ir_datasets: PubMed Central (TREC CDS)Bio-medical articles from PubMed Central. Right now, only includes subsets used for the TREC Clinical Decision Support (CDS) 2014-16 tasks.
Subset of PMC articles used for the TREC 2014 and 2015 tasks (v1). Inclues titles, abstracts, full text. Collected from the open access segment on January 21, 2014.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v1")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, journal, title, abstract, body>
You can find more details about the Python API here.
ir_datasets export pmc/v1 docs
[doc_id] [journal] [title] [abstract] [body]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:pmc/v1')
# Index pmc/v1
indexer = pt.IterDictIndexer('./indices/pmc_v1')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['journal', 'title', 'abstract', 'body'])
You can find more details about PyTerrier indexing here.
The TREC Clinical Decision Support (CDS) track from 2014.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v1/trec-cds-2014")
for query in dataset.queries_iter():
query # namedtuple<query_id, type, description, summary>
You can find more details about the Python API here.
ir_datasets export pmc/v1/trec-cds-2014 queries
[query_id] [type] [description] [summary]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:pmc/v1/trec-cds-2014')
index_ref = pt.IndexRef.of('./indices/pmc_v1') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('type'))
You can find more details about PyTerrier retrieval here.
Inherits docs from pmc/v1
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v1/trec-cds-2014")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, journal, title, abstract, body>
You can find more details about the Python API here.
ir_datasets export pmc/v1/trec-cds-2014 docs
[doc_id] [journal] [title] [abstract] [body]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:pmc/v1/trec-cds-2014')
# Index pmc/v1
indexer = pt.IterDictIndexer('./indices/pmc_v1')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['journal', 'title', 'abstract', 'body'])
You can find more details about PyTerrier indexing here.
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | not relevant |
| 1 | possibly relevant |
| 2 | definitely relevant |
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v1/trec-cds-2014")
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
You can find more details about the Python API here.
ir_datasets export pmc/v1/trec-cds-2014 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:pmc/v1/trec-cds-2014')
index_ref = pt.IndexRef.of('./indices/pmc_v1') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
[pipeline],
dataset.get_topics('type'),
dataset.get_qrels(),
[MAP, nDCG@20]
)
You can find more details about PyTerrier experiments here.
Bibtex:
@inproceedings{Simpson2014TrecCds, title={Overview of the TREC 2014 Clinical Decision Support Track}, author={Matthew S. Simpson and Ellen M. Voorhees and William Hersh}, booktitle={TREC}, year={2014} }The TREC Clinical Decision Support (CDS) track from 2015.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v1/trec-cds-2015")
for query in dataset.queries_iter():
query # namedtuple<query_id, type, description, summary>
You can find more details about the Python API here.
ir_datasets export pmc/v1/trec-cds-2015 queries
[query_id] [type] [description] [summary]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:pmc/v1/trec-cds-2015')
index_ref = pt.IndexRef.of('./indices/pmc_v1') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('type'))
You can find more details about PyTerrier retrieval here.
Inherits docs from pmc/v1
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v1/trec-cds-2015")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, journal, title, abstract, body>
You can find more details about the Python API here.
ir_datasets export pmc/v1/trec-cds-2015 docs
[doc_id] [journal] [title] [abstract] [body]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:pmc/v1/trec-cds-2015')
# Index pmc/v1
indexer = pt.IterDictIndexer('./indices/pmc_v1')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['journal', 'title', 'abstract', 'body'])
You can find more details about PyTerrier indexing here.
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | not relevant |
| 1 | possibly relevant |
| 2 | definitely relevant |
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v1/trec-cds-2015")
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
You can find more details about the Python API here.
ir_datasets export pmc/v1/trec-cds-2015 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:pmc/v1/trec-cds-2015')
index_ref = pt.IndexRef.of('./indices/pmc_v1') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
[pipeline],
dataset.get_topics('type'),
dataset.get_qrels(),
[MAP, nDCG@20]
)
You can find more details about PyTerrier experiments here.
Bibtex:
@inproceedings{Roberts2015TrecCds, title={Overview of the TREC 2015 Clinical Decision Support Track}, author={Kirk Roberts and Matthew S. Simpson and Ellen Voorhees and William R. Hersh}, booktitle={TREC}, year={2015} }Subset of PMC articles used for the TREC 2016 task (v2). Inclues titles, abstracts, full text. Collected from the open access segment on March 28, 2016.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v2")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, journal, title, abstract, body>
You can find more details about the Python API here.
ir_datasets export pmc/v2 docs
[doc_id] [journal] [title] [abstract] [body]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:pmc/v2')
# Index pmc/v2
indexer = pt.IterDictIndexer('./indices/pmc_v2')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['journal', 'title', 'abstract', 'body'])
You can find more details about PyTerrier indexing here.
The TREC Clinical Decision Support (CDS) track from 2016.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v2/trec-cds-2016")
for query in dataset.queries_iter():
query # namedtuple<query_id, type, note, description, summary>
You can find more details about the Python API here.
ir_datasets export pmc/v2/trec-cds-2016 queries
[query_id] [type] [note] [description] [summary]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:pmc/v2/trec-cds-2016')
index_ref = pt.IndexRef.of('./indices/pmc_v2') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('type'))
You can find more details about PyTerrier retrieval here.
Inherits docs from pmc/v2
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v2/trec-cds-2016")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, journal, title, abstract, body>
You can find more details about the Python API here.
ir_datasets export pmc/v2/trec-cds-2016 docs
[doc_id] [journal] [title] [abstract] [body]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:pmc/v2/trec-cds-2016')
# Index pmc/v2
indexer = pt.IterDictIndexer('./indices/pmc_v2')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['journal', 'title', 'abstract', 'body'])
You can find more details about PyTerrier indexing here.
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | not relevant |
| 1 | possibly relevant |
| 2 | definitely relevant |
Examples:
import ir_datasets
dataset = ir_datasets.load("pmc/v2/trec-cds-2016")
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
You can find more details about the Python API here.
ir_datasets export pmc/v2/trec-cds-2016 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:pmc/v2/trec-cds-2016')
index_ref = pt.IndexRef.of('./indices/pmc_v2') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
[pipeline],
dataset.get_topics('type'),
dataset.get_qrels(),
[MAP, nDCG@20]
)
You can find more details about PyTerrier experiments here.
Bibtex:
@inproceedings{Roberts2016TrecCds, title={Overview of the TREC 2016 Clinical Decision Support Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen M. Voorhees and William R. Hersh}, booktitle={TREC}, year={2016} }