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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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An ecological disturbance is a temporary disruption in the environment resulting from abiotic, biotic, or anthropogenic factors, causing a pronounced change in an ecosystem. The impact of an ecological disturbance, which can depend on its intensity, frequency, and spatial distribution, plays a significant role in shaping the species diversity within the ecosystem.
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Data Reporting and Recording01:24

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, 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 Validation01:03

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Related Experiment Video

Updated: Jan 28, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

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A data science challenge for converting airborne remote sensing data into ecological information.

Sergio Marconi1, Sarah J Graves2, Dihong Gong3

  • 1School of Natural Resources and Environment, University of Florida, Gainesville, FL, USA.

Peerj
|March 8, 2019
PubMed
Summary

Data science competitions can advance ecological methods. A competition improved tree crown segmentation, alignment, and species classification from remote sensing images, benefiting ecological data analysis.

Keywords:
Airborne remote sensingCrown delineationCrown segmentationData alignmentData science competitionNational Ecological Observatory NetworkRemote sensingSpecies classification

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Area of Science:

  • Ecology
  • Data Science
  • Remote Sensing

Background:

  • Quantitative methods are crucial for ecological research, particularly in analyzing remote sensing data for species identification.
  • Advancements in data science offer new avenues for improving ecological data analysis techniques.

Purpose of the Study:

  • To enhance quantitative methods in ecology by organizing a data science competition focused on remote sensing image analysis.
  • To improve three key tasks: individual tree crown segmentation, ground-truthed tree alignment with remote sensing data, and species classification.

Main Methods:

  • A data science competition was conducted with six teams (16 participants) submitting predictions for tree crown segmentation, alignment, and species classification.
  • Algorithms were evaluated based on their performance in identifying tree locations, sizes, and species from remote sensing images.

Main Results:

  • Crown segmentation was the most challenging task, with the best algorithm achieving only 34% overlap; performance improved for larger trees.
  • A perfect alignment was achieved for targeted trees by minimizing positional and size differences between ground-truthed and remotely sensed crowns.
  • Species classification algorithms performed well, with the top performer achieving 92% accuracy for both common and rare species.

Conclusions:

  • Data science competitions are valuable for advancing quantitative methods in ecology and biology.
  • Insights gained from comparing algorithms can improve the accuracy of extracting ecological information from remote sensing data.
  • Further development is needed for robust crown segmentation, while alignment and species classification show promising results.