Dedoose vs spaCy

Dedoose is a cloud-based application for analyzing and visualizing qualitative and mixed methods data. It handles interviews, focus groups, documents, surveys, video, images, audio, social media, and websites, and lets researchers combine qualitative and quantitative material in an analysis process. Charts, graphs, and matrices help users explore findings, with filters for examining segments. Dedoose supports real-time collaboration with other Dedoose users at no additional cost. The first 30 days of a new individual account are free; paid plans start at $12.95 a month. Student and Individual plans charge for active months, while group plans provide centralized billing. Premier and Enterprise are annual plans with SSO integration and account administration. Dedoose says communications use an AES-256 CBC-encrypted SSL/TLS tunnel, passwords are not stored, and project data remains under customer control rather than being shared with third parties. The publisher says Leviathan Security Group verified its login system. Support includes a 24/7 self-service Learning Center and assistance from trained researchers. The service identifies academic, UX, health, and policy researchers among its users.

spaCy is a free, open-source Python library for building natural language processing applications, with a focus on production use and large volumes of text. Its components handle named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphology, and entity linking. The project lists support for 75+ languages and 84 trained pipelines for 25 languages, though trained pipelines are not available for every language. Developers can configure and train custom pipelines, describe training runs with configuration files, and package pipelines for installation as Python packages. spaCy can use pretrained transformers through spacy-transformers, and supports custom models in PyTorch, TensorFlow, and other frameworks. spaCy Projects supports workflows for downloads, commands, checksum verification, and caching, along with integrations for tracking, demos, model serving, distributed training, and Hugging Face Hub. Installation is available through pip, conda, or source on Linux, macOS, and Windows. The library can run on a user's servers without data leaving them. It is not a hosted software-as-a-service product or web application. The library is free under AGPL or a commercial license; transformer models require a GPU to run effectively.