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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.”
An example of the experimental method is a public...

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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A framework for FAIR robotic datasets.

Corrado Motta^1,2, Simona Aracri³, Roberta Ferretti¹

¹Institute of Marine Engineering (INM), National Research Council of Italy (CNR), Department of Engineering, ICT and Technology for Energy and Transport (DIITET), Genoa, 16149, Italy.

Scientific Data

|September 13, 2023

Summary

This summary is machine-generated.

Publishing marine robotic data is crucial for ocean observation. This study presents Free and Open Source Software (FOSS) to make marine robotic telemetry FAIR (Findable, Accessible, Interoperable, Reusable), enhancing data transparency and usability.

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Area of Science:

Oceanography
Robotics
Data Science

Background:

Environmental data from robotic platforms are vital for the Global Ocean Observing System (GOOS).
Transparency and validation of this data require detailed robotic operational records.
The United Nations Decade of Ocean Science (2021-2030) emphasizes sustainable development through data sharing.

Purpose of the Study:

To provide a step-by-step guide for formatting and sharing marine robotic mission data.
To ensure marine robotic telemetry adheres to FAIR principles (Findable, Accessible, Interoperable, Reusable).
To promote the publication and accessibility of unique observational datasets.

Main Methods:

Utilizing Free and Open Source Software (FOSS) and established Earth Science data practices.
Applying state-of-the-art protocols for metadata and data formatting.
Automating data integration and formatting using Jupyter Notebooks.

Main Results:

A method to render marine robotic telemetry FAIR and publishable has been developed.
The proposed approach maximizes the visibility and ease of use of robotic observational data.
Jupyter Notebooks facilitate the automatic application of data formatting protocols.

Conclusions:

This manuscript offers a fundamental step towards FAIR interdisciplinary observational science.
The developed method supports the validation of environmental data and consistent in-situ robotic deployments.
Making marine robotic data FAIR contributes significantly to global ocean science initiatives.