Dataset
clinicaltrials_2017
Dataset Card for clinicaltrials/2017 The clinicaltrials/2017 dataset, provided by the ir-datasets package. For more information about the dataset, see the documentation. Data This dataset provides: docs (documents, i.e., the corpus); count=241,006 This dataset is used by: clinicaltrials_2017_trec-pm-2017, clinicaltrials_2017_trec-pm-2018 Usage from datasets import load_dataset docs = load_dataset(
Data preview
Dataset card
The clinicaltrials_2017 dataset is an indexed collection of clinical trial records distributed through the ir-datasets package. It is primarily used as a test collection for the TREC Precision Medicine tracks of 2017 and 2018.
What this dataset contains
The dataset holds documents describing individual clinical studies registered in 2017, surfaced through the ir-datasets wrapper for Hugging Face. It is configured as a text retrieval corpus and is paired with two downstream test collections, clinicaltrials_2017_trec-pm-2017 and clinicaltrials_2017_trec-pm-2018, indicating it was assembled for information-retrieval benchmarking rather than investment analysis. No licensing terms, size, or update cadence are stated.
Structure and fields
The documents are loaded via the 'docs' configuration of irds/clinicaltrials_2017. Each record exposes the fields doc_id, title, condition, summary, detailed_description, and eligibility, mirroring the public-facing fields of a ClinicalTrials.gov record. Specific row counts and split definitions are not disclosed in the provided material, so the exact coverage and partitioning cannot be summarized.
How analysts use it
Researchers use the corpus to evaluate retrieval models that match patient or disease queries to relevant trial descriptions, particularly for the TREC Precision Medicine shared tasks. The available fields support filtering on condition and eligibility text, which can be useful for building trial-search prototypes. There is no information in the provided facts to support an investment-fund use case, so none is asserted.
Documentation & references 1
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