Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Data Collection by Survey01:07

Data Collection by Survey

9.2K
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...
9.2K
Data Collection I01:30

Data Collection I

8.8K
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...
8.8K
Data Collection by Observations01:08

Data Collection by Observations

15.3K
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...
15.3K
Data Validation01:03

Data Validation

7.1K
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.
Nursing assessment guides are generally based on holistic models rather than medical...
7.1K
Data Validation01:15

Data Validation

3.3K
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
3.3K
Data Collection III01:05

Data Collection III

4.7K
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.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the...
4.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Knowledge Engineering for Open Science: Building and Deploying Knowledge Bases for Metadata Standards.

AI magazine·2026
Same author

Mapping the terminology of the early rescue chain to the Foundation of ICD-11: Registered report protocol.

PloS one·2026
Same author

The HuBMAP Framework for Advancing Data FAIRness.

bioRxiv : the preprint server for biology·2026
Same author

VO: The Vaccine Ontology.

Scientific data·2026
Same author

Desiderata for a biomedical knowledge network: opportunities, challenges and future directions.

Bioinformatics advances·2026
Same author

Toward total recall: Enhancing data FAIRness through AI-driven metadata standardization.

GigaScience·2026
Same journal

Immune biomarkers, profiles, and responses: a vaccine ontology perspective.

Journal of biomedical semantics·2026
Same journal

A pragmatist approach to bridging tables and ontologies through LinkML and punning.

Journal of biomedical semantics·2026
Same journal

FAIR in practice: minimum metadata schema for bioinformatics analytics by machines.

Journal of biomedical semantics·2026
Same journal

Prenatal monitoring in primary health care: a design science research-based approach to FHIR interoperability.

Journal of biomedical semantics·2026
Same journal

From narrative evidence to computable knowledge: a decision-relevant corpus for medicinal herb-disease relationships.

Journal of biomedical semantics·2026
Same journal

BERTopic-driven term extraction from biomedical texts toward ontology population: evaluating vaccine ontology with Plotkin's vaccines corpus.

Journal of biomedical semantics·2026
See all related articles

Related Experiment Video

Updated: Feb 25, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.8K

An ontology-driven tool for structured data acquisition using Web forms.

Rafael S Gonçalves1, Samson W Tu2, Csongor I Nyulas2

  • 1Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA. rafael.goncalves@stanford.edu.

Journal of Biomedical Semantics
|August 3, 2017
PubMed
Summary
This summary is machine-generated.

This study presents an ontology-based system for structured data acquisition in biomedicine. It enables automated reasoning and querying of semantically enriched clinical assessment data.

Keywords:
Data acquisitionForm generationOWLOntologyStructured data

More Related Videos

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

5.5K
Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

16.1K

Related Experiment Videos

Last Updated: Feb 25, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.8K
TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

5.5K
Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

16.1K

Area of Science:

  • Biomedical Informatics
  • Knowledge Representation
  • Web Science

Background:

  • Structured data acquisition is crucial in biomedicine but current methods lack automated decision-making capabilities.
  • Existing data structures are insufficient for automated reasoning systems like Web Ontology Language (OWL).
  • Clinical functional assessment for disability benefits requires data structured for automated analysis.

Purpose of the Study:

  • To develop a system for generating Web-based assessment forms from OWL ontologies.
  • To aggregate form data into a semantically enriched ontology.
  • To enable automated analysis and querying of acquired data.

Main Methods:

  • Developed an ontology-based structured data acquisition system.
  • Generated Web-based assessment forms from OWL ontologies.
  • Aggregated data into a semantically enriched ontology queried using SPARQL.

Main Results:

  • The system successfully generated Web-based assessment forms from OWL ontologies.
  • Acquired data was semantically enriched and structured as an ontology.
  • Data proved highly amenable to automatic analysis via queries.

Conclusions:

  • Ontologies can structure Web-based forms and semantically enrich data.
  • Enriched data elements with associated ontologies enable automated inferences.
  • Ontologies provide a rich vocabulary for querying structured biomedical data.