Draft data management#28
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@carriewright11 this is a draft of chapter 5 (with the updates we discussed in our last meeting). It is mostly complete, but is missing a conclusion and images. I can add these to the pull request but first wanted to make sure that this all looks good, because it is my first contribution to this course or any of the ITCR courses. I can brainstorm where to add images/what images to add but am open to any ideas that you or @kweav have, if you had anything in mind. |
| - Data Cleaning and Processing: Ensuring data consistency, accuracy, and completeness by detecting and rectifying errors, missing values, and inconsistencies. | ||
| - Data Analysis: Using statistical and analytical tools to generate insights from the data that can inform clinical decisions and study outcomes. | ||
| - Data Sharing and Reporting: Providing access to data for collaborators, regulators, or stakeholders in a secure and controlled manner and generating reports that summarize findings. | ||
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Possible figure showing how these activities relate (e.g., data is collected and then stored. Data may be checked and then shared and then analysis (with more checking) and then more sharing (of reports)) sort of thing
| #### Open source options | ||
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| Open-source tools provide a cost-effective and flexible alternative to proprietary software for handling clinical data. These tools are often developed and maintained by vibrant communities and can be customized to fit specific research needs. | ||
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Possible figure here that open source software doesn't mean data won't be protected (but still have to do work to make sure data is protected)
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May be better later in the section (looks like line 74)
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| - **Data Quality**: Refers to the accuracy, completeness, consistency, and reliability of data. High-quality data is essential for producing valid and reliable research outcomes. It involves processes such as data validation, error checking, and quality control measures. | ||
| - **Data Handling**: Encompasses the broader scope of managing the data lifecycle, from collection and storage to processing, analysis, and sharing. While data quality is a component of data handling, the latter also involves aspects like data security, access management, and regulatory compliance. | ||
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possible figure here showing that data quality is just a part of data handling
Co-authored-by: Kate Isaac <41767733+kweav@users.noreply.github.com>
…ical_Data_Management into draft_data_management
…o newer or less common tools
…can do basic repots but not advanced statistical modeling or analyses
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| - Guide to Clinical Data Management Procedures (GCDMP): GCDMP | ||
| - Books by Suzanne Prokscha: Writing and Managing SOPs for GCP and Practical Guide to Clinical Data Management | ||
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| Additionally, remember the insightful quote by [Damian Conway](https://www.questionpro.com/blog/data-documentation/): “Documentation is a love letter that you write to your future self.” This is an invaluable tidbit to keep in mind throughout the lifecycle of a study. |
3 participants
Purpose/implementation Section
Chapter 5: clinical data management
What changes are being implemented in this Pull Request?
This pull request adds a draft of chapter 5: clinical data management.
What was your approach?
I transferred the content for this chapter from the Google Drive, editing and re-ordering as needed to make it fit into the framework of the course.
What GitHub issue does your pull request address?
No current issues.
Tell potential reviewers what kind of feedback you are soliciting.
I would like to get feedback on the overall structure of this chapter, as this is my first addition to this course. I would also appreciate any ideas of where to add images.
New Content Checklist
New content/chapter is in an Rmd file with this kind of format and headers.
New content/chapter contains Learning Objectives and are in the correct format.
Bookdown successfully re-renders and any new content files have been added to the _bookdown.yml.
Spell check runs successfully in Github actions style-n-check).
Any newly necessary packages that are needed have been added to the Dockerfile and image.
Images are in the correct format for rendering.
Every new image has alt text and is in a Google Slide.
Each slide is described in the notes of the slide so learners relying on a screen reader can access the content. See https://lastcallmedia.com/blog/accessible-comics for more guidance on this.
The color palette choices of the slide are contrasted in a way that is friendly to those with color vision deficiencies.
You can check this using Color Oracle.