Document Management

Drag files directly into the file tree or click Upload. Everything you upload is processed so Academe can search, cite, and use it in context.

Know the paper identifier or source link? Paste it and Academe pulls the full metadata: title, authors, abstract, journal, and year. It downloads the full text when it is open-access.

The library is the single home for every paper in your project. Beyond folders, a few more organizational tools are worth knowing about:

• Bulk uploadDrop a whole folder of PDFs at once; imports run in parallel in the background. Papers become citable as each one finishes.
• CollectionsGroup related papers into a reading list, a chapter’s sources, or a review shortlist. A paper can sit in multiple collections without being duplicated.
• TagsApply free-form tags during upload or any time after. Filter extractions, chats, and exports down to a tag set when you need focus.
• Duplicate checksWhen you upload a paper you already have, Academe flags the likely duplicate before it creates a copy, including preprint vs. version-of-record, DOI match, or near-identical titles.
• Edit metadataFix a wrong title or author list from the paper row. Metadata edits are recorded and applied before the paper is used in extractions, so downstream tables stay clean.
• Safe deleteDeleted papers go to a 30-day recovery bin. Restoring a paper puts it back into every collection it was in before.

Build any folder structure that fits your work: by topic, chapter, course, or project phase. Drag and drop to rearrange. Your structure, your call.

Click any PDF to open it in the built-in reader. Highlight passages, jump between pages, and keep the Research Agent panel alongside with full access to the document while you read.

Academe tracks reading status as unread, reading, or done, so you always know what’s left in your pile.

Search 400 million+ scholarly works without leaving Academe. Results show peer-review status, venue quality, and open-access availability. Import records directly into the project.

Traditional reference managers tend to behave like black holes. Large paper libraries go in, very little useful comes back out. The Academe library is designed to be the launchpad for workflows, not the graveyard for PDFs.

• Auto-find full textUpload metadata (DOIs, RIS, BibTeX, EndNote XML, or .enl) and Academe tracks down abstracts and full-text PDFs for as many as possible. Institutional access can expand coverage further.
• Semantic deduplicationAssisted dedup catches duplicates that ordinary metadata checks miss, including preprint vs. version of record, near-identical titles, and author-year fingerprints. Preview a merge with color-coded differences before confirming.
• Import from anywhereBring papers in from Zotero, EndNote, Mendeley, or a folder of PDFs. Collections and tags survive the import so your existing organization is preserved.
• Collections and tagsGroup papers into reading lists, chapter sources, or review shortlists. A paper can live in multiple collections without being duplicated.
• Library-wide searchOnce your library grows past a thousand papers and tagging falls off, full-text search keeps everything findable.

• Can I upload documents that are not papers?Yes. Course readers, book chapters, grant docs, thesis drafts, meeting notes, and syllabi are all supported. Anything in PDF, DOCX, TXT, MD, TEX, XLSX, or PPTX is indexed. Google Docs, Sheets, and Slides import directly.
• Do I have to upload full-text papers?No. Metadata-only records are fine; Academe will attempt to find the full text on your behalf. Some workflows, including extraction and summarization, need full text for accurate citations. Abstract-only records are flagged so you know.
• Is there a limit to how much I can upload?Free and Starter plans have per-project storage allowances that vary by plan tier. Team and Institutional plans include higher fair-use storage allowances. If your workspace approaches its allowance, we will reach out before throttling anything.
• What do you do with the data I upload?Your documents are encrypted at rest, never shared with other users, and never used to train AI models. See the Privacy Policy for retention details.