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.travis.yml

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before_script:
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- npm install `cat npm-requirements.txt`
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script:
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- remark docs/*.md --frail
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- remark docs/*.md
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deploy:
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provider: pages
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local-dir: site
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cleanup: true
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keep-history: true
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github-token: $GITHUB_TOKEN
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on:
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branch: master

docs/05-analysis.md

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## Machine learning applications
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Resources currently [here](machine_and_deep_learning.md#Machine-learning-and-deep-learning)
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## Genetics sotfware
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Resources currently [here](genetics-software.md#genetics-software)
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<!--
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* [general linear model]

docs/genetics-software.md

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# Genetics sotfware
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## Extracting data from the Allen atlas
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The Allen atlas is an amazing resource of regional gene expression in the human brain. It contains a database of expression levels of 20,737 genes represented by 58,692 probes across the complete cortical mantle and is constructed postmortem from the brains of 6 human donors with no history of psychiatric or neuropathological disorders. The donor brains also underwent MRI postmortem and the probe locations mapped into each donor's MRI data in MNI space, providing a great opportunity to correlate regional gene expression with imaging findings!
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Data is freely available to download from AIBS [AIBS](http://human.brain-map.org/static/download).
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Extracting appropriate gene expression data from the Allen Atlas has many caveats, detailed in the excellent paper [here](https://doi.org/10.1016/j.neuroimage.2019.01.011) but tools have now been developed to make gene extraction easier.
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The following list includes software/packages and useful resources that can be used to extract gene expression data in imaging space.
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### Abagen
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- Open-source Python software for extracting gene expression data from the ALlen atlas.
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- repository URL: [Github](https://github.com/rmarkello/abagen/)
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- website URL: [http://martinos.org/mne/stable/index.html](http://martinos.org/mne/stable/index.html)
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- tutorial URL:
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- documentation: [https://abagen.readthedocs.io/en/stable/index.html](https://abagen.readthedocs.io/en/stable/index.html)
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- programming language: {python}
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- paper DOI: [https://doi.org/10.5281/zenodo.3726257.](https://doi.org/10.5281/zenodo.3726257.)
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### Matlab repository from Aurina Arnatkeviciute
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- Repository from the Aurina's paper describing best practice when extracting data from the Allen atlas.
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- repository URL: [Github](https://github.com/BMHLab/AHBAprocessing)
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- programming language: {matlab}
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- paper DOI: [https://doi.org/10.1016/j.neuroimage.2019.01.011](https://doi.org/10.1016/j.neuroimage.2019.01.011)
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- contact: aurina.arnatkeviciute@monash.edu

docs/learning_neuroimaging.md

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### Neuroimaging quality control task force
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- description:
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- URL: https://crossinvalidation.com/2018/07/31/neuroimaging-quality-control-niqc-task-force-to-develop-protocols-tools-and-manuals/
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- URL: [URL](https://crossinvalidation.com/2018/07/31/neuroimaging-quality-control-niqc-task-force-to-develop-protocols-tools-and-manuals/)
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- date: 2018
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### quality control on MRI and fMRI
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- description:
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- URL: https://practicalfmri.blogspot.com/2014/08/qa-for-fmri-part-3-facility-qa-what-to.html
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- URL: [URL](https://practicalfmri.blogspot.com/2014/08/qa-for-fmri-part-3-facility-qa-what-to.html)
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### COBIDacq
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- URL: https://practicalfmri.blogspot.com/2017/12/cobidacq.html
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- URL: [URL](https://practicalfmri.blogspot.com/2017/12/cobidacq.html)
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### wikibook neuroimaging data processing
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- description:
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- URL: https://en.wikibooks.org/wiki/Neuroimaging_Data_Processing/Data_Quality
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- URL: [URL](https://en.wikibooks.org/wiki/Neuroimaging_Data_Processing/Data_Quality)
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### References for quality control
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- URL: https://www.zotero.org/groups/2221093/niqc
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- URL: [URL](https://www.zotero.org/groups/2221093/niqc)
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### MRIQC
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- description: MRI quality control. A BIDS app that runs a pipeline to assess the quality of your data.
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- URL: https://mriqc.readthedocs.io/en/stable/
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- URL: [URL](https://mriqc.readthedocs.io/en/stable/)
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- date:
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### the PCP Quality Assessment Protocol
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- description: BIDS app based on the protocol of the connectome project data
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- URL: https://github.com/BIDS-Apps/QAP , http://preprocessed-connectomes-project.org/quality-assessment-protocol/
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- URL: [URL1](https://github.com/BIDS-Apps/QAP) , [URL2](http://preprocessed-connectomes-project.org/quality-assessment-protocol/)
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### Scripts for quality control of diffusion data
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(http://davidroalf.com/script_download/)
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[Scripts](http://davidroalf.com/script_download/)
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- description:
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- URL: https://practicalfmri.blogspot.com/2017/12/cobidacq.html
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- URL: [URL](https://practicalfmri.blogspot.com/2017/12/cobidacq.html)
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- tags: [software]
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### Qoala-t
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- description: QA for freesurfer segmentations
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- URL: https://github.com/Qoala-T/Qoala-T
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- URL: [URL](https://github.com/Qoala-T/Qoala-T)
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- date:
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- by:
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- shinyapp
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- description: QA for freesurfer segmentations
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- URL: https://qoala-t.shinyapps.io/qoala-t_app/
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- URL: [URL](https://qoala-t.shinyapps.io/qoala-t_app/)
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### Visual QC
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- URL: https://github.com/raamana/visualqc
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- URL: [URL](https://github.com/raamana/visualqc)
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- date: 2018
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## Diffusion Microstructure Imaging in Python (Dmipy)
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- description: The Dmipy software project is dedicated to fasciliting high-level, reproducible diffusion microstructure research. The Dmipy open-source repository has many examples on implementing and fitting microstructure models.
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- URL: https://github.com/AthenaEPI/dmipy
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- URL: [URL](https://github.com/AthenaEPI/dmipy)
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- date: 2018
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- tags: [software] [image processing]
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- reference: https://hal.inria.fr/hal-01873353/file/dmipy-diffusion-microstructure.pdf
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- reference: [reference](https://hal.inria.fr/hal-01873353/file/dmipy-diffusion-microstructure.pdf)
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### Computer Vision
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- description: Lecture on Computer Vision by Michael Beyeler within Neurohackademy 2018.
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- URL: https://neurohackademy.org/course/computer-vision/
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- URL: [URL](https://neurohackademy.org/course/computer-vision/)
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### Finding low-dimensional structure in large-scale neural recordings
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- URL: https://neurohackademy.org/course/finding-low-dimensional-structure-in-large-scale-neural-recordings/
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- URL: [URL](https://neurohackademy.org/course/finding-low-dimensional-structure-in-large-scale-neural-recordings/)
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### Interactive Data Visualization with D3
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- URL: https://neurohackademy.org/course/interactive-data-visualization-with-d3/
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- URL: [URL](https://neurohackademy.org/course/interactive-data-visualization-with-d3/)
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### Quantitative and diffusion MRI modeling of developmental data
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- URL: https://neurohackademy.org/course/quantitative-and-diffusion-mri-modeling-of-developmental-data/
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- URL: [URL](https://neurohackademy.org/course/quantitative-and-diffusion-mri-modeling-of-developmental-data/)
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- date: 2018
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#### video
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- description: Overview of Meta Analysis Approaches
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- URL: https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/116072
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- URL: [URL](https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/116072)
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#### slides
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- description: Overview of Meta Analysis Approaches
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- URL: https://figshare.com/articles/Overview_of_Meta-Analysis_Approaches/6723839
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- URL: [URL](https://figshare.com/articles/Overview_of_Meta-Analysis_Approaches/6723839)
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- date: 2018
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#### ALE and BrainMap
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- URL: [URL](https://www.pathlms.com/ohbm/courses/8246/sections/12542/video_presentations/116066)
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#### NiMARE
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- description: NiMARE is a Python library for coordinate- and image-based meta-analysis.
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- URL: https://github.com/neurostuff/NiMARE
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- URL: [URL](https://github.com/neurostuff/NiMARE)
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- type: [software] [python] [meta-analysis]
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- tutorial
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- URL: https://www.kaggle.com/chrisfilo/coordinate-and-image-metaanalysis-with-nimare
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- URL: [URL](https://www.kaggle.com/chrisfilo/coordinate-and-image-metaanalysis-with-nimare)
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- type: [tutorial] [juptyer] [meta-analysis]
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- URL: [URL](http://www.brainmap.org/)
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#### Sleuth
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- URL: [URL](http://www.brainmap.org/software.html#Sleuth)
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- [description](http://www.brainmap.org/)
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- URL: [URL](http://www.brainmap.org/software.html#GingerALE)
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- description: IBMA is the Image-Based Meta-Analysis toolbox for SPM.
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- URL: [URL](https://github.com/NeuroimagingMetaAnalysis/ibma)
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docs/statistics.md

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- [null hypothesis significance testing](http://rpsychologist.com/d3/NHST/)
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- [p hacking](https://www.shinyapps.org/apps/p-hacker/)
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- [positive predictive values](http://shinyapps.org/showapp.php?app=https://tellmi.psy.lmu.de/felix/PPV&by=Michael%20Zehetleitner%20and%20Felix%20Sch%C3%B6nbrodt&title=When%20does%20a%20significant%20p-value%20indicate%20a%20true%20effect?&shorttitle=When%20does%20a%20significant%20p-value%20indicate%20a%20true%20effect?)
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Partial least squares regression:
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This is particularly useful for highdimensional neuroimaging data (particularly when correlating with genetic/transcriptome data). It finds a linear regression model by projecting the predicted variables and the observable variables to a new space.
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Useful resources if you are new to PLS-R:
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- [A beginner's guide to PLS](https://www.researchgate.net/publication/228861711_A_Beginner's_Guide_to_Partial_Least_Squares_Analysis)
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- [python package](https://hoggorm.readthedocs.io/en/latest/)
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- [r package](https://cran.r-project.org/web/packages/pls/)
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- [matlab](https://uk.mathworks.com/help/stats/examples/partial-least-squares-regression-and-principal-components-regression.html)

mkdocs.yml

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- About: "05-analysis.md"
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- Statistics: "statistics.md"
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- Machine learning and Deep learning: "machine_and_deep_learning.md"
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- Genetic software: "genetics-software.md"
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- Quality control:
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- About: "06-quality-control.md"
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- Reporting methods and results:

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