Best AI tools for< Analyze Research Data >

Marvin is an AI research software that serves as the perfect AI research assistant for qualitative research. It automates tedious parts of qualitative research, allowing users to analyze hours of research in minutes. Marvin helps in centralizing, searching, and sharing user research data, making it easily accessible for the whole team. With AI-powered enhancements, Marvin assists in consolidating user insights, finding patterns, and backing decisions with evidence. The tool streamlines the research journey by managing user interview panels, recruiting participants effectively, and providing features like automatic transcripts, video clips, and privacy filters for compliance. Marvin offers different pricing plans suitable for small teams, startups, companies with multiple teams, and large organizations.

Notably is a research synthesis platform that uses AI to help researchers analyze and interpret data faster. It offers a variety of features, including a research repository, AI research, digital sticky notes, video transcription, and cluster analysis. Notably is used by companies and organizations of all sizes to conduct product research, market research, academic research, and more.

Avidnote is an AI tool designed for research writing, reading, and analysis. It enables users to write or read research papers faster, analyze research data with AI templates, summarize text, find research gaps, transcribe interviews, and more. Avidnote offers AI functionalities tailored for researchers, recommended by universities, and supported by researchers worldwide. The platform provides free and paid plans with varying features and benefits to cater to different user needs.

Looppanel is a user research analysis and repository tool that uses AI to help researchers save time and improve the quality of their work. It offers a range of features, including automated transcription, AI note-taking, video snipping, and advanced search capabilities. Looppanel is designed to make it easy for researchers to capture, organize, and analyze their research data, so they can focus on what matters most: uncovering insights and making better decisions.

TOPBOTS is a platform focused on Applied AI for Business, providing insights and resources on artificial intelligence, machine learning, automation, bots, and chatbots. The website covers a wide range of topics such as computer vision, conversational AI, natural language processing, HR & recruiting, marketing, and research summaries. TOPBOTS aims to help businesses understand and apply technical breakthroughs in AI to enhance their operations and strategies.

Elicit is a research tool that uses artificial intelligence to help researchers analyze research papers more efficiently. It can summarize papers, extract data, and synthesize findings, saving researchers time and effort. Elicit is used by over 800,000 researchers worldwide and has been featured in publications such as Nature and Science. It is a powerful tool that can help researchers stay up-to-date on the latest research and make new discoveries.

Elicit is an AI research assistant that helps researchers analyze research papers at superhuman speed. It automates time-consuming research tasks such as summarizing papers, extracting data, and synthesizing findings. Trusted by researchers, Elicit offers a plethora of features to speed up the research process and is particularly beneficial for empirical domains like biomedicine and machine learning.

Perceive Now is the world's first Large Language Model fine-tuned with IP and Market Research data. It offers custom IP and Market reports for various industries, providing detailed insights and analysis to support decision-making processes. The platform helps in identifying market trends, conducting due diligence, managing deal flow, and maximizing IP and licensing opportunities. Perceive Now is a game-changer in prior art search, increasing the odds of patent grant success. It has significantly reduced research costs and time, accessing over 100M IP and market data sources and assisting in securing funding worth $500M.

Looppanel is an AI-powered research assistant that revolutionizes the way research data is managed. It automatically records calls, transcribes them, and centralizes all research data in one place. Looppanel's highly accurate transcripts support multiple languages and accents, enabling users to focus on interviews while AI takes notes. The platform simplifies analysis, allows for time-stamped note-taking, and facilitates collaboration among team members. Looppanel ensures data security and compliance with high standards, making it a valuable tool for researchers and professionals.

mypapers.ai is an AI tool designed to assist users in managing and analyzing academic papers efficiently. The tool offers features such as exploring papers and authors, toggling between papers and authors, and tracking the journey of research. Users can also access the code on GitHub to further enhance their research capabilities.

Impact Stack is an AI-powered research platform that enables users to conduct evidence-based research with the help of specialized AI tools and expertise. It allows users to ask questions about companies, industries, or research topics in natural language, providing rigorously researched answers quickly. The platform eliminates the need for manual data entry by allowing users to interface with AI agents equipped with up-to-date knowledge bases. Impact Stack also offers the ability to search and analyze multiple databases simultaneously, streamlining the research process. Users can stay informed with the latest articles and feature updates through subscription.

Lime is an AI-powered data research assistant designed to help users with data research tasks. It offers advanced capabilities to streamline the process of gathering and analyzing data, making it easier for users to extract valuable insights. Lime is equipped with cutting-edge AI technology that enables it to handle complex data research tasks efficiently and accurately. With Lime, users can save time and effort in conducting data research, allowing them to focus on making informed decisions based on the insights generated.

DMLR (Data-centric Machine Learning Research) is an AI tool that focuses on advancing research in data-centric machine learning. It organizes workshops, research retreats, maintains a journal, and runs a working group to support infrastructure projects. The platform covers topics such as data collection, governance, bias, and drifts, as well as data-centric explainable AI and AI alignment. DMLR encourages submissions around the theme of AI for Science, using AI to tackle scientific challenges and accelerate discoveries.

Maya is an AI-powered data robot that provides personalized answers and insights for enterprise data research. It combines multiple data sources and tools into one, automates tasks, offers smart suggestions, and saves time. Maya understands the specific insights required for each workflow and provides justification for implementation. It can access data from various sources, including internal integrations and external sources, and can translate queries in up to 14 languages. Maya is constantly learning and improving through advanced machine learning and regular updates with new data. It prioritizes data privacy and security, following industry-standard protocols to keep customer data safe.

PromptLoop is an AI-powered tool that integrates with Excel and Google Sheets to enhance market research and data analysis. It offers custom AI models tailored to specific needs, enabling users to extract insights from complex information. With PromptLoop, users can leverage advanced AI capabilities for tasks such as web research, content analysis, and data labeling, streamlining workflows and improving efficiency.

David is an AI survey analyst tool that helps users turn survey, product feedback, and user research form data into strategic actions. It offers features like data analysis, insights generation, and form optimization. David is built with Metaforms AI and provides universal compatibility with various survey platforms. Users can upload CSV files to David for analysis and receive tailored recommendations based on their specific goals. The tool is free to use and aims to optimize marketing strategies, engage customers, and boost conversions.

Julius is an AI data analyst tool developed by the team behind HoopsGPT. It simplifies sports insights by providing users with comprehensive information about the game. With features like Stats Lookup, Shot Charts visualization, Parlay Analyzer, and Props/Game Comparisons, Julius offers a user-friendly platform for analyzing sports data and making informed decisions. Whether you're interested in checking complex stats, visualizing shot charts, or comparing odds, Julius has you covered. It's a valuable tool for sports enthusiasts and bettors looking to enhance their understanding of the game.

AnalyzeMe is an application that allows users to easily conduct PEST analysis. By entering industry and keywords, it provides detailed market environmental analysis. Users can use AnalyzeMe to reassess their business strategies.

The Research Center Trustworthy Data Science and Security is a hub for interdisciplinary research focusing on building trust in artificial intelligence, machine learning, and cyber security. The center aims to develop trustworthy intelligent systems through research in trustworthy data analytics, explainable machine learning, and privacy-aware algorithms. By addressing the intersection of technological progress and social acceptance, the center seeks to enable private citizens to understand and trust technology in safety-critical applications.

The National Cancer Institute's Center for Biomedical Informatics and Information Technology (CBIIT) provides a comprehensive suite of tools, resources, and training to support cancer data science research. These resources include data repositories, analytical tools, data standards, and training materials. CBIIT also develops and maintains the NCI Thesaurus, a comprehensive vocabulary of cancer-related terms, and the Cancer Data Standards Registry and Repository (caDSR), a repository of cancer data standards. CBIIT's mission is to accelerate the pace of cancer research by providing researchers with the tools and resources they need to access, analyze, and share cancer data.

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

last_layer is a security library designed to protect LLM applications from prompt injection attacks, jailbreaks, and exploits. It acts as a robust filtering layer to scrutinize prompts before they are processed by LLMs, ensuring that only safe and appropriate content is allowed through. The tool offers ultra-fast scanning with low latency, privacy-focused operation without tracking or network calls, compatibility with serverless platforms, advanced threat detection mechanisms, and regular updates to adapt to evolving security challenges. It significantly reduces the risk of prompt-based attacks and exploits but cannot guarantee complete protection against all possible threats.

Awesome-Graph-LLM is a curated collection of research papers exploring the intersection of graph-based techniques with Large Language Models (LLMs). The repository aims to bridge the gap between LLMs and graph structures prevalent in real-world applications by providing a comprehensive list of papers covering various aspects of graph reasoning, node classification, graph classification/regression, knowledge graphs, multimodal models, applications, and tools. It serves as a valuable resource for researchers and practitioners interested in leveraging LLMs for graph-related tasks.

Awesome-LLMs-for-Video-Understanding is a repository dedicated to exploring Video Understanding with Large Language Models. It provides a comprehensive survey of the field, covering models, pretraining, instruction tuning, and hybrid methods. The repository also includes information on tasks, datasets, and benchmarks related to video understanding. Contributors are encouraged to add new papers, projects, and materials to enhance the repository.

Paper-Reading-ConvAI is a repository that contains a list of papers, datasets, and resources related to Conversational AI, mainly encompassing dialogue systems and natural language generation. This repository is constantly updating.

This repository is a curated list of awesome large AI models in audio signal processing, focusing on the application of large language models to audio tasks. It includes survey papers, popular large audio models, automatic speech recognition, neural speech synthesis, speech translation, other speech applications, large audio models in music, and audio datasets. The repository aims to provide a comprehensive overview of recent advancements and challenges in applying large language models to audio signal processing, showcasing the efficacy of transformer-based architectures in various audio tasks.

This repository is an updating survey for Continual Learning of Large Language Models (CL-LLMs), providing a comprehensive overview of various aspects related to the continual learning of large language models. It covers topics such as continual pre-training, domain-adaptive pre-training, continual fine-tuning, model refinement, model alignment, multimodal LLMs, and miscellaneous aspects. The survey includes a collection of relevant papers, each focusing on different areas within the field of continual learning of large language models.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

This repository, Awesome-LLM-RAG, aims to record advanced papers on Retrieval Augmented Generation (RAG) in Large Language Models (LLMs). It serves as a resource hub for researchers interested in promoting their work related to LLM RAG by updating paper information through pull requests. The repository covers various topics such as workshops, tutorials, papers, surveys, benchmarks, retrieval-enhanced LLMs, RAG instruction tuning, RAG in-context learning, RAG embeddings, RAG simulators, RAG search, RAG long-text and memory, RAG evaluation, RAG optimization, and RAG applications.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

WritingAIPaper is a comprehensive guide for beginners on crafting AI conference papers. It covers topics like paper structure, core ideas, framework construction, result analysis, and introduction writing. The guide aims to help novices navigate the complexities of academic writing and contribute to the field with clarity and confidence. It also provides tips on readability improvement, logical strength, defensibility, confusion time reduction, and information density increase. The appendix includes sections on AI paper production, a checklist for final hours, common negative review comments, and advice on dealing with paper rejection.

ai-collective-tools is an open-source community dedicated to creating a comprehensive collection of AI tools for developers, researchers, and enthusiasts. The repository provides a curated selection of AI tools and resources across various categories such as 3D, Agriculture, Art, Audio Editing, Avatars, Chatbots, Code Assistant, Cooking, Copywriting, Crypto, Customer Support, Dating, Design Assistant, Design Generator, Developer, E-Commerce, Education, Email Assistant, Experiments, Fashion, Finance, Fitness, Fun Tools, Gaming, General Writing, Gift Ideas, HealthCare, Human Resources, Image Classification, Image Editing, Image Generator, Interior Designing, Legal Assistant, Logo Generator, Low Code, Models, Music, Paraphraser, Personal Assistant, Presentations, Productivity, Prompt Generator, Psychology, Real Estate, Religion, Research, Resume, Sales, Search Engine, SEO, Shopping, Social Media, Spreadsheets, SQL, Startup Tools, Story Teller, Summarizer, Testing, Text to Speech, Text to Image, Transcriber, Travel, Video Editing, Video Generator, Weather, Writing Generator, and Other Resources.

BambooAI is a lightweight library utilizing Large Language Models (LLMs) to provide natural language interaction capabilities, much like a research and data analysis assistant enabling conversation with your data. You can either provide your own data sets, or allow the library to locate and fetch data for you. It supports Internet searches and external API interactions.

Awesome ML is a curated list of resources and tools related to machine learning, covering a wide range of topics such as large language models, image models, video models, audio models, and marketing data science. It includes open LLM models, tools, GUIs, backends, voice assistants, code generation, libraries, fine tuning, data sets, research, image and video models, audio tasks like compression, speech recognition, and music generation, as well as resources for marketing data science. The repository aims to provide a comprehensive collection of resources for individuals interested in machine learning and its applications.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

Obsei is an open-source, low-code, AI powered automation tool that consists of an Observer to collect unstructured data from various sources, an Analyzer to analyze the collected data with various AI tasks, and an Informer to send analyzed data to various destinations. The tool is suitable for scheduled jobs or serverless applications as all Observers can store their state in databases. Obsei is still in alpha stage, so caution is advised when using it in production. The tool can be used for social listening, alerting/notification, automatic customer issue creation, extraction of deeper insights from feedbacks, market research, dataset creation for various AI tasks, and more based on creativity.

Your AI research assistant, for turning a problem into a research, developing research questions, generating plans, analyzing data and improving research workflows for project success

A UI/UX assistant for design principles, UX research, analyzing research data, and UI layout generation.

Guides scientific investigations and theoretical research methodologies.

a robot scholar reviewing social science research

Assists in psychological meta-analysis research with R language expertise.

Expert in scientific research grants, answers in Japanese with detailed references and citations.

Find recent research for your area of biological interest.

Helps you write market research briefs and survey questions.

专注于“投资策略”类型的研报分析总结，提炼对行业配置的核心观点 Focusing on the analysis and summary of research reports on the type of "investment strategy", refining core perspectives on industry allocation

Helps find and analyze ML papers, focusing on SOTA comparisons.

Summarizes academic papers, especially in medical and scientific research

Clean and structure your raw data with automatic file output for your Custom GPT knowledge.

Know the details about the Amalgamated Intermittent Computing Systems paper

CYO KATSUI

Sports betting arbitrage assistant for horse racing and other sports.

Your go-to film data guru

Hi ;) Ask me for data & insights gathered from an environmentally aware global community

Your go-to source for in-depth US zip code demographics and statistics, with easy-to-download data tables.

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