QAConv is a new question answering (QA) dataset that uses conversations as a knowledge source. We focus on informative conversations including business emails, panel discussions, and work channels. Unlike opendomain and task-oriented dialogues, these conversations are usually long, complex, asynchronous, and involve strong domain knowledge. In total, we collect 34,204 QA pairs, including span-based, free-form, and unanswerable questions, from 10,259 selected conversations with both human-written and machine-generated questions. We segment long conversations into chunks, and use a question generator and dialogue summarizer as auxiliary tools to collect multi-hop questions. The dataset has two testing scenarios, chunk mode and full mode, depending on whether the grounded chunk is provided or retrieved from a large conversational pool.