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

Levels of Use of a GIS01:29

Levels of Use of a GIS

109
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
109
Data Reporting and Recording01:24

Data Reporting and Recording

4.9K
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...
4.9K
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

190
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
190
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

71
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
71
Data Collection by Observations01:08

Data Collection by Observations

12.9K
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...
12.9K
Archival Research01:40

Archival Research

16.5K
Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
16.5K

You might also read

Related Articles

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

Sort by
Same author

Generative AI in Admission Notes and Diagnostic Completeness: A Pilot Study.

Applied clinical informatics·2026
Same author

County-level vulnerability is associated with mental health and substance use treatment among rural suicide decedents: A national multi-year cross-sectional study.

The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association·2025
Same author

LOINC implementation approaches in academic medical research centers - results from a survey of CTSA sites.

Journal of clinical and translational science·2025
Same author

Optimizing Documentation Integrity of Ophthalmic Diagnostic Test Interpretation through Electronic Health Record Clinical Decision Support.

Applied clinical informatics·2025
Same author

Transitioning Ineffective Medications on Hold Alert from Interruptive to Noninterruptive Alert to Decrease Alert Burden.

Applied clinical informatics·2025
Same author

Building and implementation of a common infrastructure for specimen and data storage at an academic medical center.

Journal of clinical and translational science·2025

Related Experiment Video

Updated: Sep 17, 2025

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

Establishing data governance for sharing and access to real-world data: a case study.

Heath A Davis1,2, Diva Kerkman2, Asher A Hoberg1,2

  • 1Office of Information Technology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, 200 Newton Road, Iowa City, IA 52242, United States.

JAMIA Open
|June 30, 2025
PubMed
Summary

Establishing robust data governance enhances clinical data sharing for research. This academic health center

Keywords:
Enterprise Data Warehouse for Researchclinical research datadata governanceexternal data sharing for researchreal-world dataresearch data

More Related Videos

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
Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
11:18

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

16.2K

Related Experiment Videos

Last Updated: Sep 17, 2025

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
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
Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
11:18

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

16.2K

Area of Science:

  • Health Informatics
  • Data Management
  • Clinical Research

Background:

  • Secondary use of clinical data is vital for research.
  • Effective data governance is crucial for managing and sharing data.
  • Academic health centers face unique challenges in data sharing.

Observation:

  • An academic health center developed a data governance program to improve research data access and sharing.
  • Qualitative and quantitative methods were used to evaluate the program's effectiveness.
  • The program focused on external data sharing processes and continuous improvement.

Findings:

  • Significant improvements in data accessibility and understanding were observed.
  • New data-driven performance indicators and strategies were implemented.
  • The formalized process enhanced data access and quality improvement.

Implications:

  • The developed data governance process can serve as a model for other institutions.
  • Key elements include data literacy, cross-office collaboration, and structured workflows.
  • Addressing bottlenecks and researcher education is essential for successful implementation.