Change-Induced Incident Causal Diagnosis Log Dataset
- Records
- 4200
- Size
- 0.3GB
- Format
- JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-05
This dataset brings together ITSM change records and related deployment, configuration, service, and incident logs, with supervised labels for incident outcomes, the change's causal role, potential failure mechanisms, and supporting evidence. Causal chains, temporal assessments, supporting or contradicting observations, alternative explanations, and diagnostic rationales help models distinguish causal evidence from mere temporal proximity. It supports IT operations troubleshooting, change-impact reviews, and the training and evaluation of causal-reasoning models.
Overview
Dataset Insights
Preview
Sample Examples
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| evidence | array | Specific observations from the raw records that support, contradict, or contextualize the causal diagnosis. |
| diagnosis | object | The supervised assessment of the incident outcome, the change's causal role, confidence, and the proposed failure mechanism. |
| causal_chain | array | Ordered events that explain the proposed path from the change to the service outcome. Include only evidence-supported links and identify uncertain links in their descriptions. |
| change_record_raw | string | The original ITSM change record serialized as JSON, including its identifier, scope, planned timing, implementation details, and status when available. |
| temporal_assessment | object | An assessment of event ordering and timing. Timing can establish consistency with a hypothesis but does not by itself establish causality. |
| diagnostic_rationale | string | A concise reasoning summary connecting the evidence, causal mechanism, temporal assessment, and alternatives to the final attribution. Do not infer causality from temporal proximity alone. |
| operational_logs_raw | string | Original deployment, configuration, service, and incident events serialized as JSON Lines, preserving timestamps and observed states when available. |
| recommended_validation | array | Checks that could confirm or refute the diagnosis, such as comparing pre- and post-change state, reviewing correlated telemetry, or reproducing the behavior. |
| alternative_explanations | array | Plausible non-change causes or competing explanations considered, with a concise account of whether the available evidence supports or weakens each one. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use
Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization
No PII, no real company names, simulated scenarios follow industry standards
Compliance System
Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs
FAQ
Frequently Asked Questions
- What does the dataset contain?
- It includes raw ITSM change records and deployment, configuration, service, or incident logs, together with annotations such as causal diagnoses, causal chains, temporal assessments, supporting evidence, alternative explanations, and recommended validation checks.
- How does it avoid treating timing alone as proof of causation?
- The annotations assess event states, log evidence, plausible failure mechanisms, and alternative explanations. Event order can show whether a hypothesis is temporally consistent, but it does not by itself prove that a change caused an incident. Uncertain links in a proposed causal chain should also be identified.
- What are the data formats and quality characteristics?
- Raw change records are represented as JSON, while operational logs are represented as JSON Lines. Supervised fields cover the diagnosis and its evidence and reasoning. Timestamps and observed states are preserved when available, so field completeness may vary across records.
- How can I obtain the data, and what are the license terms?
- Refer to the product page for the available download or delivery method, and review the applicable license before use. Commercial use, redistribution, and production training are subject to the specific terms provided with the dataset.
Can't find the data you need?
Post a request and let data providers reach out to you.
Get this Dataset
High-quality data, ready for your next AI breakthrough.
Cite this Work
@dataset{Mobiusi2026,
title={Change-Induced Incident Causal Diagnosis Log Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/9448a1fd691738da9c38dc247c659b98?dataset_task_cate_id=6},
urldate={2026-10-22},
keywords={change-induced incident,ITSM change logs,operational incident diagnosis,failure causal analysis,change impact assessment,log-based causal reasoning,reasoning SFT dataset,service failure attribution},
version={1.0}
}Using this in research? Please cite us.