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
Records
4480
Size
0.5GB
Format
JSON
Provider
MOBIUSI INCMOBIUSI INC
Updated
v1.02026-11-04
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.

Overview

Dataset Insights

Preview

Sample Examples

sample.json
{}

Schema

Technical Specifications

file_namestringFile name
source_textstringOriginal text containing a virtual experiment measurement record, observation description, or result summary.
observationsarrayPhenomena observed during the experiment and the conditions under which they occurred, extracted from the description.
result_statusstringOverall status of the experimental result, such as successful, failed, abnormal, as expected, or uncertain.
result_summarystringA concise synthesis of the experimental measurements, observations, and conclusions.
measurement_recordsarrayOne or more measured objects, values, units, and measurement methods extracted from the experimental description.
experimental_conditionsarrayConditions involved in the experiment, such as temperature, time, pressure, reagent quantities, environment, or operating procedures.

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 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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Cite 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?dataset_task_cate_id=9},
  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}
}

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