How to be a modern scientist (Hebrew Edition)
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How to be a modern scientist (Hebrew Edition)

אודות הספר

פני האקדמיה משתנים. זה לא מספיק יותר לפרסם או להיכחד. אנו נמצאים כעת בעידן שבו Twitter, Github, Figshare, ו-Alt Metrics הם חלקים קבועים מתהליך המדעי. כאן אני נותן עצות ברמה גבוהה לגבי אילו כלים להשתמש, איך להשתמש בהם, ועל מה להזהר. הספר הזה מתאים למדענים בכל הרמות שרוצים להישאר בחזית ההתפתחויות הטכנולוגיות המשפיעות על קריירות מדעיות מודרניות. הספר מבוסס בחלקו על המדריכים הפופולריים של המחבר כולל מדריכים ל

הספר כנראה הכי מתאים לתלמידי מחקר ולפוסט-דוקטורנטים במדעים, אך עשוי לעניין גם אחרים שרוצים להתאים את תהליך המדע שלהם לשימוש בכלים מודרניים.

על המחבר: Jeff Leek הוא פרופסור חבר בביוסטטיסטיקה בבית הספר לבריאות הציבור ג'ונס הופקינס בלומברג. הוא שותף מייסד ומנהל משותף של ההתמחות במדעי הנתונים של ג'ונס הופקינס ב-Coursera שכבר רשמה למעלה מ-3 מיליון מדעני נתונים פוטנציאליים. המחקר שלו תרם להבנתנו את הבסיס הגנומי של פיתוח המוח, טראומת חבטה משעממת, וסרטן. הוא כותב בבלוג Simply Statistics וניתן למצוא אותו בטוויטר ב-@jtleek וב-@simplystats

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ספר זה הוא תרגום ל-עברית של How to be a modern scientist שנכתב במקור ב-אנגלית.

אודות הסופרים

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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תוכן העניינים

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      • כתיבה - אילו כלים כדאי לי להשתמש?
      • כתיבה - טיפים נוספים ונושאים
    • הפצה
      • הפצה - מה עליי לעשות ולמה?
      • פרסום - אילו כלים כדאי לי להשתמש?
      • פרסום - טיפים נוספים ובעיות
    • ביקורת עמיתים
      • ביקורת עמיתים - מה עליי לעשות ולמה?
      • ביקורת עמיתים - אילו כלים כדאי לי להשתמש בהם?
      • ביקורת עמיתים - טיפים נוספים ונושאים לדיון
    • שיתוף נתונים
      • שיתוף נתונים - מה אני צריך לעשות ולמה?
      • שיתוף נתונים - אילו כלים כדאי לי להשתמש?
      • שיתוף נתונים - טיפים נוספים ובעיות
    • בלוגים מדעיים
      • בלוגים - מה עליי לעשות ולמה?
      • בלוגינג - אילו כלים כדאי לי להשתמש?
      • בלוגים - טיפים נוספים ובעיות
    • קוד מדעי
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      • קוד מדעי - טיפים נוספים ובעיות
    • רשתות חברתיות במדע
      • רשתות חברתיות - מה עליי לעשות ולמה?
      • רשתות חברתיות - אילו כלים כדאי לי להשתמש?
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      • הוראה - טיפים נוספים ונושאים
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      • ספרים - אילו כלים עלי להשתמש?
      • ספרים - טיפים ונושאים נוספים
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      • תקשורת פנימית - אילו כלים כדאי לי להשתמש?
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      • הרצאות מדעיות - אילו כלים כדאי לי להשתמש?
      • הרצאות מדעיות - טיפים ונושאים נוספים
    • קריאת מאמרים מדעיים
      • קריאת מאמרים מדעיים - מה עליי לעשות ולמה?
      • קריאת מאמרים מדעיים - אילו כלים כדאי לי להשתמש?
      • קריאת מאמרים מדעיים - טיפים וטריקים נוספים
    • קרדיט
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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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