cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p14rphbb
title: Detailed Grain-Size Data of Estuarine, Barrier Island, and Shoreface Environments Around Dauphin Island, Alabama, USA

description: In 2015, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) conducted sediment sampling surveys on and around the barrier islands of Dauphin Island and Little Dauphin Island, Mobile County, Alabama (AL). The study investigated the surficial sediment surrounding Dauphin Island and surrounding environments to inform sediment transport model scenarios of coastal change. Ellis and others (2017) provided a detailed account of field and laboratory methodologies as well as the statistical grain-size summary results for sediments collected from 270 fields sites; this data release provides the average detailed grain size distribution computed from multiple runs for samples from 240 sites. Samples were collected from numerous environments including dunes, beaches, protected bays, and open water. The sampling efforts by the USGS were part of a larger study to assess the current state and future function of Dauphin Island, Alabama and assess what types of restoration efforts are feasible, sustainable, and can be resilient to future storms. The data collected and presented in this data release will be utilized by modelers to address to validate hindcast and/or nowcast modeling as it relates to baseline condition and storm events. This study was funded by the National Fish and Wildlife Foundation, which is administered by the Gulf Environmental Benefit Fund, following extensive consultation with the Alabama Department of Conservation and Natural Resources. This data release serves as an archive for sedimentological data derived from detailed grain-size texture analysis.

Ellis, A.M., Marot, M.E., Smith, C.G., and Wheaton, C.J., 2017, The physical characteristics of the sediments on and surrounding Dauphin Island, Alabama: U.S. Geological Survey Data Series 1046, https://doi.org/10.3133/ds1046.
DOI: 10.15468/dl.683sf6
title: Occurrence Download

description: A dataset containing 2756 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Beetles in previous clear-cut and near natural mature spruce forests in southern Norway"
 ]
}

 The dataset includes 2756 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000619-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48522/d3mw2z
title: Tracking Bacterial, Viral, and Gene Expression Shifts throughout SCTLD Development in the Dry Tortugas

description: The rapid spread of SCTLD throughout Florida’s Coral Reef has hampered efforts to identify the causative pathogen, due in part to the difficulty of finding disease-susceptible corals with no prior disease exposure. This project leveraged integrative multi-omic and histological analyses to characterize SCTLD pathogenesis and coral immunity in four species of coral collected from Dry Tortugas National Park before and after the arrival of SCTLD. Histopathology of samples from this study showed consistent patterns to other histological analyses of SCTLD, although healthy samples from the Dry Tortugas exhibited an unusual amount of exocytosis. Initial analyses of coral microbial function and taxonomy based on shotgun sequencing data suggest that while microbial communities of corals naïve to SCTLD in Dry Tortugas National Park may differ from other corals sampled in the Florida Keys, microbial community function and composition of diseased samples from this study were similar to samples that have been previously sequenced. We found that diseased coral samples across coral species harbored viruses hypothesized to interact with the algal symbiont and viruses (including herpesvirus) that may influence host immunity capacity against other infections. Immune function in samples of healthy coral varied by species and patterns of change in function followed observed patterns of disease prevalence by month. Continued analyses supported by Florida DEP will focus on the development of robust, integrative analytical approaches to leverage the true power of this data set.
DOI: 10.15468/dl.qgwuye
title: Occurrence Download

description: A dataset containing 608371 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Potentilla erecta (L.) Raeusch."
 ]
}

 The dataset includes 608371 records from 993 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0388746-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r74s4q
title: Occurrence Download

description: A dataset containing 9456 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinguicula lusitanica L."
 ]
}

 The dataset includes 9456 records from 199 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0387633-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tefkwc
title: Occurrence Download

description: A dataset containing 117844 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinguicula vulgaris L."
 ]
}

 The dataset includes 117844 records from 468 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0387639-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60508/2m08-nf79
title: Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records

description: **Objectives:  **Unstructured and structured data in electronic health records
(EHR) are a rich source of information for research and quality improvement
studies. However, extracting accurate information from EHR is labor-intensive.
Timely and accurate identification of patients with Alzheimer's Disease,
related dementias (ADRD), or mild cognitive impairment (MCI) is critical for
improving patient outcomes through early intervention, optimizing care plans,
and reducing healthcare system burdens. Here we introduce an automated EHR
phenotyping model to streamline this process and enable efficient
identification of these conditions.

**Methods:  **We analyzed data from 3,626 outpatients seen at two hospitals
between February 2015 and June 2022. Through manual chart review, we
established ground truth labels for the presence or absence of MCI/ADRD
diagnoses. Our model combined three types of data: (1) unstructured clinical
notes, from which we extracted single words, two-word phrases (bigrams), and
three-word phrases (trigrams) as features, weighted using Term Frequency-
Inverse Document Frequency (TF-IDF) to capture their relative importance, (2)
International Classification of Diseases (ICD) codes, and (3) medication
prescriptions related to MCI/ADRD. We trained a regularized logistic
regression model to predict MCI/ADRD diagnoses and evaluated its performance
using standard metrics including area under the receiver operating curve
(AUROC), area under the precision-recall curve (AUPRC), accuracy, specificity,
precision, recall, and F1 score.

**Results:  **Thirty percent of patients in the cohort carried diagnoses of
MCI/ADRD based on manual review. When evaluated on a held-out test set, the
best model using clinical notes, ICDs, and medications, achieved an AUROC of
0.98, an AUPRC of 0.98, an accuracy of 0.93, a sensitivity (recall) of 0.91, a
specificity of 0.96, a precision of 0.96, and an F1 score of 0.93 The
estimated overall accuracy for patients randomly selected from EHRs was
99.88%.

**Conclusion:  **Automated EHR phenotyping accurately identifies patients with
MCI/ADRD based on clinical notes, ICD codes, and medication records. This
approach holds potential for large-scale MCI/ADRD research utilizing EHR
databases.
DOI: 10.15468/dl.5nd9w4
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bromus inermis",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017927-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eynuua
title: Occurrence Download

description: A dataset containing 3633 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bromus inermis",
 "Country is Poland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3633 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017926-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4h2mz7
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eleutheronema rhadinum",
 "Geometry POLYGON((119.300000 20.500000, 122.100000 20.500000, 122.100000 25.400000, 119.300000 25.400000, 119.300000 20.500000))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 43 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017928-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2sjhhk
title: Occurrence Download

description: A dataset containing 179 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cenchrus pedicellatus",
 "Country is Brazil",
 "HasCoordinate is true"
 ]
}

 The dataset includes 179 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017930-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ty8s3g
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cenchrus pedicellatus",
 "Country is Bhutan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017929-250920141307145/datasets/export for details.
DOI: 10.15468/dl.3jhvjd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cenchrus pedicellatus",
 "Country is Micronesia (Federated States of)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017932-250920141307145/datasets/export for details.
DOI: 10.60508/53gz-x407
title: Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records

description: **Objectives:  **Unstructured and structured data in electronic health records
(EHR) are a rich source of information for research and quality improvement
studies. However, extracting accurate information from EHR is labor-intensive.
Timely and accurate identification of patients with Alzheimer's Disease,
related dementias (ADRD), or mild cognitive impairment (MCI) is critical for
improving patient outcomes through early intervention, optimizing care plans,
and reducing healthcare system burdens. Here we introduce an automated EHR
phenotyping model to streamline this process and enable efficient
identification of these conditions.

**Methods:  **We analyzed data from 3,626 outpatients seen at two hospitals
between February 2015 and June 2022. Through manual chart review, we
established ground truth labels for the presence or absence of MCI/ADRD
diagnoses. Our model combined three types of data: (1) unstructured clinical
notes, from which we extracted single words, two-word phrases (bigrams), and
three-word phrases (trigrams) as features, weighted using Term Frequency-
Inverse Document Frequency (TF-IDF) to capture their relative importance, (2)
International Classification of Diseases (ICD) codes, and (3) medication
prescriptions related to MCI/ADRD. We trained a regularized logistic
regression model to predict MCI/ADRD diagnoses and evaluated its performance
using standard metrics including area under the receiver operating curve
(AUROC), area under the precision-recall curve (AUPRC), accuracy, specificity,
precision, recall, and F1 score.

**Results:  **Thirty percent of patients in the cohort carried diagnoses of
MCI/ADRD based on manual review. When evaluated on a held-out test set, the
best model using clinical notes, ICDs, and medications, achieved an AUROC of
0.98, an AUPRC of 0.98, an accuracy of 0.93, a sensitivity (recall) of 0.91, a
specificity of 0.96, a precision of 0.96, and an F1 score of 0.93 The
estimated overall accuracy for patients randomly selected from EHRs was
99.88%.

**Conclusion:  **Automated EHR phenotyping accurately identifies patients with
MCI/ADRD based on clinical notes, ICD codes, and medication records. This
approach holds potential for large-scale MCI/ADRD research utilizing EHR
databases.
DOI: 10.60508/mtk4-h048
title: Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records

description: **Objectives:  **Unstructured and structured data in electronic health records
(EHR) are a rich source of information for research and quality improvement
studies. However, extracting accurate information from EHR is labor-intensive.
Timely and accurate identification of patients with Alzheimer's Disease,
related dementias (ADRD), or mild cognitive impairment (MCI) is critical for
improving patient outcomes through early intervention, optimizing care plans,
and reducing healthcare system burdens. Here we introduce an automated EHR
phenotyping model to streamline this process and enable efficient
identification of these conditions.

**Methods:  **We analyzed data from 3,626 outpatients seen at two hospitals
between February 2015 and June 2022. Through manual chart review, we
established ground truth labels for the presence or absence of MCI/ADRD
diagnoses. Our model combined three types of data: (1) unstructured clinical
notes, from which we extracted single words, two-word phrases (bigrams), and
three-word phrases (trigrams) as features, weighted using Term Frequency-
Inverse Document Frequency (TF-IDF) to capture their relative importance, (2)
International Classification of Diseases (ICD) codes, and (3) medication
prescriptions related to MCI/ADRD. We trained a regularized logistic
regression model to predict MCI/ADRD diagnoses and evaluated its performance
using standard metrics including area under the receiver operating curve
(AUROC), area under the precision-recall curve (AUPRC), accuracy, specificity,
precision, recall, and F1 score.

**Results:  **Thirty percent of patients in the cohort carried diagnoses of
MCI/ADRD based on manual review. When evaluated on a held-out test set, the
best model using clinical notes, ICDs, and medications, achieved an AUROC of
0.98, an AUPRC of 0.98, an accuracy of 0.93, a sensitivity (recall) of 0.91, a
specificity of 0.96, a precision of 0.96, and an F1 score of 0.93 The
estimated overall accuracy for patients randomly selected from EHRs was
99.88%.

**Conclusion:  **Automated EHR phenotyping accurately identifies patients with
MCI/ADRD based on clinical notes, ICD codes, and medication records. This
approach holds potential for large-scale MCI/ADRD research utilizing EHR
databases.
DOI: 10.15468/dl.cnzspf
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eleutheronema tetradactylum",
 "Geometry POLYGON((119.300000 20.500000, 122.100000 20.500000, 122.100000 25.400000, 119.300000 25.400000, 119.300000 20.500000))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 48 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017933-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xu5nd7
title: Occurrence Download

description: A dataset containing 513 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Papilio machaon"
 ]
}

 The dataset includes 513 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017934-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gjxk8r
title: Occurrence Download

description: A dataset containing 7006907 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ageratum conyzoides, Chromolaena odorata, Praxelis clematidea, Mikania micrantha, Ageratina adenophora, Tagetes minuta, Flaveria bidentis, Galinsoga parviflora, Galinsoga quadriradiata, Tithonia diversifolia, Ambrosia artemisiifolia, Ambrosia trifida, Xanthium occidentale, Xanthium orientale, Xanthium spinosum, Parthenium hysterophorus, Sphagneticola trilobata, Bidens pilosa, Bidens frondosa, Bidens alba, Symphyotrichum subulatum, Solidago canadensis, Erigeron annuus, Erigeron bonariensis, Erigeron canadensis, Erigeron sumatrensis, Crassocephalum crepidioides, Lactuca serriola, Cyclachaena xanthiifolia, Eryngium foetidum, Lantana camara, Richardia scabra, Spermacoce alata, Solanum torvum, Solanum angustifolium, Solanum aculeatissimum, Datura stramonium, Datura metel, Ipomoea purpurea, Ipomoea cairica, Ipomoea triloba, Cuscuta campestris, Anredera cordifolia, Phytolacca americana, Spergula arvensis, Dysphania ambrosioides, Alternanthera pungens, Alternanthera philoxeroides, Gomphrena celosioides, Amaranthus dubius, Amaranthus spinosus, Amaranthus retroflexus, Amaranthus blitum, Amaranthus viridis, Amaranthus palmeri, Trifolium repens, Trifolium pratense, Melilotus albus, Melilotus suaveolens, Crotalaria trichotoma, Mimosa bimucronata, Mimosa pudica, Sicyos angulatus, Euphorbia hirta, Euphorbia prostrata, Euphorbia maculata, Lepidium virginicum, Abutilon theophrasti, Geranium carolinianum, Melinis repens, Cenchrus longispinus, Lolium multiflorum, Lolium temulentum, Phalaris paradoxa, Avena fatua, Aegilops tauschii, Bromus catharticus, Pseudosorghum fasciculare)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 7006907 records from 6817 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017903-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rrgfv9
title: Occurrence Download

description: A dataset containing 151 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Laportea decumana (Roxb.) Wedd."
 ]
}

 The dataset includes 151 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038449-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y7cvaj
title: Occurrence Download

description: A dataset containing 2723 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Arremon costaricensis (Bangs, 1907)"
 ]
}

 The dataset includes 2723 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042500-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y8r9a6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Pseudoanthaceae)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041407-240314170635999/datasets/export for details.
DOI: 10.15468/dl.jdfvxf
title: Occurrence Download

description: A dataset containing 534 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Klaineanthus gaboniae Pierre ex Prain"
 ]
}

 The dataset includes 534 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038498-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p9u537
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ixora kaniensis Valeton"
 ]
}

 The dataset includes 14 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038508-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zkpv3m
title: Occurrence Download

description: A dataset containing 424 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Searsia koefoedi Parr, 1937"
 ]
}

 The dataset includes 424 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041403-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wkppj2
title: Occurrence Download

description: A dataset containing 497 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Oriolus phaeochromus G.R.Gray, 1861"
 ]
}

 The dataset includes 497 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051568-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sy3rfx
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Laportea floribunda (Baker) Leandri"
 ]
}

 The dataset includes 71 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038464-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wmca7m
title: Occurrence Download

description: A dataset containing 11722 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Jasione laevis Lam.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 11722 records from 207 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003896-240130105604617/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.