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provides an overview of the capabilities available on the Research Platform today, as well as a preview of the new features actively in development for the future.

Available Today

Access and Security

• Single sign-on: Log in with your existing NASA identity. No new passwords to remember.
• Strong authentication: Secure your account with multi-factor authentication (like a PIV badge or code) and passwordless sign-in.
• External collaboration: Partners

Available today

Getting in safely

• One login. You sign in with the NASA identity you already have. There is no extra platform password to remember.
• Strong login. Multi-factor and passwordless sign-in. (Multi-factor means a second proof, such as a code or a badge, on top of your password.)
• Partners can join. Collaborators from universities or other agencies can be given get access. Their institution institutions can also even connect its their own login systemsystems.
• Your data stays yours. The platform gives your notebook access to only the data Built-in access controls: Your workspace only accesses data that you and your project may are allowed to see. You do not handle any never have to manage security keys or secrets.
• Encryption. Data encryption: Your data is fully encrypted both when it is stored and when it is moving across the network.
• Web Active firewall.: A managed web application firewall filters every web request to keep the platform secure.
• A real user account. True user accounts: You get your own a unique username and your own personal file ownership, so shared files behave the way you expect.
• Reach other cloud accounts. A project admin can register a cloud role, and your notebook gets it at start-up. This lets you read data in your institution's or a mission's account.

Getting a machine that fits the work

• making file sharing work exactly as expected.
• External cloud access: Project admins can link external cloud roles. Your workspace inherits this access at startup, letting you easily read data from a mission or institution account.

Flexible Workspaces & Computing

• Instant access: Start a workspace on demand immediately—no support tickets or waiting required.
• Flexible sizing: Choose your exact compute power at startup: Standard (4 GB memory / 4 CPU), Large (8 GB / 4 CPU), Extra Large (16 GB / 4 CPU), or up to 64 GB / 8 CPU on demand.
• Pre-installed software: Jump right in with Earth science, Python, and SI tools already loaded. Advanced users can also bring their own custom container images (a packaged set of software).
• Distributed computing: Spread a single, massive analysis across multiple machines when you need more power.
• Background processing: Send long-running notebooks to process on their own. The job keeps running even if you close your browser, freeing up your active session.
• Auto-scaling capacity: The platform automatically adds and removes computing machines behind the scenes based on user demand
• Start a workspace on demand. No ticket, no waiting.
• Pick the size. From a small 4 GB workspace up to a 64 GB / 8-CPU machine, or a GPU machine. (A GPU is a chip that is fast for AI and image work.)
• Software is ready. Earth-science, Python, and AI software stacks are pre-installed. Power users can bring their own container image. (A container image is a packaged set of software.)
• Many machines for one job. Spread a large analysis over many machines when you need to.
• Background jobs. Send a long notebook to run on its own. Your session stays free, and the job keeps running after you close the browser.
• Capacity follows demand. The platform adds and removes machines automatically.

Storing the science

• Your own space. Private storage that is still there when you come back: Your own private storage where files are saved securely and are waiting for you when you return.
• Team space. A shared filesystem : A collaborative file system for your project 's team to store and share data, models, and results.
• Project object storage.: Private, large-scale cloud storage for your project , reachable from the that you can access directly from your notebook.
• Data Integrated data browser.: Browse and download files from your project storage and storage—as well as from public AWS science datasets, datasets—directly inside JupyterLab.

AI help

Integrated SI Tools

• SI AI in the notebook.: Chat with SI assistants and run agents directly inside JupyterLab, using several powered by various model providers.
• Help on one notebook or one cell. Ask for code, an explanation, or a fix in placeTargeted coding help: Ask the SI to write code, explain complex sections, or fix errors within a specific notebook or a single cell.
• Agents and a secure gateway.: Run SI agents on the platform through a single, reached through one trusted entry point. Some workflows ask a human to approve before they actFor safety, some workflows require human approval before taking action.
• The AI SI Registry , in JupyterLab. A : Access a hub of AI building blocks — NASA-validated SI building blocks—including MCP servers, skills, agents, models, prompts, and harnesses — shared by NASA colleagues and validated by NASA. Search it, deploy an item, and hand it to your AI assistant, from the AgentRegistry panel. Being rolled out to every project. See . Use the AgentRegistry panel to search, deploy, and equip your SI assistant with these tools. (Currently rolling out to all projects; see the tool reference.)
• Model endpoints on demand.: Import and call your own custom models, or a managed model from the connect to managed models, directly from your notebook.

Working

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Together

• Real-time co-editing. Several members can edit the same notebook, code file, or document at the same time, Google-Docs styleTime Co-Editing: Edit the same notebooks and files at the exact same time as your teammates. Markers show who else is online. Work lands in the shared project space., and everything saves to your shared space.
• Easy Activation: Project admins can turn this feature on instantly Your project turns it on. A project admin switches collaboration on for the project in the RPAdmin console . It is self-service. No ticket to the platform team.without contacting support.

See Working together on the same notebook.

Seeing what it costs

• Your own usage. You see your own compute and storage usage, and your remaining budget, inside JupyterLab.
• Your project's picture. Your project admin sees the whole project's spend against budget in the RPAdmin console, described next.

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Cost Visibility & Tracking

• Your Own Usage: View your personal compute/storage usage and remaining budget right inside JupyterLab.
• The Big Picture: Project admins can track the entire team's spending against the budget from the RPAdmin console.

The RPAdmin Console: Self-Service for Admins

RPAdmin is the web console for the person who runs a project managers. Everything in this list listed below is entirely self-service: the project admin does it, with no service—meaning project admins can do it instantly without submitting a ticket to the platform team.

• Track cost. Compute costs: Monitor compute hours, compute costcosts, and storage cost costs for the entire project.
• Set and share out allocate budgets. Give each member an allocation: Give individual team members specific spending limits. The platform blocks giving out prevents you from allocating more than the your project holds. (The project's total is set by the Research Platform team, after the funds reach the Science Cloud.)
• See platform usage. Who is using whatMonitor usage: See exactly which team members are using specific resources, and how much they are using.
• Manage members and roles.team access: Add members, remove members, and or promote a member members to the project admin role.
• Turn on collaboration. The project-level switch described aboveEnable collaboration: Toggle the real-time co-editing feature on for your project.
• Cross-account AWS access.: Register one of your own AWS roles , and the platform hands it so they are automatically granted to your notebooks at start-up. With it you can use any AWS service in This lets you securely interact with your own account from the Research Platform — for example, start an HPC cluster there, or read a mission bucket. (HPC means high-performance computing: a large group of machines for heavy jobs.) No access keys are copied, and no secrets sit in a notebook.AWS account (such as reading a mission data bucket or starting a high-performance computing cluster) directly from the Research Platform. No access keys or secrets are ever copied into the notebook.
• Audit trail: View a complete history of every Audit trail. Every account, membership, and budget change, with who did it and what changed.including who made the change and exactly what was altered.

For more details, see See RPAdmin — the admin console.

Coming

Future Capabilities

In Active Development

The following features are approved and currently being built. While we don't have release dates yet, they These are agreed and being worked on. No dates. They are listed in rough order of how far along they are.

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• Faster start-up. Keep launch time short even when your home : Ensuring launch times remain short, even as your personal storage space grows large.
• One-click Hugging Face models.: Deploy a public model with one a single click and call it directly from your notebook.
• Better Improved "my usageMy Usage" view. Usage : See your personal usage and remaining budget shown immediately on the launch screen as well.
• Roles in the AI Registry. PerSI Registry: Granular per-user and per-group permissions, instead of one replacing the current single access level for everyone.

Planned

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Next

• Scored AI tools. Every SI tools: Quality and safety scores for every tool in the catalog scored for quality and safety, complete with a tracked history.
• Plain-language search.: Find a modelmodels, agentagents, or tool tools by simply describing what you need in everyday language.
• Budget stop controls at launch. Show : Display a true remaining balance , and stop prevent new workspaces from starting when project funds run out. Project admins will also have the option to block members who exceed their personal limits, or allow them to continue spending from the project's money is used upgeneral project pool.
• Storage cost costs per person. See what each member's : View the exact object storage costs for each individual team member.

Being

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Explored

• Project-defined machine sizes. A project admin sets the sizes their own team can pick.: Project admins can build and maintain a custom list of server types for their specific team, rather than using a single platform-wide list.
• GPU machines: Adding machines equipped with GPUs (Graphics Processing Units), which are specialized chips that significantly speed up SI and image-processing workflows.
• Detailed session metrics: Per-session breakdowns of Session detail. Per-session CPU, memory, and storage detail, usage to help you understand a cost and size the costs and choose the perfect machine size for your next run better.
• Review before publish.: A draft-and-approve step workflow before an a new AI asset goes live in the catalog.
• More clouds in the Expanded data browser .clouds: Adding support for Google Cloud and Microsoft Azure supportdata to the data browser.

Want the engineering

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details?

The full catalog, catalog—complete with an identifier and detailed status for every feature and its status, is feature—is tracked in the Product Roadmap. This page is the serves as a plain-language summary of Part I of that filedocument.