Your research needs more than an answer that just looks right. You need a method you can repeat, inspect, and trust. We compared SciSpace and Claude Science across three questions that matter to your research: 1⃣Can you repeat the workflow? 2⃣Who controls the knowledge you create? 3⃣How efficiently is AI used for your task? See how the two approaches differ, and which one better aligns with the way you work. scispace.com

Arthur Wittenburg, a PhD candidate and lecturer at King’s College London, shares how he used SciSpace as a second reviewer to get structured feedback on his thesis before submission. 🔗 Read the full case study here: scispace.com/resources/how-a… scispace.com

Making sense of dense tables and figures shouldn't take hours of manual effort. Roger Watson, Professor of Nursing at St Francis University, shows how you can use SciSpace to break down complex tables and figures in any research paper and understand the data in seconds. Trusted by 10M+ researchers worldwide. Sign up for free and see how it works: scispace.com

The closer you are to your thesis, the harder it becomes to see what needs fixing. Arthur Wittenburg, PhD Candidate and Lecturer from King’s College London, shares how he used SciSpace as a second reviewer to examine his complete thesis, receive structured feedback, and strengthen it before submission. 📖 Read the full case study here: scispace.com/resources/how-a…

Science moves fast. Your research should, too. scispace.com

To the minds steering scientific progress, thank you for letting SciSpace be a part of your research journey. ♥️ Loved by 10M+ researchers worldwide. scispace.com

AI can accelerate research. But it should never replace researcher judgment. Dr. Gali Halevi (PhD) shares how she uses SciSpace to explore literature, examine evidence, and accelerate research without giving up critical thinking or control over the final decisions. 📖 Read the full case study here: scispace.com/resources/how-a…

Your research library is now built for teamwork. 📂 Supercharge every folder in your library into an intelligent and collaborative workspace. Invite your team to a shared folder where everyone can: ☑️ Add and explore research papers ☑️ Build the library together ☑️ Start individual chats that remain private ☑️ Collaborate on shared notebooks and research documents Try it out at scispace.com

Reading through complex research papers shouldn't take hours of manual effort. Prof. Benita Olivier from Oxford Brookes University, shows how you can use SciSpace's Chat with PDF to ask questions, find related papers, and instantly verify sources directly in the text. Trusted by 9.6M+ researchers worldwide. Sign up for free and see how it works: scispace.com

🎉 Today we're excited to Introduce Citation Verifier on SciSpace. 1 in 176 papers now carries a fabricated citation, and hallucinated references are quietly ruining solid research. Citation Verifier checks every claim in your paper against real source evidence, catches unbacked citations, and auto-updates your draft in seconds. Try it today at scispace.com

🔔 The session is live! Join us now and don’t miss the conversation with the Johns Hopkins AI & Technology Collaboratory. Join here ASAP: jhjhm.zoom.us/webinar/regist…

The next AI battle isn’t about the model. It’s about the harness. Join Johns Hopkins AI & Technology Collaboratory and @saikiranchandha, CEO of SciSpace, tomorrow to explore what it takes to build AI agents that are reliable, safe & scalable! 🗓️ 12 PM ET | September 8, 2026 🔗 Register here: jhjhm.zoom.us/webinar/regist…

Building a research framework is only part of the work. The harder part is proving it holds up, against expert judgment and the published literature alike. PhD researcher Marija Đukić shares how she used SciSpace as a second reviewer to validate her big data analytics maturity model, compare it against existing frameworks, and strengthen her paper toward publication. 📖 Read the full case study here: scispace.com/resources/from-…

Turn your research into conference-ready posters with SciSpace. Organize your findings and create polished visuals in minutes. scispace.com

Some researchers hand AI their trust. Dr. Natarajan Ganesan makes it earn his. A cancer researcher and Assistant Professor at NYIT College of Osteopathic Medicine, he uses SciSpace for literature review, then checks the work: every reference mapped back to its DOI, tested for whether it's real and whether it actually supports the claim. What held up was the transparency. A structured report with a workflow he could see and inspect, not a black box handing him an answer. "I read every word of it, because my name stands behind that." That's the role we want SciSpace to play, helping researchers search, structure, and verify, without surrendering scientific judgment. Read the full case study here: scispace.com/resources/how-a…

Two teams can build on the exact same model, and one burns 40x more tokens to get the same work done. Same model. So what is actually different? Most of the conversation right now is about which model is best. It is the wrong argument. Look at what a model upgrade actually gets you. Somewhere between 4% and 10% better results, at about 1.3x the token cost. Real, but small, and you keep paying that 1.3x every month. Now try the other lever. Leave the model alone and fix the layer around it. That layer is the harness. Everything the model does not do on its own. What actions it can take. What information it sees, and in what order. Get that right and the same model runs on up to 40x fewer tokens, with up to 10x less time spent going in circles. Two costs hide in a badly built harness. 1⃣ The first is context. A sloppy harness resends everything on every call, so you pay for tokens the model did not need to see. 2⃣ The second is idle turns. The agent takes turns where it does nothing useful, and each one can still trigger another inference. Tokens and latency, no progress. Coding got there first. Claude Code and Codex put real engineering into the harness layer: the agent loop, context management, tool execution, sandboxes. The harness understands code and the specific ways things break. Science is earlier on that curve. There are research agents, but domain harnesses for science are far less mature than coding harnesses, and it shows in the reliability. A research harness is a harder build. It has to hold together 220M+ scholarly sources, 70+ connected databases, and 393 research workflows, and still hand back work a researcher can defend. That is what we are building at SciSpace.