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Author SHA1 Message Date
a1e720aa95 Slides for second session 2024-05-24 15:45:58 +02:00
137599598a Added survey results from first session 2024-05-24 15:44:31 +02:00
14 changed files with 1203 additions and 6 deletions

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Remember the strategy used over time
never gets perfected
too many people in one project
procrastination
its not fun
don't know the best tools for it
expectation of presenting results fast (time)
never having thought of it
Fear of missing something
Keeping multiple copies consistent
When should I do this task?
Other Priorities
no idea where to start
complex research design
Defining a good concept from the beginning on
Lack of planning
boring task
too much other work
public security
forget it
bad time management

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discipline
uploading under a license (CC-BY....)
report changes to dataset
Do not do it in your spare time?
Brainpower
pseudonymizing/anonymizing data
Trink about file names
regular cleaning 😊
codebook
loading data on an archive, repository etc...
Special time slot in calendar
avoid redundancy
readme
clarity
consistency
Doing the archive
report deviations from preregistration
Read-Me
checklists
clear workflow
have one place where you store the data
Document data collection in Details
Document yout code
Documentation
preregistration
Structure Structure Structure
recording the steps (taken through analysis)
Be consistent
github documentation
consitency
Reproducible code
Time Investment 😄

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#' ---
#' title: Analysis of slido surveys
#' author: Nora Wickelmaier
#' ---
#+ include = FALSE
# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/teaching/iwm/data_management/01_intro/surveys")
#' # Habits
q1 <- trimws(readLines("habits.txt"))
habits <- data.frame(q1,
habit = c("workflow", "data sharing",
"documentation", "workflow", "workflow",
"data organisation", "workflow",
"workflow", "documentation",
"data sharing", "workflow", "data organisation",
"documentation", "workflow", "workflow",
"data sharing", "documentation",
"documentation", "workflow", "workflow",
"data organisation", "documentation",
"documentation", "documentation",
"documentation", "workflow",
"documentation", "workflow",
"documentation", "workflow", "workflow",
"workflow"))
table(habits$habit)
habits[order(habits$habit),]
#print(xtable::xtable(habits[order(habits$habit),]), include.rownames = FALSE)
#' # Barriers
q2 <- trimws(readLines("barriers.txt"))
barriers <- data.frame(q2,
barrier = c("lack of consistency", "perfectionism",
"responsibility diffusion", "low priority",
"low priority", "lack of skills",
"lack of time", "low priority",
"perfectionism", "lack of consistency",
"lack of time", "low priority",
"lack of skills", "lack of skills",
"perfectionism", "lack of time",
"low priority", "lack of time",
"lack of skills", "low priority",
"low priority"))
table(barriers$barrier)
barriers[order(barriers$barrier),]
#' # Topics
q3 <- trimws(readLines("topics.txt"))
topics <- data.frame(q3,
topic = c("data sharing", "workflow",
"clean coding", "data organisation",
"workflow", "workflow", "workflow",
"data organisation", "clean coding",
"version control", "clean coding",
"data sharing", "workflow",
"data organisation", "data sharing"))
table(topics$topic)
topics[order(topics$topic),]

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important things before the open-access data
Introduction into available tools
Understandable coding
How to manage different data sources in one experiment (e.g. eye tracking, performance, questionnaire..)
Upload data before or after publishing a paper? Time mangement
going over guidelines/best practice on how to name files, folders and data as well as folder structure.
understanding where redundancy is needed (raw data?) and where to avoid it.
understanding what should always go into a readme file.
Cleaning up R code for readability
how to integrate gitHub in workflow
Documentation of a final R script
Where to store data for long-term accessibility (conventions?)
Steps and when to do what
How to best arrange the data
Tools, where I should upload my final data

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02_workflow/02_workflow.tex Normal file

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| Date | Topic | | Date | Topic |
| ---------- | --------------------------------------- | | ---------- | --------------------------------------- |
| 13.05.2024 | Introduction to data management | | 2024-05-13 | Introduction to data management |
| 27.05.2024 | Workflows | | 2024-05-27 | Workflow |
| 10.06.2024 | | | 2024-06-10 | Data organisation |
| 24.06.2024 | | | 2024-06-24 | Data sharing |
| 08.07.2024 | | | 2024-07-08 | Clean coding |
| 22.07.2024 | | | 2024-07-22 | Version control |
# Literature # Literature
@ -20,3 +20,6 @@ Frazier, M. R., O'Hara, C. C., Jiang, N., & Halpern, B. S. (2017). Our path
to better science in less time using open data science tools. _Nature to better science in less time using open data science tools. _Nature
Ecology & Evolution, 1_(6), 1-7. https://doi.org/10.1038/s41559-017-0160 Ecology & Evolution, 1_(6), 1-7. https://doi.org/10.1038/s41559-017-0160
Wilbrandt, J. (2023). Research Data Management Intro Series: Coffee Lectures &
Espresso Shots. https://doi.org/10.5281/zenodo.7573695

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doi = {10.1177/2515245917747656} doi = {10.1177/2515245917747656}
} }
@misc{Wilbrandt2023,
author = {Wilbrandt, Jeanne},
title = {{Research Data Management Intro Series: Coffee Lectures \& Espresso Shots}},
year = {2023},
publisher = {Zenodo},
version = {1.0},
url = {https://doi.org/10.5281/zenodo.7573695}
}