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Data Carpentry

Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain.

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Organisation
Data Carpentry http://www.datacarpentry.org https://dev.tess.elixir-europe.org/content_providers/data-carpentry Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. /system/content_providers/images/000/000/010/original/DC_logo_vision.png?1469458743

Owner

scraper

Editors

finn (Finn Bacall)
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  • Materials (34)
  • Events (0*)
Showing 30 materials out of 34
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  • Genomics WorkshopWorkshop OverviewTeaching Platform

  • Python for Ecologists: Glossary

  • Data Carpentry Python for Ecologists

  • Data Organization in Spreadsheets

  • Intro to Geospatial Data with R

  • Data Carpentry: R for data analysis and visualization of Ecological Data

  • Instructor notes

  • Open Refine for Ecology: Instructor Notes

  • Data Organization in Spreadsheets: Glossary

  • Cloud Genomics: Glossary

  • Open Refine for Ecology: Glossary

  • SQL for Ecology: Glossary

  • Python for Ecologists: Instructor NotesChallenge solutions

  • Data Organization in Spreadsheets: Instructor Notes

  • SQL for Social Science Data: Glossary

  • OpenRefine for Social Science Data: Glossary

  • R for Social Scientists: Glossary

  • Python for Social Science Data: Glossary

  • Python for Social Science Data: Instructor Notes

  • Genomics Organization: Instructor Notes

  • Genomics Organization: Glossary

  • Ecology Workshop Overview

  • Open Refine for Ecology

  • Shell Genomics: Instructor Notes

  • Python for Ecologists

  • Wrangling Genomics: Glossary

  • Cloud Genomics: Instructor NotesCloud Genomics Pre-WorkshopDuring the workshop

  • Data Organization in Spreadsheets for Social Scientists: Instructor Notes

  • R for Social Scientists: Instructor Notes

  • Data Organization in Spreadsheets for Social Scientists

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TeSS has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676559.