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Average Ratings 0 Ratings

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features
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support

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Description

FigCanvas serves as an AI-driven scientific figure generator, designed for researchers to produce scientific illustrations, flowcharts, and data visualizations all in one platform. By simply describing a desired figure in everyday language, inputting methodological text, or uploading a dataset, users can receive a preliminary draft in less than two minutes. The tool accommodates various workflows including scientific illustration, data visualization, and flowchart creation, enabling researchers to effortlessly create pathway diagrams, figures related to cell biology, molecular mechanisms, lab schematics, bar graphs, scatter plots, heatmaps, volcano plots, and a variety of other research visuals without requiring any design expertise or coding skills. Specifically tailored for effective scientific communication, FigCanvas utilizes visual training that focuses on research-specific standards to ensure that the generated outputs align closely with the expected structure, composition, and style of academic publications. This innovative approach allows researchers to save time and enhance their visual presentations, ultimately contributing to clearer communication of complex scientific concepts.

Description

At Iris.ai we have spent the last 6 years building an award-winning AI engine for scientific text understanding. Our algorithms for text similarity, tabular data extraction, domain-specific entity representation learning and entity disambiguation and linking measure up to the best in the world. On top of that, our machine builds a comprehensive knowledge graph containing all entities and their linkages to allow humans to learn from it, use it and also give feedback to the system. The Iris.ai Researcher Workspace is a flexible tool suite that allows to approach a project in a variety of ways. Modules include content based explorative search, machine analysis of document sets, extracting and systematizing data points, automatically writing summaries of multiple documents - and very powerful filters based on context descriptions, the machine’s analysis, or specific data points or entities. The Iris.ai engine for scientific text understanding is a powerful interdisciplinary system that can be automatically reinforced on a specific research field for much more nuanced machine understanding - without human training or annotation.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

$12.50 per month
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs No 
Webinars Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

FigCanvas

Country

United States

Website

figcanvas.com

Vendor Details

Company Name

Iris.ai

Founded

2015

Country

Norway

Website

iris.ai/

Product Features

Product Features

Data Extraction

Disparate Data Collection No 
Document Extraction No 
Email Address Extraction No 
IP Address Extraction No 
Image Extraction No 
Phone Number Extraction No 
Pricing Extraction No 
Web Data Extraction No 

Natural Language Processing

Co-Reference Resolution No 
In-Database Text Analytics No 
Named Entity Recognition No 
Natural Language Generation (NLG) No 
Open Source Integrations No 
Parsing No 
Part-of-Speech Tagging No 
Sentence Segmentation No 
Stemming/Lemmatization No 
Tokenization No 

Qualitative Data Analysis

Annotations No 
Collaboration No 
Data Visualization No 
Media Analytics No 
Mixed Methods Research No 
Multi-Language No 
Qualitative Comparative Analysis No 
Quantitative Content Analysis No 
Sentiment Analysis No 
Statistical Analysis No 
Text Analytics No 
User Research Analysis No 

Alternatives

Alternatives

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