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What is ScienceDesk?

ScienceDesk's data automation revolutionizes the incorporation of artificial intelligence in materials science. This innovative tool offers teams a practical means to consistently adopt and apply the most recent AI algorithms in their everyday tasks. It includes customizable features, universal identifiers, QR codes, and a powerful search engine that links sample data to experimental outcomes. As a pioneering platform, ScienceDesk promotes collaboration among scientists and engineers, enabling them to interact with and derive insights from their experimental data. Nevertheless, the full capabilities of this resource remain largely unexploited due to the variety of data formats and the dependence on experts for manually extracting specific information. The ScienceDesk research data management system tackles this issue by integrating documentation with data analysis within a well-structured data framework. Our algorithms give researchers and scientists complete control over their data, allowing them to not only exchange datasets but also to share their analytical knowledge, thereby nurturing a more collaborative atmosphere in research. Additionally, ScienceDesk plays a crucial role in enhancing data accessibility, sparking innovative methodologies in scientific exploration. Ultimately, the platform encourages a new wave of collaborative innovation in the materials science community.

What is Atinary SDLabs Platform?

Atinary's Self-Driving Labs (SDLabs) platform provides a no-code approach for artificial intelligence and machine learning, designed to revolutionize research and development workflows by enabling traditional laboratories to transition from manual experiments to fully autonomous experimentation. This innovative platform bolsters the design and improvement of experiments through a robust closed-loop system that integrates AI-generated hypotheses, predictions, and decision-making processes. Among its key functionalities are multi-objective optimization, effective database management, seamless workflow orchestration, and immediate data analysis. Users can define experimental parameters with specific limitations, allowing machine learning algorithms to guide the subsequent phases of the process, carry out experiments either manually or with robotic assistance, evaluate results, and refresh models with the most recent data, thereby accelerating the journey toward more efficient, cost-effective, and environmentally sustainable products. Furthermore, Atinary provides exclusive algorithms such as Emmental for addressing non-linear constrained optimization challenges, SeMOpt for facilitating transfer learning within Bayesian optimization, and Falcon, all of which significantly boost the platform's capabilities and performance. By utilizing these sophisticated tools, researchers are empowered to enhance their experimental workflows, fostering greater efficiency and driving innovation in their fields. Ultimately, the SDLabs platform represents a transformative shift in how laboratories approach experimentation, paving the way for groundbreaking discoveries.

Media

Media

Integrations Supported

Docker
Elastic Cloud
Flutter
GraphQL
Kubernetes
PostgreSQL
Redis
django CMS

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

ScienceDesk

Company Location

Germany

Company Website

sciencedesk.net

Company Facts

Organization Name

Atinary

Date Founded

2019

Company Location

Switzerland

Company Website

atinary.com/solutions/

Categories and Features

AI Science

Not specified

ELN

Not specified

Lab Automation

Not specified

LIMS

Not specified

Materials Science

Not specified

Categories and Features

Materials Science

Not specified

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