| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - video-classification |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - laboratory |
| - life-science |
| - protocol-compliance |
| - egocentric-video |
| - biology |
| - wet-lab |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: XMglass |
| data_files: |
| - path: XMglass/xm.csv |
| split: train |
| - config_name: DJI |
| data_files: |
| - path: DJI/dji.csv |
| split: train |
| --- |
| |
| # 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: |
|
|
| | Column | Description | |
| |--------|-------------| |
| | `Slice_ID` | Unique identifier (e.g., `XM_001`, `DJI-001`) | |
| | `Exp_ID` | Experiment group identifier | |
| | `Date` | Recording date | |
| | `Video Name` | Filename of the video/image | |
| | `Scene` | Recording location (`TC hood`, `bench`, `TC room`, `TC`) | |
| | `Operation` | Description of the procedure performed | |
| | `Protocol` | Filename of the corresponding protocol in the protocol folder | |
| | `Issue (if any)` | Description of intentional procedural errors, if present | |
| | `Length` | Duration of the video | |
| | `Time_stamp` | Timestamps of protocol steps within the video | |
| | `Tools` | Lab equipment used | |
|
|
| ## 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 |
|
|
| ```python |
| 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](https://creativecommons.org/licenses/by-nc/4.0/) license. |
|
|
