Contreras-Torres, Flavio F.

1 files · 461 KB · zipdeclared

v0.3.2 (2026-07-09) HARMONSMILE is a cheminformatics toolkit for preparing and harmonizing molecular structure tables across PubChem, ChEMBL, and independent SMILES datasets. It provides deterministic table loading, robust identifier handling, RDKit-based SMILES canonicalization, and an auditable harmonization layer for downstream curation, comparison, and machine learning workflows. HARMONSMILE preserves source SMILES, emits a canonical/isomeric/Kekulé RDKit representation for compatibility and deduplication, and produces a harmonized canonical/isomeric/aromatic SMILES representation using controlled RDKit-native standardization policies.

Passalacqua, Evan

1 files · 413 KB · zipdeclared

SceneSim is a turn-based EMT (Emergency Medical Technician) simulation platform. A deterministic engine owns all patient truth (vitals, timing, deterioration, scoring); an LLM is used only to render that state as natural-language roleplay, never to decide clinical outcomes.

Diehl, Martin · Eisenlohr, Philip · Roongta, Sharan · et al.

2 files · 56 MB · xz, zipdeclared

DAMASK® is a unified multi-physics crystal plasticity simulation package. The solution of continuum mechanical boundary value problems requires a constitutive response that connects deformation and stress at each material point. This problem is solved in DAMASK on the basis of crystal plasticity using a variety of constitutive models and homogenization approaches. However, treating mechanics in isolation is no longer sufficient to study emergent advanced high-strength materials. In these materials, deformation happens interrelated with displacive phase transformation, significant heating, and potential damage evolution. Therefore, DAMASK is capable of handling multi-physics problems. Following a modular approach, additional field equations are solved in a fully coupled way using a staggered approach.

Smith, C. Haden · Fields, Woodrow L. · Gonzalez, Julian · et al.

1 files · 2.2 MB · zipdeclared

Numerics is a free and open-source .NET library providing numerical methods, probability distributions, statistical analysis, and Bayesian inference tools for quantitative risk assessment in water resources engineering.

Fobbe, Sean

5 files · 155 MB · pdf, zipdeclared

Überblick Dieses Skript wertet das amtliche Internetangebot Gesetze im Internet der Bundesrepublik Deutschland vollständig aus und ist die Grundlage des Corpus des Deutschen Bundesrechts (C-DBR) . Alle mit diesem Skript erstellten Datensätze werden dauerhaft kostenlos und urheberrechtsfrei auf Zenodo, dem wissenschaftlichen Archiv des CERN, veröffentlicht. Alle Versionen sind mit einem separaten und langzeit-stabilen (persistenten) Digital Object Identifier (DOI) versehen. Die neueste Version des Datensatzes ist zusätzlich immer über den Link der Concept DOI erreichbar: https://doi.org/10.5281/zenodo.3832111 Lesen Sie bitte zuerst den Compilation Report! Dieser enthält den Source Code für die gesamte Pipeline, relevante Rechenergebnisse, alle Diagramme, Zeitstempel, sowie ein detailliertes und klickbares Inhaltsverzeichnis. Sie werden sich auf diese Weise viel schneller im eigentlichen Source Code zurechtfinden. Aktualisierung Dieser Datensatz wird ca. alle 3 Monate aktualisiert. Benachrichtigungen über neue und aktualisierte Datensätze veröffentliche ich immer zeitnah auf Mastodon unter @seanfobbe@fediscience.org NEU in Version 2026-07-09 Vollständige Aktualisierung der Daten Ausführung der Pipeline in Userland im Container Neues Skript für Zenodo Upload Funktionsweise Primäre Endprodukte des Skripts sind folgende ZIP-Archive: Der volle Datensatz im CSV-Format, unterteilt in Einzelnormen (nur Rechtsakte mit veröffentlichtem Normtext) Die Metadaten aller Einzelnormen im CSV-Format (wie 1, aber ohne Text-Variable) Der volle Datensatz im CSV-Format, unterteilt in Rechtsakte (nur Rechtsakte mit veröffentlichtem Normtext) Die Metadaten aller Rechtsakte im CSV-Format (wie 3, aber ohne Text-Variable) Die Metadaten aller veröffentlichten Rechtsakte, im CSV-Format (unabhängig davon ob Normtext veröffentlicht wurde) Der volle Datensatz im XML-Format, unterteilt in Rechtsakte (Originaldaten von GII) Alle Anlagen zu den XML-Dateien im jeweiligen Original-Format (Originaldaten von GII) Alle Rechtsakte im TXT-Format, unterteilt in Rechtsakte (deutlich reduzierter Umfang an Metadaten) Alle Rechtstexte im PDF-Format, unterteilt in Rechtsakte (deutlich reduzierter Umfang an Metadaten) Alle Rechtstexte im EPUB-Format, unterteilt in Gesetze (deutlich reduzierter Umfang an Metadaten) Alle Analyse-Ergebnisse (Tabellen als CSV, Grafiken als PDF und PNG) Netzwerk-Strukturen (Adjazenzmatrizen, Edgelists, GraphML, und Netzwerk-Diagramme) für alle Rechtsakte (experimentell!) Alle Ergebnisse werden im Ordner `output` abgelegt. Zusätzlich werden für alle ZIP-Archive kryptographische Signaturen (SHA2-256 und SHA3-512) berechnet und in einer CSV-Datei hinterlegt. Systemanforderungen Docker Docker Compose 6 GB Speicherplatz auf Festplatte Multi-core CPU empfohlen (8 cores/16 threads für die Referenzdatensätze). In der Standard-Einstellung wird das Skript vollautomatisch die maximale Anzahl an Rechenkernen/Threads auf dem System zu nutzen. Die Anzahl der verwendeten Kerne kann in der Konfigurationsatei angepasst werden. Wenn die Anzahl Threads auf 1 gesetzt wird, ist die Parallelisierung deaktiviert. Anleitung Schritt 1: Ordner vorbereiten Kopieren Sie bitte den gesamten Source Code in einen leeren Ordner (!), beispielsweise mit: $ git clone https://codeberg.org/seanfobbe/c-dbr Verwenden Sie immer einen separaten und leeren (!) Ordner für die Kompilierung. Die Skripte löschen innerhalb von bestimmten Unterordnern (files/, temp/, analysis/ und output/) alle Dateien die den Datensatz verunreinigen könnten --- aber auch nur dort. Schritt 2: Docker Image erstellen Ein Docker Image stellt ein komplettes Betriebssystem mit der gesamten verwendeten Software automatisch zusammen. Nutzen Sie zur Erstellung des Images einfach: $ bash docker-compose build --pull Schritt 3: Datensatz kompilieren Falls Sie zuvor den Datensatz schon einmal kompiliert haben (ob erfolgreich oder erfolglos), können Sie mit folgendem Befehl alle Arbeitsdaten im Ordner löschen: $ git clean -fdx Den vollständigen Datensatz kompilieren Sie mit folgendem Skript: $ bash docker-run-project.sh Ergebnis Der Datensatz und alle weiteren Ergebnisse sind nun im Ordner output/ abgelegt. Pipeline visualisieren Sie können die Pipeline visualisieren, aber nur nachdem sie die zentrale .Rmd-Datei mindestens einmal gerendert haben: > targets::tar_glimpse() # Nur Datenobjekte > targets::tar_visnetwork() # Alle Objekte Troubleshooting Hilfreiche Befehle, um Fehler zu lokalisieren und zu beheben. > tar_progress() # Zeigt Fortschritt und Fehler an > tar_meta() # Alle Metadaten > tar_meta(fields = "warnings", complete_only = TRUE) # Warnungen > tar_meta(fields = "error", complete_only = TRUE) # Fehlermeldungen > tar_meta(fields = "seconds") # Laufzeit der Targets Weitere Open Access Veröffentlichungen (Fobbe) Website - www.seanfobbe.de Open Data - https://zenodo.org/communities/sean-fobbe-data/ Source Code - https://zenodo.org/communities/sean-fobbe-code/ Volltexte regulärer Publikationen - https://zenodo.org/communities/sean-fobbe-publications/ Urheberrecht Der Source Code und alle von mir bereitgestellten Rohdaten stehen unter der GNU General Public License v3.0 oder später. Kontakt Fehler gefunden? Anregungen? Kommentieren Sie gerne im Issue Tracker oder kontaktieren Sie mich über www.seanfobbe.de

Pierre Aubert · Thibaut Oprinsen

1 files · 4.9 MB · zipdeclared

This project aims to provide a simple a powerfull lecture compilation to generate html web sites

Bravo, Santiago

1 files · 13 MB · zipdeclared

Open-source deforestation and degradation monitoring system for the Chapada do Araripe protected area (APA) in Brazil. Builds monthly per-pixel baselines from Sentinel-2, Landsat, and Harmonized Landsat Sentinel-2 imagery, then detects anomalies in vegetation moisture (NDMI), burn (NBR), and greenness (EVI2) indices via z-score thresholds with drought-adjusted sensitivity. Designed to be replicable in other regions. Source code licensed under AGPL-3.0-or-later. Data products (baselines, alerts, time series) licensed under CC-BY-SA-4.0; see DATA_LICENSE.

Duarte Rocha, Alby · Aljoumani, Basem

1 files · 36 MB · zipdeclared

rSTEMMUS_SCOPE is an open-source R package that provides a reproducible interface for configuring, executing, and post-processing simulations with the STEMMUS-SCOPE model. The package simplifies complex model workflows by automating input preparation, parameter configuration, model execution, result extraction, and visualization while preserving the original MATLAB implementation of STEMMUS-SCOPE. It supports reproducible ecohydrological modelling, facilitates software interoperability, and provides a structured workflow for research, teaching, and model development. Version 1.0 accompanies the SoftwareX publication describing the package architecture, workflow, and reproducible modelling framework. Release notes (Version 1.0) Complete R interface for STEMMUS-SCOPE Automated preprocessing workflow Enhanced parameter configuration Improved post-processing and visualization Reproducible Steglitz example Updated documentation Official software release accompanying the manuscript describing rSTEMMUS_SCOPE. Data availability: The SoilProperty dataset is distributed separately from the software and is downloaded automatically by the package from its archived Zenodo record. The dataset is unchanged for this release and has therefore not been duplicated. It can also be downloaded manually from: https://zenodo.org/api/records/15488066/files/SoilProperty.zip/content

Microsoft AI for Good Lab · Microsoft Pytorch-Wildlife project

63 files · 12 GB · zipdeclared

v0.19.0: fix the orca-cascade mobile descriptor to reference orca-detector-v3-fp16-tflite (was the superseded orca-detector-fp16-tflite id, which the cascade could not load); no other model changed. v0.18.0: TensorRT compatibility metadata. Added [inference.trt] mode to all 55 desktop ONNX manifests: mode=on_demand for TRT-compatible models (served on CUDA by default; a TensorRT engine is built only on explicit warm-up, never auto-built); mode=off for models with an in-graph spectral/DFT front-end (perch-v2, orca-ecotype-dclde2026-v1) and OWL (open-vocabulary transformer). ONNX weights are bit-identical to v0.17.0 - manifest-only metadata update. v0.17.0: add general/encoder dinov3-vitl16 (DINOv3 ViT-L/16, 1024-d image embedding, DINOv3 License); correct MDV5a manifest id (Species_Net_MDV5a -> MDV5a). v0.16.0: add BioCLIP 2 image encoder (imageomics/bioclip-2, OpenCLIP ViT-L/14, 768-d image embedding, MIT) — first general/encoder entry in the zoo. v0.15.0 — ONB-3: 4 AddaxAI TF/Keras classifiers (tropicam-ai ConvNeXt-Base/84, terai-nepal EffNetV2-M/10, tasmanian-vertebrates EffNetV2-S/96, peruvian-amazon-sdzwa EffNet/43); tf2onnx from .h5/.keras, onboard_parity GATE PASS. v0.14.0: added 2 Apache-2.0 RT-DETRv2 MegaDetector detectors (MDV6-apa-rtdetr-c [ResNet-18vd, 3 decoder layers] + MDV6-apa-rtdetr-e [ResNet-101vd, 6 decoder layers]); NMS-free rtdetr_topk contract, onboard_parity GATE PASS (c 0.974 / e 0.968). v0.13.0 : added MegaDetector MIT variants MDV6-mit-yolov9-c and MDV6-mit-yolov9-e (exported from the MIT-licensed WongKinYiu/YOLO framework, PyTorch-Lightning checkpoints), parity-gated in sparrow-engine. v0.11.0: added the 18 AddaxAI PyTorch camera-trap classifiers (ONB-1: EfficientNetV2 / ResNet50 / YOLOv8-cls / DINOv2 / SpeciesNet-FX, incl. the nz-species multi-label classifier). v0.12.0 : added 9 MegaDetector detector variants (MDV5b; MD1000 redwood/spruce/larch/cedar/sorrel; MDV6 yolov9-c/e; MDV6 rtdetr-c) and re-published MDV5a + MDV6-yolov10-c/e with cv2 (INTER_LINEAR) non-antialiased bilinear preprocessing for detector fidelity. All parity-gated in sparrow-engine. v0.11.0: added the 18 AddaxAI PyTorch camera-trap classifiers (ONB-1: EfficientNetV2 / ResNet50 / YOLOv8-cls / DINOv2 / SpeciesNet-FX, incl. the nz-species multi-label classifier). v0.11.0: added the 18 AddaxAI PyTorch camera-trap classifiers (ONB-1: EfficientNetV2 / ResNet50 / YOLOv8-cls / DINOv2 / SpeciesNet-FX, incl. the nz-species multi-label classifier). ONNX (and mobile TFLite/LiteRT) models for the sparrow-engine inference library. 24 models . v0.10.0 restructures the bundle. Per-model ZIPs are named <domain>__<task>__<id>.zip (Zenodo has no real folders; the __ delimiter groups files by domain then task when sorted). Taxonomy: Camera Trap / Acoustics / Overhead × Detector / Classifier. Latest-version-only for dev iterations (superseded orca detector v1/v2 dropped); the MegaDetector family is kept whole (MDV5a, MDV6-yolov10-c/e). Each ZIP unpacks to <id>/ with manifest.toml, the model graph, labels, MODEL_CARD.md and LICENSE.md. Multi-license — see each model's LICENSE.md.

Szoszkiewicz, Łukasz

1 files · 1.1 MB · zipdeclared

Complete harvest of the OHCHR Search Library "Voting" collection — roll-call and summary voting records of the UN Commission on Human Rights (1946–2006) and the Human Rights Council (2006–present) — parsed from customised MARC21 cataloguing into dashboard-ready CSVs, with a self-contained, zero-build interactive dashboard. Coverage: 6,346 resolutions / 80,159 individual country votes (1946–2026); record count matches the collection total exactly, verified by three independent harvest methods.

Agis-Torres, Ángel · Fernandes, Vítor Samuel · Navarro-Dorado, Jorge · et al.

1 files · 281 KB · zipdeclared

emgteach is an open-source Python package that provides a unified PySide6 desktop application for real-time acquisition, offline analysis and maximum voluntary contraction (MVC) normalisation of surface electromyography (sEMG) signals. It is designed for hands-on biopotential acquisition in undergraduate physiology teaching laboratories, and is meant to be set up by instructors with different levels of technical background and used directly by students during a practical session. The application is hardware-agnostic through a common AcquisitionDevice interface and ships with two interchangeable low-cost backends: BITalino (revolution) over Bluetooth, and an Arduino RedBoard Plus + MyoWare 2.0 over USB serial (open firmware included). A single setting switches between them. Main features: Three-tab GUI (Acquisition, Analysis, MVC normalisation) wrapping a reusable, Qt-free analytic core. Bilingual interface (English / Spanish) with automatic start-up language detection and an in-app language switch. Two-channel acquisition (e.g. agonist/antagonist) with a stacked two-channel live view. Automatic contraction-onset detection (baseline + k·SD threshold) stored as EDF+ annotations. Muscle-load analysis (Jonsson APDF): static (P10), median (P50) and peak (P90) %MVC levels, both offline and as a live monitor with warning/danger zones. One-click PDF session and MVC/muscle-load reports. Reliable EDF+ output using a buffered-write pattern that avoids a silent file-corruption artefact during continuous streaming. Reproducible synthetic signals for class assignments and hardware-free continuous integration. Assisted selection of significant fragments, with an editable fragment editor (detection parameters and envelope-filter cut-offs) applied to the analysis. Region-of-interest analysis, CSV export of results, and a live signal-quality check during recording. An ECG signal profile alongside EMG, selectable through a profile registry. emgteach runs on Windows, macOS and Linux with Python 3.10–3.12, is covered by a suite of 216 automated tests, and is released under the GPL-3.0-or-later license. Source code, documentation and issue tracker: https://github.com/aagisto-maker/emgteach

Ichsan, Mochammad Hannats Hanafi

3 files · 187 MB · zipdeclared

This record archives source-code snapshots and implementation materials for the IntuNav and EdgeNav navigation methods in desktop multi-browser virtual environments. The archive contains three GitHub repository snapshots: (1) Multi-Browser-dekstopVR-1, which provides the Unity-based desktop multi-browser virtual environment; (2) IntuNav, which contains the implementation of the IntuNav navigation algorithm; and (3) EdgeNav, which contains the implementation of the EdgeNav navigation algorithm. The archived files are provided to support transparency, reproducibility, and DOI-based citation of the custom software and algorithmic components reported in the associated manuscript.

Schwarz, Friedrich

1 files · 267 KB · zipdeclared

A Python wrapper for the GWDG SAIA (Scalable AI Accelerator) platform REST API, providing object-oriented and functional access to chat completions, voice transcription/translation, document conversion, ARCANA (RAG / knowledge bases), and an OpenAI SDK compatibility layer.

Belder, Rico · Brunner, Sebastian Georg · Steinmetz, Franz

1 files · 47 MB · zipdeclared

RAFCON uses hierarchical state machines, featuring concurrent state execution, to represent robot programs. It ships with a graphical user interface supporting the creation of state machines and contains IDE like debugging mechanisms. Alternatively, state machines can programmatically be generated using RAFCON's API.

De Pretto, Michael · Mouthon, Michaël

1 files · 139 MB · zipdeclared

Version 2.15 Change of the licence for BSD 2-Clause

Vay, Jean-Luc · Acciarri, Marco · Almgren, Ann · et al.

1 files · 7.3 MB · zipdeclared

Dependencies AMReX : 26.07 pyAMReX : 26.07 PICSAR-QED : 26.07 pybind11 : 3.0.4 picmistandard : 0.34.0 openPMD-api : 0.17.0 Python: 3.11 - 3.14 What's Changed CI: Frank GPU Runners by @ax3l in https://github.com/BLAST-WarpX/warpx/pull/6942 Dependencies: weekly update by @github-actions[bot] in https://github.com/BLAST-WarpX/warpx/pull/6938 Doc: Glossary to Agents by @ax3l in https://github.com/BLAST-WarpX/warpx/pull/6950 Tools/Parser: add input_has_value helper, use it in tests by @EZoni in https://github.com/BLAST-WarpX/warpx/pull/6850 computePhiIGF: w/o Grow of Phi by @ax3l in https://github.com/BLAST-WarpX/warpx/pull/6952 Hybrid-PIC: set unused dimensions nodal for RCYLINDER and RSPHERE by @tomzhu0225 in https://github.com/BLAST-WarpX/warpx/pull/6946 Add backward scattering angle model for two-product reactions by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/6947 Add 1D and 2D automated tests for the macroscopic EM solver by @tomzhu0225 in https://github.com/BLAST-WarpX/warpx/pull/6945 Dependencies: weekly update by @github-actions[bot] in https://github.com/BLAST-WarpX/warpx/pull/6958 Reuse GetVelocityVector for parse_momentum_function by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/6913 Unify RK4 and RKF45 execution streams by @roelof-groenewald in https://github.com/BLAST-WarpX/warpx/pull/6953 Add allocdata Python callback by @dpgrote in https://github.com/BLAST-WarpX/warpx/pull/6966 Add support for RZ + EB with effective potential ES solver by @roelof-groenewald in https://github.com/BLAST-WarpX/warpx/pull/6961 Add dependabot.yml for GitHub Actions version updates by @EZoni in https://github.com/BLAST-WarpX/warpx/pull/6959 Improve Error Message of getArrWithParser by @AlexanderSinn in https://github.com/BLAST-WarpX/warpx/pull/6964 Bump actions/upload-artifact from 4 to 7 by @dependabot[bot] in https://github.com/BLAST-WarpX/warpx/pull/6970 Bump actions/cache from 4 to 6 by @dependabot[bot] in https://github.com/BLAST-WarpX/warpx/pull/6971 Bump actions/setup-python from 5 to 6 by @dependabot[bot] in https://github.com/BLAST-WarpX/warpx/pull/6972 Bump github/codeql-action from 3 to 4 by @dependabot[bot] in https://github.com/BLAST-WarpX/warpx/pull/6973 Bump actions/checkout from 4 to 7 by @dependabot[bot] in https://github.com/BLAST-WarpX/warpx/pull/6974 use exit_status for nonlinear solvers. by @JustinRayAngus in https://github.com/BLAST-WarpX/warpx/pull/6955 setup.py : Emscripten by @ax3l in https://github.com/BLAST-WarpX/warpx/pull/6975 Support non-EB RZ cases with effective potential solver by @roelof-groenewald in https://github.com/BLAST-WarpX/warpx/pull/6977 Maxwell-Juttner: use ux_mean/uy_mean/uz_mean drift like maxwellian by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/6918 Remove redundant species_type input from test_2d_charge_exchange_dsmc by @EZoni in https://github.com/BLAST-WarpX/warpx/pull/6984 Add resampling target ratio input to picmi.py by @roelof-groenewald in https://github.com/BLAST-WarpX/warpx/pull/6983 Replace query/get_enum_sloppy with query/get_enum_case_insensitive by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/6968 Fix secondary ion emission test for multi-rank MPI runs by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/5935 Dependencies: weekly update by @github-actions[bot] in https://github.com/BLAST-WarpX/warpx/pull/6991 MultiParticleContainer.cpp : ensure that physical_species has a value before using it by @lucafedeli88 in https://github.com/BLAST-WarpX/warpx/pull/6986 openPMD-api: 0.17.1 by @ax3l in https://github.com/BLAST-WarpX/warpx/pull/6987 Used a closed interval for laser continuous injection check by @AlexanderSinn in https://github.com/BLAST-WarpX/warpx/pull/6992 C++20: use starts_with, ends_with by @lucafedeli88 in https://github.com/BLAST-WarpX/warpx/pull/6892 Fix strings in WarpX_PETSc.cpp by @dpgrote in https://github.com/BLAST-WarpX/warpx/pull/6995 Collisions: add shared parse_scattering_processes helper by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/6979 Divergence cleaning for loaded B fields in electrostatic/magnetostati… by @clarkse-he in https://github.com/BLAST-WarpX/warpx/pull/6969 C++20: use container.contains() by @lucafedeli88 in https://github.com/BLAST-WarpX/warpx/pull/6885 Update cray-python module version to 3.11.7 by @Noerr in https://github.com/BLAST-WarpX/warpx/pull/7002 DSMC refactoring: use ScatteringProcess::Executor object inside SplitAndScatterFunc kernel by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/7001 Fix name of FFT compile option in error message. by @atmyers in https://github.com/BLAST-WarpX/warpx/pull/7006 Enable RKF45 to be used at intervals to set the RK4 substep number by @roelof-groenewald in https://github.com/BLAST-WarpX/warpx/pull/6920 Capacitive discharge CI test: compare against Turner benchmark rather than flat density by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/7012 DSMC: add m_produces_products flag to ScatteringProcess::Executor by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/7011 setup.py: honor CMAKE_GENERATOR_PLATFORM for the Windows -A flag by @ax3l in https://github.com/BLAST-WarpX/warpx/pull/7019 Redistribute virtual photons after generating them with beam-size effect by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/6996 [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci[bot] in https://github.com/BLAST-WarpX/warpx/pull/7020 Fix XDim3 uninitialized warning by @AlexanderSinn in https://github.com/BLAST-WarpX/warpx/pull/7015 DSMC: support excitation processes by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/7004 Fix Load Balancing with RZ PML by @AlexanderSinn in https://github.com/BLAST-WarpX/warpx/pull/7016 CI: fix workaround for Open MPI oversubscription issue by @EZoni in https://github.com/BLAST-WarpX/warpx/pull/6014 Share hankel transformer between boxes by @AlexanderSinn in https://github.com/BLAST-WarpX/warpx/pull/7017 Expose mesh refinement ratio to Python interface by @EZoni in https://github.com/BLAST-WarpX/warpx/pull/7007 pyWarpX: Only export _PyInit_warpx_pybind by @ax3l in https://github.com/BLAST-WarpX/warpx/pull/6989 CI: check that analysis_default_regression.py is always a link by @EZoni in https://github.com/BLAST-WarpX/warpx/pull/6811 Fix Examples/CMakeLists.txt for no-MPI builds by @EZoni in https://github.com/BLAST-WarpX/warpx/pull/7033 Linear Compton: fix typo sampling of Klein-Nishina formula by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/7029 Replace Elixir pattern with The_Async_Arena for temporary GPU FABs by @RemiLehe in https://github.com/BLAST-WarpX/warpx/pull/7035 Release: WarpX 26.07 by @github-actions[bot] in https://github.com/BLAST-WarpX/warpx/pull/7005 Full Changelog : https://github.com/BLAST-WarpX/warpx/compare/26.06...26.07

Bleyer, Jeremy · Bouteiller, Paul · Hale, Jack S.

1 files · 420 KB · zipdeclared

jaxmat is an open-source library for implementing material constitutive models in a way that integrates seamlessly with modern machine learning frameworks and existing finite element software. The library is implemented in JAX and heavily relies on additional features provided by the JAX ecosystem, in particular the equinox, diffrax, and optimistix libraries.

Vincent A. Cicirello

1 files · 180 KB · zipdeclared

[1.1.2] - 2026-07-08 Release is equivalent to prior release: Release is to verify revisions to CI/CD pipeline work as expected. Only other changes are to test case code, with no changes to the libary itself. Fixed Refactored test cases to eliminate redundancy (no changes to the library itself). CI/CD Revised CI/CD pipeline.

Brenning, Alexander · Frank, Jonathan Karl · Suesse, Thomas

2 files · 100 MB · pdf, zipdeclared

Code and synthetic data accompanying the study "Bias-corrected Exceedance Mapping of Groundwater Nitrate Using Geostatistical and Machine-Learning Models", currently in press in Environmental Modelling & Software (preprint available in SSRN at https://ssrn.com/abstract=6875198 ) The research demonstrates why thresholding point predictions can lead to biased estimates of environmental exceedance areas, and shows how exceedance-probability mapping with area calibration can reduce this bias. The repository provides R scripts and mock data to illustrate the full analysis workflow. The data are entirely synthetic, or fake, but they mimic the distributional characteristics of the real-world data analyzed in the paper. The synthetic data are intended only to demonstrate the methods and reproducible workflow, not to support substantive conclusions about groundwater nitrate. The associated publication should be cited when using the code or synthetic data published in this repository.