YinkaiW/LSV

LSV: LabSuperVision Benchmark

Dataset Description

LSV is a multi-view video dataset of wet-lab biology experiments, captured from both first-person (XMglass smart glasses) and third-person (DJI action camera) perspectives. Each video records a researcher performing a laboratory protocol and is annotated with the corresponding protocol text, scene type, and—where applicable—deliberate procedural errors.

The dataset is designed for research on:

• —Protocol compliance monitoring — detecting whether a procedure was followed correctly
• —Procedural error detection — identifying specific deviations from standard protocols
• —Egocentric video understanding — understanding lab activities from a first-person view
• —Video-language grounding — linking protocol text to video segments

Dataset Structure

LSV/
├── XMglass/
│   ├── xm.csv                # Metadata (90 entries)
│   ├── XMprotocol/            # Protocol text files (22 files)
│   └── XMvideo/               # Video files (105 files, ~75 GB)
├── DJI/
│   ├── dji.csv                # Metadata (161 entries)
│   ├── DJI-Protocol/          # Protocol text files (17 files)
│   └── DJI-Video/             # Video & image files (251 files, ~219 GB)

Metadata Fields

Both CSV files share the following columns:

Data Collection

XMglass (First-Person View)

• —Device: XM smart glasses with built-in camera
• —Entries: 90 annotated video clips
• —Scenes: Tissue culture (TC) hood, bench, TC room

DJI (Third-Person View)

• —Device: DJI action camera
• —Entries: 161 (127 videos + 34 images)
• —Scenes: TC hood, bench, TC room
• —Note: Some experiments include paired first-person and third-person recordings of the same procedure

Covered Procedures

The dataset covers a range of common molecular biology and cell culture techniques, including:

• —Cell line passaging and seeding (HEK293T, iPSCs, cancer cell lines)
• —Lentiviral packaging, collection, and infection
• —CRISPR/Cas9 delivery
• —PCR reaction setup and colony PCR
• —Serial dilution
• —DNA gel electrophoresis (E-gel loading)
• —RNA extraction
• —Cell freezing and thawing
• —Restriction digestion, Gibson assembly, Golden Gate reaction
• —Transformation
• —MiniPrep and NanoDrop quantification
• —FACS staining

Error Annotations

Many videos include deliberate procedural errors with detailed descriptions. Examples:

• —Skipping a pipetting step
• —Not changing pipette tips between reagents
• —Adding reagents in the wrong order
• —Omitting incubation or mixing steps
• —Forgetting to add a critical reagent

These error annotations enable benchmarking of automated protocol-compliance systems.

Usage

from datasets import load_dataset

# Load XMglass metadata
xm = load_dataset("YinkaiW/LSV", name="XMglass", split="train")

# Load DJI metadata
dji = load_dataset("YinkaiW/LSV", name="DJI", split="train")

License

This dataset is released under the CC BY-NC 4.0 license.