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21
01_intro/surveys/barriers.txt
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Remember the strategy used over time
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never gets perfected
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too many people in one project
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procrastination
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its not fun
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don't know the best tools for it
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expectation of presenting results fast (time)
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never having thought of it
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Fear of missing something
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Keeping multiple copies consistent
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When should I do this task?
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Other Priorities
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no idea where to start
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complex research design
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Defining a good concept from the beginning on
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Lack of planning
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boring task
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too much other work
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public security
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forget it
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bad time management
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32
01_intro/surveys/habits.txt
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discipline
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uploading under a license (CC-BY....)
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report changes to dataset
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Do not do it in your spare time?
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Brainpower
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pseudonymizing/anonymizing data
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Trink about file names
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regular cleaning 😊
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codebook
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loading data on an archive, repository etc...
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Special time slot in calendar
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avoid redundancy
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readme
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clarity
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consistency
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Doing the archive
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report deviations from preregistration
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Read-Me
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checklists
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clear workflow
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have one place where you store the data
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Document data collection in Details
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Document yout code
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Documentation
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preregistration
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Structure Structure Structure
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recording the steps (taken through analysis)
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Be consistent
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github documentation
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consitency
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Reproducible code
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Time Investment 😄
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72
01_intro/surveys/surveys.R
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#' ---
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#' title: Analysis of slido surveys
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#' author: Nora Wickelmaier
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#' ---
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#+ include = FALSE
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/teaching/iwm/data_management/01_intro/surveys")
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#' # Habits
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q1 <- trimws(readLines("habits.txt"))
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habits <- data.frame(q1,
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habit = c("workflow", "data sharing",
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"documentation", "workflow", "workflow",
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"data organisation", "workflow",
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"workflow", "documentation",
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"data sharing", "workflow", "data organisation",
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"documentation", "workflow", "workflow",
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"data sharing", "documentation",
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"documentation", "workflow", "workflow",
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"data organisation", "documentation",
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"documentation", "documentation",
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"documentation", "workflow",
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"documentation", "workflow",
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"documentation", "workflow", "workflow",
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"workflow"))
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table(habits$habit)
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habits[order(habits$habit),]
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#print(xtable::xtable(habits[order(habits$habit),]), include.rownames = FALSE)
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#' # Barriers
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q2 <- trimws(readLines("barriers.txt"))
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barriers <- data.frame(q2,
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barrier = c("lack of consistency", "perfectionism",
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"responsibility diffusion", "low priority",
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"low priority", "lack of skills",
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"lack of time", "low priority",
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"perfectionism", "lack of consistency",
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"lack of time", "low priority",
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"lack of skills", "lack of skills",
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"perfectionism", "lack of time",
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"low priority", "lack of time",
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"lack of skills", "low priority",
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"low priority"))
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table(barriers$barrier)
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barriers[order(barriers$barrier),]
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#' # Topics
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q3 <- trimws(readLines("topics.txt"))
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topics <- data.frame(q3,
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topic = c("data sharing", "workflow",
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"clean coding", "data organisation",
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"workflow", "workflow", "workflow",
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"data organisation", "clean coding",
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"version control", "clean coding",
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"data sharing", "workflow",
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"data organisation", "data sharing"))
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table(topics$topic)
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topics[order(topics$topic),]
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15
01_intro/surveys/topics.txt
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important things before the open-access data
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Introduction into available tools
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Understandable coding
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How to manage different data sources in one experiment (e.g. eye tracking, performance, questionnaire..)
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Upload data before or after publishing a paper? Time mangement
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going over guidelines/best practice on how to name files, folders and data as well as folder structure.
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understanding where redundancy is needed (raw data?) and where to avoid it.
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understanding what should always go into a readme file.
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Cleaning up R code for readability
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how to integrate gitHub in workflow
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Documentation of a final R script
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Where to store data for long-term accessibility (conventions?)
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Steps and when to do what
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How to best arrange the data
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Tools, where I should upload my final data
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1045
02_workflow/02_workflow.tex
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15
README.md
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| Date | Topic |
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| Date | Topic |
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| ---------- | --------------------------------------- |
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| ---------- | --------------------------------------- |
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| 13.05.2024 | Introduction to data management |
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| 2024-05-13 | Introduction to data management |
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| 27.05.2024 | Workflows |
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| 2024-05-27 | Workflow |
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| 10.06.2024 | |
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| 2024-06-10 | Data organisation |
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| 24.06.2024 | |
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| 2024-06-24 | Data sharing |
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| 08.07.2024 | |
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| 2024-07-08 | Clean coding |
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| 22.07.2024 | |
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| 2024-07-22 | Version control |
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# Literature
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# Literature
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@ -20,3 +20,6 @@ Frazier, M. R., O'Hara, C. C., Jiang, N., & Halpern, B. S. (2017). Our path
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to better science in less time using open data science tools. _Nature
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to better science in less time using open data science tools. _Nature
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Ecology & Evolution, 1_(6), 1-7. https://doi.org/10.1038/s41559-017-0160
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Ecology & Evolution, 1_(6), 1-7. https://doi.org/10.1038/s41559-017-0160
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Wilbrandt, J. (2023). Research Data Management Intro Series: Coffee Lectures &
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Espresso Shots. https://doi.org/10.5281/zenodo.7573695
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BIN
figures/QR Code for Methodenseminar SS 2024 - Session 2.png
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figures/email_data_request_2024_01.png
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figures/email_data_request_2024_02.png
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figures/ex_filenaming_ma_01.png
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figures/ex_filenaming_website_01.png
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figures/ex_filenaming_website_02.png
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figures/xkcd_iso_8601_2x.png
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doi = {10.1177/2515245917747656}
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doi = {10.1177/2515245917747656}
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}
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}
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@misc{Wilbrandt2023,
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author = {Wilbrandt, Jeanne},
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title = {{Research Data Management Intro Series: Coffee Lectures \& Espresso Shots}},
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year = {2023},
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publisher = {Zenodo},
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version = {1.0},
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url = {https://doi.org/10.5281/zenodo.7573695}
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}
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