cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28152111.v1
title: Patient experience, understanding and self-management of asthma attacks: a qualitative study

description: Asthma attacks (AA) are potentially life-threatening complications of asthma associated with high levels of morbidity, mortality and rising healthcare costs. Patient experience, impact and understanding of AA is poorly described in the literature. Enhanced understanding will identify unmet needs in asthma care and support the development of improved personalised strategies for managing and preventing attacks. To explore patients’ experiences and understanding of AAs, the impact of AAs on their lives and self-management strategies during attacks. Single centre (UK) qualitative semi-structured interview study with 30 patients who recently had asthma attacks (≤4 weeks). Data were analysed using the framework approach. The patient experience and impact of AA varied, including recognising an impending attack. Variation in patients’ self-management behaviours during AAs was observed and was influenced by prior experience of attacks and care received for these and other life priorities. Several behaviours previously recognised as contributory to asthma deaths, including short-acting β-agonist (SABA) overuse, poor recognition of the risk of adverse outcomes, and delay in seeking medical help, were identified and reported. Most patients had a poor understanding of AAs and their management. This study describes the differing impact of AA on patient experiences and understanding of asthma attacks. These differences, combined with healthcare factors and attack characteristics, affect patient self-management approaches. These findings highlight unmet needs in asthma attack care. ClinicalTrials.gov ID NCT04410120 Academic Clinical Lecturer Starter Grant provided by Health Education England
DOI: 10.6084/m9.figshare.28219364.v1
title: Association and mechanism of montelukast on sleep disorders: insights from NHANES 2005-2018 data analysis and a network pharmacology study

description: Studies have suggested associations between montelukast and increased risks of sleep disorders, including overall sleeping problems and insomnia. However, the results of observational studies are not consistent. Understanding these associations is crucial, particularly in patients solely diagnosed with allergic rhinitis, where montelukast use remains prevalent. This study aimed to assess whether montelukast exposure is associated with sleep disorders and elucidate the possible molecular mechanism. We conducted a cross-sectional study of 16520 adults from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Multivariable regression was used to evaluate the association between montelukast exposure and sleep disorder. Network pharmacology was conducted to identify the mechanisms of montelukast on sleep disorders. Montelukast exposure had a higher prevalence of sleep disorders (25.28%). In a multivariable logistic regression model adjusted for sociodemographic, behavioural, and health characteristics, montelukast exposure was associated with sleep disorders (odds ratio [OR]: 1.72; confidence interval [CI]: 1.32-2.26). Network pharmacology was identified 39 intersection targets and 17 core targets of montelukast on sleep disorders. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis suggested montelukast mainly works through multiple pathways in chemical carcinogenesis-receptor activation, cancer, estrogen signaling pathway, etc. The study implies a potential positive association between long-term montelukast exposure and sleep disorders through multi-faceted mechanisms. It is suggested that attention be given to the possibility of sleep disorders in patients undergoing prolonged montelukast therapy.
DOI: 10.21233/s6my-6052
title: Huzenbacher See pollen dataset

description: Raw data for the Huzenbacher See pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/4zz8-vc30
title: Funny River Horse Trail testate amoebae surface sample dataset

description: Raw data for the Funny River Horse Trail testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.wmg7rk
title: Occurrence Download

description: A dataset containing 3599 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Schima wallichii (DC.) Korth."
 ]
}

 The dataset includes 3599 records from 96 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026618-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/84s3-dy85
title: Happy Valley Bison Kill [EgPn-220] vertebrate fauna dataset

description: Raw data for the Happy Valley Bison Kill [EgPn-220] vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.57703/ce-nx6qs
title: Digital surface twin #Q36

description: AFM Contact and Tapping mode images

AFM model: veeco diinova

Resolution is limited by tip radius ~ 20nm
DOI: 10.21233/vae4-3773
title: Stilwell macroinvertebrate dataset

description: Raw data for the Stilwell macroinvertebrate dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/75kx-sj35
title: McIntosh Farm water chemistry dataset

description: Raw data for the McIntosh Farm water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/c973-c009
title: LDDsite3907 ostracode surface sample dataset

description: Raw data for the LDDsite3907 ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.c3x4tx
title: Occurrence Download

description: A dataset containing 97 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sapium indicum Willd."
 ]
}

 The dataset includes 97 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026620-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6x3g9k
title: Occurrence Download

description: A dataset containing 355 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sapindus trifoliatus L."
 ]
}

 The dataset includes 355 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026619-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/37fh-7k33
title: Reed Lake water chemistry dataset

description: Raw data for the Reed Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.7910/dvn/tu5uxe
title: The south florida compound flood benchmark

description: This dataset is a collection for compound flood analysis in the South Florida area. The corresponding benchmark code is located at https://github.com/AslanDing/SFBench
DOI: 10.21233/j8n3-at60
title: Koizumi ostracode surface sample dataset

description: Raw data for the Koizumi ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.vn67zu
title: Occurrence Download

description: A dataset listing the 40825 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "BasisOfRecord is one of (Human Observation)"
 ]
}

 The dataset's 40825 records were derived from 447 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026608-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mbzke3
title: Occurrence Download

description: A dataset containing 2305 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sapium sebiferum (L.) Dum.Cours."
 ]
}

 The dataset includes 2305 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026621-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/dkx8-mq55
title: GATC54 pollen surface sample dataset

description: Raw data for the GATC54 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.qhsze9
title: Occurrence Download

description: A dataset containing 93539 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Neotoma Say & Ord, 1825"
 ]
}

 The dataset includes 93539 records from 226 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026609-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/yz9b-tb66
title: Mona Lisa Site [EgPm-3] vertebrate fauna dataset

description: Raw data for the Mona Lisa Site [EgPm-3] vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.7274/28786175
title: Overcoming Imperfect Data Challenges in Deep Learning-Based Medical Imaging

description: Deep learning (DL) techniques have demonstrated exceptional success in developing high-performing models for medical imaging applications. However, their effectiveness largely depends on access to extensive, high-quality labeled datasets, which are challenging to obtain in the medical field due to the high cost of annotation and privacy constraints. This dissertation introduces several novel deep-learning approaches aimed at addressing challenges associated with imperfect medical datasets, with the goal of reducing annotation efforts and enhancing the generalization capabilities of DL models. Specifically, two imperfect data challenges are studied in this dissertation. (1) Scarce annotation, where only a limited amount of labeled data is available for training. We propose several novel self-supervised learning techniques that leverage the inherent structure of medical images to improve representation learning. In addition, data augmentation with synthetic models is explored to generate synthetic images to improve self-supervised learning performance. (2) Weak annotation, in which the training data has only image-level annotation, noisy annotation, sparse annotation, or inconsistent annotation. We first introduce a novel self-supervised learning-based approach to better utilize the image level label for medical image semantic segmentation. Motivated by the large inter-observer variation in myocardial annotations for ultrasound images, we further propose an extended dice metric that integrates multiple annotations into the loss function, allowing the model to focus on learning generalizable features while minimizing variations caused by individual annotators.
DOI: 10.7274/28786175.v1
title: Overcoming Imperfect Data Challenges in Deep Learning-Based Medical Imaging

description: Deep learning (DL) techniques have demonstrated exceptional success in developing high-performing models for medical imaging applications. However, their effectiveness largely depends on access to extensive, high-quality labeled datasets, which are challenging to obtain in the medical field due to the high cost of annotation and privacy constraints. This dissertation introduces several novel deep-learning approaches aimed at addressing challenges associated with imperfect medical datasets, with the goal of reducing annotation efforts and enhancing the generalization capabilities of DL models. Specifically, two imperfect data challenges are studied in this dissertation. (1) Scarce annotation, where only a limited amount of labeled data is available for training. We propose several novel self-supervised learning techniques that leverage the inherent structure of medical images to improve representation learning. In addition, data augmentation with synthetic models is explored to generate synthetic images to improve self-supervised learning performance. (2) Weak annotation, in which the training data has only image-level annotation, noisy annotation, sparse annotation, or inconsistent annotation. We first introduce a novel self-supervised learning-based approach to better utilize the image level label for medical image semantic segmentation. Motivated by the large inter-observer variation in myocardial annotations for ultrasound images, we further propose an extended dice metric that integrates multiple annotations into the loss function, allowing the model to focus on learning generalizable features while minimizing variations caused by individual annotators.
DOI: 10.21233/krd4-1n63
title: Pickerel Lake water chemistry dataset

description: Raw data for the Pickerel Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/rmmj-ez04
title: Waldsea ostracode surface sample dataset

description: Raw data for the Waldsea ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/3sfd-pv57
title: LDDsite1541 water chemistry dataset

description: Raw data for the LDDsite1541 water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/dbvk-wc96
title: Menzie's Farm ostracode surface sample dataset

description: Raw data for the Menzie's Farm ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/w5m9-dg54
title: Mud Pond diatom bottom sample dataset

description: Raw data for the Mud Pond diatom bottom sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/jbvp-p252
title: Bílé Karpaty 40 pollen surface sample dataset

description: Raw data for the Bílé Karpaty 40 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.15388831
title: TABLE 1 in Tucupitermes, the largest Apicotermitinae (Isoptera, Termitidae) from the Amazon Rainforest

description: TABLE 1. Comparative measurements (range and mean in mm) of workers of Tucupitermes ubixaba sp. nov. with workers of Aparatermes thornatus (Pinzón Florian et al. 2019), Aparatermes abbreviatus (Fontes 1986), Aparatermes silvestrii (personal observations Acioli 2007) e Aparatermes cingulatus (Šobotník et al. 2010).
SpeciesHead lengthHead widthHind tibia lengthPronotumNumber ofwidthspecimensTucupitermes ubixaba sp. nov.1.98–2.12 (2.05)1.90–1.93 (1.90)1.46–1.75 (1.64)0.96–1.21 (1.03)20Aparatermes thornatus (Pinzón Florian et al. 2019)0.63–0.93 (0.80)0.82–1.11 (0.92)0.53–0.84 (0.68)-19Aparatermes abbreviatus (Fontes 1986)1.00–1.20 -1.05–1.27 -0.85–0.98 --10Aparatermes Silvestrii (personal observations Acioli 2007)0.91–1.22 (1.03)1.06–1.53 (1.26)0. –1.03 (0.89)-42Aparatermes cingulatus (Šobotník et al. 2010)-(1.38)--10
description: Published as part of Almeida-Azevedo, Rayssa, Acioli, Agno Nonato Serrão, Heleodoro, Raphael, Morais, José Wellington De, Brown, George Gardner, Kille, Peter, Cunha, Luis & Azevedo, Renato Almeida De, 2025, Tucupitermes, the largest Apicotermitinae (Isoptera, Termitidae) from the Amazon Rainforest, pp. 189 in Zootaxa 5633 (1) on page 189, DOI: 10.11646/zootaxa.5633.1.11, http://zenodo.org/record/15372700
DOI: 10.21233/4z0r-ms83
title: Lower St. Regis diatom surface sample dataset

description: Raw data for the Lower St. Regis diatom surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.6084/m9.figshare.29040524
title: Quercus robur T2T genone

description: Quercus robur T2T genone
DOI: 10.15468/dl.v2m2jz
title: Occurrence Download

description: A dataset containing 305 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Alien plants of Kyrgyzstan"
 ]
}

 The dataset includes 305 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026624-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/08p6-ac47
title: Panther P. water chemistry dataset

description: Raw data for the Panther P. water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.9vacbg
title: Occurrence Download

description: A dataset containing 28028 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-78.09909 -9.27628,-77.5118 -9.27628,-77.5118 -8.89038,-78.09909 -8.89038,-78.09909 -9.27628))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 28028 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026622-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.49nrwu
title: Occurrence Download

description: A dataset containing 247 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Korean Peninsula Flora"
 ]
}

 The dataset includes 247 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026623-250426092105405/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.23694163
title: Group sequential multi-arm multi-stage survival trial design with treatment selection

description: Multi-arm trials are increasingly of interest because for many diseases; there are multiple experimental treatments available for testing efficacy. Several novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining stopping boundaries. For example, the method of Jaki and Magirr for time-to-event endpoint, implemented in R package <b>MAMS</b>, requires complicated computational efforts to obtain stopping boundaries. In this study, we develop a group sequential MAMS survival trial design based on the sequential conditional probability ratio test. The proposed method is an improvement of the Jaki and Magirr’s method in the following three directions. First, the proposed method provides explicit solutions for both futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational efforts for the trial design. Second, the proposed method provides an accurate number of events for the fixed sample and group sequential designs. Third, the proposed method uses a new procedure for interim analysis which preserves the study power.