Tutorial: Download public data, scaffolds and run computations


In this tutorial, we'll walk through a step-by-step scenario of a user who wants to explore public datasets, download relevant data, and use the Open Simulation Platform for Advanced Research (o²S²PARC) to transform the data. Using the sparc client you will go through the steps of searching for datasets, downloading and storing data, creating an o²S²PARC instance, running a data analysis pipeline, and finally, visualizing the results.

Credit: the use case presented here was inspired by one of the projects presented at the SPARC FAIR Codeathon 2022, in particular the tutorial "Mapping 2D data to a 3D organ scaffold".


In order to run this tutorial, you need to have:

  • Basic python knowledge
  • Internet access
  • An active o²S²PARC account
    • If you don't have one, please make sure to request access by sending an email to [email protected]
  • installed SPARC Python Client and other required libraries
    • You could install it by runningpip install sparc.client requests pathlib tqdm from your console/command line.

Getting set up

To run a simulation, we need to configure access to osparc.io by providing access tokens. For that, you need to log into your o²S²PARC account, and create credentials on their website by navigating to your user profile (click on the image in the upper right corner) and choosing Preferences. Then choose API tag and generate api_key and api_secret- here is how to do it. The values will be displayed once only. More information could be found in Generating o²S²PARC Tokens section here.

Next, please create a text file config.ini in you working directory and fill the value from Key field into O2SPARC_USERNAME variable, and value from Secret into O2SPARC_PASSWORD



Your config.ini file needs to have 2 sections: global with the name of a default profile used (prod) and a section indicated by default_profile with 4 variables: pennsieve_profile_name, O2SPARC_HOST, O2SPARC_USERNAME and O2SPARC_PASSWORD.

Note: please do not use single (') or double (") quotation marks in the configuration file, just insert plain text.

Downloading files

After installation of sparc.client and other requited libraries, we will download the datasets required for the analysis. We need the following datasets that contain 3D coordinates:

  1. Dataset with ID 10: Spatial distribution and morphometric characterization of vagal afferents associated with the myenteric plexus of the rat stomach
  2. Dataset with ID 11: Spatial distribution and morphometric characterization of vagal afferents (intramuscular arrays (IMAs)) within the longitudinal and circular muscle layers of the rat stomach
  3. Dataset with ID 12: Spatial distribution and morphometric characterization of vagal efferents associated with the myenteric plexus of the rat stomach
from sparc.client import SparcClient
client = SparcClient(connect=False, config_file='config.ini') #we assume config.ini is in the current directory

#searching for the relevant files
ima_data=client.pennsieve.list_files(dataset_id=11, query="files/derivative/IMA_analyzed_data.xlsx") # Derivative file from the SPARC Portal
efferent_data=client.pennsieve.list_files(dataset_id=12, query="files/derivative/Efferent_data.xlsx") # Derivative file from the SPARC Portal
igle_data=client.pennsieve.list_files(dataset_id=10, query="files/derivative/IGLE_data.xlsx") # Derivative file from the SPARC Portal

#downloading files

We also need 2 additional files:

  • A zip file containing the Python code you want to run and a requirements.txt for your additional Python packages.
  • The 3D mesh of an organ, in .stl format. It can be downloaded here.
import requests

# Make http request for remote file with code
code_input = requests.get('https://raw.githubusercontent.com/elisabettai/sparc.client/main/docs/tutorial-readme-assets/code_input.zip')

# Save file data to local copy
with open('code_input.zip', 'wb')as file:
# Download organ mesh for simulation
organ_mesh = requests.get('https://raw.githubusercontent.com/elisabettai/sparc.client/main/docs/tutorial-readme-assets/scaffold_zinc_graphics.stl')
# Save file data to local copy
with open('scaffold_zinc_graphics.stl', 'wb')as file:

At this point you should see 6 files in your current directory: config.ini, Efferent_data.xlsx, IGLE_data.xlsx, IMA_analyzed_data.xlsx, code_input.zip and scaffold_zinc_graphics.stl.

Spinning Up an o²S²PARC Instance

Let's confirm that now that we can connect to o²S²PARC. We need to import o²S²PARC module from the SPARC Client and check if we are connected.

from sparc.client import SparcClient
from sparc.client.services.o2sparc import (

o2sparc: O2SparcService = client.o2sparc
# Check that you are connect to o²S²PARC (you should see your e-mail address)

If the response is the e-mail that we registered with, we can start our simulation!

Simulation Creation for the Downloaded Datasets

In this section we will use the o²S²PARC Service Python Runner to execute Python code on the SPARC datasets that we have just downloaded. The o²S²PARC Python Runner executes the code to transform the 2D data from the portal, expressed as distances in percentages, to 3D data that be visualized on an organ scaffold of the rat stomach.

The inputs to the Python Runner, for this particular use-case, are:

  • A zip file containing the Python code you want to run and a requirements.txt for your additional Python packages (it can be downloaded here).
  • The three data files downloaded in the previous section with the DAT-Core functionality.
  • The 3D mesh of an organ, in .stl format. It can be downloaded here.

The output is a figure showing a 2D projection of the input anatomical data onto the rat stomach scaffold.

from pathlib import Path
from tqdm import tqdm
from time import sleep
import zipfile
import shutil

with Path().absolute() as tmp_dir:
    input_file_1: Path = Path(tmp_dir) / "code_input.zip" # Zip file containing a Python script to be executed and a requirements.txt for additional Python packages
    input_file_2: Path = Path(tmp_dir) / "IMA_analyzed_data.xlsx" # Derivative file from the SPARC Portal
    input_file_3: Path = Path(tmp_dir) / "Efferent_data.xlsx" # Derivative file from the SPARC Portal
    input_file_4: Path = Path(tmp_dir) / "IGLE_data.xlsx" # Derivative file from the SPARC Portal
    input_file_5: Path = Path(tmp_dir) / "scaffold_zinc_graphics.stl" # Stomach surface mesh
    job: dict = {
        "input_5": input_file_5,
        "input_4": input_file_4,
        "input_3": input_file_3,
        "input_2": input_file_2,
        "input_1": input_file_1
    solver: O2SparcSolver = o2sparc.get_solver(solver_key="simcore/services/comp/osparc-python-runner",solver_version="1.2.0")
    job_id = solver.submit_job(job)
    pbar = tqdm(total=1.0)
    progress: float = 0
    while not solver.job_done(job_id):
        if solver.get_job_progress(job_id) > progress:
          pbar.update(solver.get_job_progress(job_id) - progress)
          progress = solver.get_job_progress(job_id)
    # Get results
    res = solver.get_results(job_id)
    # The oSPARC Python runner service has 1 output
    output_path = res['output_1']

    # The output_path contains a zip archive, let's extract it
    with zipfile.ZipFile(output_path,"r") as zip_ref:
    # Copy the content of the archive to the current working directory for convenience
    shutil.copyfile("computation_output/data_projected_on_scaffold.png", "data_projected_on_scaffold.png")

If the code run successfully, you will a file data_projected_on_scaffold.png appearing in your current directory.

This is how the output figure will look like.


If you don't get the expected output, you can retrieve the o²S²PARC job logs with:

from tempfile import TemporaryDirectory

print("job log:")
log_dir: TemporaryDirectory = solver.get_job_log(job_id)
for elm in Path(log_dir.name).rglob("*"):
    if elm.is_file():