What kinds of documents and research tasks is Datashare designed for?

Datashare (datashare.icij.org) is designed for searching and exploring large document collections — especially leaked, investigative, and public-interest archives — rather than for creating or editing documents. It fits workflows where a researcher needs to find, filter, and read across thousands of files instead of writing new ones. Because the site provides no feature list or format documentation, confirm specific supported file types and capabilities inside the application itself before relying on them.

The document sets it targets

Datashare is built around the kind of material that investigative journalists and researchers accumulate in bulk:

• Leaked archives — large dumps of emails, messages, and internal files that need to be sifted for relevant names, dates, and topics.
• Public-interest document collections — published records, reports, and correspondence that a team wants to search as one corpus.
• Mixed-format folders — real-world archives rarely arrive neatly organized, so the tool is aimed at collections where many files sit together and need to be queried at once.

The common thread is volume and heterogeneity: the value comes from querying across the whole set, not from opening files one at a time.

The research tasks it supports

The core workflow is search-and-explore, not authoring. In practice that means:

1. Loading a collection into the tool so its contents become searchable.
2. Querying by terms, names, or phrases to surface matching documents.
3. Filtering and narrowing results to a manageable set.
4. Reading and reviewing the documents that match, then iterating with new queries.

This is the opposite of a word processor or spreadsheet: you are not producing a deliverable inside the tool, you are locating and examining material that already exists.

A concrete example

Suppose a newsroom receives a folder of several thousand emails and attachments from a single organization. Rather than opening each file, an investigator loads the folder into Datashare, searches for a project name or a person's surname, filters the hits by date or file type, and reads the matching documents to build a timeline. Each answer prompts a follow-up query — a name found in one email becomes the next search term.

What to verify before you commit

The available site information does not list supported formats, indexing behavior, or specific features. Before adopting it for a project, check inside the app:

• Which file types it can ingest and search (text documents, email formats, spreadsheets, PDFs, and so on).
• Whether it handles scanned or image-only files, and how.
• What filtering options exist beyond keyword search.
• How collections are organized and whether results can be exported.

Treat any capability not confirmed in the application as unverified. If your archive contains formats the tool does not handle, plan to convert or exclude those files first.

Who it suits — and who it doesn't

The deciding question is scale and purpose: if you need to interrogate a big, messy collection of existing documents, Datashare is aimed at that job; if you need to produce documents or work with a handful of files, it is the wrong tool.