Semanticscholar

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A free, AI-powered research tool for scientific literature.

How Semanticscholar can help you:

Accelerates the research process by providing quick access to scientific papers.
Enhances understanding of scientific literature through AI-driven insights.
Facilitates discovery of relevant research using advanced semantic search algorithms.

Why choose Semanticscholar: Key features

AI-powered semantic analysis for precise search results.
Access to millions of scientific documents from various disciplines.
Intuitive interface designed for ease of use among researchers.

Who should choose Semanticscholar:

Academic researchers seeking efficient ways to access scientific literature.
Students and educators in need of comprehensive research tools.
Professionals in scientific fields requiring current literature for their work.

About Semanticscholar

Website

https://www.semanticscholar.org

Release Date

March 2024

Pricing

Free

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Related News

AI Literature Review Tools Exposed: Which Ones Are Lying to You?

What if the tool you trusted to streamline your research was quietly feeding you lies? In the race to harness AI for academic productivity, literature review tools promise to save hours of tedious work. But here's the catch: not all of them tell the truth. Imagine submitting a paper only to discover that 1 in 4 of your references is fabricated. That's the reality I uncovered while testing three popular AI-powered tools -- Manis, Gen Spark, and Gemini AI. The results? Eye-opening. Only one of them delivered the accuracy and reliability essential for serious research, while the others left me questioning their place in academic workflows. If you've ever wondered whether AI can truly be trusted with your literature reviews, this rundown might surprise you. Andy Stapleton breaks down the performance of these tools based on speed, usability, and -- most critically -- accuracy. You'll discover which AI tool churned out a 61-page report with near-perfect references, and which one sacrificed credibility for speed. Whether you're a researcher seeking to save time or just curious about the limits of AI in academia, this comparison will help you navigate the trade-offs. By the end, you'll know which tool is worth your trust -- and which might lead you astray. Because when it comes to academic integrity, the stakes are too high for guesswork. Manis demonstrated impressive speed, completing a literature review in just three minutes. It generated a 14-page document with 38 references, making it an appealing option for researchers who prioritize efficiency. However, its accuracy raised concerns. Approximately 16% of the references were either fabricated or inaccurate, posing a risk to the credibility of any research relying on its output. Manis is a viable option for generating quick overviews, but its reliability is compromised by the need for thorough manual verification. While its speed is a clear advantage, the trade-off in accuracy limits its utility for rigorous academic research. Gen Spark offered a more balanced approach, completing the task in 5-7 minutes. It produced 19 references and demonstrated a reasonable understanding of the research prompt. However, its fabrication rate was higher at 26%, and its limited output made it less suitable for in-depth academic projects. Gen Spark may serve as a starting point for preliminary research, but its higher error rate and limited scope make it less dependable for detailed academic work. Researchers seeking comprehensive and accurate results may find its limitations restrictive. Take a look at other insightful guides from our broad collection that might capture your interest in AI Literature Review Tools. Gemini AI emerged as the most reliable tool among the three tested. While it required the longest processing time -- 20 minutes -- it delivered a 61-page document with 105 references. Only 1% of these references were problematic, and the issues were related to accessibility rather than outright fabrication. Gemini AI also stood out for its inclusion of structured data, tables, and up-to-date references, providing a level of detail unmatched by the other tools. For researchers who value accuracy and depth, Gemini AI is the most dependable choice. While its longer processing time requires patience, its detailed output and low error rate make it a standout tool for academic literature reviews. After evaluating all three tools, Gemini AI clearly stands out as the most reliable option for academic literature reviews. Its detailed output, minimal error rate, and comprehensive analysis set it apart, despite its longer processing time. Manis, with its speed and moderate accuracy, is a reasonable alternative for quick overviews, while Gen Spark falls short due to its higher fabrication rate and limited scope. Final Rankings: AI tools for literature reviews hold significant potential, but they are not without flaws. Regardless of the tool you choose, manual verification remains essential to ensure the accuracy and credibility of your references. Among the tested options, Gemini AI sets the standard for academic productivity, offering a balance of precision and thoroughness that researchers can trust. While Manis and Gen Spark have their merits, they fall short of the reliability and depth required for rigorous academic work. Researchers should weigh their priorities -- whether speed, accuracy, or comprehensiveness -- when selecting the right tool for their needs.

Geeky Gadgets

Mon, 9 Jun, 4:08 PM UTC

OpenScholar AI tool surpasses GPT-4o in literature reviews and eliminates citation hallucinations

Academic researchers unveiled OpenScholar, an open-source AI tool that outperformed major LLMs like GPT-4o in scientific literature reviews. The system combines a language model with 45 million open-access articles to deliver accurate citations without hallucinations. Over 30,000 scientists have already tested the free tool since its debut.

2 Sources

Wed, 4 Feb, 11:37 PM UTC

Google's new Scholar Labs search uses AI to find relevant studies

Google has announced it's testing a new AI-powered search tool, Scholar Labs, that's designed to answer detailed research questions. But its demonstration highlighted a bigger question about finding "good" science studies. How much will scientists trust a tool that forgoes typical ways of gauging a study's popularity with the scientific establishment in favor of reading the relationships between words to help surface good research? The new search tool uses AI to identify the main topics and relationships in a user's query and is currently available to a limited set of logged-in users. The demo video from Scholar Labs featured a question about brain-computer interfaces (BCIs). I have a PhD in BCIs, so I was eager to see what Scholar Labs pulled up. The first result was a review paper of BCI research published in 2024 in a journal called Applied Sciences. Scholar Labs includes explanations for why the results matched the query, so it pointed out that the paper discusses research into a noninvasive signal called electroencephalogram and surveys some leading algorithms in the field. But I noticed that Scholar Labs lacks the filters for common metrics used to separate "good" studies from "not-so-good" ones. One metric is the number of times that a study has been cited by other studies since its publication, which loosely translates to a paper's popularity. It's also associated with time: A recently published study might have zero citations or rack up hundreds within a few months; a study from the '90s may tout thousands. Another metric is the "impact factor" of a science journal. Journals that publish widely cited studies have a higher impact factor and thus have a reputation for being more rigorous or meaningful to the scientific community. Applied Sciences self-reports an impact factor of 2.5. Nature, for comparison, says its impact factor is 48.5. The original Google Scholar has an option for ranking studies by "relevancy" and lists the number of citations for each result. The goal of the new Scholar Labs is to dig up "the most useful papers for the user's research quest," Google spokesperson Lisa Oguike told The Verge It does so by ranking papers in the same way as the researchers themselves, Google says, by "weighing the full text of each document, where it was published, who it was written by, as well as how often and how recently it has been cited in other scholarly literature." However, the new Scholar Labs will not sort or limit results based on a paper's citation count or a journal's impact factor, Oguike told The Verge. "Impact factors and citation counts depend on the research area of the papers and it can be hard for most users to guess suitable values in the context of specific research questions," Oguike wrote. "Limiting by impact factor or citation counts can often miss key papers -- in particular, papers in interdisciplinary/adjacent fields/journal or recently published articles," Oguike added. Metrics like citation count and impact factor are "pretty coarse assessments of a paper's quality," associate professor of neurology at Vanderbilt University Medical Center Matthew Schrag said in an interview with The Verge, agreeing with Google's statement. They "speak more about the social context of the paper" rather than its quality, although "those two things hopefully are correlated," he said. Schrag, who researches Alzheimer's disease, is one of the many scientists-sleuths who have flagged dubious data in published science studies. The efforts of data sleuths like Schrag, and a closer attention by the science community at large, have resulted in studies pulled from well-regarded journals because of doctored images, corrections issued by Nobel Prize winners, and federal investigations into faked data. Still, it's difficult to not use citation count or a journal's reputation to casually vet a study, especially when entering a new field. Professor of rehabilitation sciences at Tufts University, James Smoliga, a frequent user of the original Google Scholar, finds himself believing highly cited papers to be more trustworthy. "I'm guilty of it just like everybody else is," he said to The Verge. He does so despite having debunked the methods used in a study with thousands of citations. "And I know myself that's not the case but yet I still fall for that trap because what else am I going to do?" I repeated the Scholar Labs demo query about BCI research for stroke patients in PubMed, a leading repository of biomedical and health research run by the US National Institutes of Health National Library of Medicine. Unlike Scholar Labs, PubMed relies extensively on filters and terms connected with ors and ands. I narrowed my results to only review articles of clinical research, meaning only done on humans, from the past five years. I excluded preprints, which are studies posted directly to a paper repository like arXiv or bioRxiv without having gone through a review process from other scientists. Two of the six results focused exclusively on electroencephalogram as the primary type of noninvasive BCI used to help stroke patients. Users will be able to ask for "recent" papers in their query and specify a period of time in their request, and Scholar Labs uses the "full-text of research papers" to find results that match the user query, Oguike added. Google is calling Scholar Labs a "new direction for us" and says it plans to incorporate user feedback in the future. It has a waitlist for access. Schrag thinks AI-powered search, like that of the new Scholar Labs, has a place in the scientific ecosystem. It could, in theory, cast a wider net to surface papers that otherwise slipped through the cracks, or add additional context about a paper's popularity across social media platforms, he added. Studies need a holistic appraisal, he said, which AI might be able to address. "You have to have a sense of what the standards in the field are in terms of rigor and whether a study meets that," he added. Ultimately, scientists are responsible for determining what science is impactful, Schrag said. It requires reading and engaging with science literature "to be the final arbiters and not to let algorithms be the final arbiter of what we consider high quality."

The Verge

Wed, 19 Nov, 10:31 PM UTC

AI research tools benefit individual scientists but may slow ...

An analysis of over 41 million research papers from the past 40 years has revealed that researchers who use artificial intelligence (AI) tools in their work publish more papers and receive more citations. However, using these tools may narrow academics research to those areas that are richest in data. AI tools can help scientists identify patterns in large datasets, automate certain processes for high-throughput experiments or help with scientific writing. A team led by James Evans at the University of Chicago in the US, along with researchers in China, has sought to better understand how adopting such tools affects both individual scientists and the wider scientific community. What do we mean when we say AI? To do this, the team used a large language model to analyse over 41 million research papers, published over the past 40 years, that used AI tools. The model identified papers that used AI tools and split such studies into three AI 'eras': traditional machine learning (1980-2014), deep learning (2015-2022) and generative AI (2023 to the present). Studies were analysed from across the natural sciences, including biology, medicine, chemistry, physics, materials science and geology. Evans explains that the team did not choose fields like mathematics, computer science and social sciences as they often describe development or use of AI tools, rather than using such tools to do scientific research. Human expertise was then used to evaluate a sample of papers classified by the model and they agreed with its choices in nearly 90% of cases. Analysis revealed that the share of papers published using AI tools has rapidly increased over the past four decades, as has the number of researchers taking advantage of them, likely the result of such tools becoming more easily accessible. This result was consistent across all three AI eras. Additionally, scientists using AI in their research published around three times more papers and received just under five times more citations, on average, compared with those not adopting AI. However, Milad Abolhasani, who researches autonomous flow chemistry at North Carolina State University in the US, suggests that the model may miss certain papers if the use of AI is not clear, leading to an undercount of AI-adopting studies. Evans and his team also extracted the career paths of over 2 million scientists from the dataset, classifying them as either junior, who had not yet led a research project, or established. Teams that adopted AI tools were smaller with fewer junior researchers, yet early-career members in these groups were 13% more likely to stay in academia. Such AI-adopting junior researchers also tended to become established researchers around one-and-a-half years quicker than their peers. However, the team was unable to fully explain why adopting AI tools increased scientific impact. Molly Crockett, a neuroscientist at Princeton University, cautions that the researchers' definition of 'impact' is based on citation count and requires scientists to reach the level of principal investigator. 'These metrics could equally reflect the hype and economic incentives to use a novel technology, aside from the quality of research, and the data cannot disentangle these explanations,' Crockett adds. The wider scientific community While adopting AI tools can help individual scientists, further analysis suggested that using AI tools can contract research focus onto niche problems within already established fields. Areas with an abundance of available data are more amenable to research using AI, compared with fields that explore fundamental questions, such as the origin of natural phenomena. 'Entire research areas may be at risk of extinction if they are not tractable for tracking with automation,' says Crockett. Results from the study also revealed that AI research leads to 22% less engagement between scientists, which can create 'lonely crowds' in popular scientific research fields. 'We're bringing these [artificial] intelligences into our research, but really only applying them on one part of the knowledge intelligence spectrum,' says Evans. He thinks that this creates an imbalance between focusing on existing research problems, rather than exploring new ones. 'The problem is that there's a conflict between individual and collective incentive,' he explains. However, Abolhasani says that the results from this study doesn't mean that scientists must use AI to have career progression. He adds that this study 'should not encourage scientists to replace their critical thinking with a large language model'.

Chemistry World

Fri, 23 Jan, 10:32 AM UTC

10 Best AI Tools for Research: Consensus, Scite, Elicit & More

AI tools are not just transforming the Generative AI space, but they are also being used to discover and analyze academic research papers. The AI boom has led to many AI-powered search engines for research, citation generation, finding key insights, and more. The AI tools for research can help you find relevant research papers and check emerging trends. On that note, find our list of best AI tools for research. 1. Consensus If you are doing research for academic work, Consensus is the best AI tool you can use. It's an AI-powered academic search engine that collates millions of research papers from various fields. The best part about Consensus is that it has a Consensus Meter which shows where the research leans for your question or research study. It shows 'Yes', 'Possibly,' or 'No' based on published data from various papers. It can analyze up to 20 papers at once. Apart from that, Consensus shows a quick AI-powered Study Snapshot from the scientific literature to show key information about the study. Not to mention, you get AI-powered summaries, key takeaways, and insights. Finally, you can chat with Copilot and ask questions or get assistance to draft your research. Simply put, to find relevant studies and aid in research work, Consensus is the best AI tool out there. 2. Scite Scite is another powerful AI-powered tool that is ideal for students and researchers. It evaluates scientific studies to answer your questions, backed by real research and citations. Scite is widely used by universities, publishers, and corporations across the world. The feature called Smart Citations adds context to each citation and checks references to provide reliability of studies. For each topic, you can find research papers that support or dispute the claim. It helps researchers quickly find scientific literature relevant to their area of study. Moreover, for those unaware, Scite is funded by the National Institutes of Health (NIH), and the National Science Foundation (NSF). 3. SciSpace If you wish to understand research papers in simple words, I strongly recommend giving SciSpace a try. It offers a 'Chat with PDF' tool that explains and summarizes research papers. Next, you can ask questions, and it generates AI-powered insights from the top five research papers. Right below, SciSpace displays all the top papers with the journal name, key findings, PDF access, external links, and more. There are also tons of filter options that can help you narrow down recent publications, find papers based on citation count, and more. What I find interesting is that you can add multiple columns to display results, summarized abstracts, limitations, and much more next to research papers. 4. Semantic Scholar Next, we have Semantic Scholar, the free AI-powered research tool to find scientific literature and academic research papers to back your claim. It has a vast database of more than 200 million papers from various fields. It offers a deep search functionality and shows the citation count below every research paper. You can further filter out the field of study and find papers based on journals, publication dates, authors, and more. Furthermore, you can check out all the references and move to the DOI link to find the paper. The standout feature of Semantic Scholar is that it can perform advanced citation analysis across its large library of databases to provide relevant papers. If you want to discover scientific literature, Semantic Scholar is a great free AI tool. 5. Elicit In academic research, the process of literature review takes up a lot of time. Elicit is one such AI tool for research that automates summarizing papers, extracting data, and evaluating new findings seamlessly. You can search, ask research questions, and get relevant papers from its massive library of 125 million research papers. The best part about Elicit is that you can extract details in a tabular format. Next, you can choose a relevant paper and find more papers like it. Not to mention, Elicit can generate AI-powered abstract summaries to quickly get the gist of the paper. Apart from that, it can connect concepts across various domains and show papers backing the claim. Finally, you can also upload your PDF research papers and chat with them. 6. ScholarGPT ScholarGPT is one of the best custom GPTs for ChatGPT that you can use for research purposes. It's the top-ranking GPT on OpenAI's GPT store. It researches on your behalf and answers questions backed by various research papers from leading journals and publications. The GPT connects to the internet to find high-quality sources to generate a response. And now that OpenAI has opened access to GPTs for all, free ChatGPT users can also chat with ScholarGPT or any other GPT without subscribing to the paid plan. So to find answers to your research questions, you must check out ScholarGPT. 7. Coral AI If you are looking for an AI tool that allows you to upload research papers and lets you chat with the document, meet Coral AI. It's widely used by researchers to process local papers. Coral AI uses AI to generate summaries and translations in 90+ languages. The best part about Coral AI is that it gives you citations with page numbers. You can upload multiple documents and find key information by collating information from all of the papers. To conclude, if you have several local research papers, and want to analyze them using AI, Coral AI is the best tool out there. 8. ResearchRabbit ResearchRabbit is heavily used by researchers for literature search and review. It allows you to search through papers and visualize them interactively. You can visualize citations among networks of papers, and find authors neatly. You can create a collection of research papers, and ResearchRabbit learns from your preferences and improves its recommendation engine. Not only that, it also has Zotero integration, which means you can easily manage your existing research materials. 9. Connected Papers Connected Papers is not an AI tool per se, but it's incredibly useful for researchers. It helps students discover new and relevant academic papers that might be under the radar. This is done through a visual graph of papers, showing a starting point and connections based on similarity. You can see the trends and find where the research area is heading using this platform. On top of that, along the connected points, you can also find important research papers to stay updated on emerging trends. If you are doing literature review, Connected Papers is an impressive tool you must use. 10. ChatGPT and Perplexity Although ChatGPT and Perplexity are general-purpose AI chatbots, you can use them for research purposes as well. The ChatGPT API is particularly used by many research AI tools to generate summaries, key insights, and more. ChatGPT lets you upload files, and it can also connect to the internet to find the latest papers on a research subject. And the best part is that now you can use all these features for free. Perplexity, on the other hand, is very good at combing through the internet and finding relevant information on any subject. It cites sources from where it collates the information, which can be pretty useful for researchers. I would suggest getting the paid Perplexity Pro subscription if you intend to do heavy research. The free plan tends to hallucinate a bit. So these are the best AI tools for research that students and researchers can check out. Most of the tools are free while some are paid ones. If you want to check out more free AI tools for various use cases, go through our linked article. And if we have missed out on your favorite AI tool for research work, let us know in the comments below.

Beebom

Thu, 25 Jul, 2:00 PM UTC

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