Alternatives to Visdom

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

Amazon SageMaker is a comprehensive machine learning platform that integrates powerful tools for model building, training, and deployment in one cohesive environment. It combines data processing, AI model development, and collaboration features, allowing teams to streamline the development of custom AI applications. With SageMaker, users can easily access data stored across Amazon S3 data lakes and Amazon Redshift data warehouses, facilitating faster insights and AI model development. It also supports generative AI use cases, enabling users to develop and scale applications with cutting-edge AI technologies. The platform’s governance and security features ensure that data and models are handled with precision and compliance throughout the entire ML lifecycle. Furthermore, SageMaker provides a unified development studio for real-time collaboration, speeding up data discovery and model deployment.

C++Builder is an integrated design and development tool for modern apps. Are you ready to create the best UIs in your life? Our VCL framework for Windows (FMX) visual framework to create cross-platform user interfaces provides the foundation for beautiful, intuitive user interfaces that work on all platforms: Windows, macOS and iOS. You can design your master UI layout once and then easily modify platform- or device-specific views without having to duplicate your design efforts. Use our visual designer to drag-and-drop visual or non-visual components. The LiveBindings Designer allows you to visually connect user interface elements with data sources. Live On-Device Preview allows you to validate your design and broadcast it to multiple devices simultaneously. Responsive design can be added to desktop, tablet, and smartphone devices with resolution-aware components. For a better user experience, use native controls that are platform-specific.

TensorBoard serves as a robust visualization platform within TensorFlow, specifically crafted to aid in the experimentation process of machine learning. It allows users to monitor and illustrate various metrics, such as loss and accuracy, while also offering insights into the model architecture through visual representations of its operations and layers. Users can observe the evolution of weights, biases, and other tensors via histograms over time, and it also allows for the projection of embeddings into a more manageable lower-dimensional space, along with the capability to display various forms of data, including images, text, and audio. Beyond these visualization features, TensorBoard includes profiling tools that help streamline and enhance the performance of TensorFlow applications. Collectively, these functionalities equip practitioners with essential tools for understanding, troubleshooting, and refining their TensorFlow projects, ultimately improving the efficiency of the machine learning process. In the realm of machine learning, accurate measurement is crucial for enhancement, and TensorBoard fulfills this need by supplying the necessary metrics and visual insights throughout the workflow. This platform not only tracks various experimental metrics but also facilitates the visualization of complex model structures and the dimensionality reduction of embeddings, reinforcing its importance in the machine learning toolkit.

TensorFlow is a comprehensive open-source machine learning platform that covers the entire process from development to deployment. This platform boasts a rich and adaptable ecosystem featuring various tools, libraries, and community resources, empowering researchers to advance the field of machine learning while allowing developers to create and implement ML-powered applications with ease. With intuitive high-level APIs like Keras and support for eager execution, users can effortlessly build and refine ML models, facilitating quick iterations and simplifying debugging. The flexibility of TensorFlow allows for seamless training and deployment of models across various environments, whether in the cloud, on-premises, within browsers, or directly on devices, regardless of the programming language utilized. Its straightforward and versatile architecture supports the transformation of innovative ideas into practical code, enabling the development of cutting-edge models that can be published swiftly. Overall, TensorFlow provides a powerful framework that encourages experimentation and accelerates the machine learning process.

With Determined, you can engage in distributed training without needing to modify your model code, as it efficiently manages the provisioning of machines, networking, data loading, and fault tolerance. Our open-source deep learning platform significantly reduces training times to mere hours or minutes, eliminating the lengthy process of days or weeks. Gone are the days of tedious tasks like manual hyperparameter tuning, re-running failed jobs, and the constant concern over hardware resources. Our advanced distributed training solution not only surpasses industry benchmarks but also requires no adjustments to your existing code and seamlessly integrates with our cutting-edge training platform. Additionally, Determined features built-in experiment tracking and visualization that automatically logs metrics, making your machine learning projects reproducible and fostering greater collaboration within your team. This enables researchers to build upon each other's work and drive innovation in their respective fields, freeing them from the stress of managing errors and infrastructure. Ultimately, this streamlined approach empowers teams to focus on what they do best—creating and refining their models.

Neptune.ai serves as a robust platform for machine learning operations (MLOps), aimed at simplifying the management of experiment tracking, organization, and sharing within the model-building process. It offers a thorough environment for data scientists and machine learning engineers to log data, visualize outcomes, and compare various model training sessions, datasets, hyperparameters, and performance metrics in real-time. Seamlessly integrating with widely-used machine learning libraries, Neptune.ai allows teams to effectively oversee both their research and production processes. Its features promote collaboration, version control, and reproducibility of experiments, ultimately boosting productivity and ensuring that machine learning initiatives are transparent and thoroughly documented throughout their entire lifecycle. This platform not only enhances team efficiency but also provides a structured approach to managing complex machine learning workflows.

MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models.

Utilize Weights & Biases (WandB) for experiment tracking, hyperparameter tuning, and versioning of both models and datasets. With just five lines of code, you can efficiently monitor, compare, and visualize your machine learning experiments. Simply enhance your script with a few additional lines, and each time you create a new model version, a fresh experiment will appear in real-time on your dashboard. Leverage our highly scalable hyperparameter optimization tool to enhance your models' performance. Sweeps are designed to be quick, easy to set up, and seamlessly integrate into your current infrastructure for model execution. Capture every aspect of your comprehensive machine learning pipeline, encompassing data preparation, versioning, training, and evaluation, making it incredibly straightforward to share updates on your projects. Implementing experiment logging is a breeze; just add a few lines to your existing script and begin recording your results. Our streamlined integration is compatible with any Python codebase, ensuring a smooth experience for developers. Additionally, W&B Weave empowers developers to confidently create and refine their AI applications through enhanced support and resources.

DagsHub serves as a collaborative platform tailored for data scientists and machine learning practitioners to effectively oversee and optimize their projects. By merging code, datasets, experiments, and models within a cohesive workspace, it promotes enhanced project management and teamwork among users. Its standout features comprise dataset oversight, experiment tracking, a model registry, and the lineage of both data and models, all offered through an intuitive user interface. Furthermore, DagsHub allows for smooth integration with widely-used MLOps tools, which enables users to incorporate their established workflows seamlessly. By acting as a centralized repository for all project elements, DagsHub fosters greater transparency, reproducibility, and efficiency throughout the machine learning development lifecycle. This platform is particularly beneficial for AI and ML developers who need to manage and collaborate on various aspects of their projects, including data, models, and experiments, alongside their coding efforts. Notably, DagsHub is specifically designed to handle unstructured data types, such as text, images, audio, medical imaging, and binary files, making it a versatile tool for diverse applications. In summary, DagsHub is an all-encompassing solution that not only simplifies the management of projects but also enhances collaboration among team members working across different domains.

Guild AI serves as an open-source toolkit for tracking experiments, crafted to introduce systematic oversight into machine learning processes, thereby allowing users to enhance model creation speed and quality. By automatically documenting every facet of training sessions as distinct experiments, it promotes thorough tracking and evaluation. Users can conduct comparisons and analyses of different runs, which aids in refining their understanding and progressively enhancing their models. The toolkit also streamlines hyperparameter tuning via advanced algorithms that are executed through simple commands, doing away with the necessity for intricate trial setups. Furthermore, it facilitates the automation of workflows, which not only speeds up development but also minimizes errors while yielding quantifiable outcomes. Guild AI is versatile, functioning on all major operating systems and integrating effortlessly with pre-existing software engineering tools. In addition to this, it offers support for a range of remote storage solutions, such as Amazon S3, Google Cloud Storage, Azure Blob Storage, and SSH servers, making it a highly adaptable choice for developers. This flexibility ensures that users can tailor their workflows to fit their specific needs, further enhancing the toolkit’s utility in diverse machine learning environments.

Aim captures all your AI-related metadata, including experiments and prompts, and offers a user interface for comparison and observation, as well as a software development kit for programmatic queries. This open-source, self-hosted tool is specifically designed to manage hundreds of thousands of tracked metadata sequences efficiently. Notably, Aim excels in two prominent areas of AI metadata applications: experiment tracking and prompt engineering. Additionally, Aim features a sleek and efficient user interface that allows users to explore and compare different training runs and prompt sessions seamlessly. This capability enhances the overall workflow and provides valuable insights into the AI development process.

Data Version Control (DVC) is an open-source system specifically designed for managing version control in data science and machine learning initiatives. It provides a Git-like interface that allows users to systematically organize data, models, and experiments, making it easier to oversee and version various types of files such as images, audio, video, and text. This system helps structure the machine learning modeling process into a reproducible workflow, ensuring consistency in experimentation. DVC's integration with existing software engineering tools is seamless, empowering teams to articulate every facet of their machine learning projects through human-readable metafiles that detail data and model versions, pipelines, and experiments. This methodology promotes adherence to best practices and the use of well-established engineering tools, thus bridging the gap between the realms of data science and software development. By utilizing Git, DVC facilitates the versioning and sharing of complete machine learning projects, encompassing source code, configurations, parameters, metrics, data assets, and processes by committing the DVC metafiles as placeholders. Furthermore, its user-friendly approach encourages collaboration among team members, enhancing productivity and innovation within projects.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

Amazon SageMaker equips users with an extensive suite of tools and libraries essential for developing machine learning models, emphasizing an iterative approach to experimenting with various algorithms and assessing their performance to identify the optimal solution for specific needs. Within SageMaker, you can select from a diverse range of algorithms, including more than 15 that are specifically designed and enhanced for the platform, as well as access over 150 pre-existing models from well-known model repositories with just a few clicks. Additionally, SageMaker includes a wide array of model-building resources, such as Amazon SageMaker Studio Notebooks and RStudio, which allow you to execute machine learning models on a smaller scale to evaluate outcomes and generate performance reports, facilitating the creation of high-quality prototypes. The integration of Amazon SageMaker Studio Notebooks accelerates the model development process and fosters collaboration among team members. These notebooks offer one-click access to Jupyter environments, enabling you to begin working almost immediately, and they also feature functionality for easy sharing of your work with others. Furthermore, the platform's overall design encourages continuous improvement and innovation in machine learning projects.

A comprehensive platform designed for reproducible and scalable applications in Machine Learning and Deep Learning. Explore the array of features and products that support the leading platform for managing data science workflows today. Polyaxon offers an engaging workspace equipped with notebooks, tensorboards, visualizations, and dashboards. It facilitates team collaboration, allowing members to share, compare, and analyze experiments and their outcomes effortlessly. With built-in version control, you can achieve reproducible results for both code and experiments. Polyaxon can be deployed in various environments, whether in the cloud, on-premises, or in hybrid setups, ranging from a single laptop to container management systems or Kubernetes. Additionally, you can easily adjust resources by spinning up or down, increasing the number of nodes, adding GPUs, and expanding storage capabilities as needed. This flexibility ensures that your data science projects can scale effectively to meet growing demands.

Keepsake is a Python library that is open-source and specifically designed for managing version control in machine learning experiments and models. It allows users to automatically monitor various aspects such as code, hyperparameters, training datasets, model weights, performance metrics, and Python dependencies, ensuring comprehensive documentation and reproducibility of the entire machine learning process. By requiring only minimal code changes, Keepsake easily integrates into existing workflows, permitting users to maintain their usual training routines while it automatically archives code and model weights to storage solutions like Amazon S3 or Google Cloud Storage. This capability simplifies the process of retrieving code and weights from previous checkpoints, which is beneficial for re-training or deploying models. Furthermore, Keepsake is compatible with a range of machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, enabling efficient saving of files and dictionaries. In addition to these features, it provides tools for experiment comparison, allowing users to assess variations in parameters, metrics, and dependencies across different experiments, enhancing the overall analysis and optimization of machine learning projects. Overall, Keepsake streamlines the experimentation process, making it easier for practitioners to manage and evolve their machine learning workflows effectively.

Streamline the entire machine learning lifecycle from start to finish. Equip developers and data scientists with an extensive array of efficient tools for swiftly building, training, and deploying machine learning models. Enhance the speed of market readiness and promote collaboration among teams through leading-edge MLOps—akin to DevOps but tailored for machine learning. Drive innovation within a secure, reliable platform that prioritizes responsible AI practices. Cater to users of all expertise levels with options for both code-centric and drag-and-drop interfaces, along with automated machine learning features. Implement comprehensive MLOps functionalities that seamlessly align with existing DevOps workflows, facilitating the management of the entire machine learning lifecycle. Emphasize responsible AI by providing insights into model interpretability and fairness, securing data through differential privacy and confidential computing, and maintaining control over the machine learning lifecycle with audit trails and datasheets. Additionally, ensure exceptional compatibility with top open-source frameworks and programming languages such as MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R, thus broadening accessibility and usability for diverse projects. By fostering an environment that promotes collaboration and innovation, teams can achieve remarkable advancements in their machine learning endeavors.

The world's leading Free Open Source survey software. LimeSurvey Cloud - LimeSurvey can be used in the cloud. LimeSurvey is automatically updated and installed by us. In accordance with data privacy regulations, we will install and update LimeSurvey for you at one of our highly accessible data centers. LimeSurvey Cloud also offers exclusive features. LimeSurvey Community edition: LimeSurvey Community edition allows you to run any research project as easily as possible. You can create complex research designs and share surveys, tests, and measurement instruments with other researchers in the open LSS format file format. You can also make your validated questionnaires accessible to the scientific community.

AI engineering can be transparent rather than opaque. With a suite of tools for tracing, assessment, prompt management, and more, HoneyHive emerges as a comprehensive platform for AI observability and evaluation, aimed at helping teams create dependable generative AI applications. This platform equips users with resources for model evaluation, testing, and monitoring, promoting effective collaboration among engineers, product managers, and domain specialists. By measuring quality across extensive test suites, teams can pinpoint enhancements and regressions throughout the development process. Furthermore, it allows for the tracking of usage, feedback, and quality on a large scale, which aids in swiftly identifying problems and fostering ongoing improvements. HoneyHive is designed to seamlessly integrate with various model providers and frameworks, offering the necessary flexibility and scalability to accommodate a wide range of organizational requirements. This makes it an ideal solution for teams focused on maintaining the quality and performance of their AI agents, delivering a holistic platform for evaluation, monitoring, and prompt management, ultimately enhancing the overall effectiveness of AI initiatives. As organizations increasingly rely on AI, tools like HoneyHive become essential for ensuring robust performance and reliability.

The Suite is designed to be both secure and scalable, featuring a contemporary and user-friendly interface that empowers scientists to have full control over the configuration of workflows for various techniques, modalities, and data types. The latest iteration, Signals VitroVivo 3.0, formerly known as Signal’s Screening, effectively converts raw data into practical insights, while Signals Inventa 3.0, previously called Signals Lead Discovery, serves as advanced analytics software that allows researchers to publish results effortlessly from diverse data sources. Additionally, it facilitates the capture of experimental data, oversees materials management, and streamlines collaboration workflows within an easy-to-navigate cloud-based electronic notebook. With adaptable visualizations and the ability to automate instrument data processing, the Suite enhances data quality and ensures reproducibility in research. It also provides a unified data management system for scientific outcomes, enriched with dynamic, interactive analytics. Furthermore, its extensibility allows for seamless integration with internal systems and partner processes, making it an invaluable tool for scientific discovery and innovation.

BIOVIA solutions foster an unparalleled environment for scientific management, enabling organizations focused on science to develop and interlink innovations in biology, chemistry, and materials to enhance our quality of life. The leading BIOVIA portfolio emphasizes the seamless integration of diverse scientific disciplines, experimental workflows, and information needs throughout the entire spectrum of research, development, quality assurance, quality control, and manufacturing. It boasts capabilities spanning Scientific Informatics, Molecular Modeling and Simulation, Data Science, Laboratory Informatics, Formulation Design, BioPharma Quality and Compliance, as well as Manufacturing Analytics. BIOVIA is dedicated to accelerating innovation, boosting productivity, enhancing quality and compliance, lowering costs, and expediting product development for clients across various sectors. Additionally, it plays a crucial role in managing and unifying scientific innovation processes and information throughout the entire product lifecycle, ensuring a comprehensive approach to scientific advancement.

BIOVIA COSMO-RS serves as an extensive toolkit for modeling and forecasting fluid phase characteristics, which empowers professionals such as chemical engineers, chemists, formulation experts, and materials scientists to innovate and develop solutions more rapidly and effectively than relying solely on traditional testing and experimentation methods, thereby enhancing innovation while minimizing costs. The simulations conducted using COSMO-RS are grounded in a solid scientific framework, guaranteeing dependable predictions across the entire spectrum of liquid-state chemistry. By employing a first-principle methodology, it is capable of forecasting the properties of new compounds that have yet to be synthesized, pushing the boundaries of known chemical territory. The team behind BIOVIA consists of the original creators of COSMO-RS, providing prompt assistance and unparalleled expertise to tackle even the most complex challenges in solution thermodynamics. Additionally, the primary advantages encompass a solid scientific basis that merges quantum chemistry with thermodynamics to assure both accuracy and reliability, fostering a deeper understanding of fluid behaviors in various contexts. This comprehensive approach not only streamlines the research process but also opens up new avenues for exploration within the field.

Bokeh simplifies the creation of standard visualizations while also accommodating unique or specialized scenarios. It allows users to publish plots, dashboards, and applications seamlessly on web pages or within Jupyter notebooks. The Python ecosystem boasts a remarkable collection of robust analytical libraries such as NumPy, Scipy, Pandas, Dask, Scikit-Learn, and OpenCV. With its extensive selection of widgets, plotting tools, and user interface events that can initiate genuine Python callbacks, the Bokeh server serves as a vital link, enabling the integration of these libraries into dynamic, interactive visualizations accessible via the browser. Additionally, Microscopium, a project supported by researchers at Monash University, empowers scientists to uncover new functions of genes or drugs through the exploration of extensive image datasets facilitated by Bokeh’s interactive capabilities. Another useful tool, Panel, which is developed by Anaconda, enhances data presentation by leveraging the Bokeh server. It streamlines the creation of custom interactive web applications and dashboards by linking user-defined widgets to a variety of elements, including plots, images, tables, and textual information, thus broadening the scope of data interaction possibilities. This combination of tools fosters a rich environment for data analysis and visualization, making it easier for researchers and developers to share their insights.

Cease the struggle with cloud infrastructure and unreliable informatics tools; begin uncovering biological insights immediately. The scientific exploration process is hindered by the disjointed nature of tools utilized by biology and bioinformatics teams. To address this issue, we developed a unified bioinformatics platform that bridges the gap between wet lab and dry lab operations in the cloud, enabling teams to expedite their research and development efforts. You can easily import raw data from your cloud, your service provider, or your team's instruments with minimal hassle. Create and implement tailored bioinformatics workflows in various programming languages without the frustration of complex infrastructure management. Effortlessly execute any workflow while maintaining a comprehensive log of every analysis performed. Our platform features ready-to-use interactive visualizations for NGS data that allow you to create point-and-click plots with ease. Additionally, Latch seamlessly integrates with your organization’s AWS S3, granting access to hundreds of terabytes of data within a user-friendly organic filesystem. You can define bioinformatics workflows and dynamically generate no-code interfaces using Python, with adjustable compute and storage options to suit your needs. This innovative approach not only streamlines the research process but also fosters collaboration among teams, ultimately leading to more impactful scientific discoveries.

An integrated platform that bridges scientific reasoning, compound development, and computational tools is now available. Chemaxon’s Design Hub supports medicinal chemistry by facilitating the analysis and prioritization of innovative ideas. This unified platform allows users to design compounds and manage concepts seamlessly. Transitioning from traditional PowerPoint presentations to dynamic, visually engaging, and chemically searchable hypotheses enhances the compound design workflow. Users can conveniently utilize established physicochemical properties, computational models, novelty considerations, or accessible compound catalogs within an interactive visual setting. Collaborate with your Contract Research Organizations (CROs) in the compound development journey through this secure online resource. Furthermore, assess the gathered data from biological assays or experimental structural insights to derive Structure-Activity Relationships (SAR) and generate fresh hypotheses for subsequent optimization cycles. Your scientific ideas can be conveniently organized in a “designer's electronic lab notebook,” which features chemically aware drawing tools that streamline the design process. This comprehensive approach ensures that every step of compound development is efficient and well-documented, paving the way for future innovations.

Discover your new go-to browser that offers cutting-edge features, impressive speed, and essential development tools for creating on the open web. It includes the latest beta developer tools and innovative additions such as a multi-line console editor and a WebSocket inspector. You can easily run this alongside the standard or beta versions of Firefox, thanks to a distinct profile and path. Tailored specifically for web developers, it comes with default settings for browser and remote debugging, a dark theme, and a handy developer toolbar button. Notably, Firefox DevTools now highlights CSS rules that do not apply to the page, and hovering over the info icon reveals insightful messages about the non-functional CSS, along with suggestions for resolution. The newly upgraded Firefox DevTools are not only powerful and flexible but also customizable to suit your needs. Among its standout features is a top-tier JavaScript debugger capable of targeting multiple browsers, all built with React and Redux, making it an ideal choice for developers. With these enhancements, developing on the web has never been more accessible or efficient.

Tomviz is a versatile open-source application that operates across different platforms, specifically tailored for processing, visualizing, and analyzing 3D tomographic datasets, with an emphasis on electron tomography. Its user-friendly graphical interface empowers users to portray objects in various forms, such as shaded contours or volumetric projections, which enhances the exploration and examination of extensive 3D tomograms. The software allows simultaneous handling of multiple datasets, offering customizable colormaps and visualization options for tasks like rotation, slicing, animation, and exporting visual content as images or videos. Users can engage in sophisticated data analysis using tools like histograms, multicorrelative statistics, various filtering options, and personalized Python scripts. Furthermore, Tomviz supports the reconstruction of tomographic data from experimental sources and includes an extensive array of Python tools aimed at 3D analysis to facilitate the implementation of custom algorithms. This powerful platform is designed to work seamlessly on 64-bit versions of Windows, macOS, and Linux operating systems, making it accessible for a wide range of users and applications. Overall, Tomviz stands out as a comprehensive solution for anyone involved in the field of electron tomography and 3D data analysis.

Microsoft Discovery is an advanced AI-powered platform designed to accelerate scientific discovery by enabling researchers to collaborate with a team of specialized AI agents. This platform leverages a graph-based knowledge engine that connects diverse scientific data, allowing for deep, contextual reasoning over complex and often contradictory theories. Researchers can customize AI agents to align with their specific domains and tasks, making it easier to manage and orchestrate research efforts. Built on Microsoft Azure, Discovery ensures a high level of trust, transparency, and compliance, offering an enterprise-ready solution. The platform has already been used to accelerate the development of a novel coolant for data centers, cutting the discovery time from months to just 200 hours. This demonstrates the transformative potential of AI in R&D, providing researchers with the tools to unlock new possibilities and innovations at scale.

Walling serves as a dynamic visual platform for organizing your thoughts, managing tasks, and gaining an overarching view of your projects through collaborative visual walls. The engaging visual format of these walls enhances clarity and offers insight into both your ideas and project specifics. Designed for your concepts, initiatives, and research, Walling enables you to juxtapose your thoughts, allowing for a broader perspective on your work. You can maintain an organized collection of bookmarks on a wall and easily design stunning mood boards. By gathering bookmarks and crafting mood boards, you can visualize the complete context of your projects, plans, tasks, research, creative briefs, and more, all neatly arranged in one location. Walling facilitates a streamlined approach to working through tasks, plans, and research, eliminating the need for excessive clicks to access your ideas. Additionally, you have the option to invite your team or clients to collaborate on your walls in real time, where they can contribute their own insights and provide feedback, or you can share a public link to your wall for broader access. This collaborative feature not only enhances teamwork but also enriches the creative process, making it easier to bring ideas to fruition.

The MPLAB® X Integrated Development Environment (IDE) is a versatile and customizable software solution that integrates robust tools designed to assist you in exploring, configuring, developing, debugging, and validating embedded designs for a wide range of microcontrollers and digital signal controllers. It operates flawlessly within the MPLAB development ecosystem, which includes numerous software and tools, many of which are available at no cost. There's no need for additional visualization tools, as real-time data can be monitored through the Data Visualizer. The I/O View feature allows for quick verification and manipulation of pin states, facilitating efficient hardware validation. Furthermore, it streamlines your workflow by automatically providing convenient access to software libraries, datasheets, and user guides. Additionally, the MPLAB Integrated Programming Environment (IPE) is included for high-level production programming, ensuring that you have all the essential resources at your fingertips. With its extensive array of features, MPLAB X IDE significantly enhances your ability to debug projects effectively, ultimately reducing your overall development time. This makes it an invaluable asset for engineers working on embedded systems development.

Explore innovative strategies for overseeing your projects and data management. Whether your organization is small, medium, or large, Laby seamlessly integrates with your specific structure and expertise. Become part of our user community and collaborate with us to shape future advancements. Effectively structure your projects, experimental protocols, and academic papers. Create detailed recipes for producing finished goods. Streamline item management and ensure traceability with automation. Oversee laboratory equipment and handle their upkeep efficiently. Laby offers a unified platform for all your documents, facilitating straightforward access and retrieval. Prepare and approve quality assurance documents prior to their release. Easily search, share, and review all relevant scientific literature. Generate reports compliant with 21 CFR part 11 and EIDAS regulations. Monitor time spent on each project and export your findings as needed. This comprehensive management system ensures a high level of oversight and efficiency. Additionally, receive training to help you transition smoothly and maximize your productivity every day. Engage with others in the community to exchange ideas and enhance your experience.

ProtoSphere is an innovative business collaboration platform that enables organizations to swiftly establish a 3D immersive space tailored for various applications such as e-learning, virtual meetings, conferences, scientific research, and collaborative efforts in sales and manufacturing that require advanced data visualization. This software facilitates global teams to seamlessly connect their top talent and pertinent information, empowering them to make informed business decisions anytime and from anywhere. It provides all the essential components needed to build your custom 3D virtual environment, including server and client software, comprehensive management and administration tools, as well as user-friendly tools for integrating content and data feeds into the virtual space. Additionally, ProtoSphere is designed to operate on the widely used Microsoft Windows server and desktop platforms, ensuring compatibility and ease of use for a broad range of users. By leveraging these capabilities, businesses can enhance their collaborative efforts and drive productivity in a dynamic virtual setting.

NuVerve is a collaborative SaaS that offers Scientific Data Intelligence for scientists. It provides a set easy-to-use features to structure data, create custom analyses, and automatically generate reports. NuVerve, priced at $75/month, is available to all scientists.

Plot.ai is a sophisticated mapping tool powered by artificial intelligence, specifically tailored to aid real estate developers in the strategic planning of new construction initiatives by delivering valuable insights on the best locations, suitable building types, and effective pricing tactics informed by both current market dynamics and historical patterns. The platform is equipped with functionalities such as area analysis, predictive forecasting, and project evaluations, allowing users to delve into the historical development trends of a region to enhance their pricing forecasts and keep track of rival activities to secure a competitive advantage in their sales strategies. It boasts an array of layered data, including points of interest, ambient noise levels, transportation access, educational institutions, and more, which collectively enrich users’ understanding of their targeted investment locales. Furthermore, Plot.ai offers comprehensive heatmaps that illustrate past, present, and anticipated trends, encompassing metrics like sales performance, square meter pricing, and population density. Users also have the capability to assess ongoing and upcoming construction projects within their vicinity, gain access to exhaustive unit inventories, and visualize data trends over time to make informed decisions. By leveraging these features, developers can enhance their project planning and execution, ultimately leading to more successful real estate ventures.

ParaView is a versatile, open-source application designed for data analysis and visualization across multiple platforms, allowing users to create visual representations for both qualitative and quantitative data analysis. It features interactive 3D exploration capabilities alongside programmatic data processing through its batch processing functionality. Engineered to manage extremely large datasets, ParaView leverages distributed memory computing resources, making it ideal for use on supercomputers that process terascale data as well as on laptops for smaller datasets. The application is built with a client-server architecture, enabling remote visualization of data while generating level-of-detail models to ensure smooth interactive performance even with extensive data. Its extensible framework is grounded in open standards, promoting customization and integration with existing tools and workflows. ParaView supports a wide array of well-known file formats and boasts over 200 filters and tools for effective data processing and visualization. This extensive feature set allows users to tailor their analysis experiences to meet specific needs and enhances collaboration in data-intensive projects.

Streamline your data collection methods by establishing standardized operational procedures. Combine manual lab techniques with automated instruments to enhance efficiency. Facilitate smoother collaborations by providing your partners and service providers with the necessary data to perform their tasks effectively. Boost the consistency of your processes through comprehensive standardization. Assess reproducibility by analyzing multiple iterations of identical operations. Amplify the statistical significance of your experiments by generating additional data points and minimizing errors in experimentation. This heightened statistical power distinguishes robust scientific findings from mere anecdotal evidence. Solid scientific outcomes contribute to stronger patent submissions, more impactful publications, and faster innovation of superior products. Utilize the lab's dashboard to monitor ongoing projects, manage your to-do list, and prioritize outstanding requests, ensuring a well-organized workflow. Ultimately, adopting these strategies will not only enhance productivity but also foster a culture of continuous improvement within the lab environment.

TotalView debugging software offers essential tools designed to expedite the debugging, analysis, and scaling of high-performance computing (HPC) applications. This software adeptly handles highly dynamic, parallel, and multicore applications that can operate on a wide range of hardware, from personal computers to powerful supercomputers. By utilizing TotalView, developers can enhance the efficiency of HPC development, improve the quality of their code, and reduce the time needed to bring products to market through its advanced capabilities for rapid fault isolation, superior memory optimization, and dynamic visualization. It allows users to debug thousands of threads and processes simultaneously, making it an ideal solution for multicore and parallel computing environments. TotalView equips developers with an unparalleled set of tools that provide detailed control over thread execution and processes, while also offering extensive insights into program states and data, ensuring a smoother debugging experience. With these comprehensive features, TotalView stands out as a vital resource for those engaged in high-performance computing.

Streamline the entire selection process for reagents and model systems to eliminate costly inefficiencies and errors that lead to experimental failures. Accelerate project timelines by facilitating the selection of reagents and model systems in a mere 30 seconds, compared to the traditional 12-week duration. This transformation can significantly cut the hard costs associated with consumables, saving organizations millions annually. By restoring valuable research time to scientists, you can enhance the organization’s mission. Experience tangible business benefits from AI through a proven, ready-to-use application. More than 41,200 scientists across 15 of the leading 20 pharmaceutical companies, as well as over 4,450 academic institutions, leverage BenchSci’s AI-Assisted Antibody Selection to design more effective experiments, resulting in documented savings of millions per year in hard costs alone. However, it's important to note that antibodies account for only 40-50% of reagent-related failures. Access a comprehensive array of experimental evidence, along with catalog data for reagents and model systems, all within one user-friendly interface. This platform integrates real-world experiment data sourced from 11.2 million scientific publications, including those published in closed-access journals, providing an unparalleled resource for researchers. With this level of detailed information, scientists can make informed decisions that significantly enhance their research outcomes.

ENVI stands as the leading choice in the realm of image processing and analysis, utilized by image analysts, GIS experts, and researchers to derive accurate, timely, and dependable insights from geospatial imagery. Its scientific validation, user-friendly interface, and seamless integration with Esri’s ArcGIS platform have made it a favored tool in the industry. For over thirty years, ENVI has consistently embraced innovation, accommodating a diverse array of data types such as multispectral, hyperspectral, thermal, LiDAR, and SAR. The platform democratizes deep learning by providing intuitive tools and workflows that do not necessitate programming expertise. Additionally, ENVI offers customization options through an API and visual programming environment, allowing users to tailor the software to their specific project needs. By employing scientifically validated analytics, ENVI ensures the delivery of professional-grade results. Organizations opt for ENVI due to its ability to fit seamlessly into existing workflows, compatibility with the most prevalent sensors today, and the flexibility to adapt to unique project specifications. Ultimately, ENVI's comprehensive capabilities make it an indispensable asset for professionals seeking to maximize the value of geospatial data.

Arm DDT stands out as the premier debugger for servers and high-performance computing (HPC) in research, industry, and educational settings, serving software engineers and scientists who work with C++, C, and Fortran in parallel and threaded environments across both CPUs and GPUs, including those from Intel and Arm. Renowned for its robust capabilities, Arm DDT excels at automatically identifying memory issues and divergent behavior, enabling users to attain exceptional performance across various scales. This versatile tool supports multiple server and HPC architectures, offering seamless cross-platform functionality. Additionally, it provides native parallel debugging for Python applications, ensuring comprehensive support for a range of programming needs. Arm DDT is distinguished by its leading memory debugging features and exceptional support for C++ and Fortran debugging, along with an offline mode that allows for non-interactive debugging sessions. It is also equipped to manage and visualize substantial data sets effectively. Available as a standalone tool or as a component of the Arm Forge debug and profile suite, Arm DDT boasts an intuitive graphical interface that simplifies the process of detecting memory bugs and divergent behaviors across diverse computational scales. This makes it an invaluable resource for engineers and researchers alike, ultimately facilitating the development of high-performance applications.

Numerical analysis, also known as scientific computing, focuses on the study of techniques for approximating solutions to mathematical challenges. Scilab features an array of graphical functions that allow users to visualize, annotate, and export data, as well as numerous options for creating and personalizing diverse plots and charts. As a high-level programming language designed for scientific applications, Scilab facilitates rapid algorithm prototyping while alleviating the burdens associated with lower-level languages like C and Fortran, where issues like memory management and variable declarations can complicate the process. With Scilab, complex mathematical computations can often be expressed in just a few lines of code, whereas other programming languages might necessitate significantly more extensive coding. Additionally, Scilab is equipped with sophisticated data structures, including polynomials, matrices, and graphic handles, and it provides a user-friendly development environment that enhances productivity and ease of use for researchers and engineers. Overall, Scilab's capabilities streamline the process of scientific computing and make it accessible to a wider audience.

Your entire team can visually design and optimize high-speed apps and sites. A platform that has been approved by developers gives whole-team autonomy. Builder's platform makes it easier than ever to create, manage, and optimize digital experiences on any website or app. Keep your favorite apps and platforms. Builder can be integrated seamlessly with any software, whether it is homegrown or third-party. Builder allows anyone to create rich digital experiences. Drag and drop to personalize, edit, and A/B-test your entire website. You can update your websites and apps instantly. Our visual editor supports page building and instantly creates a speed-optimized webpage when you hit "Publish." You can also drag and drop to add rich digital experiences directly to your site without relying on developers. Builder's visual editor gives you 100% flexibility, complete control over your layout, and no limitations.

SpectreUQ™ enhances the advanced DAKOTA software created by Sandia National Laboratories by incorporating a user-friendly wizard that assists engineers throughout the uncertainty quantification (UQ) process. The intricate details of the UQ method are managed seamlessly in the background. It employs a database to systematically organize results from both experiments and simulations, along with offering interactive visualization and graphing capabilities. Available through an annual subscription without any limits on the number of users, it also comes in source code format. Unlike many other techniques, SpectreUQ™ operates in a non-intrusive manner, allowing it to run alongside existing simulations rather than integrating directly within them. Users can leverage their own high-performance computing resources to assess numerous flight conditions and develop the built-in surrogate models. It is designed to be intuitive and user-friendly, having been refined through extensive real-world applications in UQ studies. The process of incorporating experimental data and computational fluid dynamics (CFD) results is guided by the Oberkampf-Roy method, ensuring a robust approach. Additionally, the interactive plots generated facilitate easy exploration and sharing of results, enhancing collaboration and communication among engineers. This comprehensive capability makes SpectreUQ™ a valuable tool for any engineer looking to integrate UQ into their workflow.