cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/6ez-669-9ba
title: Carex liparocarpos subsp. bordzilowskii (V.I.Krecz.) T.V.Egorova

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/1n2-she-6kj
title: Crepis pulchra L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/j6l-s2s-c8c
title: Melica picta K.Koch

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/b62-127-bcj
title: Crepis tectorum L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/myc-ssw-h8l
title: Asplenium L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/4l1-bj1-22g
title: Potentilla collina Wibel

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/z43-tkf-gez
title: Sclerochloa dura (L.) P.Beauv.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/dyc-lk7-w5a
title: Cerastium tenoreanum Ser.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/73f-l0q-f28
title: Chenopodium album L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/yab-nnb-yzk
title: Alhagi maurorum Medik.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/1sk-g5t-lry
title: Chenopodium album L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/7rq-rse-fdw
title: Chenopodium album L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/3b3-cqa-k22
title: Lathyrus tuberosus L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/sbp-k8s-519
title: Arabidopsis thaliana (L.) Heynh.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/lw9-vqg-zjb
title: Solidago canadensis L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/wpx-qs3-28e
title: Veronica polita Fr.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/tll-tth-w2z
title: Solanum villosum Mill.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/7jm-cwk-ddg
title: Solanum villosum Mill.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/g4h-y53-jqz
title: Solanum villosum Mill.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.5281/zenodo.16598196
title: Diffraction images of the myoVa-MICAL1 complex

description: PEG-ION2-48#
30%Glycerol
DOI: 10.5281/zenodo.16598197
title: Diffraction images of the myoVa-MICAL1 complex

description: PEG-ION2-48#
30%Glycerol
DOI: 10.3535/00r-1ad-p0p
title: Persicaria maculosa Gray

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/w3z-ztt-t8a
title: Euphrasia kerneri Wettst.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/eyq-7ha-yt0
title: Anthyllis L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/lv1-wgz-pq9
title: Vinca L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/7tn-dns-bha
title: Buglossoides tenuiflora (L. fil.) I. M. Johnst.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/b2s-gfc-gc5
title: Tripleurospermum caucasicum (Willd.) Hayek

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/8c1-z71-72w
title: Senecio vernalis Waldst. & Kit.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.hbd2sn
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Desmognathus quadramaculatus)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072818-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/8x0-yq6-t99
title: Catabrosa aquatica (L.) P.Beauv.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.axqm2n
title: Occurrence Download

description: A dataset containing 5560 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cephalopoda)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((140 -20,150 -20,150 -10,140 -10,140 -20))"
 ]
}

 The dataset includes 5560 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015158-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4fcxzu
title: Occurrence Download

description: A dataset containing 6993 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus taeda",
 "HasCoordinate is true",
 "BasisOfRecord is Human Observation"
 ]
}

 The dataset includes 6993 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015160-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rwyzbt
title: Occurrence Download

description: A dataset containing 254 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cephalopoda)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((150 -20,160 -20,160 -10,150 -10,150 -20))"
 ]
}

 The dataset includes 254 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015159-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6xam2y
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Solenogastres)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((110 20,120 20,120 30,110 30,110 20))"
 ]
}

 The dataset includes 49 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015161-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zbabmf
title: Occurrence Download

description: A dataset containing 260 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cephalopoda)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((160 -20,170 -20,170 -10,160 -10,160 -20))"
 ]
}

 The dataset includes 260 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015162-251009101135966/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.29582648
title: A Machine-Insight-Based Geocomputational Approach to Decode the Complexities of the Restless Landscapes: Toward Integration of Imaginations and Planning Interventions

description: The proliferating urban influence brought a complex sociospatial, economic structure over the urban periphery. Previous studies focused on different approaches to capturing this intricate process; generalization was done by setting their own criteria. This study builds a decision-making model to comprehend the nature of Varanasi city’s periphery. Settlement-wise data are collected to calculate the study’s initial parameters: functional index, physical infrastructure dynamics score, physiological density, and nonagricultural land to total land ratio. The independent component analysis calculates the composite score. The learning cloud incorporates settlement-wise data in an encoded format to run the XGBoost machine learning algorithm. The findings highlight a competitive pattern of settlements and identify four periurban zones based on the deviation diffusion of calculated parameters. The study implements the Monte Carlo simulation for visualizing periurban decision space and recorded an asymptotic one-sample Kolmogorov–Smirnov test score of 0.051 at a <i>p</i> value of 0.076. The findings suggest four peripheral settlement clusters where the Gaussian mixture model confirms the reliability. This study highlights the importance of these clusters in attracting government and private players to invest and innovate functioning urban like institutions. This study’s methodological framework and inclusive approaches for city region planning and policy formulation remain significant.
DOI: 10.6084/m9.figshare.29582648.v1
title: A Machine-Insight-Based Geocomputational Approach to Decode the Complexities of the Restless Landscapes: Toward Integration of Imaginations and Planning Interventions

description: The proliferating urban influence brought a complex sociospatial, economic structure over the urban periphery. Previous studies focused on different approaches to capturing this intricate process; generalization was done by setting their own criteria. This study builds a decision-making model to comprehend the nature of Varanasi city’s periphery. Settlement-wise data are collected to calculate the study’s initial parameters: functional index, physical infrastructure dynamics score, physiological density, and nonagricultural land to total land ratio. The independent component analysis calculates the composite score. The learning cloud incorporates settlement-wise data in an encoded format to run the XGBoost machine learning algorithm. The findings highlight a competitive pattern of settlements and identify four periurban zones based on the deviation diffusion of calculated parameters. The study implements the Monte Carlo simulation for visualizing periurban decision space and recorded an asymptotic one-sample Kolmogorov–Smirnov test score of 0.051 at a <i>p</i> value of 0.076. The findings suggest four peripheral settlement clusters where the Gaussian mixture model confirms the reliability. This study highlights the importance of these clusters in attracting government and private players to invest and innovate functioning urban like institutions. This study’s methodological framework and inclusive approaches for city region planning and policy formulation remain significant.
DOI: 10.6084/m9.figshare.28276096
title: A Novel Longitudinal Rank-Sum Test for Multiple Primary Endpoints in Clinical Trials: Applications to Neurodegenerative Disorders

description: Neurodegenerative disorders such as Alzheimer’s disease (AD) present a significant global health challenge, characterized by cognitive decline, functional impairment, and other debilitating effects. Current AD clinical trials often assess multiple longitudinal primary endpoints to comprehensively evaluate treatment efficacy. Traditional methods, however, may fail to capture global treatment effects, require larger sample sizes due to multiplicity adjustments, and may not fully use the available longitudinal data. To address these limitations, we introduce the Longitudinal Rank Sum Test (LRST), a novel nonparametric rank-based omnibus test statistic. The LRST enables a comprehensive assessment of treatment efficacy across multiple endpoints and time points without the need for multiplicity adjustments, effectively controlling Type I error while enhancing statistical power. It offers flexibility for various data distributions encountered in AD research and maximizes the utilization of longitudinal data. Simulations across realistic clinical trial scenarios, including those with conflicting treatment effects, and real-data applications demonstrate the LRST’s performance, underscoring its potential as a valuable tool in AD clinical trials.
DOI: 10.6084/m9.figshare.28276096.v1
title: A novel longitudinal rank-sum test for multiple primary endpoints in clinical trials: Applications to neurodegenerative disorders

description: Neurodegenerative disorders such as Alzheimer’s disease (AD) present a significant global health challenge, characterized by cognitive decline, functional impairment, and other debilitating effects. Current AD clinical trials often assess multiple longitudinal primary endpoints to comprehensively evaluate treatment efficacy. Traditional methods, however, may fail to capture global treatment effects, require larger sample sizes due to multiplicity adjustments, and may not fully utilize the available longitudinal data. To address these limitations, we introduce the Longitudinal Rank Sum Test (LRST), a novel nonparametric rank-based omnibus test statistic. The LRST enables a comprehensive assessment of treatment efficacy across multiple endpoints and time points without the need for multiplicity adjustments, effectively controlling Type I error while enhancing statistical power. It offers flexibility for various data distributions encountered in AD research and maximizes the utilization of longitudinal data. Simulations across realistic clinical trial scenarios, including those with conflicting treatment effects, and real-data applications demonstrate the LRST’s performance, underscoring its potential as a valuable tool in AD clinical trials.
DOI: 10.6084/m9.figshare.28276096.v2
title: A Novel Longitudinal Rank-Sum Test for Multiple Primary Endpoints in Clinical Trials: Applications to Neurodegenerative Disorders

description: Neurodegenerative disorders such as Alzheimer’s disease (AD) present a significant global health challenge, characterized by cognitive decline, functional impairment, and other debilitating effects. Current AD clinical trials often assess multiple longitudinal primary endpoints to comprehensively evaluate treatment efficacy. Traditional methods, however, may fail to capture global treatment effects, require larger sample sizes due to multiplicity adjustments, and may not fully use the available longitudinal data. To address these limitations, we introduce the Longitudinal Rank Sum Test (LRST), a novel nonparametric rank-based omnibus test statistic. The LRST enables a comprehensive assessment of treatment efficacy across multiple endpoints and time points without the need for multiplicity adjustments, effectively controlling Type I error while enhancing statistical power. It offers flexibility for various data distributions encountered in AD research and maximizes the utilization of longitudinal data. Simulations across realistic clinical trial scenarios, including those with conflicting treatment effects, and real-data applications demonstrate the LRST’s performance, underscoring its potential as a valuable tool in AD clinical trials.
DOI: 10.15468/dl.tjyhss
title: Occurrence Download

description: A dataset containing 167 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aristolochia littoralis",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 167 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015166-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cah3ra
title: Occurrence Download

description: A dataset containing 758 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 one of (Erodium manescavi)"
 ]
}

 The dataset includes 758 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015163-251009101135966/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.27854806
title: Random Effects Meta-Analysis of Contingency Tables with Complete and Partially Complete Data, with Application to COVID-19 Research

description: We present a random effects meta-analytic approach for analyzing exchangeable 2×2×2 tables of COVID-19 deaths classified by two comorbidities, for example, diabetes and hypertension. We take the marginal tables of comorbidities as multinomial with table-specific cell probabilities drawn from a Dirichlet distribution. Conditionally hereon, we model the death counts for cell in the 2 × 2 table of possible comorbidity combinations as independent binomial random variables. We allow for a randomly drawn normally distributed frailty to model the correlation of the death counts within the same study. For complete tables, this model can be fitted by standard statistical software. We propose an approximate maximum likelihood (ML) imputation procedure by which tables with missing entries can be completed enabling the use of the same standard procedures and giving a better estimate of model parameters than one would get by leaving partial tables out. The properties of the method are illustrated by simulations.
DOI: 10.6084/m9.figshare.27854806.v2
title: Random Effects Meta-Analysis of Contingency Tables with Complete and Partially Complete Data, with Application to COVID-19 Research

description: We present a random effects meta-analytic approach for analyzing exchangeable 2×2×2 tables of COVID-19 deaths classified by two comorbidities, for example, diabetes and hypertension. We take the marginal tables of comorbidities as multinomial with table-specific cell probabilities drawn from a Dirichlet distribution. Conditionally hereon, we model the death counts for cell in the 2 × 2 table of possible comorbidity combinations as independent binomial random variables. We allow for a randomly drawn normally distributed frailty to model the correlation of the death counts within the same study. For complete tables, this model can be fitted by standard statistical software. We propose an approximate maximum likelihood (ML) imputation procedure by which tables with missing entries can be completed enabling the use of the same standard procedures and giving a better estimate of model parameters than one would get by leaving partial tables out. The properties of the method are illustrated by simulations.
DOI: 10.6084/m9.figshare.27854806.v1
title: Random Effects Meta-Analysis of Contingency Tables with Complete and Partially Complete Data, with Application to COVID-19 Research

description: We present a random effects meta-analytic approach for analyzing exchangeable 2×2×2 tables of COVID-19 deaths classified by two comorbidities, for example, diabetes and hypertension. We take the marginal tables of comorbidities as multinomial with table-specific cell probabilities drawn from a Dirichlet distribution. Conditionally hereon, we model the death counts for cell in the 2×2 table of possible comorbidity combinations as independent binomial random variables. We allow for a randomly drawn normally distributed frailty to model the correlation of the death counts within the same study. For complete tables, this model can be fitted by standard statistical software. We propose an approximate maximum likelihood (ML) imputation procedure by which tables with missing entries can be completed enabling the use of the same standard procedures and giving a better estimate of model parameters than one would get by leaving partial tables out. The properties of the method are illustrated by simulations.
DOI: 10.15468/dl.geyavs
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Solenogastres)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((100 20,110 20,110 30,100 30,100 20))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015167-251009101135966/datasets/export for details.
DOI: 10.15468/dl.nrg3pk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Solenogastres)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((90 20,100 20,100 30,90 30,90 20))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015169-251009101135966/datasets/export for details.
DOI: 10.15468/dl.ugw86m
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aristolochia littoralis",
 "Country is Seychelles",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015165-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17308742
title: Dataset: Dataset Documentation for AI Paper

description: This is the dataset associated with our paper titled "Dataset Documentation for Responsible AI: Analysis of Suitability and Usage for Health Datasets". Artificial Intelligence (AI) is rapidly transforming healthcare, but also raising concerns about algorithmic biases that mostly stem from the training data. It is widely supported that transparent dataset documentation is key to enabling responsible AI development. Several standardized dataset documentation approaches have been established, such as Datasheet, Dataset Nutrition Label, Accountability Documentation, Healthsheet, and Data Card. However, their suitability and usage for health datasets remain unclear. In this paper, we compared all five approaches and evaluated their alignment with the STANDING Together Recommendations for Documentation of Health Datasets. We also investigated their real-world usage and gathered insights from generators and consumers of health datasets.

The paper will be linked here when published: https://github.com/AI-READI/dataset-documentation-paper-inventory