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Related Concept Videos

Data Collection by Experiments01:13

Data Collection by Experiments

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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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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Experimental Designs01:16

Experimental Designs

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An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
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Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Controls in Experiments01:13

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When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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Data pipeline for managing field experiments.

Jian Liu1, Rogerio Cichota1, Stephanie Langer1

  • 1The New Zealand Institute for Plant and Food Research Limited, New Zealand.

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|February 16, 2023
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Summary
This summary is machine-generated.

Researchers developed a custom, interactive dashboard using open-source software to aid complex agricultural and environmental science experiments. This system offers timely data interpretation and decision support, reducing costs and specialist requirements.

Keywords:
Data pipeline for managing field experimentsData streamliningExperiment managementReal-time visualisation

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Area of Science:

  • Agricultural science
  • Environmental science
  • Data science

Background:

  • Increasing complexity in agricultural and environmental research necessitates advanced experiment management and data handling.
  • Interactive visualization tools offer user-friendly, real-time data interpretation for improved decision-making.
  • Commercial visualization solutions can be costly and require specialized expertise for development.

Purpose of the Study:

  • To develop a cost-effective, customized interactive dashboard system for science experiment decision support.
  • To leverage open-source software for creating a near real-time data visualization solution.
  • To address the growing demand for efficient data handling in complex research projects.

Main Methods:

  • Utilized open-source software to design and implement a customized interactive dashboard.
  • Integrated digitalized domain knowledge into the decision support system.
  • Developed an automated workflow for efficient execution of necessary components.
  • Employed modular design principles for maintainability and scalability.

Main Results:

  • Successfully created a near real-time interactive dashboard tailored to specific research needs.
  • Enabled digitalized domain knowledge integration through open-source tools.
  • Implemented an automated workflow reducing computational overhead.
  • Achieved lower maintenance costs and facilitated easier upgrades due to modularity.

Conclusions:

  • Customized, open-source interactive dashboards provide a viable and cost-effective solution for supporting complex scientific research.
  • The developed system enhances data interpretation and decision-making capabilities in agricultural and environmental studies.
  • This approach offers a flexible, low-maintenance alternative to expensive, off-the-shelf visualization tools.