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Iris.ai

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Facilitates content-based search, automates data extraction, and personalizes researcher workspaces.

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Overview

Iris.ai is an advanced AI-driven platform designed to streamline the research process for academics and professionals alike. By offering a suite of innovative features, it addresses common challenges faced in research, such as information overload and inefficient data extraction. The platform's content-based search allows users to discover relevant interdisciplinary research without the constraints of traditional keyword searches. Additionally, its adaptable workspace and robust data extraction tools empower researchers to personalize their experience and systematically organize their findings, ultimately enhancing the quality and efficiency of their work.

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Features:

Leverage a content-based recommendation engine to explore interdisciplinary research, moving beyond traditional keyword searches.

Utilize advanced filtering options to refine document sets based on contextual sentences or specific data points, enhancing search precision.

Automatically extract and systematize data from research documents, streamlining the organization and analysis of findings.

Adapt the Researcher Workspace to fit your specific research needs without requiring human labeling or extensive training.

Access a wide range of document formats, including research papers, patents, and internal documentation, to meet varied research requirements.

Employ an extraction tool with pre-defined layouts to systematically gather specific information from articles, enhancing data analysis.

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Use Cases for Iris.ai

Streamlining Literature Review for Academic Research

  • Leveraging AI for Comprehensive Literature Searches
  • An academic researcher embarks on a literature review for their thesis on a niche topic within cognitive psychology. Aware of the limitations of traditional search engines, they turn to Iris.ai's content-based recommendation engine. This allows them to bypass keyword limitations and discover a broader range of relevant articles, including interdisciplinary studies that offer unique insights into their topic.
  • Customized Filtering for Efficient Data Management
  • Faced with an overwhelming amount of literature, the researcher uses the Context and Data Filtering feature to refine their search results. By inputting specific criteria related to their research question, they efficiently narrow down the search to the most relevant studies, saving valuable time and effort in the review process.
  • Data Extraction for Effective Analysis
  • To analyze the selected studies systematically, the researcher employs the Extraction and Systematizing Data feature. This tool automatically extracts key data points and organizes them into a pre-defined format, enabling the researcher to easily compare results, identify trends, and draw conclusions for their thesis. This streamlined process not only enhances the quality of their literature review but also significantly reduces the time spent on manual data extraction and organization.

Enhancing Pharmaceutical Research with AI-driven Insights

  • Content-Based Search for Drug Discovery
  • A pharmaceutical researcher is exploring potential compounds for a new drug targeting a specific disease. Instead of relying on traditional keyword searches, which can miss relevant studies due to terminology differences, they use Iris.ai's Content-Based Search. This allows them to discover research based on the content and context of their initial research inputs, uncovering a wider array of potential compounds and related studies.
  • Context and Data Filtering for Precision
  • To narrow down the vast amount of data to the most pertinent studies, the researcher uses the Context and Data Filtering feature. By specifying the disease context and desired compound characteristics, they efficiently filter out irrelevant studies, focusing only on those with the highest potential for contributing to their drug development process.
  • Researcher Workspace Modules for Collaboration
  • As the drug discovery process advances, the researcher collaborates with colleagues across different departments. Utilizing the Researcher Workspace Modules, they share search results, analyses, and data extractions seamlessly within a unified platform. This collaborative environment not only speeds up the research process but also ensures that all team members are aligned and informed, facilitating more effective decision-making.

Accelerating Interdisciplinary Research for Climate Change Solutions

  • Identifying Relevant Research Across Disciplines
  • A climate scientist is tasked with developing innovative solutions to combat climate change. Recognizing the interdisciplinary nature of the challenge, they use Iris.ai's Interdisciplinary Research Discovery feature to identify relevant research across fields such as environmental science, engineering, and social sciences. This broadens their perspective and uncovers novel approaches previously unconsidered.
  • Custom Training for Deep Domain Focus
  • To refine their search and ensure the most relevant and up-to-date research is included, the scientist utilizes the Custom Training for Deep Domain Focus service. By feeding the AI a curated set of documents specific to their current focus—such as renewable energy sources or carbon capture technologies—they tailor the AI's search capabilities to their precise needs, enhancing the relevance of discovered documents.
  • Systematic Analysis and Comparison
  • With a comprehensive set of interdisciplinary research at their disposal, the scientist employs the Extraction and Systematizing Data feature to systematically analyze and compare findings from various studies. This process helps in identifying patterns, gaps, and opportunities for innovation in the field of climate change mitigation, guiding the scientist towards promising areas of further research or application.

FAQs

Frequently Asked Questions

Iris.ai allows for custom training of its AI tools on specific domains using a collection of relevant documents, improving the accuracy and relevance of search results for specialized research areas.

Iris.ai enhances interdisciplinary research discovery by utilizing AI to pinpoint relevant studies across various fields, broadening the scope of research exploration.

Iris.ai's Researcher Workspace Modules encompass tools such as Search, Filter, Analyze, Extract, Summarize, Automate, and Report, facilitating a flexible and tailored research process.

Iris.ai automatically extracts and organizes data from research documents into a predefined format, allowing for systematic analysis and comparison across various sources.

Iris.ai enables users to filter document sets based on contextual criteria articulated in sentences or specific data points and ranges, providing a more refined search capability.

Iris.ai's content-based recommendation engine allows users to bypass traditional keyword searches, facilitating exploration of interdisciplinary research and enriching the discovery process.

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