Flexible Structure
The datasets are presented in a searchable list instead of predefined project structure.
Semantic Search
Use the semantic search to find datasets you need.
Organize and Sharing
Create folders and produce a data structure you like. Share it and collaborate.
Controlled Access
Choose the access level for each dataset individually.
Application Ready
Use the full featured API to connect to the repository from your software.
Standard File Formats
DICOM, ITK images, CDISC, STL, Statismo and ontology based meta-data.
Scientific Data
Scientific Data is an open-access, online-only publication for descriptions of scientifically valuable datasets
PLOS
List of the biological repositories and standards recommended by the PLOS publishing group journals
bioCADDIE
Offers a joint publisher-curated list of appropriate data deposition repositories in the field of life sciences
Our Competences
Storing medical image data requires special knowledge. SICAS offers a unique combination of competence in acquiring and storing medical images, in processing and visualising data for research and applications in medicine.
Anonymisation
Anonymisation of patient information and image features like face soft tissue.
Data Storage
Certified, efficient, and innovative data center based in Switzerland.
Research expertise
Sound expertise and broad experience in research support and collaboration management.
Acquisition
Acquisition of high quality image data and curation by experts.
Controlled
Controlled distribution of medical image data.
Anatomy
Anatomy based search to find the correct images.
With the API
Please consider developing on the demo (https://demo.smir.ch) server before you interact with the live system.
API Reference Python 3 API Connector Scala (JAVA) API Connector
Chunked file upload
Python code
## import libs
from pathlib import Path
from vsdConnect import connect
## connect to demo
api = connect.VSDConnecter()
## define filepath
fp = Path('C:' + os.sep, 'test', 'test.nii')
## Define chunk size to eg. 8 MB if you dont want to use the default 4MB
chunk = 1024 * 4096 * 2
## upload using the chunkFileUpload
obj = api.chunkFileUpload(fp, chunksize = chunk)
## print the selfUrl of the generated object
print(obj.selfUrl)
Console output
uploading part 1 of 3 uploaded part 1 of 3 uploading part 2 of 3 uploaded part 2 of 3 uploading part 3 of 3 uploaded part 3 of 3 https://demo.smir.ch/api/objects/1
Download a folder
Python code
from pathlib import Path
from vsdConnect import connect
# connect to the demo API
api = connectVSD.VSDConnecter()
## search for the test folder and retrieve the folder object
folder = api.getFolderByName('One')
for obje in folder.containedObjects:
## get each object in the folder
obj = api.getObject(obje.selfUrl)
## Download directory
home_dir = Path('/Home/User/Downloads')
obj.download(api, working_dir=home_dir,)
