Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Data Collection by Survey

Data Collection by Survey

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...

Systematic Sampling Method

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...

Surveys

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Conservation of Protein Domains

Conservation of Protein Domains

You might also read

Related Articles

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

Sort by

Same author

Ecological momentary assessment suggests greater sensitivity to clinical change in a compensatory strategy pilot clinical trial.

Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists·2026

Same author

Temperature discomfort impairs everyday cognition: a pilot study using smartwatch-based ecological momentary assessment.

Environmental research communications·2026

Same author

Promoting digital memory aid use in older adults with cognitive concerns: A pilot randomized controlled trial of adaptive web-based training.

Neuropsychology·2026

Same author

Introductory editorial for a special issue on artificial intelligence in neuropsychology.

The Clinical neuropsychologist·2026

Same author

Smart Home Technologies for Monitoring Cancer Symptoms and Enhancing Palliative Care.

Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science·2026

Same author

Detecting the Impact of Older Adult Healthy Brain Aging Behaviour Adoption Using Smart Home Technology.

Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science·2026

Same journal

Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation.

ACM transactions on intelligent systems and technology·2026

Same journal

SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification.

ACM transactions on intelligent systems and technology·2025

Same journal

HydraGAN: A Cooperative Agent Model for Multi-Objective Data Generation.

ACM transactions on intelligent systems and technology·2024

Same journal

Indirectly-Supervised Anomaly Detection of Clinically-Meaningful Health Events from Smart Home Data.

ACM transactions on intelligent systems and technology·2021

Same journal

Topic-Aware Physical Activity Propagation with Temporal Dynamics in a Health Social Network.

ACM transactions on intelligent systems and technology·2019

Same journal

SNAP: A General Purpose Network Analysis and Graph Mining Library.

ACM transactions on intelligent systems and technology·2017

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 26, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

A Survey of Unsupervised Deep Domain Adaptation.

Garrett Wilson¹, Diane J Cook¹

¹Washington State University, USA.

ACM Transactions on Intelligent Systems and Technology

|August 2, 2021

Summary

This summary is machine-generated.

This survey explores unsupervised deep domain adaptation, a method using deep learning to adapt models trained on one data type (source domain) to perform well on another (target domain) without target labels. It compares various approaches and discusses future research directions.

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Oct 26, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision

Background:

Deep learning excels in supervised learning but assumes identical data distributions for training and testing.
Discrepancies between training (source) and testing (target) data distributions pose a significant challenge.
Single-source unsupervised domain adaptation addresses this by leveraging labeled source data and unlabeled target data.

Purpose of the Study:

To survey and compare single-source unsupervised deep domain adaptation methods.
To analyze commonalities, differences, and theoretical underpinnings of these approaches.
To highlight application areas and identify future research opportunities in deep domain adaptation.

Main Methods:

Review and categorization of existing unsupervised deep domain adaptation techniques.
Comparative analysis of different methods based on their architectural components and adaptation strategies.
Examination of theoretical frameworks supporting domain adaptation in deep learning.

Main Results:

Identification of key strategies for aligning source and target domain representations.
Evaluation of the effectiveness of various methods across different tasks and datasets.
Synthesis of insights into the factors driving successful domain adaptation.

Conclusions:

Unsupervised deep domain adaptation is crucial for real-world applications where labeled target data is scarce.
Continued research is needed to address challenges like domain shift and improve model generalizability.
Exploring novel architectures and theoretical foundations will advance the field.