From 95e6ec339f351ce1f02441d469ad0a810be555fe Mon Sep 17 00:00:00 2001 From: tjkessler Date: Wed, 5 Aug 2026 13:42:39 -0400 Subject: [PATCH] Record Zenodo DOI for the v0.12.0 software archive Co-authored-by: Cursor --- CITATION.cff | 4 ++-- README.md | 4 ++-- paper.md | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/CITATION.cff b/CITATION.cff index 607ff59..12f8425 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -15,10 +15,10 @@ url: "https://koopmangraph.readthedocs.io/" license: Apache-2.0 version: 0.12.0 date-released: 2026-08-05 -doi: 10.5281/zenodo.21763908 +doi: 10.5281/zenodo.21811756 identifiers: - type: doi - value: 10.5281/zenodo.21763908 + value: 10.5281/zenodo.21811756 description: "Archived software (version DOI)" keywords: - koopman-operator diff --git a/README.md b/README.md index e0587f2..e3fbd99 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ **Topology-aware Koopman autoencoders for forecasting and analyzing networked dynamics** [![Tests](https://github.com/tjkessler/KoopmanGraph/actions/workflows/ci.yml/badge.svg)](https://github.com/tjkessler/KoopmanGraph/actions/workflows/ci.yml) -[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.21763908.svg)](https://doi.org/10.5281/zenodo.21763908) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.21811756.svg)](https://doi.org/10.5281/zenodo.21811756) [![PyPI version](https://img.shields.io/pypi/v/koopman-graph)](https://pypi.org/project/koopman-graph/) [![codecov](https://codecov.io/gh/tjkessler/KoopmanGraph/graph/badge.svg)](https://codecov.io/gh/tjkessler/KoopmanGraph) [![Documentation Status](https://readthedocs.org/projects/koopmangraph/badge/?version=latest)](https://koopmangraph.readthedocs.io/en/latest/?badge=latest) @@ -162,7 +162,7 @@ If you use KoopmanGraph in research, please cite: title = {KoopmanGraph: Topology-Aware Koopman Autoencoders for Networked Dynamics}, year = {2026}, publisher = {Zenodo}, - doi = {10.5281/zenodo.21763908}, + doi = {10.5281/zenodo.21811756}, url = {https://github.com/tjkessler/KoopmanGraph}, version = {0.12.0}, } diff --git a/paper.md b/paper.md index 4500458..f2b79c2 100644 --- a/paper.md +++ b/paper.md @@ -55,9 +55,9 @@ Tutorials under `examples/` cover synthetic dynamics, IEEE 118-bus, METR-LA, and # Research impact statement -`KoopmanGraph` is research software with no publications or downstream adoptions to cite at submission. Impact is shown through reproducible tutorials on synthetic, IEEE 118-bus, METR-LA, and epidemic graphs---per-horizon evaluation, topology-versus-vector baselines in the spirit of prior GNN-versus-Koopman power-grid studies [@Nandanoori2022], dynamic topology, spectral/stability analysis, and latent-space control---plus v0.5.0 tooling and notebooks for deep-ensemble / latent-Gaussian uncertainty, hierarchical multi-resolution forecasting, auxiliary-spectral continuous generators, and sparsity / worst-case reconstruction losses (with a GNN encoder zoo). Open artifacts include PyPI releases through v0.11.0, CI with coverage and notebook smoke tests, Sphinx docs, and a security policy. +`KoopmanGraph` is research software with no publications or downstream adoptions to cite at submission. Impact is shown through reproducible tutorials on synthetic, IEEE 118-bus, METR-LA, and epidemic graphs---per-horizon evaluation, topology-versus-vector baselines in the spirit of prior GNN-versus-Koopman power-grid studies [@Nandanoori2022], dynamic topology, spectral/stability analysis, and latent-space control---plus v0.5.0 tooling and notebooks for deep-ensemble / latent-Gaussian uncertainty, hierarchical multi-resolution forecasting, auxiliary-spectral continuous generators, and sparsity / worst-case reconstruction losses (with a GNN encoder zoo). Open artifacts include PyPI releases through v0.12.0, CI with coverage and notebook smoke tests, Sphinx docs, and a security policy. -The author uses the library to experiment with Koopman forecasting on networked systems and to lower the barrier for topology-aware Koopman models on standard PyG workflows. Zenodo archives are available through v0.11.0 (DOI: \url{https://doi.org/10.5281/zenodo.21763908}; earlier version DOIs are linked from that record). +The author uses the library to experiment with Koopman forecasting on networked systems and to lower the barrier for topology-aware Koopman models on standard PyG workflows. Zenodo archives are available through v0.12.0 (DOI: \url{https://doi.org/10.5281/zenodo.21811756}; earlier version DOIs are linked from that record). # Conclusion