Experimental Measurement and Observation Result Information Extraction Dataset
#Experimental Information Extraction #Measurement Value Recognition #Experimental Condition Extraction #Structured JSON Generation #Virtual Lab Education #Experimental Result Analysis #Research Data Structuring #Educational AI Applications
- 4480
- 0.5GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-11-04
AI Analysis & Value Prop
Designed for virtual laboratory scenarios, this dataset contains text describing measurement records, observed phenomena, and result summaries, with structured annotations for measured objects, values, units, methods, experimental conditions, and result status. Its JSON representation supports training models to extract information from experimental descriptions and produce consistent records. Typical uses include virtual lab education, experimental result analysis, and research data structuring.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| source_text | string | Original text containing a virtual experiment measurement record, observation description, or result summary. |
| observations | array | Phenomena observed during the experiment and the conditions under which they occurred, extracted from the description. |
| result_status | string | Overall status of the experimental result, such as successful, failed, abnormal, as expected, or uncertain. |
| result_summary | string | A concise synthesis of the experimental measurements, observations, and conclusions. |
| measurement_records | array | One or more measured objects, values, units, and measurement methods extracted from the experimental description. |
| experimental_conditions | array | Conditions involved in the experiment, such as temperature, time, pressure, reagent quantities, environment, or operating procedures. |
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 |
Frequently Asked Questions
- What experimental information does the dataset contain?
- It includes original virtual experiment descriptions and extracted measurement records, observations, experimental conditions, result status, and result summaries. Measurement records may specify the measured object, value, unit, and method, while conditions may include temperature, time, pressure, quantities, or operating environment.
- What is the data structure?
- Results are organized as JSON, with fields for the source description, measurement records, observations, experimental conditions, result status, and result summary. This structure is suitable for structured-output training and experimental information processing workflows.
- Which training and application tasks does it support?
- It can support experimental text information extraction, measurement and unit recognition, condition parsing, result status classification, and structured JSON generation. It may also be used in virtual lab education tools and automated experiment record management.
- How can the data be obtained, and what should users check?
- Follow the acquisition method, file format, and delivery details provided on the product page. Review the license and permitted uses before use; for a specific discipline or experimental workflow, check that the field definitions and examples meet project requirements.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Experimental Measurement and Observation Result Information Extraction Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/d359872ecdcbc79ac2aa8e13e79bce67},
urldate={2026-10-27},
keywords={experimental information extraction dataset,virtual lab data,experimental measurement records,observation recognition,experimental condition extraction,result status classification,structured JSON data,experimental result summaries},
version={1.0}
}Using this in research? Please cite us.
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