---
license: cc-by-4.0
language:
- en
size_categories:
- n<1K
task_categories:
- text-retrieval
tags:
- academic-search
- federated-search
- arxiv
- pubmed
- semantic-scholar
- openalex
- retrieval
- benchmark
- deduplication
- research-papers-mcp
configs:
- config_name: default
  data_files:
  - split: train
    path: traces.json
---

# Federated Academic Search Traces (research-papers-mcp benchmark)

## What this is

30 curated academic search queries run through the `research-papers-mcp` federated search pipeline. For each query, the dataset captures:

- The query text + domain label + expected-strong-sources annotation
- Per-source raw result counts (before dedup)
- Total deduplicated count + number of duplicates removed
- Top 10 deduplicated paper records (title, year, source, DOI, URL)
- Per-source wall-clock timing

The queries span 30 domains across ML, biomedicine, physics, philosophy, economics, computer science, and astronomy — chosen to exercise each source's coverage strengths.

### Sources active during this trace

This snapshot was captured against the `research-papers-mcp` package as installed from `main` branch at the time of run. The registry contained three live sources:

| Source | Status during trace |
|---|---|
| arXiv | Active (rate-limited on bulk queries — see `per_source_timing_sec` for evidence) |
| PubMed | Active, 30/30 queries returned results |
| Semantic Scholar | Active but throttled (HTTP 429 on 23/30 queries) |
| OpenAlex | **Not registered on the `main` branch at trace time.** A `PaperSource`-protocol package abstraction exists on a feature branch but had not been merged. Future traces will include OpenAlex once that branch lands. |

This is itself a useful signal: the trace honestly records which sources a `pip install -e .` from main got at trace time. Users running newer versions of the package will see different (likely fuller) coverage.

## Schema

| Field | Type | Description |
|---|---|---|
| `id` | string | Query identifier (`q01` to `q30`) |
| `query` | string | The search query text |
| `domain` | string | Coarse-grained topic domain |
| `expected_strong_sources` | list[string] | Sources we expect to return strong matches |
| `per_source_raw_count` | dict[str, int] | Records returned by each source before dedup |
| `raw_total` | int | Sum across sources |
| `deduped_count` | int | After DOI-then-(source, source_id) deduplication |
| `duplicates_removed` | int | `raw_total - deduped_count` |
| `per_source_timing_sec` | dict[str, float] | Wall-clock seconds per source |
| `top_papers` | list[object] | First 10 deduplicated results (title, year, source, doi, url) |

## Load it

```python
from datasets import load_dataset
ds = load_dataset("barissozudogru/federated-search-traces")
print(ds["train"][0])
```

Or directly:

```python
import json
import requests
data = requests.get("https://huggingface.co/datasets/barissozudogru/federated-search-traces/resolve/main/traces.json").json()
for r in data[:5]:
    print(r["query"], "->", r["deduped_count"], "results")
```

## Generating method

Queries were run through the `research-papers-mcp` pipeline at <https://github.com/barissozudogru/deep-research-digest>. Each source is called concurrently with `max_results=15`. Deduplication is by DOI first (lowercased, stripped), then by `(source, source_id)`. Trace is captured at one point in time — re-running may give different counts as the live indices update.

The full reproduction script is `run_benchmark.py` in this dataset's repo.

## Use cases

- Regression-testing federated search dedup logic
- Comparing source coverage across domains
- Benchmarking new sources (e.g., does adding CORE or bioRxiv change dedup ratios meaningfully?)
- Sanity-checking ranking algorithms on stable inputs

## Caveats

- **Single snapshot**, not a live mirror. External indices update; re-running may produce different counts.
- **Sources may rate-limit**. If `per_source_raw_count[name] == 0` for a query, that source either truly had no matches OR was throttled at trace time. Compare with `per_source_timing_sec` for hints.
- **Query selection is editorial**, not random. We picked queries to exercise the four sources' coverage strengths. Inferences about typical user behavior should treat this as a stress-test corpus, not a representative one.
- **No ground-truth relevance labels**. This is a trace dataset (what came back), not an annotated benchmark (what should have come back). For relevance evaluation, pair with human-annotated relevance judgments.

## Related

- [research-papers-mcp on GitHub](https://github.com/barissozudogru/deep-research-digest) — the federated-search MCP server
- [Live Space demo](https://huggingface.co/spaces/barissozudogru/research-papers-mcp) — interactive federated search
- [OpenAlex concepts dataset](https://huggingface.co/datasets/barissozudogru/openalex-concepts) — taxonomy used for topic tagging in the pipeline