# con/catinate

All the resources and conText a new conScript needs, smushed together into a single doc.

## Welcome

Welcome to the Center for Open Neuroscience, where we are working together to make Neuroscience a better science! We are developing standards, workflows, tools, and automation that improve the effieniency, transparency, and reproducibility of the next generation of Neuroscience.

## Accounts

### Github

1. [CON Org](https://github.com/con)

### Chat clients

No CON centralized IM ATM, only per project and for different communities.

1. DANDI slack workspace: registration comes with registering a user on https://dandiarchive.org. Then user needs to be invited to "internal" room.
2. ReproNIM slack workspace (ask Yarik)
3. DataLad matrix.io: [public room](https://matrix.to/#/#datalad:matrix.org), [internal](not-sure-if-not-private)
4.[NWB slack workspace](https://join.slack.com/t/nwb-users/shared_invite/enQtNzMwOTcwNzQ2MDM5LWMyZDUwODJjYjM3MzMzYzZiNDk4ZTU3ZjQ3MmMxMmY5MDUyNzc0ZDI5ZjViYmJjYTQ5NjljOGFjZmMwOGIwZmQ)
5. [mattermost brainhack](https://mattermost.brainhack.org/): for various open science projects

### Calendar

1. [DANDI cal](https://calendar.google.com/calendar/embed?src=6a48akicfittlo932phrhdm84g%40group.calendar.google.com&ctz=America%2FNew_York)
2. [ReproNIM cal](https://calendar.google.com/calendar/embed?src=ahfj9rg32tmb459up8gkv2t7ek%40group.calendar.google.com&ctz=America%2FNew_York)

### Drive

1. [Repronim grant directory](https://drive.google.com/drive/folders/1AbpaqrCnInU-0V7KCxIn0RdG7578JrzI?ths=true)

### Compute

1. Send public key to Yarik for development box(es): smaug, typhon, etc
2. [Apply for Discovery Account](https://rcweb.dartmouth.edu/accounts/index.php)
3. ReproNim: request iam from David for AWS Access
4. DANDI: request credentials for DANDI from Satra

### Boilerplate

1. Add yourself to [whoweare](https://github.com/con/centerforopenneuroscience.org/blob/master/content/pages/whoweare.html) to be displayed on [the website](https://centerforopenneuroscience.org/whoweare).

## Community Projects & Definitions

[Amazon FSx](https://aws.amazon.com/fsx/)
: AWS Launch, run, and scale feature-rich and highly-performant file systems with just a few clicks

[ANCP-bids](https://github.com/ANCPLabOldenburg/ancp-bids)
: A Python package to read/write/query/validate BIDS datasets. Ligher weight than PyBids.

[Apptainer](https://apptainer.org/)
: Apptainer/Singularity is the most widely used container system for HPC. It is designed to execute applications at bare-metal performance while being secure, portable, and 100% reproducible. Apptainer is an open-source project with a friendly community of developers and users. The user base continues to expand, with Apptainer/Singularity now used across industry and academia in many areas of work.

[BEP](https://bids.neuroimaging.io/governance.html#a-bep-procedure-key-definitions)
: BIDS Enhancement Proposal

[BIDS](https://bids-specification.readthedocs.io/en/stable/)
: Brain Imaging Data Structure

[BIDS-schema](https://github.com/bids-standard/bids-schema)
: Prospective repository of BIDS schema versions. For now, [use current source of truth](https://github.com/bids-standard/bids-specification/tree/master/src/schema)

[BOLD MRI](https://en.wikipedia.org/wiki/Blood-oxygen-level-dependent_imaging)
: Blood Oxygen Level Dependent MRI -- basis of most fMRI

[BossDB](https://bossdb.org/)
: Brain Observatory Storage Service & Database

[Brain Hack](https://brainhack.org/)
: Collaborative workshops, hackathons, etc

[CalVer](https://calver.org/)
: Versioning based on the date

[ChRIS](http://chrisproject.org/)
: ChRIS is a fully-open source distributed data and computation platform.

[CON](http://centerforopenneuroscience.org/)
: Center for Open Neuroscience

[con/tinuous](https://github.com/con/tinuous/)
: is a command for downloading build logs and (for GitHub only) artifacts & release assets for a GitHub repository from GitHub Actions, Travis-CI.com, and/or Appveyor.

[con/catinate](https://github.com/con/catinate/)
: This doc!

[COS](http://cos.io)
: Unaffiliated but friendly, the Center for Open Science and their flagship the Open Science Framework

[DataLad](https://www.datalad.org/)
: Allows distributed data management under a single interface (git), uses git-annex under the hood.

[DANDI](https://www.dandiarchive.org/)
: Distributed Archives for Neurophysiology Data Integration

[DICOM](https://www.dicomstandard.org/)
: Digital Imaging and Communications in Medicine
: Primarily for clinical use, does not include research-friendly metadata

[Docker](https://en.wikipedia.org/wiki/Docker_(software))
: brand name for container platform as a service(PaaS)

[DueCredit](https://github.com/duecredit/duecredit)
: Makes it easy to cite code you used. It embeds publication or other references in the original code so they are automatically collected and reported to the user at the necessary level of reference detail for citation.

[FAIR](https://www.go-fair.org/fair-principles/)
: Findable, Accessible, Interoperable, Reproducible

[FastSurfer](https://github.com/Deep-MI/FastSurfer)
: FastSurfer - a fast and accurate deep-learning based neuroimaging pipeline

[fMRI](???)
: Functional MRI -- MRI hoping to reflect functioning of the brain, not only anatomy, and typically acquired multiple brain volumes through time to see the change in the signal.

[fMRI Prep](https://fmriprep.org/en/stable/)
: NiPreps application for the preprocessing of task-based and resting-state functional MRI (fMRI).

[git-annex](https://git-annex.branchable.com/)
: git-annex allows managing large files with git, without storing the file contents in git. It can sync, backup, and archive your data, offline and online. Checksums and encryption keep your data safe and secure. Bring the power and distributed nature of git to bear on your large files with git-annex. Used under the hood by DataLad

[HeudiConv](https://github.com/nipy/heudiconv)
: heudiconv is a flexible DICOM converter for organizing brain imaging data into structured directory layouts.FastSurfer - a fast and accurate deep-learning based neuroimaging pipeline

[HED](https://www.hedtags.org/)

[jq](https://stedolan.github.io/jq/)
: filter and pretty print json

[Jupyter](https://jupyter.org/)
: Python notebooks (formerly iPython Notebook)

[Kubernetes](https://kubernetes.io/)
: Container orchestrator

[Minder](https://github.com/phase1geo/Minder)
: Mind mapper tool. Used to produce e.g. [DataLad standards mindmap](https://datasets.datalad.org/centerforopenneuroscience/talks/2022-tx-big-neuroscience.html#/4/5)

[MOC](https://massopen.cloud/)
: Mass Open Cloud

[NERC](https://nerc.mghpcc.org/)
: New England Research Cloud

[Niceman](https://github.com/ReproNim/reproman)
: now called reproman

[NifTI](https://nifti.nimh.nih.gov/)
: Neuroimaging Informatics Technology Initiative

[NiPreps](www.nipreps.org)
: (NeuroImaging PREProcessing toolS) application

[NWB](https://www.nwb.org/)
: Neurodata Without Borders (NWB) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build analysis tools for neurophysiology data. NWB is designed to store a variety of neurophysiology data, including data from intracellular and extracellular electrophysiology experiments, data from optical physiology experiments, and tracking and stimulus data.

[NeuroScout](https://neuroscout.org/)
: A platform for fast and flexible re-analysis of (naturalistic) fMRI studies

[NeuroSynth](https://neurosynth.org/)
: Neurosynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data. It takes thousands of published articles reporting the results of fMRI studies, chews on them for a bit, and then spits out images that look like this:

[Open Brain Consent](http://open-brain-consent.readthedocs.org/)
: Helps make consent forms easier, and easier to share data.

[Open Neuro](https://openneuro.org/)
: A free and open platform for validating and sharing BIDS-compliant MRI, PET, MEG, EEG, and iEEG data.

[Open Neuroscience](https://open-neuroscience.com/)
: We are a user-driven database of open neuroscience projects. All should be here, add them if they aren't.

[Orthanc](https://www.orthanc-server.com/)
: Open-source, lightweight DICOM servhttps://www.repronim.org/5stepser

[PACS](https://en.wikipedia.org/wiki/Picture_archiving_and_communication_system)
: picture archiving and communication system

[Pelican](https://docs.getpelican.com/en/latest/)
: static site generator (used by CON website)

PBS
: 1. [Portable Batch System](https://en.wikipedia.org/wiki/Portable_Batch_System)
: 2. [Psychology and Brain Science @ Dartmouth](https://pbs.dartmouth.edu/)

[Polars](https://www.pola.rs/)
: Lightning-fast DataFrame library for Rust and Python

[PyBIDs](https://github.com/bids-standard/pybids)
: Python tools for querying and manipulating BIDS datasets.

[Pylabber](https://github.com/TheLabbingProject/pylabber)
: "XNAT reimplemented in Django" ((C) Chris) ;)

[PyMVPA](http://www.pymvpa.org/)
: PyMVPA is a Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn, shogun, MDP, etc. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. (Pioneering in machine learning analytics in neuroimaging in 2007-2016, currently not actively developed)

[NeuroDebian](https://neuro.debian.net/)
: NeuroDebian is a distribution of popular neuroscience research software for Debian (and derivatives)

[RN/ReproMan](https://github.com/ReproNim/reproman)
: creation and management of computing environments (in Neuroimaging)?

[RN/containers](https://github.com/ReproNim/containers)
: with a collection of popular neuroscience computational tools provided within ready to use containerized environmentshttps://github.com/CenterForOpenScience/modular-file-renderer

[RN/reproin](https://github.com/ReproNim/reproin)
: Its goal is to provide a turnkey flexible setup for automatic generation of shareable, version-controlled BIDS datasets from MR scanners. Provids heuristic for HeudiConv and convention for naming exam cards and sequences in the MRI scanner.

[RN/reprostim](https://github.com/ReproNim/reprostim)
: ReproStim is a video capture and recording suite for neuroimaging and psychology experiments. Its goal is to provide experimenters with a complete record of audio and visual stimulation for every data collection session by making it possible to easily collect high fidelity copies of the actual stimuli shown to each subject in the form of video files that can be stored alongside behavioral or neuroimaging data in public repositories.

[RN/neurodocker](https://github.com/ReproNim/neurodocker)
: Neurodocker is a command-line proghttps://www.repronim.org/5stepsram that generates custom Dockerfiles and Singularity recipes for neuroimaging and minifies existing containers. Its purpose is to make it easier for scientists (and others) to easily create reproducible computational environments.

[RN/ReproLake](https://repronim.wordpress.com/)
: Cloud storage of metadata about studies and analyses

[RN/ReproPond](https://repronim.wordpress.com/)
: Local storage of metadata

[ReproPub](https://zenodo.org/record/3336609#.Y5IAjH7MIUE)
: Publish ReproPond to ReproLake

[SemVer](https://semver.org/)
: Semantic Versioning

[Singularity]
: See Apptainer

[SLURM](https://en.wikipedia.org/wiki/Slurm_Workload_Manager)
: Simple Linux Utility for Resource Management

TR&D
: Technology Research and Development Programs

[YODA](https://github.com/myyoda)
: YODA's Organigram on Data Analysis
: [poster](https://raw.githubusercontent.com/ReproNim/containers-artwork/master/repronim-containers-yoda_30dpi.png)
: Best practice(s) for organizing and managing digital objects of science

## Annotated Bibliography

[5 Steps to More Reproducible Neuroimaging Research](https://www.repronim.org/5steps)
: High level of how many of the ReproNim projects fit together into a Reproducibility-friendly workflow.

1. Study Design
2. Data Collection
3. Data Processing
4. Statistical Analysis
5. Publication

[Dartmouth neuroimaging fundamentals](https://dartbrains.org/content/intro.html)
: Course that covers basic MRI physics, and high level explanations of programming, data analysis, and more!

[Open and reproducible neuroimaging: From study inception to publication](https://www.sciencedirect.com/science/article/pii/S1053811922007388)
: Outdated guide found on OSF from someone unaffiliated.

[ReproNim grant 2021 drive](https://drive.google.com/drive/folders/1AbpaqrCnInU-0V7KCxIn0RdG7578JrzI?ths=true)
: All the drafts and responses of the 2021 ReproNim Grant.

3 parts:
- TR&D1:
- TR&D2
- TR&D3

[Open is not enough. Let’s take the next step: an integrated, community-driven computing platform for neuroscience](https://www.frontiersin.org/articles/10.3389/fninf.2012.00022/full)
: note written in 2012

[Open Science and Neuroimaging - A Practical Guide](https://docs.google.com/document/d/1KW2KTN-iBDo4MyrItH5kvkoqJweoeNrLcmjTYdXxzFU/edit)

[A new virtue of phantom MRI data: explaining variance in human participant data](https://f1000research.com/articles/9-1131/v1)

[Promoting an open research culture](https://www.science.org/doi/10.1126/science.aab2374)