Frequently asked questions

Scientists and researchers in life sciences and healthcare—from students to senior experts—can leverage Nextnet as their AI-powered second brain to generate insights and make informed decisions at the speed of thought.

We built Nextnet to solve a fundamental challenge in scientific research—information overload. Researchers spend countless hours sifting through PubMed, Google Scholar, bioRxiv, grant reports, dissertations, patents, policy documents, and specialized biomedical databases, only to get bogged down by irrelevant information and fragmented data. We’ve experienced this frustration firsthand, so we created the world’s largest semantic web for life sciences, breaking down data silos and making research more accessible. Powered by AI, Nextnet helps you uncover hidden connections, ask and answer mission-critical questions, visualize complex data intuitively, and collaborate seamlessly—so you can focus on groundbreaking discoveries instead of tedious searches.

Modern biomedical research is hindered by fragmented knowledge bases and a lack of semantic unity. Scientific discovery relies on the ability to integrate and contextualize evidence, yet existing systems fail to provide a cohesive framework for connecting disparate data sources. While efforts like the Unified Medical Language System (UMLS) and Medical Subject Headings (MeSH) have attempted to standardize terminology, they remain limited in scope and interoperability. Similarly, the Open Biomedical and Biological Ontology (OBO) Foundry, which houses over 500 ontologies, suffers from inconsistencies, redundancies, and poor interconnectivity—making large-scale research inefficient. The problem extends beyond semantics to the data ecosystem itself. Despite open science initiatives producing vast repositories of biomedical data, inadequate standardization and lack of cross-compatibility make it difficult to extract meaningful insights. AI and machine learning hold transformative potential for biomedical research, but their effectiveness is undermined by fragmented or incomplete datasets, leading to unreliable and biased outputs. Nextnet addresses this fundamental challenge by building a proprietary ontology—an intelligently structured, semantically unified knowledge base designed to bridge these gaps. By integrating diverse data sources into a common framework, our ontology enables seamless exploration, analysis, and discovery, empowering researchers with AI-driven insights that are both accurate and actionable.

Nextnet is an AI knowledge companion designed to help life sciences teams discover, visualize, and share research effortlessly. Our platform includes two core modules:

• Copilot: Delivers precise, evidence-backed answers instantly, so you can focus on breakthrough discoveries.
• Explorer: A dynamic visual workspace that helps you map complex ideas, uncover hidden connections, and navigate research intuitively.

You do. Nextnet does not collect, mine, or sell your data. We are not a data broker or aggregator—we focus on augmenting and empowering researchers, not monetizing personal information.

Nextnet works seamlessly on Google Chrome, Microsoft Edge, Safari, and Firefox. While we haven’t tested it on every browser, it should be compatible with most modern options. Let us know if you run into any issues—we’re happy to help!

Join the Nextnet Community on Slack to connect with other researchers, meet Nextnet staff, ask questions, and share feedback. Prefer a private conversation? Reach out to us via “Talk to us.”

To register an account at Nextnet, go to our website and click on the “Get Nextnet Free” button at on the far right of the header navigation menu. You can sign up using your email address, Google account, or Microsoft account. After choosing a sign up method, fill in the required details, such as your name and email. Depending on the method, you may need to verify your email address by entering a code sent to your inbox. Once your account is created, you can set up your profile, organization details, team spaces, and share with colleagues and guests.

Yes, you can sign up with your Google account. Coming soon, you can also use your Microsoft account from Office365 and Live.

Nextnet accounts have no password, so there is no password reset action. If you registered with a Google or Microsoft account, choose the appropriate social login Nextnet again. Otherwise, login with your email address, to recieve a one-time magic link or verification sent to your inbox.

If you need to delete your account, please reach out to the support team. Coming soon, we will include account deletion in the settings.

Nextnet accounts have no password. If you register with a social login like Google or Microsoft, access is granted to validated accounts on those platforms. If you registered with your email address only, login with a one-time magic link or verification code sent to your inbox. Nextnet never needs to store a password. By removing this point-of-attack, we have removed many risks of a compromised account.

Yes, please do! You can find our community by the link in our navigation menu under “Company.”

Currently, our support team can be reached from our “Talk to us” contact form under “Company” in the navigation menu. Or email to support@nextnetinc.com.

Our entry-level Free Plan is free and designed for individuals and small teams to begin using Nextnet. You can share your research with unlimited viewers, making collaboration seamless. Upgrade options are coming soon.

Our entry-level Free Plan is free. We are introducing subscription plans soon to support the cost of running large AI models and maintaining our data infrastructure while ensuring sustainable growth and high-quality service. Reach out to sales via “Talk to us” or “Request a demo” to learn more.

Nextnet extracts knowledge from a vast range of scientific databases, cross-referencing information at an incredibly granular level. Our proprietary Ontology unifies disparate datasets into a common language. We pull from sources such as:

• Biomedical Research: PubMed, bioRxiv, OpenAlex, CrossRef
• Genomics & Drug Discovery: ChEMBL, NCBI, Ensembl, HGNC, MarkerDB
• Medical & Clinical Data: MeSH, SNOMED-CT, OMIM, WikiPathways
• Emerging Data: We’re actively adding clinical trial data, patents, grant reports, and commercially relevant insights.

Nextnet Copilot is your AI research collaborator, purpose-built for life sciences teams. Think of it as a smarter, more specialized version of ChatGPT—combining the intelligence of ChatGPT, Perplexity, and Microsoft Copilot with evidence drawn from the world’s largest semantic knowledge web for life sciences. Copilot doesn’t just read millions of scientific documents—it extracts information from gene and protein data, pharmacological insights, and more, cross-referencing and contextualizing everything to deliver intelligent, evidence-backed insights in seconds. With every response grounded in real data, the risk of AI hallucinations is significantly reduced. Our goal is to give scientists superhuman research capabilities and accelerate the pace of discovery.

In your Copilot chat, responses are always backed by primary sources and suggested follow-up questions. To access all sources from your session, simply click the Sources icon in the upper right corner of your interface. This will open a detailed list of primary sources along with key excerpts. Need the full document? Click “View Full Text” to open the complete scientific paper and dive deeper into the research.

Nextnet is built on a custom retrieval-augmented generation (RAG) pipeline, advanced large language models (LLMs), and a powerful knowledge graph explicitly designed for life sciences. Our AI understands context, intent, and the vast landscape of scientific literature and biomedical databases, helping you retrieve relevant studies and generate meaningful insights. By selecting the right models for each task and rapidly pinpointing critical information, Nextnet acts as your most intelligent research companion. You also gain access to a rich semantic ontology developed by leading scientists, allowing you to discover deeper conceptual relationships—not just references. With Nextnet, you can explore your existing knowledge resources more efficiently and unlock new, interconnected insights with ease.

Nextnet makes collaboration effortless while keeping your research secure. You can share insights with peers, team members, or even collaborators outside your organization—engaging multiple researchers on the same data without conflicts. To share your session, simply click the three-dot menu (⋯) in the upper right corner and choose to export as a PDF or copy the session link. You can then share the PDF or link via email, Slack, or another communication channel. Your collaborators can sign up for Nextnet in seconds and immediately start engaging with your shared research. We’re continuously enhancing our collaboration features, with upcoming additions like version control, change logs, and advanced privacy and access controls, empowering scientists to build on each other’s work more effectively.

Copilot is designed to enhance scientific inquiry by making research an iterative, dynamic process. After answering your initial question, it suggests relevant follow-ups or variations, allowing you to refine your search, test new hypotheses, and uncover deeper insights. This rapid, interactive approach helps scientists analyze data more effectively, explore connections, and accelerate discoveries, ultimately leading to a clearer understanding of complex scientific phenomena.

Copilot automatically provides relevant sources alongside its responses. If you don’t see them, simply ask a follow-up question like “Can you cite the relevant sources?” within the same chat session.

Nextnet Explorer is your interactive thought partner for exploring complex, interconnected data. It visually presents information as dynamic maps, showing how concepts, topics, and data entities are related. Data entities include research institutions, authors, genes, proteins, diseases, and much more. Each data node represents an object, and the links between nodes represent data relationships. Explorer enables powerful link-based analysis and relational discovery, helping you uncover hidden insights. Soon, you’ll also be able to explore connections between concepts surfaced in your Copilot sessions, further enhancing your research experience.

A Connected Search Engine revolutionizes how teams access and utilize information by breaking down data silos, reducing app-switching, and transforming fragmented knowledge into real-time, actionable insights. With Nextnet, you can unify search across multiple applications, leverage AI-driven relevance and personalization, enhance collaboration, and seamlessly integrate with your existing tools—all in one powerful platform.

Yes, they represent the same concept. A knowledge graph is a widely recognized term in software development and bioinformatics, referring to structured data connections. However, for broader accessibility, Nextnet uses knowledge maps to emphasize connectivity, visualization, and intuitive exploration. While “graph” often brings to mind line charts or bar graphs, a knowledge map better conveys the power of discovering relationships and insights within interconnected data.

The Map View is an interactive visual workspace that helps you brainstorm, explore, and connect complex ideas in an intuitive, dynamic way. It allows you to see relationships between concepts at a glance. The List View, on the other hand, presents results in a familiar card-based format, similar to Google search results, ranking information by relevance. You can switch between these views anytime based on your preferred way of analyzing data.

Nodes in Explorer are color-coded and icon-labeled to distinguish different data types. Hover over a node to see an At-a-Glance Card, a pop-up preview with key details. To explore deeper, click “View Details”, which opens a Data Card in a side panel with multiple sub-tabs for additional insights. We’re continuously expanding these data layers to provide richer context.

We have an exciting roadmap of features rolling out in 2025 to make Nextnet even more powerful:

• AI Overview – Get a high-level summary and ranking of key concepts surfaced by Explorer, helping you quickly understand relevance before diving deeper.
• Real-time collaborative comments – Team members with access to your workspace will be able to comment directly on the Map View, enabling real-time multi-player collaboration.
• Research Article Reader – Read and annotate scientific papers within a beautifully designed in-app reader.
• Expanded Node Exploration – Perform broad semantic web searches by expanding nodes to second, third, or higher-degree connections.
• Advanced Filtering Options – Including Temporal Analysis, which allows you to explore patterns over time and trace event sequences interactively.
• Common Property Finder – Discover shared properties between nodes to spot patterns, trends, and insights across massive datasets. This will be a game-changer for finding hidden connections, analyzing and exploring trends, and combining different filters and visualizations to generate some hypotheses worth sharing.

An ontology is a structured framework that systematically maps data to meaningful semantic concepts. Think of it as the language that connects data points, defining relationships between concepts such as diseases, genes, proteins, research articles, institutions, and more. Ontologies create a shared vocabulary that unifies disparate data sources, enabling collaboration, deeper analysis, and AI-powered research. At Nextnet, we define ontology as a foundational set of building blocks that map scientific and commercial concepts to the data that describes them. By standardizing semantics and defining categories of meaning, our ontology transforms scattered information into a structured knowledge system—empowering researchers to uncover hidden connections and accelerate discovery. We will publish a detailed blog post on the Nextnet ontology soon—stay tuned!

Nextnet has ingested a catalogue of research articles and other scientific textual sources. “Catalogued Sources” represent documents that Nextnet has ingested into its own database with authorship and other data connections. Sometimes the data sources Nextnet draws from have references to sources which have not yet been catalogued. For example, a research article may have been published more recently than Nextnet’s latest catalogue update. In those instances, these are considered “External Sources” within which we show external links. These will be added to our queue for data ingestion as “Catalogued Sources” in the future.