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

¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

1.7K
The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
1.7K
Manipulation and Analysis01:21

Manipulation and Analysis

59
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
59
Data Validation01:03

Data Validation

5.3K
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...
5.3K
Dimensional Analysis02:19

Dimensional Analysis

16.8K
The concept of dimension is important because every mathematical equation linking physical quantities must be dimensionally consistent, implying that mathematical equations must meet the following two rules. The first rule is that, in an equation, the expressions on each side of the equal sign must have the same dimensions. This is fairly intuitive since we can only add or subtract quantities of the same type (dimension). The second rule states that, in an equation, the arguments of any of the...
16.8K
Reliability and Validity01:29

Reliability and Validity

13.2K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
13.2K
Statgraphics01:10

Statgraphics

195
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
195

You might also read

Related Articles

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

Sort by
Same author

Evaluating the quality of tabular synthetic data in health care.

PLOS digital health·2026
Same author

Selecting medical research data platforms for translational biomedical research: a five-tier overview and requirement-weighted assessment framework.

Frontiers in digital health·2026
Same author

Scaling Electronic Consent for Research Integrated into Clinical Routine Processes.

Studies in health technology and informatics·2026
Same author

Accurate Yet Privacy-Preserving Determination of Case Numbers Across German University Hospital Health Data.

Studies in health technology and informatics·2026
Same author

A FHIR-Based Dashboard as an Integrated Research Patient Record and Quality Assurance Tool.

Studies in health technology and informatics·2026
Same author

Setting up a DataSHIELD Hub for the German Medical Informatics Initiative: Challenges and Lessons Learned.

Studies in health technology and informatics·2026

Related Experiment Video

Updated: Sep 12, 2025

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

6.4K

Real-World Data Integration in Practice: Why Communication with Domain Experts is Key.

Lena Baum1, Marco Johns1, Armin Müller1

  • 1Medical Informatics Group, Center of Health Data Sciences, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.

Studies in Health Technology and Informatics
|August 8, 2025
PubMed
Summary

Informatics infrastructures enhance translational research by enabling data access. Addressing data quality and representation issues requires customized, use-case-specific solutions aligned with domain experts and agile ETL processes for meaningful data reuse.

Keywords:
Clinical Data WarehouseData IntegrationData QualityData SharingETL-ProcessesReal-world Data

More Related Videos

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.4K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K

Related Experiment Videos

Last Updated: Sep 12, 2025

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

6.4K
Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.4K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K

Area of Science:

  • Health Informatics
  • Translational Research
  • Data Science

Background:

  • Informatics infrastructures are crucial for translational research.
  • These systems provide researchers with self-service access to health data.
  • Real-world data integration presents significant challenges.

Purpose of the Study:

  • To identify common data quality and representation issues in health data integration.
  • To present recommendations and best practices for addressing these issues.
  • To emphasize the need for use-case-specific solutions in data integration.

Main Methods:

  • Analysis of common data quality and representation issues in real-world data integration.
  • Development of recommendations and best practices.
  • Emphasis on aligning technical solutions with domain expertise and use cases.

Main Results:

  • Data quality and representation issues are prevalent in health data integration.
  • Technical solutions alone are insufficient; customization based on intended use case is essential.
  • Alignment with domain experts and agile ETL processes are key for meaningful data reuse.

Conclusions:

  • Effective informatics infrastructures require addressing data quality and representation challenges.
  • Solutions must be tailored to specific use cases and involve domain experts.
  • Agile ETL processes facilitate the meaningful reuse of integrated health data.