sparc-marker-paper-code
Code associated with the SPARC Marker Paper
Science Score: 75.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 5 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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✓Institutional organization owner
Organization nih-sparc has institutional domain (commonfund.nih.gov) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (12.5%) to scientific vocabulary
Keywords
Repository
Code associated with the SPARC Marker Paper
Basic Info
Statistics
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
Code: SPARC Marker Paper
About
This is the code associated with the SPARC Marker Paper. SPARC is one of the NIH Common Fund's Program that seeks to accelerate the development of therapeutic devices that modulate electrical activity in the ANS to improve internal organ functions and suppress dysfunction. The SPARC Marker paper currently being prepared is aimed at providing an overview of the SPARC program, presenting its major outcomes, and giving an assessment of the impact of the program on bioelectronic medicine-related research and development. The repository contains the Jupyter notebook developed to analyze data for the paper and generate figures. See this inventory for all related resources, including the paper.
Standards followed
The overall code is structured according to the FAIR-BioRS guidelines. The Python code in the Jupyter notebook main.ipynb follows the PEP8 guidelines. Functions are documented with docstring formatted following Google's style guide. All the dependencies are documented in the environment.yml file.
Using the Jupyter notebook on your computer
Prerequisites
We recommend using Anaconda to create and manage your development environment and using JupyterLab to run the notebook. All the subsequent instructions are provided assuming you are using Anaconda (Python 3 version) and JupyterLab.
Clone repo
Clone the repo or download as a zip and extract.
cd into the code folder
Open Anaconda prompt (Windows) or the system Command line interface then naviguate to the code ```sh cd .sparc-marker-paper
```
Setup conda env
sh
$ conda env create -f environment.yml
Setup kernell for Jupyter lab
sh
$ conda activate sparc-marker-paper
$ conda install ipykernel
$ ipython kernel install --user --name=<any_name_for_kernel>
$ conda deactivate
Launch Jupyter lab
Launch Jupyter lab and naviguate to open the main.ipynb file. Make sure to change the kernel to the one created above (e.g., see here). We recommend to use the JupyterLab code formatter along with the Black and isort formatters to facilitate compliance with PEP8 if you are editing the notebook.
Using the Jupyter notebook on o²S²PARC (no installation required)
SPARC has also developed o²S²PARC, a cloud platform to build and explore modeling and data analysis pipelines. You will find the code in this repository, all the necessary software dependencies, and the input data at this o²S²PARC link.
To access the o²S²PARC link, you need an o²S²PARC account; you will find more information on how to create an account at the o²S²PARC platform URL osparc.io.
Inputs/outputs
The Jupyter notebook makes use of files in the dataset associated with the SPARC Marker Paper (see here). You will need to download the dataset at add it in the input folder.
Outputs of the code include tables displayed directly in the notebook and plots displayed in the notebook but also saved as files. These saved plot files are included in the output folder.
License
This work is licensed under MIT. See LICENSE for more information.
Feedback and contribution
Use the GitHub issues for submitting feedback or making suggestions. You can also work the repository and submit a pull request with suggestions.
How to cite
If you use this code, please cite the SPARC Marker Paper (it will be listed here when available) and also cite this repository as:
bash
Patel, Bhavesh. Code: SPARC Marker Paper [Software]. Zenodo. https://doi.org/10.5281/zenodo.11361363
Owner
- Name: NIH SPARC
- Login: nih-sparc
- Kind: organization
- Website: https://commonfund.nih.gov/sparc
- Repositories: 5
- Profile: https://github.com/nih-sparc
Citation (CITATION.cff)
# This CITATION.cff file was generated with FAIRshare. # Visit https://fairdataihub.org/fairshare to learn more! abstract: Code associated with the SPARC Marker Paper authors: - affiliation: FAIR Data Innovations Hub, California Medical Innovations Institute email: bpatel@calmi2.org family-names: Patel given-names: Bhavesh orcid: 0000-0002-0307-262X - affiliation: IT'IS Foundation, Zürich (Switzerland) email: iavarone@itis.swiss family-names: Elisabetta given-names: Iavarone orcid: 0000-0001-5157-247X cff-version: 1.2.0 date-released: '2025-05-28' identifiers: - description: DOI for this software's record on Zenodo type: doi value: 10.5281/zenodo.11361363 keywords: - sparc - paper - bioelectrionic - neuromodulation license: MIT message: If you use this software, please cite it as below. repository-code: https://github.com/nih-sparc/sparc-marker-paper title: 'Code: SPARC Marker Paper' type: software url: https://github.com/nih-sparc/sparc-marker-paper version: 1.0.0
CodeMeta (codemeta.json)
{
"@context": "https://doi.org/10.5063/schema/codemeta-2.0",
"@type": "SoftwareSourceCode",
"license": "https://spdx.org/licenses/MIT",
"codeRepository": "https://github.com/nih-sparc/sparc-marker-paper",
"dateCreated": "2023-05-28",
"datePublished": "2023-05-28",
"downloadUrl": "https://github.com/nih-sparc/sparc-marker-paper",
"issueTracker": "https://github.com/nih-sparc/sparc-marker-paper/issues",
"name": "Code: SPARC Marker Paper",
"version": "1.0.0",
"identifier": "10.5281/zenodo.11361363",
"description": "Code associated with the SPARC Marker Paper",
"applicationCategory": "Scientific",
"releaseNotes": "First version release",
"developmentStatus": "active",
"keywords": [
"sparc",
"paper",
"biolectronic",
"neuromodulation"
],
"programmingLanguage": [
"Jupyter Notebook"
],
"relatedLink": [
"https://sparc.science"
],
"author": [
{
"@type": "Person",
"@id": "https://orcid.org/0000-0002-0307-262X",
"givenName": "Bhavesh",
"familyName": "Patel",
"email": "bpatel@calmi2.org",
"affiliation": {
"@type": "Organization",
"name": "FAIR Data Innovations Hub, California Medical Innovations Institute"
}
},
{
"@type": "Person",
"@id": "https://orcid.org/0000-0001-5157-247X",
"givenName": "Elisabetta",
"familyName": "Iavarone",
"email": "iavarone@itis.swiss",
"affiliation": {
"@type": "Organization",
"name": "IT'IS Foundation, Zrich (Switzerland)"
}
}
]
}
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Dependencies
- ipykernel 6.19.2.*
- ipywidgets
- openpyxl 3.0.10.*
- pip
- python 3.11.4.*