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Data Collection by Experiments

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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Related Experiment Video

Updated: Feb 12, 2026

Automated Delivery of Microfabricated Targets for Intense Laser Irradiation Experiments

Automated Delivery of Microfabricated Targets for Intense Laser Irradiation Experiments

Published on: January 28, 2021

Automating data citation: the eagle-i experience.

Abdussalam Alawini¹, Leshang Chen¹, Susan B Davidson¹

¹University of Pennsylvania, Philadelphia, PA.

Proceedings of the ... ACM/IEEE Joint Conference on Digital Libraries. ACM/IEEE Joint Conference on Digital Libraries

|March 31, 2018

Summary

This summary is machine-generated.

Automating data citation for curated databases ensures proper credit for contributors and allows data examination. This study presents a framework for automated data citation across diverse database types.

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Area of Science:

Computer Science
Data Management
Information Science

Background:

Data citation is crucial for crediting database contributors and enabling data reproducibility.
Current manual citation methods are inefficient and prone to errors.
Automated citation is needed for curated datasets.

Purpose of the Study:

To automate data citation for the eagle-i RDF dataset.
To develop a generalized citation framework applicable to various database types (relational, XML, RDF).
To provide a method for database administrators to implement automated citation.

Main Methods:

Developed an automated data citation system for the eagle-i RDF dataset.
Designed a generalized citation framework adaptable to different database structures.
Described the implementation process for database administrators.

Main Results:

Successfully automated data citation for the eagle-i dataset.
Demonstrated the framework's flexibility across relational, XML, and RDF databases.
Provided a practical approach for database administrators to implement automated citation.

Conclusions:

Automated data citation is feasible and beneficial for curated databases.
The proposed framework offers a scalable solution for diverse data sources.
Implementing automated citation enhances data integrity and acknowledges data provenance.