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741 to 750 of 25,631 Results
sparql_gpt5_mini_generated_query.rq
Jul 10, 2026 - Replication Data for: AI-Assisted RDF Authoring from JSON Data
Unknown - 879 B - MD5: 0011be7eea39c453a0e2be1826ecb6f5
detailed_hint_gpt4_failed_rdf_result.json
Jul 10, 2026 - Replication Data for: AI-Assisted RDF Authoring from JSON Data
JSON - 24.3 KB - MD5: 99e780dc5d934240415367072aefd107
minimal_hint.txt
Jul 10, 2026 - Replication Data for: AI-Assisted RDF Authoring from JSON Data
Plain Text - 26 B - MD5: d64236525eb61bc10a35386eae28a1e9
validation_shapes.ttl
Jul 10, 2026 - Replication Data for: AI-Assisted RDF Authoring from JSON Data
Unknown - 1010 B - MD5: 069a4901fa2a82d4cc90fd2a2ca7ee2e
minimal_hint_gpt4_rdf_result.json
Jul 10, 2026 - Replication Data for: AI-Assisted RDF Authoring from JSON Data
JSON - 6.8 KB - MD5: 3cb93085c20a10a3a8aaa93fcefc3a5e
detailed_hint_gpt5_mini_success_rml_mapping.ttl
Jul 10, 2026 - Replication Data for: AI-Assisted RDF Authoring from JSON Data
Unknown - 16.3 KB - MD5: a97dc13b39733dbb3e49b61cc3e505bd
README.md
Jul 10, 2026 - Replication Data for: AI-Assisted RDF Authoring from JSON Data
Markdown Text - 5.2 KB - MD5: 907c0f808274ac94847e926c3f476693
The DUNE-Phasefield Module (release 2.0)
Jun 25, 2026 - PN 5-13
Riethmüller, Cedric, 2026, "The DUNE-Phasefield Module (release 2.0)", https://doi.org/10.18419/DARUS-6262, DaRUS, V1
The dune-phasefield module contains local operators and grid function spaces for the solution of Cahn-Hilliard and coupled Cahn-Hilliard Navier-Stokes systems in a FEM framework. The module additionally provides option-classes for physical properties, multiple Cahn-Hilliard potentials, a time-step manager and grid adaptivity. Compared to the initia...
dune-phasefield.tar.gz
Jun 25, 2026 - The DUNE-Phasefield Module (release 2.0)
Gzip Archive - 470.6 KB - MD5: ac1ad4d8a9fd4198ec7ba70ab53148ec
Replication Data for: A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling
Jun 22, 2026 - PN 6A-4
Álvarez Chaves, Manuel, 2026, "Replication Data for: A variational approach at uncertainty estimation in data-driven rainfall-runoff modeling", https://doi.org/10.18419/DARUS-5118, DaRUS, V1
This is repository contains code and archived results for the paper "A variational approach at uncertainty estimation in rainfall-runoff modeling" submitted for review to the ML:Earth journal. Instructions for running the training or evaluation workflows can be found in the README.md file located within the scripts/ folder. This dataset primarily c...
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