Computer-assisted qualitative data analysis software (CAQDAS)
Computer-assisted qualitative data analysis software (CAQDAS)
Computer-assisted qualitative data analysis software (CAQDAS) can summarize scholarly content such as publications and PDF files, make document groups, with advanced features with collaboration features, and review and summarize scholarly literature. It can also use machine learning with a few clicks. While these tools were originally designed to facilitate qualitative data analysis, they are equally valuable for managing and analyzing vast amounts of literature, making CAQDAS a powerful tool for literature reviews.
ATLAS.ti
ATLAS.ti is known for its intuitive and user-friendly interface, making it a popular choice among qualitative researchers. It offers robust tools for data coding, visualization, and network analysis. ATLAS.ti supports various data formats, including text, multimedia, and geographic data. Key features include advanced coding options, sophisticated visualization tools (such as word clouds and networks) and the ability to handle large datasets. It also supports collaborative work, making it suitable for research teams.
Researchers can import their references from their preferred bibliography manager into ATLAS.ti Windows and Mac. Researchers frequently start with references organized in software such as Zotero or EndNote. ATLAS.ti allows for the import of data from these programs using EndNote XML files or BibTeX files. It enables the transfer of references from almost any reference management software.
Paper Search 2.0 in ATLAS.ti Web is a state-of-the-art AI tool designed to optimize research workflows by rapidly evaluating the relevance of scientific papers. Researchers input their questions, and the tool conducts extensive searches, providing concise summaries of the most pertinent papers. This technology significantly reduces the time and effort needed, allowing researchers to focus on deeper analysis.
With access to over 200 million scientific resources from Semantic Scholar, Paper Search 2.0 offers a vast database for relevant studies. The streamlined search function efficiently identifies and imports key scientific resources. The AI delivers focused summaries of the top five papers, highlighting content that directly answers the research question. Additionally, it facilitates easy citation within ATLAS.ti projects, ensuring a cohesive workflow.
Supporting the entire literature review process on one platform, the tool uses advanced Natural Language Processing to fully understand research needs, delivering highly relevant search results and summaries. When users input their questions, the AI refines them, generates relevant keywords, and searches the Semantic Scholar database for pertinent papers. It then summarizes the findings and provides actionable insights. This AI-driven functionality, developed by the ATLAS.ti AI Lab, transforms raw data into valuable knowledge, streamlining the literature review process. Future updates will expand database access, refine search capabilities, and integrate advanced AI features for deeper insights.
