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Related Concept Videos

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.
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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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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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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 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 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 22, 2026

Patterning Bioactive Proteins or Peptides on Hydrogel Using Photochemistry for Biological Applications
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Bioactivity-explorer: a web application for interactive visualization and exploration of bioactivity data.

Lu Liang1, Chunfeng Ma1,2, Tengfei Du1

  • 1State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin, 300353, China.

Journal of Cheminformatics
|July 12, 2019
PubMed
Summary
This summary is machine-generated.

Bioactivity-explorer is a new web tool that helps researchers visualize and explore the vast ChEMBL database. It integrates disease-target information, offering extensive data on molecular interactions and targets for drug discovery.

Keywords:
ChEMBLMatched molecular pairMolecule scaffoldNetwork pharmacologyWeb server

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • The ChEMBL database contains extensive bioactivity data crucial for drug discovery.
  • Leveraging this large-scale data for interactive exploration presents a significant challenge.

Purpose of the Study:

  • To develop Bioactivity-explorer, a web application for interactive visualization and exploration of ChEMBL bioactivity data.
  • To integrate Therapeutic Target Database (TTD) disease-target mappings into the ChEMBL database.

Main Methods:

  • Developed a web application for interactive data visualization.
  • Integrated TTD disease-target mappings with ChEMBL bioactivity data.
  • Utilized large-scale data mining and integration techniques.

Main Results:

  • Bioactivity-explorer includes 493,430 scaffolds and 31,400,000 matched molecular pairs.
  • The tool encompasses 1,330,220 target-target interactions (shared active compounds) and 4,526,718 (shared active scaffolds).
  • It also features 97,041,700 molecule-molecule interactions and 14,974 disease-target mappings.

Conclusions:

  • Bioactivity-explorer provides a powerful platform for exploring ChEMBL bioactivity data.
  • The integrated disease-target information enhances the utility for drug discovery research.
  • The web tool and its source code are publicly available for scientific use.