What is AutoDiscovery?
AutoDiscovery is a sophisticated software solution crafted for automated exploratory data analysis, enabling biomedical researchers to uncover complex relationships hidden within their experimental and clinical trial data. This innovative tool autonomously identifies the appropriate statistical tests required to assess the connections between different variable combinations in each data subset. AutoDiscovery effectively tackles prevalent challenges in biomedical research, such as discerning cause-effect relationships, managing false discovery rates, addressing small sample sizes, organizing treatment groups, and ensuring result traceability. Specifically designed for Principal Investigators, who often face time constraints and may lack extensive statistical knowledge, it allows them to focus on conducting significant and productive research endeavors. Additionally, this software streamlines research workflows, promoting faster insights and breakthroughs in the biomedical domain while empowering researchers to make data-driven decisions with confidence. By leveraging AutoDiscovery, researchers can enhance the overall quality of their findings and contribute more effectively to advancements in health science.
Pricing
Price Starts At:
€1.795 per year
Integrations
No integrations listed.
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Screenshots and Video
Company Facts
Company Name:
Butler Scientifics
Date Founded:
2014
Company Location:
Spain
Company Website:
www.butlerscientifics.com
Product Details
Deployment
Windows
Mac
Linux
Training Options
Documentation Hub
Support
Standard Support
Web-Based Support
Product Details
Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English
AutoDiscovery Categories and Features
Data Discovery Software
Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics