove razvoja mozga, tupih sila trauma i raka. Bloga na Simply Statistics i može se naći na Twitteru na @jtleek i @simplystats

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

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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

    • Uvod
    • Pisanje radova
      • Pisanje - što bih trebao učiniti i zašto?
      • Pisanje - koji alati bih trebao koristiti?
      • Pisanje - dodatni savjeti i problemi
    • Objavljivanje
      • Objavljivanje - što bih trebao učiniti i zašto?
      • Objavljivanje - koje alate trebam koristiti?
      • Objavljivanje - dodatni savjeti i problemi
    • Recenzija
      • Stručno recenziranje - što bih trebao raditi i zašto?
      • Recenziranje - koje alate trebam koristiti?
      • Recenzentski postupak - dodatni savjeti i problemi
    • Dijeljenje podataka
      • Dijeljenje podataka - što ja trebam raditi i zašto?
      • Dijeljenje podataka - koje alate trebam koristiti?
      • Dijeljenje podataka - dodatni savjeti i problemi
    • Znanstveno bloganje
      • Bloganje - što bih trebao raditi i zašto?
      • Bloganje - koje alate trebam koristiti?
      • Bloganje - dodatni savjeti i problemi
    • Znanstveni kod
      • Znanstveni kod - što bih trebao napraviti i zašto?
      • Znanstveni kod - koji alati bih trebao koristiti?
      • Znanstveni kod - dodatni savjeti i problemi
    • Društveni mediji u znanosti
      • Društveni mediji - što bih trebao raditi i zašto?
      • Društvene mreže - koje alate trebam koristiti?
      • Društvene mreže - dodatni savjeti i pitanja
    • Poučavanje u znanosti
      • Poučavanje - što bih trebao raditi i zašto?
      • Predavanje - koje alate trebam koristiti?
      • Predavanje - dodatni savjeti i problemi
    • Knjige
      • Knjige - što bih trebao učiniti i zašto?
      • Knjige - koje alate bih trebao koristiti?
      • Knjige - dodatni savjeti i problemi
    • Interna znanstvena komunikacija
      • Interna komunikacija - što bih trebao raditi i zašto?
      • Interna komunikacija - koji alati bih trebao koristiti?
      • Interna komunikacija - dodatni savjeti i problemi
    • Znanstvena predavanja
      • Znanstvena predavanja - što bih trebao raditi i zašto?
      • Znanstveni govori - koji alati bih trebao koristiti?
      • Znanstveni govori - dodatni savjeti i problemi
    • Čitanje znanstvenih radova
      • Čitanje znanstvenih radova - što bih trebao napraviti i zašto?
      • Čitanje znanstvenih radova - koji alati mi trebaju?
      • Čitanje znanstvenih radova - dodatni savjeti i trikovi
    • Zasluge
      • Zasluge - što bih trebao učiniti i zašto?
      • Zasluge - koje alate trebam koristiti?
      • Zasluge - dodatni savjeti i problemi
    • Planiranje karijere
      • Planiranje karijere - što bih trebao učiniti i zašto?
      • Planiranje karijere - koje alate trebam koristiti?
    • Vaš online identitet
      • Vaš online identitet - što bih trebao učiniti i zašto?
      • Vaš online identitet - koje alate biste trebali koristiti?
      • Vaš internetski identitet - dodatni savjeti i trikovi
    • O autoru
      • Trebate li slijediti moj primjer?

Causes

Causes Supported

Data Carpentry
Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Our goal is to provide researchers high-quality, domain-specific training covering the full lifecycle of data-driven research.
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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