How to be a modern scientist (Norsk Utgave)
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How to be a modern scientist (Norsk Utgave)

Om boken

Ansiktet til akademia er i endring. Det er ikke lenger tilstrekkelig å bare publisere eller forsvinne. Vi er nå i en æra der Twitter, Github, Figshare, og Alt Metrics er vanlige deler av den vitenskapelige arbeidsflyten. Her gir jeg råd på høyt nivå om hvilke verktøy du bør bruke, hvordan du bruker dem, og hva du bør se opp for. Denne boken passer for vitenskapsfolk på alle nivåer som ønsker å holde seg oppdatert på de nåværende teknologiske utviklingene som påvirker moderne vitenskapelige karrierer. Boken er basert delvis på forfatterens populære guider, inkludert guider for

Boken er sannsynligvis mest egnet for doktorgradsstudenter og postdoktorer i vitenskapene, men kan også være av interesse for andre som ønsker å tilpasse sin vitenskapelige prosess til å bruke moderne verktøy. 

Om forfatteren: Jeff Leek er en assosiert professor i biostatistikk ved Johns Hopkins Bloomberg School of Public Health. Han er medgrunnlegger og meddirektør for Johns Hopkins Data Science Specialization på Coursera som har registrert over 3 millioner håpefulle dataforskere. Hans forskning har bidratt til vår forståelse av den genomiske basisen for hjerneutvikling, stump voldstraume, og kreft. Han blogger på Simply Statistics og kan finnes på twitter på @jtleek og @simplystats

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

Om forfatterne

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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To see the output of TranslateAI right now, you can check out these two books: How To Be a Modern Scientist and The Elements of Data Analytic Style by bestselling Leanpub author Jeffrey Leek.

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Innhold

    • Introduksjon
    • Skrivearbeid
      • Skriving - hva bør jeg gjøre og hvorfor?
      • Skriving - hvilke verktøy bør jeg bruke?
      • Skriving - ytterligere tips og problemer
    • Publisering
      • Publisering - hva bør jeg gjøre og hvorfor?
      • Publisering - hvilke verktøy bør jeg bruke?
      • Publisering - ytterligere tips og problemstillinger
    • Fagfellevurdering
      • Fagfellevurdering - hva skal jeg gjøre og hvorfor?
      • Fagfellevurdering - hvilke verktøy bør jeg bruke?
      • Fagfellevurdering - ytterligere tips og problemer
    • Deling av data
      • Deling av data - hva skal jeg gjøre og hvorfor?
      • Deling av data - hvilke verktøy bør jeg bruke?
      • Datadeling - ytterligere tips og problemer
    • Vitenskapelig blogging
      • Blogging - hva bør jeg gjøre og hvorfor?
      • Blogging - hvilke verktøy bør jeg bruke?
      • Blogging - ytterligere tips og problemstillinger
    • Vitenskapelig kode
      • Vitenskapelig kode - hva bør jeg gjøre og hvorfor?
      • Vitenskapelig kode - hvilke verktøy bør jeg bruke?
      • Vitenskapelig kode - ytterligere tips og problemstillinger
    • Sosiale medier i vitenskapen
      • Sosiale medier - hva bør jeg gjøre og hvorfor?
      • Sosiale medier - hvilke verktøy bør jeg bruke?
      • Sosiale medier - ytterligere tips og problemstillinger
    • Undervisning i naturvitenskap
      • Undervisning - hva skal jeg gjøre og hvorfor?
      • Undervisning - hvilke verktøy bør jeg bruke?
      • Undervisning - flere tips og problemstillinger
    • Bøker
      • Bøker - hva skal jeg gjøre og hvorfor?
      • Bøker - hvilke verktøy bør jeg bruke?
      • Bøker - ytterligere tips og problemer
    • Intern vitenskapelig kommunikasjon
      • Intern kommunikasjon - hva bør jeg gjøre og hvorfor?
      • Intern kommunikasjon - hvilke verktøy bør jeg bruke?
      • Intern kommunikasjon - ytterligere tips og problemer
    • Vitenskapelige presentasjoner
      • Vitenskapelige presentasjoner - hva bør jeg gjøre og hvorfor?
      • Vitenskapelige presentasjoner - hvilke verktøy bør jeg bruke?
      • Vitenskapelige presentasjoner - flere tips og problemer
    • Lesing av vitenskapelige artikler
      • Lesing av vitenskapelige artikler - hva bør jeg gjøre og hvorfor?
      • Å lese vitenskapelige artikler - hvilke verktøy bør jeg bruke?
      • Å lese vitenskapelige artikler - flere tips og triks
    • Kreditt
      • Kreditt - hva bør jeg gjøre og hvorfor?
      • Kreditt - hvilke verktøy bør jeg bruke?
      • Kreditt - ytterligere tips og problemer
    • Karriereplanlegging
      • Karriereplanlegging - hva bør jeg gjøre og hvorfor?
      • Karriereplanlegging - hvilke verktøy bør jeg bruke?
    • Din nettidentitet
      • Din nettidentitet - hva bør jeg gjøre og hvorfor?
      • Din nettidentitet - hvilke verktøy bør du bruke?
      • Din online identitet - flere tips og triks
    • Om forfatteren
      • Bør du følge mitt eksempel?

Årsaker som Støttes

Data Carpentry

http://www.datacarpentry.org

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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