- Introduktion
- Skrivning af artikler
- Skrivning - hvad skal jeg gøre og hvorfor?
- Skrivning - hvilke værktøjer skal jeg bruge?
- Skrivning - yderligere tips og problemer
- Udgivelse
- Udgivelse - hvad skal jeg gøre og hvorfor?
- Udgivelse - hvilke værktøjer skal jeg bruge?
- Publicering - yderligere tips og spørgsmål
- Peer review
- Peer review - hvad skal jeg gøre, og hvorfor?
- Peer review - hvilke værktøjer skal jeg bruge?
- Peer review - yderligere tips og problemer
- Datadeling
- Datadeling - hvad skal jeg gøre, og hvorfor?
- Datadeling - hvilke værktøjer skal jeg bruge?
- Data deling - yderligere tips og problemer
- Videnskabelig blogging
- Blogging - hvad skal jeg gøre, og hvorfor?
- Blogging - hvilke værktøjer skal jeg bruge?
- Blogging - yderligere tips og problemer
- Videnskabelig kode
- Videnskabelig kode - hvad skal jeg gøre og hvorfor?
- Videnskabelig kode - hvilke værktøjer skal jeg bruge?
- Videnskabelig kode - yderligere tips og problemer
- Sociale medier i videnskaben
- Sociale medier - hvad skal jeg gøre, og hvorfor?
- Sociale medier - hvilke værktøjer skal jeg bruge?
- Sociale medier - yderligere tips og problemer
- Undervisning i naturvidenskab
- Undervisning - hvad skal jeg gøre og hvorfor?
- Undervisning - hvilke værktøjer skal jeg bruge?
- Undervisning - yderligere tips og problemer
- Bøger
- Bøger - hvad skal jeg gøre og hvorfor?
- Bøger - hvilke værktøjer skal jeg bruge?
- Bøger - yderligere tips og problemer
- Intern videnskabelig kommunikation
- Intern kommunikation - hvad skal jeg gøre og hvorfor?
- Intern kommunikation - hvilke værktøjer skal jeg bruge?
- Intern kommunikation - yderligere tips og problemstillinger
- Videnskabelige foredrag
- Videnskabelige foredrag - hvad skal jeg gøre, og hvorfor?
- Videnskabelige foredrag - hvilke værktøjer skal jeg bruge?
- Videnskabelige foredrag - yderligere tips og problemer
- Læsning af videnskabelige artikler
- Læsning af videnskabelige artikler - hvad skal jeg gøre, og hvorfor?
- Læsning af videnskabelige artikler - hvilke værktøjer skal jeg bruge?
- Læsning af videnskabelige artikler - yderligere tips og tricks
- Kredit
- Kredit - hvad skal jeg gøre, og hvorfor?
- Kredit - hvilke værktøjer skal jeg bruge?
- Kredit - yderligere tips og problemer
- Karriereplanlægning
- Karriereplanlægning - hvad skal jeg gøre og hvorfor?
- Karriereplanlægning - hvilke værktøjer bør jeg bruge?
- Din online identitet
- Din online identitet - hvad skal jeg gøre og hvorfor?
- Din online identitet - hvilke værktøjer skal du bruge?
- Din online identitet - yderligere tips og tricks
- Om forfatteren
- Skal du følge mit eksempel?
How to be a modern scientist (Dansk Udgave)
Free With Membership
This book is a translation into Danish of How to be a modern scientist which was originally written in English
The authors are letting you choose the price you pay for this book!
En bog om, hvordan man er en forsker på den moderne, open source-måde.
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About
About the Book
Akademias ansigt er i forandring. Det er ikke længere nok bare at publicere eller forsvinde. Vi er nu i en æra, hvor Twitter, Github, Figshare og Alt Metrics er faste dele af det videnskabelige workflow. Her giver jeg råd på højt niveau om, hvilke værktøjer man skal bruge, hvordan man bruger dem, og hvad man skal være opmærksom på. Denne bog er passende for forskere på alle niveauer, der ønsker at holde sig ajour med de nuværende teknologiske udviklinger, der påvirker moderne videnskabelige karrierer. Bogen er delvist baseret på forfatterens populære guider, herunder guider til
Bogen er nok mest egnet til ph.d.-studerende og postdocs inden for videnskaben, men kan også være af interesse for andre, der ønsker at tilpasse deres videnskabelige proces til at bruge moderne værktøjer.
Om forfatteren: Jeff Leek er lektor i biostatistik ved Johns Hopkins Bloomberg School of Public Health. Han er medstifter og meddirektør for Johns Hopkins Data Science Specialization på Coursera, som har tilmeldt over 3 millioner aspirerende dataloger. Hans forskning har bidraget til vores forståelse af den genomiske basis for hjernens udvikling, stumpe traumer og kræft. Han blogger på Simply Statistics og kan findes på Twitter på @jtleek og @simplystats
This book is a translation into Danish of How to be a modern scientist which was originally written in English
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$10.00Authors earn
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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.
Episode 19
An Interview with Jeffrey Leek
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Data Carpentry
http://www.datacarpentry.orgData 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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