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

description: A dataset containing 165 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Pakistan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Pavo cristatus Linnaeus, 1758"
 ]
}

 The dataset includes 165 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013879-241126133413365/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.27899754.v1
title: <b>InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving</b>

description: We provide a dense interaction dataset, InterHub, derived from extensive naturalistic driving records to address the scarcity of real-world datasets capturing rich interaction events. The dataset provided on this page include:<b>A</b><b> CSV file </b>(all_results.csv) containing the <b>indexed list</b> of the extracted interaction events<b>.</b><b>Relevant unified data cache files </b>(lyft_train_full, interaction_multi, interaction_single, and nuplan_train)<b>.</b>The Python codes used to process and analyze the dataset can be found at https://github.com/zxc-tju/InterHub. The tools for navigating InterHub involve the following three parts:0_data_unify.py converts various data resources into a unified format for seamless interaction event extraction.1_interaction_extract.py extracts interactive segments from unified driving records.2_case_visualize.py showcases interaction scenarios in InterHub.<br><br>
DOI: 10.6084/m9.figshare.25270694
title: Deep eutectic solvents for sustainable extraction of polyphenols and saponins from plant sources: assessment of the impact of influencing factors

description: With the requirement to increase the safety and sustainability of the separation processes, there has been a shift toward the utilization of green solvents in the extraction processes. Deep eutectic solvents (DESs) are neoteric solvents with several advantages over conventional organic solvents or ionic liquids (ILs). They are nontoxic, nonflammable, biodegradable, and less costly. Moreover, they can be regenerated and reused in the extraction process in repeated cycles. The extraction yield using DESs can be improved through the employment of ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), ultrahigh pressure extraction (UHPE), or negative pressure cavitation extraction (NPCE). These techniques not only shorten the processing time but also intensify the mass transfer and facilitate the diffusion and extraction of bioactive ingredients. With the raised interest in the separation and purification of polyphenol and saponin compounds with several therapeutic properties, DESs have found their applications as alternative solvents. This review aims to highlight recent applications of DESs for the extraction of flavonoids, curcuminoids, and saponins from plant sources and discuss and compare the factors that should be considered for enhancing the extraction efficiency of these bioactive ingredients. The discussed works lay the foundations for further development of the extraction processes using these solvents.
DOI: 10.6084/m9.figshare.25270694.v1
title: Deep eutectic solvents for sustainable extraction of polyphenols and saponins from plant sources: assessment of the impact of influencing factors

description: With the requirement to increase the safety and sustainability of the separation processes, there has been a shift toward the utilization of green solvents in the extraction processes. Deep eutectic solvents (DESs) are neoteric solvents with several advantages over conventional organic solvents or ionic liquids (ILs). They are nontoxic, nonflammable, biodegradable, and less costly. Moreover, they can be regenerated and reused in the extraction process in repeated cycles. The extraction yield using DESs can be improved through the employment of ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), ultrahigh pressure extraction (UHPE), or negative pressure cavitation extraction (NPCE). These techniques not only shorten the processing time but also intensify the mass transfer and facilitate the diffusion and extraction of bioactive ingredients. With the raised interest in the separation and purification of polyphenol and saponin compounds with several therapeutic properties, DESs have found their applications as alternative solvents. This review aims to highlight recent applications of DESs for the extraction of flavonoids, curcuminoids, and saponins from plant sources and discuss and compare the factors that should be considered for enhancing the extraction efficiency of these bioactive ingredients. The discussed works lay the foundations for further development of the extraction processes using these solvents.
DOI: 10.6084/m9.figshare.27950145
title: Supplementary Material for: Systemic Cancer Hallmarks as Novel Markers Associated with Progression-free Survival in Gastroenteropancreatic Neuroendocrine Tumor Patients Undergoing PRRT

description: Introduction: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are a heterogeneous group of tumors often detected at the metastatic stage. The aim of this study was to profile the peripheral blood transcriptome through RNA-Seq and investigate the association of the systemic cancer hallmarks with progression-free survival in PRRT-treated GEP-NET patients. Methods: The cohorts were: discovery cohort [PRRT-naïve well-differentiated GEP-NETs, n=59; age- and sex-matched healthy individuals, n=38], and independent evaluation cohort [GEP-NETs, n=66]. Peripheral blood transcriptomes were profiled through RNA sequencing and cancer hallmarks were identified via Gene Set Enrichment Analysis (GSEA). Activities of cancer hallmarks in each sample were calculated using Gene Set Variation Analysis (GSVA). Differentially expressed genes were identified with DESeq2. Progression-free survival was used as a primary endpoint and prognostic association was evaluated using univariate and multivariate COXPH analyses. Results: RNA-Seq captured global changes in the peripheral blood transcriptome of GEP-NET patients. Peripheral blood transcriptome of NET patients showed differential enrichment of 30 systemic cancer hallmarks viz., TNF-α signaling via NF-κB, IL2/STAT5 signaling, TNF-α response, TNF-γ response, IL6/JAK/STAT signaling, TGF-β signaling, heme metabolism. etc. In the univariate analyses, two cancer hallmarks were prognostically significant (p&lt;0.05) in GEP-NETs. Heme metabolism and IL2/STAT5 signaling were statistically significant in the Discovery cohort (n=58) and independent evaluation cohort (n=66). In multivariate COXPH analyses, heme metabolism and IL2/STAT5 signaling were independently associated with PFS in GEP-NET patients undergoing PRRT. Conclusions: This study provides comprehensive coverage of the peripheral blood transcriptome of GEP-NET patients via RNA-Seq and identifies systemic cancer hallmarks as independent prognostic factors in NETs.
DOI: 10.18126/q7rx-9597
title: Structural reconfiguration of interacting multi-particle systems through parametric pumping
DOI: 10.15468/dl.apu5re
title: Occurrence Download

description: A dataset containing 1016 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Carnivora (Mammalia) Collection from the Museu Nacional de História Natural e da Ciência, Universidade de Lisboa, Portugal"
 ]
}

 The dataset includes 1016 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001124-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gah2t4
title: Occurrence Download

description: A dataset containing 1144 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material citation, Human Observation, Specimen, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "TaxonKey is Caulerpa okamurae Weber Bosse, 1897"
 ]
}

 The dataset includes 1144 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001125-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fsqw3x
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "TaxonKey is Onosma tricerosperma Lag.",
 "Year 1975-2025"
 ]
}

 The dataset includes 7 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001126-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ajk429
title: Occurrence Download

description: A dataset containing 120 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ulva reticulata Forsskal, 1775)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation, Literature Occurrence)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 120 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001128-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2tyszq
title: Occurrence Download

description: A dataset containing 1075 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Soliva anthemifolia R.Br.)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation, Literature Occurrence)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1075 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001140-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13905734
title: Reproducibility Test Data of Foundation Model for Cancer Imaging x Mhub

description: This dataset provides test data for the FMCIB model integrated within the MHub platform, a robust solution for deploying, managing, and testing deep learning models tailored for medical imaging. 

Dataset Composition:



Sample Folder: Contains the input data utilized for testing the model’s functionality.

Reference Folder: Contains the corresponding output provided by the original model contributor.

Test.yml File: This file includes the original contributor’s test setup, which has been accepted by the MHub team.


Sample Data Source:

The sample images used in this dataset are sourced from public datasets available through the Imaging Data Commons (IDC), a repository that provides access to a wide range of medical imaging data. This ensures that the test cases reflect real-world clinical scenarios, facilitating robust validation of model performance.

Purpose and Utility:

The primary objective of this dataset is to enable the rigorous testing and validation of model performance within MHub workflows. To assess the performance of a model, users can process the sample data and compare the resulting output to the reference data. Additionally, users may inspect the sample and reference data independently to better understand the input-output structure that defines each model’s workflow.

This dataset streamlines the process of model validation. By providing a standardized testing framework, the dataset facilitates reproducible results and accelerates the development of reliable AI models for medical imaging.

About MHub:

MHub (mhub.ai) is an innovative platform designed to simplify the deployment, management, and testing of deep learning models for medical imaging. It enables researchers and clinicians to integrate AI-based solutions into clinical workflows while ensuring reproducibility and scalability. The platform provides a modular framework where users can execute complex workflows, such as image segmentation, classification, and registration, leveraging state-of-the-art AI models. MHub's goal is to accelerate the development and clinical adoption of medical imaging models by providing a streamlined, user-friendly environment for testing and validating new algorithms.

For more information on the platform and its capabilities, visit mhub.ai.
DOI: 10.5281/zenodo.14205464
title: Reproducibility Test Data of Foundation Model for Cancer Imaging x Mhub

description: This dataset provides test data for the FMCIB model integrated within the MHub platform, a robust solution for deploying, managing, and testing deep learning models tailored for medical imaging. 

Dataset Composition:



Sample Folder: Contains the input data utilized for testing the model’s functionality.

Reference Folder: Contains the corresponding output provided by the original model contributor.

Test.yml File: This file includes the original contributor’s test setup, which has been accepted by the MHub team.


Sample Data Source:

The sample images used in this dataset are sourced from public datasets available through the Imaging Data Commons (IDC), a repository that provides access to a wide range of medical imaging data. This ensures that the test cases reflect real-world clinical scenarios, facilitating robust validation of model performance.

Purpose and Utility:

The primary objective of this dataset is to enable the rigorous testing and validation of model performance within MHub workflows. To assess the performance of a model, users can process the sample data and compare the resulting output to the reference data. Additionally, users may inspect the sample and reference data independently to better understand the input-output structure that defines each model’s workflow.

This dataset streamlines the process of model validation. By providing a standardized testing framework, the dataset facilitates reproducible results and accelerates the development of reliable AI models for medical imaging.

About MHub:

MHub (mhub.ai) is an innovative platform designed to simplify the deployment, management, and testing of deep learning models for medical imaging. It enables researchers and clinicians to integrate AI-based solutions into clinical workflows while ensuring reproducibility and scalability. The platform provides a modular framework where users can execute complex workflows, such as image segmentation, classification, and registration, leveraging state-of-the-art AI models. MHub's goal is to accelerate the development and clinical adoption of medical imaging models by providing a streamlined, user-friendly environment for testing and validating new algorithms.

For more information on the platform and its capabilities, visit mhub.ai.
DOI: 10.5281/zenodo.14941936
title: Reproducibility Test Data of Foundation Model for Cancer Imaging x Mhub

description: This dataset provides test data for the FMCIB model integrated within the MHub platform, a robust solution for deploying, managing, and testing deep learning models tailored for medical imaging. 

Dataset Composition:



Sample Folder: Contains the input data utilized for testing the model’s functionality.

Reference Folder: Contains the corresponding output provided by the original model contributor.

Test.yml File: This file includes the original contributor’s test setup, which has been accepted by the MHub team.


Sample Data Source:

The sample images used in this dataset are sourced from public datasets available through the Imaging Data Commons (IDC), a repository that provides access to a wide range of medical imaging data. This ensures that the test cases reflect real-world clinical scenarios, facilitating robust validation of model performance.

Purpose and Utility:

The primary objective of this dataset is to enable the rigorous testing and validation of model performance within MHub workflows. To assess the performance of a model, users can process the sample data and compare the resulting output to the reference data. Additionally, users may inspect the sample and reference data independently to better understand the input-output structure that defines each model’s workflow.

This dataset streamlines the process of model validation. By providing a standardized testing framework, the dataset facilitates reproducible results and accelerates the development of reliable AI models for medical imaging.

About MHub:

MHub (mhub.ai) is an innovative platform designed to simplify the deployment, management, and testing of deep learning models for medical imaging. It enables researchers and clinicians to integrate AI-based solutions into clinical workflows while ensuring reproducibility and scalability. The platform provides a modular framework where users can execute complex workflows, such as image segmentation, classification, and registration, leveraging state-of-the-art AI models. MHub's goal is to accelerate the development and clinical adoption of medical imaging models by providing a streamlined, user-friendly environment for testing and validating new algorithms.

For more information on the platform and its capabilities, visit mhub.ai.
DOI: 10.5281/zenodo.14947299
title: Reproducibility Test Data of Foundation Model for Cancer Imaging x Mhub

description: This dataset provides test data for the FMCIB model integrated within the MHub platform, a robust solution for deploying, managing, and testing deep learning models tailored for medical imaging. 

Dataset Composition:



Sample Folder: Contains the input data utilized for testing the model’s functionality.

Reference Folder: Contains the corresponding output provided by the original model contributor.

Test.yml File: This file includes the original contributor’s test setup, which has been accepted by the MHub team.


Sample Data Source:

The sample images used in this dataset are sourced from public datasets available through the Imaging Data Commons (IDC), a repository that provides access to a wide range of medical imaging data. This ensures that the test cases reflect real-world clinical scenarios, facilitating robust validation of model performance.

Purpose and Utility:

The primary objective of this dataset is to enable the rigorous testing and validation of model performance within MHub workflows. To assess the performance of a model, users can process the sample data and compare the resulting output to the reference data. Additionally, users may inspect the sample and reference data independently to better understand the input-output structure that defines each model’s workflow.

This dataset streamlines the process of model validation. By providing a standardized testing framework, the dataset facilitates reproducible results and accelerates the development of reliable AI models for medical imaging.

About MHub:

MHub (mhub.ai) is an innovative platform designed to simplify the deployment, management, and testing of deep learning models for medical imaging. It enables researchers and clinicians to integrate AI-based solutions into clinical workflows while ensuring reproducibility and scalability. The platform provides a modular framework where users can execute complex workflows, such as image segmentation, classification, and registration, leveraging state-of-the-art AI models. MHub's goal is to accelerate the development and clinical adoption of medical imaging models by providing a streamlined, user-friendly environment for testing and validating new algorithms.

For more information on the platform and its capabilities, visit mhub.ai.
DOI: 10.5281/zenodo.13905733
title: Reproducibility Test Data of Foundation Model for Cancer Imaging x Mhub

description: This dataset provides test data for the FMCIB model integrated within the MHub platform, a robust solution for deploying, managing, and testing deep learning models tailored for medical imaging. 

Dataset Composition:



Sample Folder: Contains the input data utilized for testing the model’s functionality.

Reference Folder: Contains the corresponding output provided by the original model contributor.

Test.yml File: This file includes the original contributor’s test setup, which has been accepted by the MHub team.


Sample Data Source:

The sample images used in this dataset are sourced from public datasets available through the Imaging Data Commons (IDC), a repository that provides access to a wide range of medical imaging data. This ensures that the test cases reflect real-world clinical scenarios, facilitating robust validation of model performance.

Purpose and Utility:

The primary objective of this dataset is to enable the rigorous testing and validation of model performance within MHub workflows. To assess the performance of a model, users can process the sample data and compare the resulting output to the reference data. Additionally, users may inspect the sample and reference data independently to better understand the input-output structure that defines each model’s workflow.

This dataset streamlines the process of model validation. By providing a standardized testing framework, the dataset facilitates reproducible results and accelerates the development of reliable AI models for medical imaging.

About MHub:

MHub (mhub.ai) is an innovative platform designed to simplify the deployment, management, and testing of deep learning models for medical imaging. It enables researchers and clinicians to integrate AI-based solutions into clinical workflows while ensuring reproducibility and scalability. The platform provides a modular framework where users can execute complex workflows, such as image segmentation, classification, and registration, leveraging state-of-the-art AI models. MHub's goal is to accelerate the development and clinical adoption of medical imaging models by providing a streamlined, user-friendly environment for testing and validating new algorithms.

For more information on the platform and its capabilities, visit mhub.ai.