Find sources, inspect originals and correct classification and tags.
Reviewed · 2026-10-12
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1. Find a document
- Open Materials and enter a title or body keyword in the basic search field.
- Expand Advanced search and type to select grants, projects, researchers or tags. Add multiple selections and remove them with ×.
- Choose the relevant kind: report, minutes, code, data, general material or reference, then search. Remove filters one by one if no results appear.
- Open a title to inspect its body, source date, collection date and original link. Extracted text may differ from the original PDF or table layout.
2. Correct metadata
- Open the classification and related-items area on a material page. Its connection creator, direct uploader or manager may save changes while able to read it; other readers may propose changes.
- Correct its kind, assign grants, projects, researchers and tags, then save. Source paths drive automatic code classification, while manual classification is retained separately.
- Review whether it should remain reference context. This makes it a background candidate for AI, not proof of work in the reporting period.
- Return to search and verify the kind and tags. Assigning metadata does not grant other users access.
3. Use references
- Open Project home → References for connected Zotero items.
- Review titles, authors, dates, abstracts, DOIs and tags and follow DOI or original links.
- Select Reference in Materials to search accessible bibliography across projects.
- Edit metadata in Zotero, sync online and collect again. Zotero collection currently reads bibliography and abstracts, not automatically acquired PDF full text.
Find materials in the grouped overview
The Materials page shows counts and recent items grouped by type. Expand an item to see its dates and a short excerpt, then choose Open material for its full copy and relationships. View all inside a group keeps your filters and that type; View all at the top shows all current matches. Advanced grant, project, researcher, tag, date and type filters remain available.
4. Generate tags and review them in place
- Expand metadata editing on a material/report and choose Generate beside Tags (comma-separated).
- The title, headings and body supply one to five keywords. Review the chips/field and status in place without returning to the top.
- Use Keep existing tags to retain current tags while adding suggestions; when replacing them, review the proposed set.
- Edit unsuitable tags and save metadata. Depending on your role, changes may become an approval proposal rather than an immediate edit; read the status message.
- Tags neither rewrite the body nor grant access. Check other unsaved fields and the final saved/proposal state.
Find materials with AI
- Enter keywords in Materials and select Find with AI. The search opens on the right on desktop and fills the screen on mobile. Current date, grant, project, researcher, type and tag filters are retained.
- Cards show excerpts, match reasons and dates; open the title to read the source. Metadata-only sources are labelled. Search uses no AI generation allowance.
- Choose Ask about this source, or enter a summary/comparison question and choose Ask AI. This starts a private consultation using your personal AI allowance and the saved search boundary. Return to List and search filters to change the scope.
- AI receives bounded excerpts, not every original, image or attachment. If there are no results, review the query and filters.
Search terms and relevance
- Enter several terms such as imagery eeg. Exact phrases rank first, then all terms, curated related terms and partial matches. Supported bilingual terms include EEG/뇌파 and imagery/심상. This does not translate arbitrary terms or find every semantically similar document.
- Use "imagery eeg" for a required phrase, imagery OR eeg for either term, and eeg -animal to exclude a term. Quoted terms are not expanded. Use up to 200 characters and 16 terms. Suggested queries apply only when selected.
- Choose Relevance or Default order. Date, grant, visibility, tag and current access restrictions stay in effect. Calendar items stay on their dates; the list below can rank by relevance. Announcements search only the published edition in the current language.
- Basic search makes no external AI calls and consumes no personal or lab AI allowance. It searches the fields available on each page, such as a material title and stored body, without fetching inaccessible external files. Citation search uses titles only and preserves the conversation readers' access restrictions.
Find and edit literature in Materials
- Open References in Materials or search by title, author or DOI. References saved in a research space are discoverable in the same catalog; do not register another copy.
- Select a card title to open its wide permanent detail page; View all opens the dedicated literature list. Translation, sharing, favorites and print are at the top, followed by authors, venue, publication date, abstract and source links. Original abstracts and AI summaries are separate. Generate an AI summary only with the explicit button or request.
- Edit only the required bibliography fields. Materials and the space use the same reference ID and correction. Check the applicable research space; other spaces and Zotero originals are not silently overwritten.
- Select references and choose BibTeX, RIS or CSL JSON under Export. Distinguish selected references, all search matches and the complete current scope. All includes every page in that scope, not just the visible rows.
- Use File → Import in Zotero to read the exported file. This is a bibliographic snapshot, not automatic two-way synchronization. Unpinning does not delete the original material.
Register references and request source storage in conversation
- Tell native AI or ChatGPT the research space and the DOI, source URL or bibliography to register. Example: “Register these papers in this space, reuse existing references and do not generate new AI summaries.”
- Check the destination project/grant, audience, new/existing references and original attachments. The host must actually expose the required tools. Search-only tools cannot prove registration.
- Check each returned material link and status. With a linked research work, the actual registration request and receipt also appear in its history. Ask for a destination if work context is unclear; never invent earlier conversation that was not delivered. Bibliography registration, pinning, source storage, text extraction and AI analysis are distinct. Reuse the same request ID when retrying to prevent duplicates.
- Add a PDF later from the reference detail page. Active authorized participants can contribute files, preserving contributor and file versions. Identical files are not linked twice and earlier PDFs are not overwritten. Distinguish published papers, preprints and supplements. In conversation, identify the existing reference before attaching a PDF. Example: “Attach this PDF to the existing reference without creating another paper.” Only delivered files or accessible originals can be stored.
- A ZIP or download link generated in ChatGPT is not proof of platform storage. Check file delivery and the platform receipt. Registration, reading, pinning and exporting do not themselves generate AI summaries.
- The tool sequence is consultant_material_prepare → consultant_material_commit → consultant_material_status. Review the destination and audience, then pass the unchanged preview_token and review. A cached host can use the existing consultant_thread_prepare save / materials envelope for the same service. If neither route is exposed, refresh the connection tool list.
- A request accepts up to 50 items and 100 MB of combined attachments. Distinguish “register bibliography only” from “also store an accessible PDF.” A paper landing page is not reported as a saved PDF; supply an actual PDF file when needed.
Understand storage, PDF and extraction states
- Once registered, bibliography can be searched and edited before source collection finishes. Do not create duplicate references because earlier files are still processing.
- Queued and running storage are distinct. Stored means a verified preservation receipt exists. An archived HTML page is not a PDF.
- If the original is stored but text extraction fails, it remains downloadable. Combined source attachments may total 100 MB; safe text extraction supports up to 20 MB per file. Larger originals are preserved with an extraction-limit status. Scans, protected documents or unsupported formats may not yield readable text.
- Retryable storage failures use bounded attempts and backoff. For persistent failures or access requirements, inspect the reason and supply an accessible file. Access and login restrictions are not bypassed.
- A PDF downloadable from its reference is distinct from source archiving and text extraction. Linking a PDF does not prove external synchronization, extraction or AI analysis. AI analysis is a separate request with its own usage. Stored originals or extracted text do not mean AI analysis has completed.
Bibliography and PDF visibility
New public-paper bibliography is visible by default to active signed-in platform users, not anonymous visitors. PDF originals, private analysis and research spaces retain their existing access. Reusing an existing private source does not publish it. Edit bibliographic fields and grant/project/researcher metadata on the permanent literature page. External Zotero originals are not overwritten.
Find and attach PDFs from ChatGPT
Example: “Find references in this space that lack a PDF. Locate public originals, verify the title and DOI, and attach them to the existing references while preserving earlier files.” Tools import a public HTTPS PDF or an actually transferable ChatGPT file and verify bytes, size and hash. They never bypass login or paid access. Original preservation, text preparation and AI analysis are separate states; an unavailable file transfer is not a saved result.
Compare literature and save to the same work
Example: “Compare these three papers for the Paper Goal and Literature Survey work. State the PDF version and actual coverage; tabulate model structure, data, metrics and limitations. Save the delivered analysis as another request/result pair in this same work and attach the comparison file.” The existing work ID and earlier results are preserved. An excerpt is never labelled a complete-paper reading. Specify explicitly when a new derived work is intended.
File transfer and retry
Example: “Save the actual image and BibTeX files just created as outputs of this work. Report each saved status and download link.” Attach up to 50 files, combined 100 MB. Reuse the request key for retries; reselect files to renew expired transfer links. The file tool must actually be available in the ChatGPT host. If unavailable, transfer the files through Add sources on the platform. A filename or temporary link is not a preserved file.
Edit, export and apply requests
Search, select required references or all references, then use the download icon beside search for BibTeX, RIS or CSL JSON. Example: “Correct the selected authors and DOI while preserving the original.” AI summaries run only after a button action or explicit request. Applying the refinement preview updates both the current composer and its local draft, ready to inspect before sending. Dashed graph edges indicate references; solid edges indicate research flow.
Start paper analysis in the same work
In the existing work composer, use Add sources to select one reference or PDF. Enter a goal-specific question, choose Paper analysis and discovery in the Send to AI menu, and send. Source reading, backward/forward/topic search, registration, synthesis and manuscript drafts are checkpointed. Search uses the public paper title or your explicit public query, not private goals. Verified references and final recommendations are registered automatically; PDF collection is queued with actual storage status. Missing originals limit analysis to the abstract. Forward and similar searches cover the first 25 results each; request further searches separately. Paywalls and institutional login are not bypassed; unavailable originals retain a reason. Registration, original collection, AI analysis and author review are separate states.
Goals, notifications and collaborative review
Use work menu → Research conditions to edit the question, venue, deadline and constraints. Expand Collaborative guidance to opt into a reviewed goal snapshot and next-action notifications. Only the published goal version is shared; private edits do not silently replace it. Notifications open the same suggestion ID used by the composer and do not execute AI. Deferred or dismissed suggestions are not resurrected. Before joint use, check work sharing, access to originals and the shared goal separately. Approved methods also require use and provider-processing permission. Participants use the research team destination for follow-up analysis. Private results are not published automatically. A researcher confirms submission or goal completion.
Improve v1 into v2
Use work menu → Know-how settings to select exact approved versions. The default direction and relevant common/activity guidance are pinned; four stages are not required for every work. Existing work-only know-how remains available in collapsed history. Select currently accessible, non-deleted request/result evidence and distinguish paper-specific fixes from reusable improvements. Compare against the actual active v1 using matched input/model/work conditions, held-out input and human review receipts before considering v2 promotion. Promotion changes future defaults and is reversible; it does not rewrite existing results. The built-in method before initialization is distinct from a reviewed and evaluated v1. Method versions, manuscript revisions and model IDs are distinct. Output-only use, source reading and derivation are separate permissions.
Delete and restore request/result pairs
The author or an authorized space manager can move an accessible request/result pair to trash through its menu. Removed content is excluded from new analysis, method improvement and downloads; other researchers’ follow-ups and existing quotations remain. Restore through Deleted requests and results; this never restarts AI. Management does not grant access to another person’s private conversation. External copies and provider costs already incurred are unaffected.
ChatGPT, consultation, Overleaf and costs
Continue in ChatGPT preserves the same work ID and selected evidence, saving only delivered results. Survey request/status/resume/external, method reads/improvements and pair delete/restore reuse consultant_thread_prepare/commit reviews. Independent host analysis never claims to have executed a protected method. Core instructions are excluded from ordinary results, history and exports. Provider processing permission is reviewed separately; this does not guarantee that outputs cannot reveal or imply knowledge. Download the draft and BibTeX, review them, then incorporate them in Overleaf; there is no automatic bidirectional sync. Per-request, daily and provider limits pause work while preserving completed stages. Inspect saved stages and usage before explicitly resuming. Team consultation tools currently support requests and status; use the web for resuming and method management. Host tool availability, new server PDF collection, AI quality and Lightsail latency require separate verification.
Keep only the know-how you choose from a discussion
In the same work composer, ask: “Separate the literature-analysis and manuscript-writing know-how from this discussion. Propose only the changes to each existing method.” Only delivered, saved requests and results are evidence. External ChatGPT or Codex can prepare structured proposals using returned evidence IDs and current permissions, then use the existing reviewed save contract. On the proposal page, select only the drafts you want to keep and review the save. Unselected proposals and active versions remain unchanged. Preparing or saving a draft does not validate quality or approve v2.
Apply several methods together
Use the applied know-how count below the work title, or work menu → Know-how settings, to manage defaults. No empty badge appears for zero items. Search names, handles or activities and review exact versions and roles before saving. Add to a work on the independent detail page lets you choose a space and work and pin an exact version. Type @ in a follow-up, new research request or personal AI consultation to search researchers and know-how together. Choosing know-how adds it only to that request without changing defaults. [[ source citations keep their existing behavior. Multiple selections never auto-merge assets or adopt newer versions. Web, consultation and ChatGPT handoff check the same IDs and current permissions.
Split, merge, originals and shared evidence
One discussion can produce independent proposals that reference the same segment as shared evidence. Drafts start privately. Separate paper-specific corrections and unverified scientific conclusions from reusable guidance. Change organization prepares a split or merge request in the existing composer for review and sending. A merge proposes a new asset linked to exact parent versions. It does not delete or overwrite the versions, history or active state of assets A and B. Changing how a proposal is organized requires a newly reviewed proposal, not an unreviewed browser-side rewrite.
Version inheritance and evaluation
Derived work inherits the exact selected versions of independent assets. For existing work-specific methods, the collapsed Work know-how controls retain version and candidate selection in the original work. Review generalized instructions and create a separate asset before reuse in other work. Execution applies modules relevant to the current stage, such as literature, synthesis, writing or review, while retaining links to other stages. New versions are not adopted automatically. Initial approval reviews the actual instructions and their generalization; it does not claim measured quality improvement. Improvement candidates require actual runs with matched questions, inputs, models and effort, held-out cases and human review before adoption. A candidate trial is a one-off selection that does not change the default and uses the normal AI allowance.
Protection, storage and execution boundaries
An independent detail page shows introduction, ID, handle, authors, usage, current permissions and versions. Editing the introduction or image link does not change execution guidance; image access is checked separately. The default direction is protected guidance. Write a draft or propose a direction improvement, review it and select which drafts to keep. Saving an introduction or candidate does not change the active version. Ordinary catalogs and proposal cards exclude protected instructions. Source review requires current permission and AI-provider processing permission is separate. Reads, selections and structured proposal saves do not run AI, while sending analysis or reorganization requests may use your allowance. Independent external analysis does not prove protected-service execution. Instructions belong in encrypted backups, not code or static files. AI quality, actual ChatGPT tool availability and server speed require separate validation.