cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14742885
title: Extracting Clinical Significance for Drug-Gene Interactions using FDA Label Packages

description: The drug-gene interaction database (DGIdb) is a resource that aggregates interaction data from over 40 different resources into one platform with the primary goal of making the druggable genome accessible to clinicians and researchers. By providing a public, computationally accessible database, DGIdb enables therapeutic insights through broad aggregation of drug-gene interaction data.

As part of our aggregation process, DGIdb preserves data regarding interaction types, directionality, and other attributes that enable filtering or biochemical insight. However, source data are often incomplete and may not contain the therapeutic relevance of the interaction. In this report, we address these missing data and demonstrate a pipeline for extracting physiological context from free-text sources. We apply existing large language models (LLMs) to tag and extract indications, cancer types, and relevant pharmacogenomics from free-text, FDA approved labels. We are then able to utilize the Variant Interpretation for Cancer Consortium (VICC) normalization services to ground extracted data back to formally grouped concepts.

In a preliminary test set of 355 FDA labels, we were able to normalize 86.5% of extracted chemical entities back to ontologically-grounded therapeutic concepts. We can link this therapeutic context data back to interaction records already searchable within DGIdb. By using LLMs to extract this data set, we can supplement our existing interaction data with relevant indications, pharmacogenomic data and mutational statuses that may inform the therapeutic relevance of a particular interaction. Inclusion of these data will be invaluable for variant interpretation pipelines where mutational status can lead to the identification of a lifesaving therapeutic.
DOI: 10.5281/zenodo.14742886
title: Extracting Clinical Significance for Drug-Gene Interactions using FDA Label Packages

description: The drug-gene interaction database (DGIdb) is a resource that aggregates interaction data from over 40 different resources into one platform with the primary goal of making the druggable genome accessible to clinicians and researchers. By providing a public, computationally accessible database, DGIdb enables therapeutic insights through broad aggregation of drug-gene interaction data.

As part of our aggregation process, DGIdb preserves data regarding interaction types, directionality, and other attributes that enable filtering or biochemical insight. However, source data are often incomplete and may not contain the therapeutic relevance of the interaction. In this report, we address these missing data and demonstrate a pipeline for extracting physiological context from free-text sources. We apply existing large language models (LLMs) to tag and extract indications, cancer types, and relevant pharmacogenomics from free-text, FDA approved labels. We are then able to utilize the Variant Interpretation for Cancer Consortium (VICC) normalization services to ground extracted data back to formally grouped concepts.

In a preliminary test set of 355 FDA labels, we were able to normalize 59.4% of extracted chemical entities back to ontologically-grounded therapeutic concepts. We can link this therapeutic context data back to interaction records already searchable within DGIdb. By using LLMs to extract this data set, we can supplement our existing interaction data with relevant indications, pharmacogenomic data and mutational statuses that may inform the therapeutic relevance of a particular interaction. Inclusion of these data will be invaluable for variant interpretation pipelines where mutational status can lead to the identification of a lifesaving therapeutic.
DOI: 10.5281/zenodo.14757388
title: Extracting Clinical Significance for Drug-Gene Interactions using FDA Label Packages

description: The drug-gene interaction database (DGIdb) is a resource that aggregates interaction data from over 40 different resources into one platform with the primary goal of making the druggable genome accessible to clinicians and researchers. By providing a public, computationally accessible database, DGIdb enables therapeutic insights through broad aggregation of drug-gene interaction data.

As part of our aggregation process, DGIdb preserves data regarding interaction types, directionality, and other attributes that enable filtering or biochemical insight. However, source data are often incomplete and may not contain the therapeutic relevance of the interaction. In this report, we address these missing data and demonstrate a pipeline for extracting physiological context from free-text sources. We apply existing large language models (LLMs) to tag and extract indications, cancer types, and relevant pharmacogenomics from free-text, FDA approved labels. We are then able to utilize the Variant Interpretation for Cancer Consortium (VICC) normalization services to ground extracted data back to formally grouped concepts.

In a preliminary test set of 355 FDA labels, we were able to normalize 86.5% of extracted chemical entities back to ontologically-grounded therapeutic concepts. We can link this therapeutic context data back to interaction records already searchable within DGIdb. By using LLMs to extract this data set, we can supplement our existing interaction data with relevant indications, pharmacogenomic data and mutational statuses that may inform the therapeutic relevance of a particular interaction. Inclusion of these data will be invaluable for variant interpretation pipelines where mutational status can lead to the identification of a lifesaving therapeutic.
DOI: 10.5281/zenodo.14757436
title: Extracting Clinical Significance for Drug-Gene Interactions using FDA Label Packages

description: The drug-gene interaction database (DGIdb) is a resource that aggregates interaction data from over 40 different resources into one platform with the primary goal of making the druggable genome accessible to clinicians and researchers. By providing a public, computationally accessible database, DGIdb enables therapeutic insights through broad aggregation of drug-gene interaction data.

As part of our aggregation process, DGIdb preserves data regarding interaction types, directionality, and other attributes that enable filtering or biochemical insight. However, source data are often incomplete and may not contain the therapeutic relevance of the interaction. In this report, we address these missing data and demonstrate a pipeline for extracting physiological context from free-text sources. We apply existing large language models (LLMs) to tag and extract indications, cancer types, and relevant pharmacogenomics from free-text, FDA approved labels. We are then able to utilize the Variant Interpretation for Cancer Consortium (VICC) normalization services to ground extracted data back to formally grouped concepts.

In a preliminary test set of 355 FDA labels, we were able to normalize 86.5% of extracted chemical entities back to ontologically-grounded therapeutic concepts. We can link this therapeutic context data back to interaction records already searchable within DGIdb. By using LLMs to extract this data set, we can supplement our existing interaction data with relevant indications, pharmacogenomic data and mutational statuses that may inform the therapeutic relevance of a particular interaction. Inclusion of these data will be invaluable for variant interpretation pipelines where mutational status can lead to the identification of a lifesaving therapeutic.
DOI: 10.15468/dl.z3k2fw
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Podarcis raffoneae (Mertens, 1952)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2013",
 "Year is less than or equal to 2018",
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002761-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bzcszg
title: Occurrence Download

description: A dataset containing 1116 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Loliginidae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1116 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002762-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.436tye
title: Occurrence Download

description: A dataset containing 448 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stenothoidae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 448 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002764-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26781631
title: Bulk sequencing datasets for <i>itga4-vcam1b</i> paper

description: Bulk RNA-seq @ 5 dpfRunx:mCherry+ HSPCs were sorted via flow cytometry from pooled WT and <i>itga4</i> mutant embryos at 5dpf (immediately after CHT interaction or bypass, respectively).Sorted cell counts (n) as follows: WT samples: n<sub>1</sub>= 7,504, n<sub>2</sub>= 8,556, n<sub>3</sub>= 4,307, n<sub>4</sub>= 26,345 & <i>itga4 </i>mutant samples: n<sub>1</sub>= 3,517, n<sub>2</sub>= 1,610, n<sub>3</sub>= 1,286RNA isolation from sorted cells was prepared in the Tamplin lab before library prep & QC was done at the UW-Madison Gene Expression Center. Sequencing was completed by the UW-Madison Biotech Center and alignment, QC, and preliminary analysis was performed by the UW-Madison Bioinformatics Resource Center before delivery to the Tamplin lab.After preliminary analysis and discussion with the BRC we decided to drop the WT#1 replicate, as they advised it was "at least a putative outlier" (we can provide more information as requested). Both analyses are provided and labeled accordingly (with and without WT#1 replicate).Bulk ATAC-seq @ 5 dpfRunx:mCherry+ HSPCs were sorted via flow cytometry from pooled WT and <i>itga4</i> mutant embryos at 5dpf (immediately after CHT interaction or bypass, respectively).Sorted cell counts (n) as follows: WT samples: n<sub>1</sub>= 1,279, n<sub>2</sub>= 11,056, n<sub>3</sub>= 11,189 & <i>itga4 </i>mutant samples: n<sub>1</sub>= 21,801, n<sub>2</sub>= 330, n<sub>3</sub>= 395Libraries were prepared in the Tamplin lab before QC & sequencing by the UW-Madison Biotech Center and alignment, QC, peak calling, and Homer motif analysis was performed by the UW-Madison Bioinformatics Resource Center before delivery to the Tamplin lab.All replicates clustered within sample sets and had peaks align after analysis by the Bioinformatics Research Core (BRC) at UW-Madison, but reps WT#3 and <i>itga4</i> mut#1 had the deepest/cleanest signal. Therefore, there are some analysis files of just these two in comparison for visualization purposes.Bulk ATAC-seq @ 6-24 mpfRunx:mCherry+ HSPCs were sorted via flow cytometry from dissected WKM of 6 mpf WT, <i>itga4</i> mutant, and <i>vcam1b</i> mutant and 24 mpf WT adult zebrafish.Aging studies are not well established in zebrafish, so for the purpose of this study we label 6 mpf as young adults (relative to 8-10 week old mice) and 24 mpf as old adults (relative to 2 year old mice).Libraries were prepared in the Tamplin lab before QC & sequencing by the UW-Madison Biotech Center and alignment, QC, peak calling, and Homer motif analysis was performed by the UW-Madison Bioinformatics Resource Center before delivery to the Tamplin lab.Preliminary analysis was performed by the Tamplin lab after delivery of the sequencing files, although there was no direct link found to the current results that will be published in this <i>itga4</i>-<i>vcam1b </i>paper. We believe this dataset to be of good quality and will use it for later experiments. If new/further analysis uncovers a link to the current manuscript, we would be happy to include this in the paper.
DOI: 10.6084/m9.figshare.26781631.v1
title: Bulk sequencing datasets for <i>itga4-vcam1b</i> paper

description: Bulk RNA-seq @ 5 dpfRunx:mCherry+ HSPCs were sorted via flow cytometry from pooled WT and <i>itga4</i> mutant embryos at 5dpf (immediately after CHT interaction or bypass, respectively).Sorted cell counts (n) as follows: WT samples: n<sub>1</sub>= 7,504, n<sub>2</sub>= 8,556, n<sub>3</sub>= 4,307, n<sub>4</sub>= 26,345 & <i>itga4 </i>mutant samples: n<sub>1</sub>= 3,517, n<sub>2</sub>= 1,610, n<sub>3</sub>= 1,286RNA isolation from sorted cells was prepared in the Tamplin lab before library prep & QC was done at the UW-Madison Gene Expression Center. Sequencing was completed by the UW-Madison Biotech Center and alignment, QC, and preliminary analysis was performed by the UW-Madison Bioinformatics Resource Center before delivery to the Tamplin lab.After preliminary analysis and discussion with the BRC we decided to drop the WT#1 replicate, as they advised it was "at least a putative outlier" (we can provide more information as requested). Both analyses are provided and labeled accordingly (with and without WT#1 replicate).Bulk ATAC-seq @ 5 dpfRunx:mCherry+ HSPCs were sorted via flow cytometry from pooled WT and <i>itga4</i> mutant embryos at 5dpf (immediately after CHT interaction or bypass, respectively).Sorted cell counts (n) as follows: WT samples: n<sub>1</sub>= 1,279, n<sub>2</sub>= 11,056, n<sub>3</sub>= 11,189 & <i>itga4 </i>mutant samples: n<sub>1</sub>= 21,801, n<sub>2</sub>= 330, n<sub>3</sub>= 395Libraries were prepared in the Tamplin lab before QC & sequencing by the UW-Madison Biotech Center and alignment, QC, peak calling, and Homer motif analysis was performed by the UW-Madison Bioinformatics Resource Center before delivery to the Tamplin lab.All replicates clustered within sample sets and had peaks align after analysis by the Bioinformatics Research Core (BRC) at UW-Madison, but reps WT#3 and <i>itga4</i> mut#1 had the deepest/cleanest signal. Therefore, there are some analysis files of just these two in comparison for visualization purposes.Bulk ATAC-seq @ 6-24 mpfRunx:mCherry+ HSPCs were sorted via flow cytometry from dissected WKM of 6 mpf WT, <i>itga4</i> mutant, and <i>vcam1b</i> mutant and 24 mpf WT adult zebrafish.Aging studies are not well established in zebrafish, so for the purpose of this study we label 6 mpf as young adults (relative to 8-10 week old mice) and 24 mpf as old adults (relative to 2 year old mice).Libraries were prepared in the Tamplin lab before QC & sequencing by the UW-Madison Biotech Center and alignment, QC, peak calling, and Homer motif analysis was performed by the UW-Madison Bioinformatics Resource Center before delivery to the Tamplin lab.Preliminary analysis was performed by the Tamplin lab after delivery of the sequencing files, although there was no direct link found to the current results that will be published in this <i>itga4</i>-<i>vcam1b </i>paper. We believe this dataset to be of good quality and will use it for later experiments. If new/further analysis uncovers a link to the current manuscript, we would be happy to include this in the paper.
DOI: 10.15468/dl.7x2vzx
title: Occurrence Download

description: A dataset containing 15345 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Anas platyrhynchos Linnaeus, 1758"
 ]
}

 The dataset includes 15345 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002765-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4ju6u7
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is North America",
 "TaxonKey is Pinus georginae Pérez de la Rosa"
 ]
}

 The dataset includes 20 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002766-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.84vbkd
title: Occurrence Download

description: A dataset containing 1683 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Melitidae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1683 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002767-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60520/ieda/113671
title: test test test

description: geochemical data for table 1-5
description: LIU, D, BERTRAND, S., VILLASEÑOR, T., VAN DIJCK, T., FAGEL, N., MATTIELLI, N. (2020) — Provenance of northwestern Patagonian river sediments (44–48°S): A critical evaluation of mineralogical, geochemical and isotopic tracers. Sedimentary Geology 408, 105744. doi:jadaldjalk
DOI: 10.15468/dl.ty4dfh
title: Occurrence Download

description: A dataset containing 1045 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dogielinotidae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1045 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002768-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f6ps9a
title: Occurrence Download

description: A dataset containing 2416 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ampeliscidae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2416 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002769-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e216901
title: Data and Code for "Central Banks as Dollar Lenders of Last Resort: Implications for Regulation and Reserve Holdings"

description: This paper explores how non-U.S. central banks behave when firms in their economies 
engage in currency mismatch, borrowing more heavily in dollars than justified by their operating 
exposures. We begin by documenting that, in a panel of 56 countries, central bank holdings of 
dollar reserves are correlated with the dollar-denominated bank borrowing of their non-financial 
corporate sectors, controlling for a number of known covariates of reserve accumulation. We then 
build a model in which the central bank can deal with private-sector mismatch, and the associated 
risk of a domestic financial crisis, in two ways: (i) by imposing ex ante financial regulations such 
as bank capital requirements; or (ii) by building a stockpile of dollar reserves that allow it to serve 
as an ex post dollar lender of last resort. The model highlights a novel externality: individual 
central banks may over-accumulate dollar reserves, relative to what a global planner would choose. 
This is because, in the presence of imperfect regulation of currency mismatch, individual central 
banks do not internalize that their hoarding of reserves exacerbates a global scarcity of dollar-denominated safe assets, which lowers dollar interest rates and encourages firms to further increase 
the currency mismatch of their liabilities. Relative to the decentralized outcome, a global planner 
may therefore prefer higher capital requirements and reduced holdings of dollar reserves.
description: Nonfinancial corporates and central banks in 56 countries from all regions of the world, observed between 2013 and 2020 inclusive. . Smallest Geographic Unit: Country
DOI: 10.3886/e216901v1
title: Data and Code for "Central Banks as Dollar Lenders of Last Resort: Implications for Regulation and Reserve Holdings"

description: This paper explores how non-U.S. central banks behave when firms in their economies 
engage in currency mismatch, borrowing more heavily in dollars than justified by their operating 
exposures. We begin by documenting that, in a panel of 56 countries, central bank holdings of 
dollar reserves are correlated with the dollar-denominated bank borrowing of their non-financial 
corporate sectors, controlling for a number of known covariates of reserve accumulation. We then 
build a model in which the central bank can deal with private-sector mismatch, and the associated 
risk of a domestic financial crisis, in two ways: (i) by imposing ex ante financial regulations such 
as bank capital requirements; or (ii) by building a stockpile of dollar reserves that allow it to serve 
as an ex post dollar lender of last resort. The model highlights a novel externality: individual 
central banks may over-accumulate dollar reserves, relative to what a global planner would choose. 
This is because, in the presence of imperfect regulation of currency mismatch, individual central 
banks do not internalize that their hoarding of reserves exacerbates a global scarcity of dollar-denominated safe assets, which lowers dollar interest rates and encourages firms to further increase 
the currency mismatch of their liabilities. Relative to the decentralized outcome, a global planner 
may therefore prefer higher capital requirements and reduced holdings of dollar reserves.
description: Nonfinancial corporates and central banks in 56 countries from all regions of the world, observed between 2013 and 2020 inclusive. . Smallest Geographic Unit: Country
DOI: 10.15468/dl.vwhp2z
title: Occurrence Download

description: A dataset containing 107 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Atylidae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 107 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002771-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.98fa7y
title: Occurrence Download

description: A dataset containing 729 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dexaminidae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 729 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002772-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14757485
title: Table S.3.10. Number of BINs and MOTUs identified by ASAP for each of the 280 species analysed. The number of MOTUs corresponds to the partition with the lowest ASAP score; in cases where the ASAP score was tied, the partition with the lowest p-value was selected.
DOI: 10.5281/zenodo.14757486
title: Table S.3.10. Number of BINs and MOTUs identified by ASAP for each of the 280 species analysed. The number of MOTUs corresponds to the partition with the lowest ASAP score; in cases where the ASAP score was tied, the partition with the lowest p-value was selected.