cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.6z4g7f
title: Occurrence Download

description: A dataset containing 56105 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-128.28516 5.96573,-75.69141 5.96573,-75.69141 37.32824,-128.28516 37.32824,-128.28516 5.96573))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "or" : [
 "TaxonKey is Aquila chrysaetos (Linnaeus, 1758)",
 "TaxonKey is Aquila chrysaetus (Linnaeus, 1758)"
 ]
 }
 ]
}

 The dataset includes 56105 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0259109-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.twc4qb
title: Occurrence Download

description: A dataset containing 94 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lagisca extenuata (Grube, 1840)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is MANAGED"
 ]
 }
 },
 {
 "not" : {
 "EstablishmentMeans" : [
 "is not null"
 ]
 }
 }
 ]
 },
 {
 "or" : [
 "OccurrenceStatus is Present",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is not null"
 ]
 }
 }
 ]
 }
 ]
}

 The dataset includes 94 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056836-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.citxf1
title: Occurrence Download

description: A dataset containing 4497014 species occurrences available in GBIF matching the query:
{
 "or" : [
 "Country is China",
 "Country is Viet Nam",
 "Country is Myanmar",
 "Country is Lao People’s Democratic Republic"
 ]
}

 The dataset includes 4497014 records from 2393 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011626-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6sajjg
title: Occurrence Download

description: A dataset containing 7216 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bombycilla cedrorum Vieillot, 1808",
 "Year 2011-2018"
 ]
}

 The dataset includes 7216 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0171407-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cpwqa9
title: Occurrence Download

description: A dataset containing 17743 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Rosa glauca Pourr."
 ]
}

 The dataset includes 17743 records from 326 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013320-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cbwzfq
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-108.37552 13.49507,-90.1132 13.49507,-90.1132 25.35236,-108.37552 25.35236,-108.37552 13.49507))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Craterium rubronodum G.Lister, 1915"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069284-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bw6n36
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Palapedia yongshuensis (Dai, Cai & Yang, 1994)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1970",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((108.080642 16.439832, 121.84182 16.084389, 123.975906 41.313386, 108.669355 40.867975, 108.080642 16.439832))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013278-230828120925497/datasets/export for details.
DOI: 10.15468/dl.6y3yjh
title: Occurrence Download

description: A dataset containing 179795 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Verbena officinalis L."
 ]
}

 The dataset includes 179795 records from 1704 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013264-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13026/ccbh-z832
title: FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark

description: Automatic medical report generation (MRG) towards describing life-threatening
lesions from given medical images, such as Chest X-ray and Fundus Fluorescein
Angiography (FFA), has been a long-standing research topic in machine learning
and automatic medical diagnosis fields. However, existing MRG benchmarks only
provide medical images and free-text reports without explainable annotations
and reliable evaluation tools, hindering the current research advances from
two aspects: First, existing methods can only predict reports without accurate
explanation, undermining the trustworthiness of the diagnostic methods;
Second, the comparison between predicted reports from MRG methods is
unreliable based on the natural language generation (NLG) metrics.

To address these issues, we propose an explainable and reliable MRG benchmark
based on FFA Images and Reports (FFA-IR). Specifically, our FFA-IR dataset is
featured from the following aspects: 1) Large-scale medical dataset. FFA-IR
collects 10,790 reports along with 1,048,584 FFA images from clinical
practice. 2) Explainable annotation. FFA-IR annotates 46 categories of lesions
with a total of 12,166 regions. 3) Bilingual reports. FFA-IR provides both
English and Chinese reports for each case. We hope that our FFA-IR can
significantly advance research from both vision-and-language and medicine
fields and improve the conventional retinal disease diagnosis procedure.
DOI: 10.13026/f7w3-gm74
title: FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark

description: Automatic medical report generation (MRG) towards describing life-threatening
lesions from given medical images, such as Chest X-ray and Fundus Fluorescein
Angiography (FFA), has been a long-standing research topic in machine learning
and automatic medical diagnosis fields. However, existing MRG benchmarks only
provide medical images and free-text reports without explainable annotations
and reliable evaluation tools, hindering the current research advances from
two aspects: First, existing methods can only predict reports without accurate
explanation, undermining the trustworthiness of the diagnostic methods;
Second, the comparison between predicted reports from MRG methods is
unreliable based on the natural language generation (NLG) metrics.

To address these issues, we propose an explainable and reliable MRG benchmark
based on FFA Images and Reports (FFA-IR). Specifically, our FFA-IR dataset is
featured from the following aspects: 1) Large-scale medical dataset. FFA-IR
collects 766 reports along with 47,247 FFA images from clinical practice. 2)
Explainable annotation. FFA-IR annotates 46 categories of lesions with a total
of 12,166 regions. 3) Bilingual reports. FFA-IR provides both English and
Chinese reports for each case. We hope that our FFA-IR can significantly
advance research from both vision-and-language and medicine fields and improve
the conventional retinal disease diagnosis procedure.
DOI: 10.13026/k5rp-9h43
title: FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark

description: Automatic medical report generation (MRG) towards describing life-threatening
lesions from given medical images, such as Chest X-ray and Fundus Fluorescein
Angiography (FFA), has been a long-standing research topic in machine learning
and automatic medical diagnosis fields. However, existing MRG benchmarks only
provide medical images and free-text reports without explainable annotations
and reliable evaluation tools, hindering the current research advances from
two aspects: First, existing methods can only predict reports without accurate
explanation, undermining the trustworthiness of the diagnostic methods;
Second, the comparison between predicted reports from MRG methods is
unreliable based on the natural language generation (NLG) metrics.

To address these issues, we propose an explainable and reliable MRG benchmark
based on FFA Images and Reports (FFA-IR). Specifically, our FFA-IR dataset is
featured from the following aspects: 1) Large-scale medical dataset. FFA-IR
collects 766 reports along with 47,247 FFA images from clinical practice. 2)
Explainable annotation. FFA-IR annotates 46 categories of lesions with a total
of 12,166 regions. 3) Bilingual reports. FFA-IR provides both English and
Chinese reports for each case. We hope that our FFA-IR can significantly
advance research from both vision-and-language and medicine fields and improve
the conventional retinal disease diagnosis procedure.
DOI: 10.13026/20e2-1g10
title: TAME Pain: Trustworthy AssessMEnt of Pain from Speech and Audio for the Empowerment of Patients

description: Precise pain assessment is essential for medical professionals to provide
appropriate treatment. However, not every patient can verbalize their pain due
to various reasons, such as speech disorders or language barriers. In these
cases, medical practitioners must rely on non-verbal signs to determine the
pain level. The TAME Pain project aims to pave the way for the development of
reliable pain assessment tools through advanced audio analysis.

We aim to create a comprehensive dataset that captures acoustic signals to
accurately predict pain levels. This dataset, approved by the University of
Texas at Austin's institutional review board (IRB number: STUDY00004954), will
enable the investigation of whether acoustic and non-acoustic signals
extracted from healthy individuals subjected to pain can reliably indicate
pain levels. We augment this dataset by annotating every single audio file,
including every sentence spoken by the participant, with details such as
background and foreground noise, speech errors, and non-speech vocal features.
These annotations enable thorough audio analysis, facilitate pain studies, and
aid in identifying both speech and non-speech pain cues. This dataset provides
a resource for researchers and developers working on pain assessment
technologies.

The data collection and creation efforts are based at the University of Texas
at Austin, with collaborative input from the University of Nottingham and the
University of Southampton in the UK.
DOI: 10.13026/wp85-ea58
title: TAME Pain: Trustworthy AssessMEnt of Pain from Speech and Audio for the Empowerment of Patients

description: Precise pain assessment is essential for medical professionals to provide
appropriate treatment. However, not every patient can verbalize their pain due
to various reasons, such as speech disorders or language barriers. In these
cases, medical practitioners must rely on non-verbal signs to determine the
pain level. The TAME Pain project aims to pave the way for the development of
reliable pain assessment tools through advanced audio analysis.

We aim to create a comprehensive dataset that captures acoustic signals to
accurately predict pain levels. This dataset, approved by the University of
Texas at Austin's institutional review board (IRB number: STUDY00004954), will
enable the investigation of whether acoustic and non-acoustic signals
extracted from healthy individuals subjected to pain can reliably indicate
pain levels. We augment this dataset by annotating every single audio file,
including every sentence spoken by the participant, with details such as
background and foreground noise, speech errors, and non-speech vocal features.
These annotations enable thorough audio analysis, facilitate pain studies, and
aid in identifying both speech and non-speech pain cues. This dataset provides
a resource for researchers and developers working on pain assessment
technologies.

The data collection and creation efforts are based at the University of Texas
at Austin, with collaborative input from the University of Nottingham and the
University of Southampton in the UK.
DOI: 10.13026/eskc-3e36
title: TherLid: A Thermometry Linked Dataset

description: A recent study showed that infrared (IR) sensors may be prone to calibration
discrepancies among darker-pigmented patients. Similar disparities have
already been verified in pulse oximetry. This raises questions about whether
thermometry measurements may inadvertently overlook hypothermia, fever, or
sepsis cases, potentially leading to delayed diagnoses and ultimately
exacerbating poorer outcomes among vulnerable subpopulations.

TherLiD is a derived dataset from 3 Electronic Health Record databases: MIMIC-
IV, eICU-CRD-1, and eICU-CRD-2. It consists of 13,251 temperature pairs, along
with comprehensive demographic data and time-synchronized hospital
information, offering a detailed profile of each patient. These pairs have one
reference (contact thermometers - oral, core, and rectal) and one infrared-
based (temporal) temperature value measured within a 1-hour time window, with
temperature values between 30°C to 45°C. TherLiD not only provides high-
quality, clinically relevant data but also offers a reproducible framework,
allowing researchers to tailor the dataset to their specific research needs,
including training machine learning models. This dataset was also built to
facilitate temperature-related retrospective studies and promote research on
racial and ethnic healthcare disparities.
DOI: 10.13026/tkww-8b64
title: TherLid: A Thermometry Linked Dataset

description: A recent study showed that infrared (IR) sensors may be prone to calibration
discrepancies among darker-pigmented patients. Similar disparities have
already been verified in pulse oximetry. This raises questions about whether
thermometry measurements may inadvertently overlook hypothermia, fever, or
sepsis cases, potentially leading to delayed diagnoses and ultimately
exacerbating poorer outcomes among vulnerable subpopulations.

TherLiD is a derived dataset from 3 Electronic Health Record databases: MIMIC-
IV, eICU-CRD-1, and eICU-CRD-2. It consists of 13,251 temperature pairs, along
with comprehensive demographic data and time-synchronized hospital
information, offering a detailed profile of each patient. These pairs have one
reference (contact thermometers - oral, core, and rectal) and one infrared-
based (temporal) temperature value measured within a 1-hour time window, with
temperature values between 30°C to 45°C. TherLiD not only provides high-
quality, clinically relevant data but also offers a reproducible framework,
allowing researchers to tailor the dataset to their specific research needs,
including training machine learning models. This dataset was also built to
facilitate temperature-related retrospective studies and promote research on
racial and ethnic healthcare disparities.
DOI: 10.5281/zenodo.10015378
title: Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020

description: Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".

 

Please cite the original paper:

Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework,  2000–2020.” Applied Geography  167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.

WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).

All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.

Three WUI maps were generated using structures built on of before the years indicated below:

2000 - "CA_WUI_2000.tif"

2010 - "CA_WUI_2010.tif"

2020 - "CA_WUI_2020.tif" 

 

Acknowledgments -

We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.
DOI: 10.5281/zenodo.10015379
title: Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020

description: Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".

WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).

All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.

Three WUI maps were generated using structures built on of before the years indicated below:

2000 - "CA_WUI_2000.tif"

2010 - "CA_WUI_2010.tif"

2020 - "CA_WUI_2020.tif" 

 

Acknowledgments -

We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data  use  agreement between the  University of Colorado Boulder and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. We thank Johannes Uhl and Stefan Leyk for the incredible amount of work they dedicated to  preparing this Zillow ZTRAX. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.
DOI: 10.5281/zenodo.11043572
title: Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020

description: Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".

 

Please cite the original paper:

Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework,  2000–2020.” Applied Geography  167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.

WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).

All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.

Three WUI maps were generated using structures built on of before the years indicated below:

2000 - "CA_WUI_2000.tif"

2010 - "CA_WUI_2010.tif"

2020 - "CA_WUI_2020.tif" 

 

Acknowledgments -

We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.
DOI: 10.5281/zenodo.13381455
title: Wearable microneedle graphene field effect transistor sensors (supporting data)

description: Supporting data for our paper on GFET microneedle sensors.
DOI: 10.5281/zenodo.13381456
title: Wearable microneedle graphene field effect transistor sensors (supporting data)

description: Supporting data for our paper on GFET microneedle sensors.