- Uvod
- Pisanje radova
- Pisanje - šta treba da radim i zašto?
- Pisanje - koji alati bi trebalo da koristim?
- Pisanje - dodatni saveti i problemi
- Objavljivanje
- Objavljivanje - šta treba da radim i zašto?
- Objavljivanje - koje alate treba da koristim?
- Objavljivanje - dodatni saveti i pitanja
- Recenzija
- Recenzija - šta bi trebalo da radim i zašto?
- Recenzija - koji alati bi trebalo da se koriste?
- Recenzija - dodatni saveti i problemi
- Deljenje podataka
- Deljenje podataka - šta ja treba da radim i zašto?
- Deljenje podataka - koji alati bi trebali da se koriste?
- Deljenje podataka - dodatni saveti i problemi
- Naučno blogovanje
- Blogovanje - šta treba da radim i zašto?
- Blogovanje - koje alate treba da koristim?
- Blogovanje - dodatni saveti i problemi
- Naučni kod
- Naučni kod - šta treba da radim i zašto?
- Naučni kod - koji alati bi trebalo da koristim?
- Naučni kod - dodatni saveti i problemi
- Društvene mreže u nauci
- Društvene mreže - šta treba da radim i zašto?
- Društvene mreže - koji alati bi trebalo da koristim?
- Društvene mreže - dodatni saveti i pitanja
- Podučavanje u nauci
- Podučavanje - šta treba da radim i zašto?
- Podučavanje - koji alati bi trebalo da koristim?
- Podučavanje - dodatni saveti i problemi
- Knjige
- Knjige - šta bih trebao da radim i zašto?
- Knjige - koji alati bi trebalo da se koriste?
- Knjige - dalji saveti i problemi
- Interna naučna komunikacija
- Interna komunikacija - šta treba da radim i zašto?
- Interna komunikacija - koji alati bi trebalo da se koriste?
- Interna komunikacija - dodatni saveti i problemi
- Naučna predavanja
- Naučna predavanja - šta bih trebao da radim i zašto?
- Naučna predavanja - koji alati bi trebalo da se koriste?
- Naučna izlaganja - dodatni saveti i problemi
- Čitanje naučnih radova
- Čitanje naučnih radova - šta bih trebao da radim i zašto?
- Čitanje naučnih radova - koji alati bi trebali da se koriste?
- Čitanje naučnih radova - dalji saveti i trikovi
- Kredit
- Kredit - šta bi trebalo da radim i zašto?
- Kredit - koje alate treba da koristim?
- Kredit - dalji saveti i pitanja
- Planiranje karijere
- Planiranje karijere - šta bih trebao da radim i zašto?
- Planiranje karijere - koji alati su mi potrebni?
- Vaš online identitet
- Vaš online identitet - šta bih trebao da uradim i zašto?
- Vaš online identitet - koji alati bi trebali da se koriste?
- Vaš online identitet - dodatni saveti i trikovi
- O autoru
- Da li bi trebalo da sledite moj primer?
How to be a modern scientist (Izdanje na Srpskom)
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This book is a translation into Serbian (Latin) 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!
Knjiga o tome kako biti naučnik na moderan, open-source način.
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About
About the Book
Lice akademije se menja. Više nije dovoljno samo objaviti rad ili nestati. Sada smo u eri gde su Twitter, Github, Figshare i Alt Metrics redovni delovi naučnog rada. Ovde dajem savete visokog nivoa o tome koje alate koristiti, kako ih koristiti i na šta obratiti pažnju. Ova knjiga je prikladna za naučnike na svim nivoima koji žele da ostanu u toku sa trenutnim tehnološkim razvojem koji utiče na moderne naučne karijere. Knjiga je delimično zasnovana na popularnim vodičima autora koji uključuju vodiče za
Knjiga je verovatno najprikladnija za diplomce i postdoktorande u naukama, ali može biti zanimljiva i drugima koji žele da prilagode svoj naučni proces korišćenju modernih alata.
O autoru: Jeff Leek je docent biostatistike na Bloomberg školi javnog zdravlja Univerziteta Johns Hopkins. On je suosnivač i su-direktor Specializacije za data science na Univerzitetu Johns Hopkins na Courseri koja je upisala preko 3 miliona budućih stručnjaka za podatke. Njegova istraživanja su pomogla u razumevanju genomske osnove razvoja mozga, posledica tupih udaraca i raka. Bloguje na Simply Statistics i može se naći na Twitteru na @jtleek i @simplystats
This book is a translation into Serbian (Latin) of How to be a modern scientist which was originally written in English
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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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