ir_datasets : Clinical Trials

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2017 docs



[doc_id]    [title]    [condition]    [summary]    [detailed_description]    [eligibility]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017')
# Index clinicaltrials/2017
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2017')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.clinicaltrials.2017')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

{
  "docs": {
    "count": 241006,
    "fields": {
      "doc_id": {
        "max_len": 11,
        "common_prefix": "NCT0"
      }
    }
  }
}

`"clinicaltrials/2017/trec-pm-2017"`

The TREC 2017 Precision Medicine clinical trials subtask.

30 queries

Language: en

Query type:

TrecPm2017Query: (namedtuple)

query_id: str
disease: str
gene: str
demographic: str
other: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2017")
for query in dataset.queries_iter():
    query # namedtuple<query_id, disease, gene, demographic, other>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2017/trec-pm-2017 queries



[query_id]    [disease]    [gene]    [demographic]    [other]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2017')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('disease'))

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.clinicaltrials.2017.trec-pm-2017.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

241K docs

Inherits docs from clinicaltrials/2017

Language: en

Document type:

ClinicalTrialsDoc: (namedtuple)

doc_id: str
title: str
condition: str
summary: str
detailed_description: str
eligibility: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2017")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2017/trec-pm-2017 docs



[doc_id]    [title]    [condition]    [summary]    [detailed_description]    [eligibility]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2017')
# Index clinicaltrials/2017
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2017')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.clinicaltrials.2017.trec-pm-2017')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

13K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int
iteration: str

Relevance levels

Rel.	Definition	Count	%
0	not relevant	`12K`	91.0%
1	possibly relevant	`735`	5.6%
2	definitely relevant	`436`	3.3%

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2017")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2017/trec-pm-2017 qrels --format tsv



[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2017')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('disease'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.clinicaltrials.2017.trec-pm-2017.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

Citation

ir_datasets.bib:

\cite{Roberts2017TrecPm}

Bibtex:

@inproceedings{Roberts2017TrecPm, title={Overview of the TREC 2017 Precision Medicine Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar and Shubham Pant}, booktitle={TREC}, year={2017} }

{
  "docs": {
    "count": 241006,
    "fields": {
      "doc_id": {
        "max_len": 11,
        "common_prefix": "NCT0"
      }
    }
  },
  "queries": {
    "count": 30
  },
  "qrels": {
    "count": 13019,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "0": 11848,
          "1": 735,
          "2": 436
        }
      }
    }
  }
}

`"clinicaltrials/2017/trec-pm-2018"`

The TREC 2018 Precision Medicine clinical trials subtask.

50 queries

Language: en

Query type:

TrecPmQuery: (namedtuple)

query_id: str
disease: str
gene: str
demographic: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2018")
for query in dataset.queries_iter():
    query # namedtuple<query_id, disease, gene, demographic>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2017/trec-pm-2018 queries



[query_id]    [disease]    [gene]    [demographic]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2018')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('disease'))

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.clinicaltrials.2017.trec-pm-2018.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

241K docs

Inherits docs from clinicaltrials/2017

Language: en

Document type:

ClinicalTrialsDoc: (namedtuple)

doc_id: str
title: str
condition: str
summary: str
detailed_description: str
eligibility: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2018")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2017/trec-pm-2018 docs



[doc_id]    [title]    [condition]    [summary]    [detailed_description]    [eligibility]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2018')
# Index clinicaltrials/2017
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2017')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.clinicaltrials.2017.trec-pm-2018')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

14K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int
iteration: str

Relevance levels

Rel.	Definition	Count	%
0	not relevant	`12K`	85.6%
1	possibly relevant	`1.2K`	8.3%
2	definitely relevant	`873`	6.2%

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2018")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2017/trec-pm-2018 qrels --format tsv



[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2018')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('disease'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.clinicaltrials.2017.trec-pm-2018.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

Citation

ir_datasets.bib:

\cite{Roberts2018TrecPm}

Bibtex:

@inproceedings{Roberts2018TrecPm, title={Overview of the TREC 2018 Precision Medicine Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar}, booktitle={TREC}, year={2018} }

{
  "docs": {
    "count": 241006,
    "fields": {
      "doc_id": {
        "max_len": 11,
        "common_prefix": "NCT0"
      }
    }
  },
  "queries": {
    "count": 50
  },
  "qrels": {
    "count": 14188,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "0": 12141,
          "2": 873,
          "1": 1174
        }
      }
    }
  }
}

`"clinicaltrials/2019"`

A snapshot of ClinicalTrials.gov from May 2019 for use with the clinicaltrials/2019/trec-pm-2019 Clinical Trials subtask.

Dataset information

docs

306K docs

Language: en

Document type:

ClinicalTrialsDoc: (namedtuple)

doc_id: str
title: str
condition: str
summary: str
detailed_description: str
eligibility: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2019")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2019 docs



[doc_id]    [title]    [condition]    [summary]    [detailed_description]    [eligibility]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2019')
# Index clinicaltrials/2019
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2019')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.clinicaltrials.2019')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

{
  "docs": {
    "count": 306238,
    "fields": {
      "doc_id": {
        "max_len": 11,
        "common_prefix": "NCT0"
      }
    }
  }
}

`"clinicaltrials/2019/trec-pm-2019"`

The TREC 2019 Precision Medicine clinical trials subtask.

40 queries

Language: en

Query type:

TrecPmQuery: (namedtuple)

query_id: str
disease: str
gene: str
demographic: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2019/trec-pm-2019")
for query in dataset.queries_iter():
    query # namedtuple<query_id, disease, gene, demographic>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2019/trec-pm-2019 queries



[query_id]    [disease]    [gene]    [demographic]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2019/trec-pm-2019')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2019') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('disease'))

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.clinicaltrials.2019.trec-pm-2019.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

306K docs

Inherits docs from clinicaltrials/2019

Language: en

Document type:

ClinicalTrialsDoc: (namedtuple)

doc_id: str
title: str
condition: str
summary: str
detailed_description: str
eligibility: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2019/trec-pm-2019")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2019/trec-pm-2019 docs



[doc_id]    [title]    [condition]    [summary]    [detailed_description]    [eligibility]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2019/trec-pm-2019')
# Index clinicaltrials/2019
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2019')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.clinicaltrials.2019.trec-pm-2019')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

13K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int
iteration: str

Relevance levels

Rel.	Definition	Count	%
0	not relevant	`11K`	83.2%
1	possibly relevant	`1.7K`	13.1%
2	definitely relevant	`485`	3.7%

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2019/trec-pm-2019")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2019/trec-pm-2019 qrels --format tsv



[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2019/trec-pm-2019')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2019') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('disease'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.clinicaltrials.2019.trec-pm-2019.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

Citation

ir_datasets.bib:

\cite{Roberts2019TrecPm}

Bibtex:

@inproceedings{Roberts2019TrecPm, title={Overview of the TREC 2019 Precision Medicine Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar and Shubham Pant and Funda Meric-Bernstam}, booktitle={TREC}, year={2019} }

{
  "docs": {
    "count": 306238,
    "fields": {
      "doc_id": {
        "max_len": 11,
        "common_prefix": "NCT0"
      }
    }
  },
  "queries": {
    "count": 40
  },
  "qrels": {
    "count": 12996,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "0": 10811,
          "1": 1700,
          "2": 485
        }
      }
    }
  }
}

`"clinicaltrials/2021"`

A snapshot of ClinicalTrials.gov from April 2021 for use with the TREC Clinical Trials 2021 Track.

Dataset information

docs

376K docs

Language: en

Document type:

ClinicalTrialsDoc: (namedtuple)

doc_id: str
title: str
condition: str
summary: str
detailed_description: str
eligibility: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2021")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2021 docs



[doc_id]    [title]    [condition]    [summary]    [detailed_description]    [eligibility]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2021')
# Index clinicaltrials/2021
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2021')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.clinicaltrials.2021')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

{
  "docs": {
    "count": 375580,
    "fields": {
      "doc_id": {
        "max_len": 11,
        "common_prefix": "NCT0"
      }
    }
  }
}

`"clinicaltrials/2021/trec-ct-2021"`

The TREC Clinical Trials 2021 track.

Shared Task Website

75 queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2021/trec-ct-2021")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2021/trec-ct-2021 queries



[query_id]    [text]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2021/trec-ct-2021')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2021') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.clinicaltrials.2021.trec-ct-2021.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

376K docs

Inherits docs from clinicaltrials/2021

Language: en

Document type:

ClinicalTrialsDoc: (namedtuple)

doc_id: str
title: str
condition: str
summary: str
detailed_description: str
eligibility: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2021/trec-ct-2021")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2021/trec-ct-2021 docs



[doc_id]    [title]    [condition]    [summary]    [detailed_description]    [eligibility]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2021/trec-ct-2021')
# Index clinicaltrials/2021
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2021')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.clinicaltrials.2021.trec-ct-2021')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

36K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int
iteration: str

Relevance levels

Rel.	Definition	Count	%
0	Not Relevant	`24K`	67.7%
1	Excluded	`6.0K`	16.8%
2	Eligible	`5.6K`	15.5%

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2021/trec-ct-2021")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2021/trec-ct-2021 qrels --format tsv



[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2021/trec-ct-2021')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2021') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.clinicaltrials.2021.trec-ct-2021.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

{
  "docs": {
    "count": 375580,
    "fields": {
      "doc_id": {
        "max_len": 11,
        "common_prefix": "NCT0"
      }
    }
  },
  "queries": {
    "count": 75
  },
  "qrels": {
    "count": 35832,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "1": 6019,
          "0": 24243,
          "2": 5570
        }
      }
    }
  }
}

`"clinicaltrials/2021/trec-ct-2022"`

The TREC Clinical Trials 2022 track.

Shared Task Website

50 queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2021/trec-ct-2022")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2021/trec-ct-2022 queries



[query_id]    [text]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2021/trec-ct-2022')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2021') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.clinicaltrials.2021.trec-ct-2022.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

376K docs

Inherits docs from clinicaltrials/2021

Language: en

Document type:

ClinicalTrialsDoc: (namedtuple)

doc_id: str
title: str
condition: str
summary: str
detailed_description: str
eligibility: str

Examples:

import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2021/trec-ct-2022")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>

You can find more details about the Python API here.

CLI

ir_datasets export clinicaltrials/2021/trec-ct-2022 docs



[doc_id]    [title]    [condition]    [summary]    [detailed_description]    [eligibility]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2021/trec-ct-2022')
# Index clinicaltrials/2021
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2021')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.clinicaltrials.2021.trec-ct-2022')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore