The SHARE Research Portal is a publication-discovery and research-support environment for the SHARE (Survey of Health, Ageing and Retirement in Europe) community. It brings together semantic search, a browsable publication index, topic-based exploration, and SARA — the AI research assistant that works over the SHARE-related scientific literature.

What it does

• Research assistant (SARA): conversational access to the publication corpus with citation support, summarisation, and methodology questions grounded in the SHARE literature. SARA runs in two modes: a research mode over the paper index, and a data analyst mode for questions about SHARE variables, cross-wave harmonisation, and analytical workflows.
• Search publications: semantic and hybrid (BM25 + dense vector) search over the full text of indexed publications, with optional neural reranking.
• Browse by topic: OpenAlex-taxonomy-based topic tree (domain → field → subfield → topic) with publication timelines.
• Publication index: bibliographic inventory compiled from the SHARE repository crawler, including entries whose full text is available and those known by reference only.

Semantic search — what it is, and why to use it

Traditional keyword search only finds papers that contain the exact words you type. Semantic search goes a step further: queries and paper passages are both converted into high-dimensional numerical vectors (embeddings) that capture the meaning of the text rather than its surface form. Two passages that talk about the same concept end up close together in that vector space even if they share no vocabulary.

This has several concrete advantages for literature discovery:

• Paraphrase and synonym tolerance: a query for “cognitive decline in old age” also surfaces papers written as “neurocognitive ageing trajectories” or “memory loss in the elderly”.
• Natural-language queries: you can ask full questions (“how does early retirement affect depression risk?”) instead of guessing the right keyword boolean.
• Multilingual robustness: because the embedding model places semantically related terms close together, near-synonyms and closely related concepts across languages or disciplines tend to match.
• Better recall on conceptual questions: semantic search finds relevant material that keyword search would miss because the authors used different terminology.

The portal uses a hybrid retrieval strategy: semantic (dense-vector) scoring is combined with classical BM25 keyword scoring, so you keep the precision of exact term matches (for example specific variable names, author names, or waves) while also benefiting from meaning-based recall. An optional neural cross-encoder reranker then re-scores the top candidates by looking at the query and each passage together, which sharpens the ordering of the final results.

Data sources

• SHARE document repository (scientific publications, working papers, methodology reports, questionnaires, codebooks).
• Bibliographic inventory produced by the SHARE repository crawler (repository_inventory.bib), enriched with DOI and OpenAlex-topic assignments where available.
• Topic assignments derived from the OpenAlex topic taxonomy; papers without an OpenAlex match receive zero-shot assignments as a fallback.

How it works

• Text extraction: PDF content is extracted, cleaned, and chunked before indexing.
• Embeddings: chunks are embedded with BAAI/bge-large-en-v1.5 and stored in a FAISS vector index.
• Hybrid retrieval: queries are answered by combining BM25 lexical scoring with dense-vector similarity; an optional cross-encoder reranker (BAAI/bge-reranker-large) refines the top results.
• Research assistant: SARA runs a locally-hosted instruction-tuned LLM with retrieval-augmented generation against the publication index; it cites the source publications it draws from.
• Topic exploration: per-paper topic assignments are aggregated into a domain/field/subfield/topic tree and visualised as a publication-per-year timeline.

Index refresh

The index is refreshed on a regular schedule. New publications added to the SHARE repository are picked up by the next indexing pass, which reconciles the docstore against the live repository, re-runs text extraction and embedding for new files, and rebuilds the topic assignments.

Access

Access to the research portal is limited to registered SHARE-ERIC data users in good standing. Use of the service is governed by the SHARE-ERIC Conditions of Use and is restricted to scientific research consistent with each user's registered SHARE project.

Technical stack

Vector store

FAISS

Embeddings

BAAI/bge-large-en-v1.5

Lexical index

BM25

Reranker

BAAI/bge-reranker-large cross-encoder

Back end

FastAPI (Python)

Front end

Static HTML, CSS and JavaScript — no build step

Assistant inference

Locally hosted, on this server

Topic taxonomy

OpenAlex, with zero-shot fallback

Using SARA as a data analyst

SARA has a dedicated data-analysis mode for questions about the SHARE dataset itself — variables, coding, harmonisation across waves, analytical workflows, and exploratory statistics — rather than the scientific literature. It complements the research-mode assistant: where research mode reasons over published papers, data-analysis mode reasons over the SHARE data structure and your analytical question.

How to use it:

1. Open the research assistant tab and type your question.
2. If the question clearly looks analytical (for example “how do I compute a change score between waves 6 and 8 for the EURO-D depression scale?”), SARA routes it to the data-analysis mode automatically.
3. If the question is ambiguous, SARA shows a small disambiguation card asking whether you want the research and literature assistant or the data analysis assistant — choose the one that matches your intent.
4. Once you pick a mode, the conversation stays locked to that mode until you start a new chat, so follow-up questions are answered consistently.

The data-analysis mode can execute small analytical snippets, draw plots, and reference SHARE questionnaires and codebooks that are also part of the publication index. No extra setup is required beyond being signed in to the research portal.

Connecting external AI tools (MCP)

SHARE is exposed as a Model Context Protocol server, so external assistants (Claude.ai, ChatGPT, Claude Desktop, any MCP client) can query the literature and the survey documentation mid-conversation — and get back records rather than a plausible-sounding guess.

What it answers:

search_share_variables

Which variable measures a construct — across 57,679 variable labels and the wording of the questions behind them.

get_share_question

What a question actually asked, as fielded in any of 42 language versions — including regional variants such as CH-de, BE-fr, IL-ar and LU-pt. Your model never has to translate SHARE wording, because it can ask for the original.

compare_share_waves

Whether an item was worded the same way across waves — the check that should happen before anyone pools them.

search_papers, get_topic_tree, get_topic_timeline, search_by_topic

The publication index and its OpenAlex topic taxonomy.

Metadata only. No microdata is reachable through this server — not filtered, not aggregated, not one row. That is a property of what the endpoint can execute, not a policy it promises to follow. Analysis stays inside the portal, where you are signed in and the data never leaves.

Two habits are built in. Every record states which documentation release it came from, because documentation and data are not always the same vintage. And where something is not documented, the answer says so and returns what is — constructed variables and national add-on questionnaires sit outside the central instrument, and a label is not a question.

Connection details:

MCP server URL

https://research.share-austria.at/mcp

Authentication

OAuth 2.0 (authorization code + PKCE)

OAuth client ID

your own portal Client ID — the same one you sign in with above.

OAuth client secret

your own portal client secret. If you have mislaid it, ask the portal administrator to reissue one; it is stored only as a hash and cannot be looked up.

Use your personal credentials, never a shared one. Every connection is attributable to one account, which is what makes access revocable for a single user without disrupting anyone else, and what keeps the usage metrics meaningful. Do not paste your secret into a shared machine, a group chat, or a config file you commit to a repository.

Claude.ai (web):

1. Open claude.ai/settings/connectors and choose “Add custom connector”.
2. Paste the server URL, then your own portal Client ID and client secret.
3. Choose Connect; you are redirected through the OAuth flow and back to Claude.
4. Ask, for example, “which SHARE variable measures life satisfaction, what exactly was asked in Italian, and was it worded the same way in wave 4?” — three tool calls, three records, no paraphrase.

ChatGPT: under Settings → Integrations (or Custom Actions), choose “Add MCP server”, paste the same credentials, and complete the OAuth authorisation.

Claude Desktop: add the following to your claude_desktop_config.json (~/Library/Application Support/Claude/ on macOS, ~/.config/Claude/ on Linux, %APPDATA%\Claude\ on Windows), then restart the app:

{
  "mcpServers": {
    "share-research-portal": {
      "url": "https://research.share-austria.at/mcp",
      "auth": {
        "type": "oauth2",
        "client_id": "YOUR-PORTAL-CLIENT-ID",
        "client_secret": "YOUR-PORTAL-CLIENT-SECRET"
      }
    }
  }
}

External MCP clients are subject to the same SHARE-ERIC Conditions of Use as the portal itself — only registered SHARE users in good standing may connect, and all queries are logged with the same privacy-safe usage metrics as direct portal traffic.

Contact

Questions, bug reports, or feedback can be directed to the SHARE Austria research support team.