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Related Concept Videos

Data Collection II01:29

Data Collection II

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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Data Collection I01:30

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Data Collection by Experiments01:13

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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.”
An example of the experimental method is a public...
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Data Collection by Survey01:07

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Data Collection III01:05

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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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.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Updated: Jan 23, 2026

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Beyond the clipboard: data collection with GridScore NEXT.

Sebastian Raubach1, Miriam Schreiber1, Ruth Hamilton1

  • 1The James Hutton Institute/International Barley Hub, Invergowrie, Dundee, DD2 5DA, UK.

BMC Bioinformatics
|January 21, 2026
PubMed
Summary
This summary is machine-generated.

GridScore NEXT is a new plant phenotyping application that improves data collection accuracy and standardization in crop research. This tool reduces errors and speeds up decision-making for better genetic variation analysis.

Keywords:
Data qualityPlant genetic resourcesPlant phenotypingResearch software engineering

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Area of Science:

  • Plant genetics and breeding
  • Agricultural research
  • Bioinformatics

Background:

  • Accurate phenotypic data is crucial for understanding genetic variation in crops.
  • Traditional handwritten data collection methods are prone to errors.
  • Electronic data collection ensures error prevention and data standardization.

Purpose of the Study:

  • To introduce GridScore NEXT, an advanced plant phenotyping application for field trial data collection.
  • To enhance the state-of-the-art in plant genetics, pre-breeding, and crop improvement research.
  • To address limitations of previous data collection methods through user-driven design.

Main Methods:

  • Iterative design methodology based on real-world field interactions with expert users.
  • Incorporation of feedback from diverse crop research teams (rice, grasspea, alfalfa, barley, potato, etc.).
  • Development and testing of new features for enhanced data capture and analysis.

Main Results:

  • GridScore NEXT features enhanced data collection tools, including individual plant phenotyping, GPS, and image traits.
  • Customizable validation rules, barcode scanning, and advanced visualizations (heatmaps, box plots) reduce errors and improve data quality.
  • Cross-platform compatibility, offline functionality, and open-source availability facilitate widespread adoption and data sharing.
  • Standardized methods, significant error reduction, and timely data sharing accelerate decision-making in crop improvement.

Conclusions:

  • GridScore NEXT significantly improves the accuracy, efficiency, and standardization of plant phenotyping data collection.
  • The application's features and open-source nature make it broadly applicable to various experiments requiring accurate data.
  • Adoption of GridScore NEXT leads to quicker insights and enhanced decision-making in crop research and development.