cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14031766
title: Guidelines for Adoption Micro-frontend Architecture

description: Context: The rapid evolution of software development has led to challenges when integrating new technologies into monolithic frontend architectures. While microservices advanced backend modularity, frontends often face issues like code redundancy, scalability, and lack of modularity. The micro-frontend architecture emerged as a modular approach to address these challenges.

Problem: Current monolithic frontend structures hinder agility and scalability in corporate environments, affecting both technology performance and business adaptability. This study targets challenges such as redundancy and the complexities of team coordination when scaling frontend projects.

Proposed Solution: We developed a Guide for Adoption of Micro-Frontends (GAM), providing structured guidelines to support the effective implementation of micro-frontends in corporate contexts.

IS Theory: This research draws upon the Technology Acceptance Model (TAM), evaluating the factors influencing the adoption of micro-frontend architecture within development teams.

Method: We conducted a systematic mapping study (SMS) to investigate the impacts and challenges associated with adopting micro-frontends in software development. Next, we proposed the GAM and carried out a case study at [Blind Review] to assess the feasibility of the proposed guide. Additionally, we conducted a survey with development practitioners to explore their perceptions of the micro-frontend architecture and the workflows suggested by the GAM.

Results: Results indicate that GAM is viewed positively by practitioners, assisting in the structured adoption of micro-frontends. Our case study highlights critical areas impacted by this approach, including performance, scalability, and parallel development, as well as issues with module complexity and developer experience.

Contributions and Impact on IS: This research contributes a practical framework for the industry, addressing current gaps in micro-frontend implementation and setting a basis for further study on modular frontend architectures in Information Systems (IS). GAM provides actionable insights for practitioners, enhancing frontend modernization efforts with measurable impacts in agility and maintenance.
DOI: 10.5281/zenodo.14063897
title: Guidelines for Adoption Micro-frontend Architecture

description: Context: The rapid evolution of software development has led to challenges when integrating new technologies into monolithic frontend architectures. While microservices advanced backend modularity, frontends often face issues like code redundancy, scalability, and lack of modularity. The micro-frontend architecture emerged as a modular approach to address these challenges.

Problem: Current monolithic frontend structures hinder agility and scalability in corporate environments, affecting both technology performance and business adaptability. This study targets challenges such as redundancy and the complexities of team coordination when scaling frontend projects.

Proposed Solution: We developed a Guide for Adoption of Micro-Frontends (GAM), providing structured guidelines to support the effective implementation of micro-frontends in corporate contexts.

IS Theory: This research draws upon the Technology Acceptance Model (TAM), evaluating the factors influencing the adoption of micro-frontend architecture within development teams.

Method: We conducted a systematic mapping study (SMS) to investigate the impacts and challenges associated with adopting micro-frontends in software development. Next, we proposed the GAM and carried out a case study at [Blind Review] to assess the feasibility of the proposed guide. Additionally, we conducted a survey with development practitioners to explore their perceptions of the micro-frontend architecture and the workflows suggested by the GAM.

Results: Results indicate that GAM is viewed positively by practitioners, assisting in the structured adoption of micro-frontends. Our case study highlights critical areas impacted by this approach, including performance, scalability, and parallel development, as well as issues with module complexity and developer experience.

Contributions and Impact on IS: This research contributes a practical framework for the industry, addressing current gaps in micro-frontend implementation and setting a basis for further study on modular frontend architectures in Information Systems (IS). GAM provides actionable insights for practitioners, enhancing frontend modernization efforts with measurable impacts in agility and maintenance.
DOI: 10.15468/dl.usakdt
title: Occurrence Download

description: A dataset containing 282 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ternstroemia lineata DC.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 282 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017178-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kayq44
title: Occurrence Download

description: A dataset containing 2604 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen)",
 "Geometry POLYGON((-160.14342 22.28463,-160.53261 21.65903,-159.88396 21.65903,-158.94991 21.41769,-158.56072 20.95804,-157.60072 20.95804,-157.02991 20.42405,-156.48504 20.03453,-156.04396 18.68824,-155.47315 18.56531,-154.8764 19.03198,-154.33153 19.44843,-154.33153 20.0589,-155.39531 20.47268,-155.70667 21.00649,-156.77045 21.58667,-157.57477 21.75546,-158.56072 21.75546,-158.69045 22.28463,-159.31315 22.54846,-160.14342 22.28463))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Basidiomycota"
 ]
}

 The dataset includes 2604 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017179-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u6s3vu
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ternstroemia maltbyi Rose)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017180-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a6nhn4
title: Occurrence Download

description: A dataset containing 5346 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-78.51138 17.52119,-76.08493 17.52119,-76.08493 18.83569,-78.51138 18.83569,-78.51138 17.52119))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Fungi"
 ]
}

 The dataset includes 5346 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017181-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m4vpfp
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ternstroemia oocarpa (Rose) Melch.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017182-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e8vtm6
title: Occurrence Download

description: A dataset listing the 59 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((105.62375 -6.62684,105.68456 -6.6272,105.68271 -6.4843,105.63796 -6.50179,105.62375 -6.62684))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset's 59 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017173-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14807428
title: Lattice Light-Sheet Microscopy Datasets and Workflow for Omero

description: Dataset Overview

This dataset provides a structured workflow for Lattice Light-Sheet Microscopy image processing, including raw data acquisition (.czi), summarised data (extract the .zarr compressed file), metadata extraction, and image enhancement techniques such as deskewing and deconvolution that can be found as a script (main.py). The dataset is intended for researchers working with high-resolution microscopy data.

Contents





Raw Data: Original microscopy images in CZI format 



It is recommended to store the raw data (e.g., CZI files) as a baseline for reproducibility. If raw data is too large (e.g., 500 GB), consider downsampling it for testing and archival purposes.





Metadata: Embedded data extracted from Zeiss software can be found directly after processing .czi file, while external metadata is synthetically generated (https://github.com/onionsp/Synthetic-WGS-Dataset-Generator/).




Processing Scripts: Python scripts (as found in main.py) for deskewing, deconvolution, and data summarization.



Use the provided processing scripts to perform deskewing, deconvolution, and other preprocessing steps. Note that processed data can become significantly larger (e.g., a 500 GB raw dataset may expand to 700 GB after processing).





Summarized Data: Processed image outputs in .zarr/.tiff format, reducing storage overhead while maintaining key insights.



Save summarized data to reduce storage requirements. Summarized data could include key metrics, visualizations, or compressed outputs.





Data Transfer Agreement: Documentation regarding data sharing policies and agreements.



Workflow Overview





Deskewing: Corrects image distortions caused during acquisition.




Deconvolution: Enhances image clarity and sharpness.




Downsampling: Reduces resolution for efficient processing and sharing.




Conversion: CZI to Zarr or TIFF format for optimized storage and computational use.



Data Access & Usage





The dataset, including raw and processed files, is hosted on Zenodo.




Users are encouraged to download downsampled versions for testing before using full-resolution data.




Processing scripts enable reproducibility and customization for different research applications.




Data transfer policies are outlined in the included Data Transfer Agreement.



 

https://github.com/DBK333/Omero-DataPortal/tree/main/OmeroImageSamples

https://github.com/BioimageAnalysisCoreWEHI/napari_lattice
DOI: 10.5281/zenodo.14807429
title: Lattice Light-Sheet Microscopy Datasets and Workflow for Omero

description: Dataset Overview

This dataset provides a structured workflow for Lattice Light-Sheet Microscopy image processing, including raw data acquisition (.czi), summarised data (extract the .zarr compressed file), metadata extraction, and image enhancement techniques such as deskewing and deconvolution that can be found as a script (main.py). The dataset is intended for researchers working with high-resolution microscopy data.

Contents





Raw Data: Original microscopy images in CZI format 



It is recommended to store the raw data (e.g., CZI files) as a baseline for reproducibility. If raw data is too large (e.g., 500 GB), consider downsampling it for testing and archival purposes.





Metadata: Embedded data extracted from Zeiss software can be found directly after processing .czi file, while external metadata is synthetically generated (https://github.com/onionsp/Synthetic-WGS-Dataset-Generator/).




Processing Scripts: Python scripts (as found in main.py) for deskewing, deconvolution, and data summarization.



Use the provided processing scripts to perform deskewing, deconvolution, and other preprocessing steps. Note that processed data can become significantly larger (e.g., a 500 GB raw dataset may expand to 700 GB after processing).





Summarized Data: Processed image outputs in .zarr/.tiff format, reducing storage overhead while maintaining key insights.



Save summarized data to reduce storage requirements. Summarized data could include key metrics, visualizations, or compressed outputs.





Data Transfer Agreement: Documentation regarding data sharing policies and agreements.



Workflow Overview





Deskewing: Corrects image distortions caused during acquisition.




Deconvolution: Enhances image clarity and sharpness.




Downsampling: Reduces resolution for efficient processing and sharing.




Conversion: CZI to Zarr or TIFF format for optimized storage and computational use.



Data Access & Usage





The dataset, including raw and processed files, is hosted on Zenodo.




Users are encouraged to download downsampled versions for testing before using full-resolution data.




Processing scripts enable reproducibility and customization for different research applications.




Data transfer policies are outlined in the included Data Transfer Agreement.



 

https://github.com/DBK333/Omero-DataPortal/tree/main/OmeroImageSamples

https://github.com/BioimageAnalysisCoreWEHI/napari_lattice
DOI: 10.5281/zenodo.14807502
title: Lattice Light-Sheet Microscopy Datasets and Workflow for Omero

description: Dataset Overview

This dataset provides a structured workflow for Lattice Light-Sheet Microscopy image processing, including raw data acquisition (.czi), summarised data (extract the .zarr compressed file), metadata extraction, and image enhancement techniques such as deskewing and deconvolution that can be found as a script (main.py). The dataset is intended for researchers working with high-resolution microscopy data.

Contents





Raw Data: Original microscopy images in CZI format 



It is recommended to store the raw data (e.g., CZI files) as a baseline for reproducibility. If raw data is too large (e.g., 500 GB), consider downsampling it for testing and archival purposes.





Metadata: Embedded data extracted from Zeiss software can be found directly after processing .czi file, while external metadata is synthetically generated (https://github.com/onionsp/Synthetic-WGS-Dataset-Generator/).




Processing Scripts: Python scripts (as found in main.py) for deskewing, deconvolution, and data summarization.



Use the provided processing scripts to perform deskewing, deconvolution, and other preprocessing steps. Note that processed data can become significantly larger (e.g., a 500 GB raw dataset may expand to 700 GB after processing).





Summarized Data: Processed image outputs in .zarr/.tiff format, reducing storage overhead while maintaining key insights.



Save summarized data to reduce storage requirements. Summarized data could include key metrics, visualizations, or compressed outputs.





Data Transfer Agreement: Documentation regarding data sharing policies and agreements.



Workflow Overview





Deskewing: Corrects image distortions caused during acquisition.




Deconvolution: Enhances image clarity and sharpness.




Downsampling: Reduces resolution for efficient processing and sharing.




Conversion: CZI to Zarr or TIFF format for optimized storage and computational use.



Data Access & Usage





The dataset, including raw and processed files, is hosted on Zenodo.




Users are encouraged to download downsampled versions for testing before using full-resolution data.




Processing scripts enable reproducibility and customization for different research applications.




Data transfer policies are outlined in the included Data Transfer Agreement.



 

https://github.com/DBK333/Omero-DataPortal/tree/main/OmeroImageSamples

https://github.com/BioimageAnalysisCoreWEHI/napari_lattice
DOI: 10.5281/zenodo.14807924
title: Lattice Light-Sheet Microscopy Datasets and Workflow for Omero

description: Dataset Overview

This dataset provides a structured workflow for Lattice Light-Sheet Microscopy image processing, including raw data acquisition (.czi), summarised data (extract the .zarr compressed file), metadata extraction, and image enhancement techniques such as deskewing and deconvolution that can be found as a script (main.py). The dataset is intended for researchers working with high-resolution microscopy data.

Contents





Raw Data: Original microscopy images in CZI format 



It is recommended to store the raw data (e.g., CZI files) as a baseline for reproducibility. If raw data is too large (e.g., 500 GB), consider downsampling it for testing and archival purposes.





Metadata: Embedded data extracted from Zeiss software can be found directly after processing .czi file, while external metadata is synthetically generated (https://github.com/onionsp/Synthetic-WGS-Dataset-Generator/).




Processing Scripts: Python scripts (as found in main.py) for deskewing, deconvolution, and data summarization.



Use the provided processing scripts to perform deskewing, deconvolution, and other preprocessing steps. Note that processed data can become significantly larger (e.g., a 500 GB raw dataset may expand to 700 GB after processing).





Summarized Data: Processed image outputs in .zarr/.tiff format, reducing storage overhead while maintaining key insights.



Save summarized data to reduce storage requirements. Summarized data could include key metrics, visualizations, or compressed outputs.





Data Transfer Agreement: Documentation regarding data sharing policies and agreements.



Workflow Overview





Deskewing: Corrects image distortions caused during acquisition.




Deconvolution: Enhances image clarity and sharpness.




Downsampling: Reduces resolution for efficient processing and sharing.




Conversion: CZI to Zarr or TIFF format for optimized storage and computational use.



Data Access & Usage





The dataset, including raw and processed files, is hosted on Zenodo.




Users are encouraged to download downsampled versions for testing before using full-resolution data.




Processing scripts enable reproducibility and customization for different research applications.




Data transfer policies are outlined in the included Data Transfer Agreement.



 

https://github.com/DBK333/Omero-DataPortal/tree/main/OmeroImageSamples

https://github.com/BioimageAnalysisCoreWEHI/napari_lattice
DOI: 10.15468/dl.uz72bd
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ternstroemia sylvatica Choisy)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017183-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c2arec
title: Occurrence Download

description: A dataset containing 61 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ternstroemia tepezapote Schltdl. & Cham.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 61 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017184-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u3ar5x
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pera barbellata Standl.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017185-250127130748423/datasets/export for details.
DOI: 10.15468/dl.r5hc3a
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Usnea orientalis Motyka & Lich.Gen.Usnea Stud.Monogr."
 ]
}

 The dataset includes 54 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017186-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wx95jx
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Petenaea cordata Lundell)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017187-250127130748423/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.28356242
title: CHM for anxiety disorders - Data spreadsheet

description: This is the dataset of the meta-analysis on Chinese herbal medicine for anxiety disorders
DOI: 10.15468/dl.ehc8ms
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ledenbergia macrantha Standl.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017188-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8a7sjj
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Astrocasia peltata Standl.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017189-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.