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How to be a modern scientist (Nederlandse Editie)

Een boek over hoe je op de moderne, open-source manier een wetenschapper kunt zijn.

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

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About

About

About the Book

The face of academia is changing. It is no longer sufficient to just publish or perish. We are now in an era where Twitter, Github, Figshare, and Alt Metrics are regular parts of the scientific workflow. Here I give high level advice about which tools to use, how to use them, and what to look out for. This book is appropriate for scientists at all levels who want to stay on top of the current technological developments affecting modern scientific careers. The book is based in part on the author's popular guides including guides for

The book is probably most suited to graduate students and postdocs in the sciences, but may be of interest to others who want to adapt their scientific process to use modern tools. 

About the author: Jeff Leek is an Associate Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He is a co-founder and co-director of the Johns Hopkins Data Science Specialization on Coursera that has enrolled over 3 million aspiring data scientists. His research has helped contributed to our understanding of the genomic basis of brain development, blunt force trauma, and cancer. He is blogs at Simply Statistics and can be found on twitter at @jtleek and @simplystats

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This book is a translation into Dutch 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

    • Inleiding
    • Het schrijven van wetenschappelijke artikelen
      • Schrijven - wat moet ik doen en waarom?
      • Schrijven - welke hulpmiddelen moet ik gebruiken?
      • Schrijven - verdere tips en kwesties
    • Publiceren
      • Publiceren - wat moet ik doen en waarom?
      • Publiceren - welke hulpmiddelen moet ik gebruiken?
      • Publiceren - verdere tips en kwesties
    • Peer review
      • Peer review - wat moet ik doen en waarom?
      • Peer review - welke hulpmiddelen moet ik gebruiken?
      • Peer review - verdere tips en problemen
    • Gegevens delen
      • Gegevens delen - wat moet ik doen en waarom?
      • Gegevens delen - welke tools moet ik gebruiken?
      • Gegevensdeling - verdere tips en problemen
    • Wetenschappelijk bloggen
      • Bloggen - wat moet ik doen en waarom?
      • Bloggen - welke tools moet ik gebruiken?
      • Bloggen - verdere tips en kwesties
    • Wetenschappelijke code
      • Wetenschappelijke code - wat moet ik doen en waarom?
      • Wetenschappelijke code - welke tools moet ik gebruiken?
      • Wetenschappelijke code - verdere tips en problemen
    • Sociale media in de wetenschap
      • Sociale media - wat moet ik doen en waarom?
      • Sociale media - welke hulpmiddelen moet ik gebruiken?
      • Sociale media - verdere tips en kwesties
    • Onderwijzen in de wetenschap
      • Onderwijs - wat moet ik doen en waarom?
      • Onderwijs - welke tools moet ik gebruiken?
      • Onderwijs - verdere tips en kwesties
    • Boeken
      • Boeken - wat moet ik doen en waarom?
      • Boeken - welke tools moet ik gebruiken?
      • Boeken - verdere tips en kwesties
    • Interne wetenschappelijke communicatie
      • Interne communicatie - Wat moet ik doen en waarom?
      • Interne communicatie - welke hulpmiddelen moet ik gebruiken?
      • Interne communicatie - verdere tips en problemen
    • Wetenschappelijke presentaties
      • Wetenschappelijke presentaties - wat moet ik doen en waarom?
      • Wetenschappelijke presentaties - welke hulpmiddelen moet ik gebruiken?
      • Wetenschappelijke presentaties - verdere tips en kwesties
    • Wetenschappelijke papers lezen
      • Wetenschappelijke papers lezen - wat zou ik moeten doen en waarom?
      • Wetenschappelijke papers lezen - welke tools zou ik moeten gebruiken?
      • Wetenschappelijke artikelen lezen - verdere tips en trucs
    • Credit
      • Credit - wat moet ik doen en waarom?
      • Credit - welke tools moet ik gebruiken?
      • Credit - verdere tips en problemen
    • Carrièreplanning
      • Carrièreplanning - wat moet ik doen en waarom?
      • Carrièreplanning - welke hulpmiddelen moet ik gebruiken?
    • Je online identiteit
      • Je online identiteit - wat moet ik doen en waarom?
      • Je online identiteit - welke tools zou je moeten gebruiken?
      • Je online identiteit - verdere tips en trucs
    • Over de auteur
      • Moet u mijn voorbeeld volgen?

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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