Wound Healing Assay Time-Lapse Series
Description
Qualified researchers may request access for validation purposes by contacting the corresponding author via institutional email. Data sharing requires a formal Data Transfer Agreement (DTA).
Dataset Metadata: Wound Healing Assay Time-Lapse Series
1. General Identification
- Dataset Title: Time-Lapse Microscopy Dataset for Cellular Migration and Wound Healing Analysis
- Version: 1.0.0
- Data Content: In-vitro scratch assay images capturing cell migration over a 72-hour period.
- Keywords: Cell Migration, Wound Healing Assay, Scratch Assay, Time-lapse Microscopy, Computer Vision.
2. Access and Rights (Controlled Access)
- Access Status: Controlled Access
- Reason for Restriction: Data contains sensitive experimental parameters and is part of an ongoing institutional research project.
- Metadata DOI/URL: https://zenodo.org/records/19653335
- Access Request Procedure: Qualified researchers may request access for validation purposes by contacting the corresponding author via institutional email. Data sharing requires a formal Data Transfer Agreement (DTA).
3. Technical Metadata (Dataset Structure)
|
Attribute |
Description |
|
Total Samples |
32 Time-lapse sequences |
|
Time Points |
0h, 24h, 48h, 72h |
|
Replicates |
Identified by parenthetical indices (e.g., (2), (10), (34), (43)) |
|
File Format |
JPEG |
|
Image Type |
Phase-contrast / Brightfield Microscopy |
|
Spatial Resolution |
2560 x 1920 pixels |
|
Bit Depth |
24-bit |
4. Data Dictionary (Naming Convention)
The file naming system is as follows: {Time_Point} ({Replicate_ID}).JPG
- Example: 24 (10).JPG -> Image captured at the 24th hour for replicate sample number 10.
5. Experimental Context
- Imaging Platform: Olympus CX31Light Microscope / Olympus E330 Digital Camera
- Magnification: x20
- Cell Line: Human Breast Adenocarcinoma - MCF-7 / Human Hepatocellular Carcinoma - HepG2
6. Summary Statistics for Reviewers
- Dataset Integrity: The dataset includes complete sequences for all time points (0-72h).
- Target-to-Background: High-contrast boundaries for optimal segmentation performance.