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Get a URL to download or embed the file. For private assets, the URL is temporary and will expire after 1 hour.
Update file metadata (name, description, visibility, etc.) and optionally replace the file data with a new file. Requires write or admin permission.
Permanently delete a file from the platform. Requires admin permission. This action cannot be undone.
# Delete a file (requires admin permission)
ouro.files.delete(id=file_id)# Get signed URL to download the file
file_data = file.read_data()
print(file_data.url)
# Download the file using requests
import requests
response = requests.get(file_data.url)
with open('downloaded_file', 'wb') as output_file:
output_file.write(response.content)# Update file metadata
updated = ouro.files.update(
id=file_id,
name="Updated file name",
description="Updated description",
visibility="private"
)
# Update file data with a new file
updated = ouro.files.update(
id=file_id,
file_path="./new_file.txt"
)import os
from ouro import Ouro
# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")
ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))
file_id = "06df3603-8fda-44f5-8782-de9a195f9ea7"
# Retrieve file metadata
file = ouro.files.retrieve(file_id)
print(file.name, file.visibility)
print(file.metadata)Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset (‘3DSC’), featuring the critical temperature Tc of superconducting materials additionally to tested non-superconductors.
Orb latent space Tc classifier evaluation
Careful evaluation of the classifier model is important so that we can truly understand the capabilities and performance of a Tc predicting model. Particularly important to us is the ability for the m
Orb latent space to Tc prediction
After reading the MatterSim paper, the authors proposed the idea of using the MLFF's latent space as a direct property prediction feature set. Earlier, @will and I had been thinking about using a VAE
Critical temperature prediction models
Literature review of existing studies done on predicting with machine learning.
Superconductor databases
Literature review of databases with materials and . See literature review on ML models which utilize these datasets:
Building on Belli, Zurek & Errea: ML predictions vs quantum nuclear effect descriptors in hydride superconductors
Deep-read and ML analysis of the Belli-Zurek-Errea 2026 npj Computational Materials paper on bonding descriptors for QNEs in hydride superconductors. Ran Tc, Debye, and DOS predictions on 6 hydride systems (4 SB, 2 AB). ML fails to capture QNE direction; the paper's S_a descriptor fills the gap.
Superconductor & Thermoelectric Outreach: 10 prospects, all emails drafted
All 10 personalized outreach emails finalized for superconductors (5) and thermoelectrics (5). Each references specific papers and connects to concrete Ouro resources. Blocked on Resend API access.