[+] croissant 🥐

croissant.json

@@ -0,0 +1,235 @@
+{
+  "@context": {
+    "@language": "en",
+    "@vocab": "https://schema.org/",
+    "sc": "https://schema.org/",
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+    "dct": "http://purl.org/dc/terms/",
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+    "field": "cr:field",
+    "recordSet": "cr:recordSet",
+    "dataType": "cr:dataType",
+    "examples": "cr:examples",
+    "extract": "cr:extract",
+    "source": "cr:source",
+    "annotation": "cr:annotation",
+    "references": "cr:references",
+    "format": "cr:format",
+    "jsonPath": "cr:jsonPath",
+    "regex": "cr:regex",
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+    "rai": "http://mlcommons.org/croissant/RAI/",
+    "prov": "http://www.w3.org/ns/prov#"
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+  "@type": "sc:Dataset",
+  "name": "ORC-bench",
+  "description": "ORC-bench is a benchmark dataset for reasoning over power systems, optimal power flow, control decisions, and cybersecurity tasks. The dataset contains 11 JSON test files spanning 7 tasks, including topological path finding, topological connectivity, linear power flow, contingency analysis, agentic grid control, optimal power flow optimization, credit scoring, botnet detection, and phishing URL reasoning.",
+  "url": "https://huggingface.co/datasets/orc-bench",
+  "sameAs": "https://huggingface.co/datasets/orc-bench",
+  "license": "https://creativecommons.org/licenses/by/4.0/",
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+  "citeAs": "ORC-bench dataset (2026). https://huggingface.co/datasets/orc-bench",
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+    "@type": "sc:Organization",
+    "name": "ORC-bench"
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+  "publisher": {
+    "@type": "sc:Organization",
+    "name": "ORC-bench"
+  },
+  "datePublished": "2026-05-06",
+  "version": "1.0.0",
+  "sdVersion": "1.0",
+  "keywords": [
+    "power-systems",
+    "electricity",
+    "graph-reasoning",
+    "optimization",
+    "optimal-power-flow",
+    "benchmark",
+    "json",
+    "tabular",
+    "cybersecurity",
+    "credit-scoring",
+    "urls",
+    "ieee",
+    "pglib-opf",
+    "pypsa",
+    "networkx",
+    "pytorch-geometric"
+  ],
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+  "rai:dataLimitations": "ORC-bench focuses on static, simplified settings (e.g., small IEEE/PGLib grids and linearized/steady-state power-flow) and curated cybersecurity/credit/URL corpora, so results may not transfer to larger, dynamic, noisy, or real-time environments. It is not recommended for production grid operations, safety-critical control, security incident response, or real-world lending/eligibility decisions without independent validation and regulatory review.",
+  "rai:dataBiases": "The grid tasks center on canonical IEEE/PGLib test cases that represent well-studied topologies and operating conditions, while the URL and botnet corpora are susceptible to time-based drift and family/host skew, and credit data (e.g., Lending Club) can embed selection and survivorship biases. These biases may cause models to overfit to specific network motifs or attacker behaviors.",
+  "rai:personalSensitiveInformation": "No direct Personal & Sensitive Information like names or SSNs is expected in these test JSONs.",
+  "rai:dataUseCases": "The dataset measures multi-step reasoning over power-system topology/physics and optimization (OPF), agentic grid control decision-making, and classification tasks in cybersecurity (botnets, phishing) and credit risk. It is validated as a benchmarking suite for task accuracy/feasibility on IEEE/PGLib OPF cases and widely used security/credit corpora.",
+  "rai:dataSocialImpact": "ORC-Bench aims to expose, rather than enhance, LLM capabilities in constrained reasoning. We anticipate broader societal impacts by helping practitioners avoid premature deployment of LLMs in high-stakes domains such as power grid operations, financial underwriting, and cyber-security where unsatisfied physical or domain constraints could translate into blackouts, discriminatory credit decisions, or missed intrusions. A potential negative impact is that improvements against the benchmark could lend over-confidence to operators who deploy LLMs autonomously in safety-critical infrastructure.",
+  "rai:hasSyntheticData": false,
+  "prov:wasDerivedFrom": [
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+      "prov:label": "gridopt-dataset",
+      "sc:license": "CC BY 4.0",
+      "prov:wasAttributedTo": {
+        "@id": "https://arxiv.org/abs/2406.07234"
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+      "prov:label": "TabularBench",
+      "sc:license": "CC BY 4.0",
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+        },
+        {
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+          "@id": "fabien_bernier",
+          "prov:label": "Fabien Bernier",
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+      "prov:wasAttributedTo": [
+        {
+          "@type": "prov:Agent",
+          "@id": "fabien_bernier",
+          "prov:label": "Fabien Bernier",
+          "sc:description": "Postdoctoral Researcher @ SnT, University of Luxembourg"
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+          "prov:label": "Salah Ghamizi",
+          "sc:description": "Research Scientist @ SnT, University of Luxembourg"
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+    }
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+}