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

Longitudinal Research02:20

Longitudinal Research

13.5K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.5K
Longitudinal Studies01:26

Longitudinal Studies

563
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
563

You might also read

Related Articles

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

Sort by
Same author

Determinants of body mass index during early life: findings from an exposome-wide association study with follow-up replication and Mendelian randomization analyses.

Exposome·2026
Same author

Life lost due to the COVID-19 pandemic: A model-based cohort analysis of mortality displacement in the registered population of England.

PloS one·2026
Same author

Peristalsis of the gastric conduit post-esophagectomy: is it relevant? Detailed conduit analysis using dynamic magnetic resonance imaging.

Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus·2026
Same author

Faecal haemoglobin-based referral and investigation prioritisation is associated with colorectal cancer-specific survival in symptomatic patients: a retrospective observational study.

British journal of cancer·2026
Same author

Socio-economic disparities in clinical outcomes of transfusion-dependent β-thalassaemia patients.

Journal of health, population, and nutrition·2026
Same author

Is All Weight Loss Equal Following Sleeve Gastrectomy? Defining Body Composition and Anthropometric Thresholds for Hyperglycemia Remission in Women from a Prospective Cohort Study.

Obesity surgery·2026

Related Experiment Video

Updated: Feb 21, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

793

Synthetic ALSPAC longitudinal datasets for the Big Data VR project.

Demetris Avraam1, Rebecca C Wilson1, Paul Burton1

  • 1Data 2 Knowledge Research Group, Institute of Health and Society, Newcastle Biomedical Research Building, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK.

Wellcome Open Research
|October 10, 2017
PubMed
Summary

Synthetic datasets mirroring the ALSPAC birth cohort study were created for broader use in data analysis software development. These datasets enable collaboration without sharing sensitive participant information.

Keywords:
ALSPACSimulated datadata visualisationsynthetic datavirtual realityvisual analytics

More Related Videos

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.9K
An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
08:59

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Published on: March 3, 2023

2.8K

Related Experiment Videos

Last Updated: Feb 21, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

793
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.9K
An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
08:59

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Published on: March 3, 2023

2.8K

Area of Science:

  • Biostatistics
  • Data Science
  • Epidemiology

Background:

  • The ALSPAC birth cohort study involves sensitive participant data.
  • Sharing such data is restricted, limiting its use in method and software development.

Purpose of the Study:

  • To create synthetic datasets that replicate key properties of the ALSPAC study data.
  • To enable wider use of ALSPAC data characteristics for research and development.

Main Methods:

  • Simulated three synthetic datasets with varying observation sizes (15,000, 155,000, 1,555,000 participants).
  • Modeled eleven cardiac and anthropometric variables across nine collection ages.
  • Ensured synthetic data retained covariance matrices, means, and variances of the original ALSPAC data.

Main Results:

  • Successfully generated synthetic datasets that mirror original data properties without containing sensitive information.
  • Demonstrated the utility of these datasets in an academia-industry collaboration for developing virtual reality data analysis software.

Conclusions:

  • Synthetic data offers a viable solution for collaborative research and software development involving sensitive cohort data.
  • These datasets can facilitate advancements in statistical methods and data analysis tools where data sharing is a challenge.