Search results for Codebook

Synthace is a digital experiment platform built for high-performance life science R&D teams to help them run more powerful experiments and accelerate scientific progress. It allows biologists to design, plan, and automate experiments in a single place, then automatically gather structured experiment data and metadata in a unified no-code environment.

Amanote is a GenAI-powered learning notebook designed to enhance student engagement and streamline the educational experience. By integrating seamlessly with Learning Management Systems (LMS), it transforms traditional course materials—such as slides, PDFs, and textbooks—into interactive, annotatable content. This allows students to take structured notes directly alongside their study materials, fostering a more organized and effective learning process. Amanote's contextual chatbot, powered by advanced language models, provides instant, relevant answers to student queries by understanding the specific context of the course material being studied. Educators can customize the chatbot's behavior and control the resources it accesses, ensuring alignment with institutional standards for data protection and educational quality. Additionally, Amanote supports both typed and handwritten notes, accommodates various learning styles, and offers collaborative features that enable students to share notes and insights, promoting a more interactive and engaging educational environment. Key Features and Functionality: - Interactive Note-Taking: Enables students to annotate course materials directly, supporting both typed and handwritten notes for versatile learning preferences. - Contextual AI Chatbot: Provides instant, context-aware responses to student inquiries, enhancing understanding and engagement with the material. - Seamless LMS Integration: Integrates effortlessly with popular LMS platforms, ensuring a smooth user experience without disrupting existing workflows. - Collaborative Learning Tools: Facilitates student collaboration through shared notes, comments, and resource linking, enriching the learning process. - Customizable Educator Controls: Allows educators to tailor the chatbot's behavior and resource access, maintaining control over educational content and data privacy. Primary Value and Problem Solved: Amanote addresses the challenge of passive learning by transforming static course materials into interactive, engaging content. It empowers students to actively participate in their education through structured note-taking and immediate access to contextual information. For educators, Amanote offers tools to enhance course delivery, monitor student engagement, and ensure that learning resources are utilized effectively. By integrating advanced AI capabilities within a secure and customizable framework, Amanote bridges the gap between traditional educational methods and modern technological advancements, leading to improved learning outcomes and a more dynamic educational environment.

WordPerfect Lightning digital notebook

Labfolder is an electronic lab notebook software (ELN) designed for scientists.

Eversports is an online marketplace for sport activities, where sport enthusiasts can search, book and pay sport activities online with a few clicks on their smartphone or notebook.

VigiaOT is an OT asset inventory and vulnerability management tool built for factories and industrial sites, including small and mid-size plants without a dedicated OT security team. A technician’s laptop visits each cell and runs read-only active discovery over Modbus TCP, Siemens S7, EtherNet/IP, OPC UA, BACnet/IP, SNMP, MQTT and other common protocols. It identifies vendor, model, firmware, open ports and network exposure (isolated, segmented, bridged to IT or internet-facing). Scans only run in windows the plant schedules, and no probe ever writes to a device. Each device is correlated against NVD and the CISA Known Exploited Vulnerabilities catalog, with KEV-listed findings at the top. Every finding gets a decision (risk accepted, mitigation planned, false positive, remediated) with a written justification, so the review history is auditable. Results export to CSV and to an executive Excel report. VigiaOT scales from a single laptop (Solo) to a site-wide deployment, where several technicians feed one on-site appliance. Optional modules add passive listening on the IT network for OT devices crossing the IT/OT boundary, and an AI module that reads manufacturer security advisories, matches them to the exact firmware installed, and drafts short alerts that a person reviews. Discovery needs no internet. Air-gapped sites receive vulnerability updates by USB. Free trial for up to 5 devices. Built by CodeBox, a Portuguese industrial software and hardware company with 9 years in manufacturing automation.

The AISE PyTorch 0.4 Python 3.6 CPU Notebook is a pre-configured, fully integrated runtime environment designed for machine learning and data science applications. It combines PyTorch 0.4, an open-source machine learning library, with Python 3.6 and Jupyter Notebook, a browser-based interactive platform for programming and data analysis. Optimized for CPU performance, this environment facilitates efficient development and execution of machine learning models without the need for GPU resources. Key Features and Functionality: - Integrated Software Stack: Includes PyTorch 0.4, Python 3.6, and Jupyter Notebook, providing a cohesive environment for machine learning tasks. - CPU Optimization: Tailored for high-performance execution on CPU architectures, ensuring efficient training and inference without GPU dependency. - Development Tools: Equipped with essential development tools such as a C compiler and build utilities, supporting comprehensive program development. - Stability and Support: Offers a stable, production-ready environment with long-term support and regular updates to maintain reliability. Primary Value and User Solutions: This notebook environment addresses the need for a ready-to-use, CPU-optimized platform for machine learning practitioners and data scientists. By eliminating the complexities of manual setup and configuration, it enables users to focus on developing and deploying machine learning models efficiently. Its integration of key tools and libraries streamlines workflows, making it particularly beneficial for those without access to GPU resources or requiring a stable CPU-based solution.

The AISE TensorFlow 1.7 Python 3.6 CPU Notebook is a pre-configured, fully integrated runtime environment designed for machine learning and data science applications. It combines TensorFlow 1.7, an open-source machine learning library, with Python 3.6 and Jupyter Notebook, a browser-based interactive platform for programming and data analysis. This setup is optimized for CPU performance, providing a stable and efficient environment for developing and deploying machine learning models. Key Features and Functionality: - TensorFlow 1.7 Integration: Leverage the capabilities of TensorFlow 1.7 for building and training machine learning models. - Python 3.6 Support: Utilize Python 3.6, offering a robust and versatile programming language for data science tasks. - Jupyter Notebook Interface: Access a user-friendly, interactive environment for coding, visualization, and documentation. - CPU Optimization: The environment is tailored for high-performance execution on CPU architectures, ensuring efficient model training and inference without the need for specialized hardware. - Development Tools: Includes essential development tools such as C compilers and build utilities, facilitating seamless program development and deployment. Primary Value and User Solutions: This notebook environment addresses the challenges of setting up and configuring machine learning frameworks by providing a ready-to-use platform. Users can focus on developing and experimenting with machine learning models without the overhead of environment setup. Its CPU optimization ensures accessibility for users without GPU resources, making it suitable for a wide range of applications, from educational purposes to professional development and deployment of machine learning solutions.

Lab Thread is an intuitive, user-friendly platform to make collaboration – and scientists’ lives – easier. It combines molecular design, an electronic lab notebook, lab processes and sample tracking with project management built for biology to tie all the strands of your research into a single digital thread.

Noteshelf is an award-winning note-taking application designed to provide a seamless and intuitive digital writing experience across various platforms, including iOS, Android, and Windows. It offers a comprehensive suite of tools that cater to diverse note-taking needs, from handwritten notes and typed documents to audio recordings and PDF annotations. With its realistic pen and highlighter options, customizable templates, and AI-powered features, Noteshelf transforms the way users capture, organize, and share their ideas. Key Features and Functionality: - Handwriting and Typing: Utilize lifelike pens and highlighters to create beautiful handwritten notes, or opt for typing to produce professional documents. - PDF Annotation: Import PDFs and annotate them by highlighting, underlining, or adding notes directly onto the document. - Audio Recording: Record lectures, meetings, or discussions while taking notes, with the ability to replay recordings at any time. - Image Editing: Insert images into your notes, then crop, resize, or annotate them to enhance your content. - Shape Recognition: Draw and hold to convert strokes into perfect shapes, aiding in the creation of diagrams and illustrations. - Handwriting to Text Conversion: Easily convert your handwritten notes into typed text for easy editing and sharing. - Customizable Toolbar: Tailor the toolbar to include the tools you use most frequently, streamlining your workflow. - Templates and Covers: Access over 200 templates for various needs, such as journals, planners, and to-do lists, and personalize notebooks with a wide range of covers. - AI Assistance: Leverage Noteshelf AI to generate handwritten notes, create summaries, translate text, and more, enhancing productivity and learning. - Cross-Platform Syncing: Sync your notes across devices, ensuring access anytime, anywhere. Primary Value and User Solutions: Noteshelf addresses the need for a versatile and user-friendly digital note-taking solution that replicates the natural feel of writing on paper while offering the advantages of digital organization and accessibility. It caters to students, professionals, and creatives by providing tools that enhance productivity, facilitate effective learning, and support efficient information management. By integrating handwriting recognition, audio recording, and AI-powered features, Noteshelf empowers users to capture and organize their thoughts in a manner that suits their individual preferences and workflows.

Hopsworks is an open-source Enterprise platform for the development and operation of Machine Learning (ML) pipelines at scale, based around the industry’s first Feature Store for ML. You can easily progress from data exploration and model development in Python using Jupyter notebooks and conda to running production quality end-to-end ML pipelines, without having to learn how to manage a Kubernetes cluster.

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With Vhumo Bookmark Manager, organize your favorite websites into personal categories and access them from all devices, wherever you are. You can import bookmarks from other systems and share them on the platform with your confirmed contacts. Vhumo can be customized and used via smartphone, notebook, tablet or desktop, ProgressiveWebApp or extension for the most common browsers.

Turn any AI generated content, such as HTML, Markdown, notebook, or PDF into a private, live website protected by real authentication, so only the people you invite can access it. And it stays up to date. Connect your AI to the latest data, whether it’s Power BI, a CSV, or virtually any source, and Thryvate automatically refreshes the site. You control access, and you can see exactly who viewed it.

Ganymede is a cutting-edge, AI-native assay analysis tool designed to revolutionize scientific workflows in the life sciences industry. It connects directly to instruments and integrates with Electronic Laboratory Notebooks (ELNs) and Laboratory Information Management Systems (LIMS), automating data analysis and reducing manual labor. Modular Analysis offers customizable apps for various workflows, starting with bioprocess analysis, and supports applications in small molecules, biologics, cell & gene therapies, and synthetic biology.

NuGenesis ELN, the analytical electronic laboratory notebook (ELN) capability of the NuGenesis Lab Management System, allows laboratories to document observations, control procedures, exchange information with other software solutions, and easily find and collaborate on scientific results. NuGenesis ELN optimizes the utilization of information collected and cataloged by NuGenesis SDMS. With an open and flexible design, NuGenesis ELN easily adapts to fit and support existing free-flow or prescribed workflows in your lab.

The AISE TensorFlow 1.10 Python 2.7 CUDA 9.2 Notebook is a pre-configured, fully integrated runtime environment designed for deep learning applications. It combines TensorFlow 1.10, Python 2.7, and NVIDIA CUDA 9.2, providing a seamless platform for developing and deploying machine learning models. This environment is optimized for high-performance execution, enabling efficient training and inference processes. Key Features and Functionality: - TensorFlow 1.10 Integration: Leverages the capabilities of TensorFlow 1.10 for building and training machine learning models. - Python 2.7 Support: Ensures compatibility with legacy Python codebases and libraries. - CUDA 9.2 Optimization: Utilizes NVIDIA's CUDA 9.2 to accelerate computations on compatible GPUs, enhancing performance for complex models. - Pre-Configured Environment: Includes essential tools and libraries, reducing setup time and potential configuration issues. - Jupyter Notebook Integration: Provides an interactive interface for coding, visualization, and debugging, streamlining the development workflow. Primary Value and User Solutions: This notebook environment addresses the challenges of setting up and configuring deep learning frameworks by offering a ready-to-use platform. Users can focus on developing and refining their models without the overhead of environment setup. The integration of CUDA 9.2 ensures that computations are efficiently offloaded to GPUs, significantly reducing training times. This solution is particularly beneficial for data scientists and machine learning practitioners seeking a reliable and performance-optimized environment for their projects.

The AISE PyTorch 0.4 Python 3.6 CUDA 9.1 Notebook is a pre-configured, fully integrated runtime environment designed for machine learning and data science applications. It combines PyTorch 0.4, an open-source machine learning library, with Python 3.6 and Jupyter Notebook, providing a seamless platform for developing and deploying deep learning models. Optimized for NVIDIA GPUs, this environment leverages CUDA 9.1 to accelerate computations, making it ideal for both training and inference tasks. The stack also includes development tools such as a C compiler and make, facilitating a comprehensive development experience. Key Features and Functionality: - Integrated Environment: Combines PyTorch 0.4, Python 3.6, and Jupyter Notebook for a cohesive development experience. - GPU Optimization: Utilizes CUDA 9.1 to harness NVIDIA GPU capabilities, enhancing performance for intensive computations. - Development Tools: Includes essential tools like a C compiler and make for building and compiling code. - Versatile Deployment: Can be installed on various platforms, including Linux servers, virtual machines, Docker containers, and cloud instances. Primary Value and User Solutions: This product addresses the complexities of setting up a machine learning environment by offering a ready-to-use, optimized stack. Users can focus on developing and deploying models without the overhead of configuring software dependencies. The GPU optimization ensures faster execution of deep learning tasks, making it suitable for researchers, data scientists, and developers aiming to accelerate their machine learning workflows.

The AISE TensorFlow 1.10 Python 3.6 CUDA 9.2 Notebook is a pre-configured, fully integrated runtime environment designed for machine learning and deep learning applications. It combines TensorFlow 1.10, Python 3.6, and CUDA 9.2, providing a robust platform for developing and deploying complex models. This environment is optimized for high-performance execution, ensuring efficient training and inference processes. Key Features and Functionality: - TensorFlow 1.10 Integration: Offers the capabilities of TensorFlow 1.10 for building and training machine learning models. - Python 3.6 Support: Utilizes Python 3.6, ensuring compatibility with a wide range of libraries and tools. - CUDA 9.2 Compatibility: Leverages CUDA 9.2 to accelerate computations on NVIDIA GPUs, enhancing performance for deep learning tasks. - Jupyter Notebook Interface: Includes Jupyter Notebook, providing an interactive environment for code development and visualization. - Pre-installed Libraries: Comes with essential libraries such as Keras and cuDNN, facilitating seamless development and deployment of neural networks. Primary Value and Problem Solved: This notebook environment addresses the challenges of setting up and configuring a deep learning workspace. By offering a ready-to-use platform with integrated tools and libraries, it allows data scientists and developers to focus on model development and experimentation without the overhead of environment setup. The inclusion of CUDA 9.2 ensures that users can fully exploit GPU acceleration, leading to faster training times and more efficient workflows.

Apache Zeppelin is an open-source tool that equips you with a web-based notebook that can be used for machine learning operations, interactive data analytics, visualization, and exploration. Vibrant designs and pictures generated can save time for users in the identification of key trends in data and ultimately accelerates the decision-making processes. This offer not only contains different pre-installed interpreters but also allows you to plug in your own various language backends for desirability. Apache Zeppelin supports many data sources which allow you to synthesize your data to visualize into interactive plots and charts. You can also create dynamic forms in your notebook and can share your notebook with collaborators. Note: You’ll have to sign up to Azure, for free, if you do not have an existing account.