A systematic review mapping the application of artificial intelligence in behavioral analysis for invertebrate and larval model organisms. The dataset, authored by Zuzanna Stępnicka and published on figshare in May 2026, analyzes 97 eligible studies published between 2015 and May 2025, tracking trends in model organisms, AI methods, and data characteristics.
Use Cases
- Benchmarking AI method adoption trends based on the analysis of 97 studies from 2015-2025.
- Identifying common preprocessing pipelines and model architectures for behavioral video analysis.
- Evaluating geographic and organism-specific research focus based on publication origin and species data.
- Developing standardized reporting frameworks for AI-driven behavioral analysis based on identified gaps in reproducibility.
Strengths
- Follows PRISMA 2020 guidelines for systematic review methodology.
- Analyzes 97 studies, showing a steep increase from 2 in 2015 to 97 by mid-2025.
- Identifies dominant AI techniques, such as convolutional neural networks and pose estimation frameworks like DeepLabCut since 2021.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- The dataset is 48.8 KB, indicating a limited scope likely containing summary tables rather than raw experimental data.
Provenance
- Source
- Zuzanna Stępnicka
- Collection Method
- Systematic literature review screened according to PRISMA 2020 guidelines.
- Time Range
- 2015 to May 2025
- Freshness
- Last updated 2026-05-19 05:41:11; freshness should be verified.
- Geography
- Studies primarily originated from the USA, China, and Germany.