cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14909963
title: Stable Diffusion Face Dataset

description: Description:







This dataset contains AI-generated human faces created using various versions of Stable Diffusion Face Dataset models. The primary goal is to create highly realistic human faces without adhering to any specific style, primarily focusing on facial realism. The dataset includes:



Images in 512x512px resolution, generated using Stable Diffusion 1.5.

Images in 768x768px resolution, created with Stable Diffusion 2.1.

Images in 1024x1024px, produced by SDXL 1.0.








Download Dataset









This dataset provides a wide array of human faces that can be employed for AI training, machine learning models, facial recognition, and various computer vision tasks. The collection represents diverse human appearances, including different ages, ethnicities, and genders, making it ideal for applications requiring human likeness simulations, security algorithms, and research into AI biases. The focus is on achieving photorealism while utilizing the generative power of modern AI models like Stable Diffusion. The images have been meticulously curated to ensure high quality, versatility, and a wide scope of applications across industries such as entertainment, marketing, security, and virtual avatars.

Potential Applications:



Training deep learning models for facial recognition.

Enhancing synthetic media and virtual avatar creation.

Researching biases in AI-generated content.

Improving performance in security algorithms that require realistic face datasets.


This dataset is particularly useful for:



AI Research & Development: Train machine learning models to recognize and classify human faces across diverse parameters.

Synthetic Media Creation: Leverage this dataset to create lifelike characters for games, movies, and virtual environments.

Security Applications: Enhance facial recognition systems by training them on AI-generated faces that mirror real-world complexities.

AI Ethics & Bias Testing: Evaluate how well AI models handle diversity in age, gender, and ethnicity with a balanced dataset.


This dataset is sourced from Kaggle.
DOI: 10.15468/dl.6uz5q8
title: Occurrence Download

description: A dataset containing 785 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Lynx pardinus (Temminck, 1827)",
 "Year 2008-2024"
 ]
}

 The dataset includes 785 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001085-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13918422
title: Replication Package for "On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?"

description:  

Authors

Matteo Esposito, University of Oulu, Finland Francesco Palagiano, Multitel di Lerede Alessandro & C. s.a.s. Valentina Lenarduzzi, University of Oulu, Finland Davide Taibi, University of Oulu, Finland

Content Overview

This replication package contains the following materials:



Data: RAW Questionnaire dataset.

Data Description: We have now included a new file detailing the columns of the raw excel files and their connections with the Research Questions of our work.

Contact Information


For any issues, questions, or further assistance, please do not hesitate to contact the paper's authors. We are here to help!
DOI: 10.5281/zenodo.13918423
title: Replication Package for "On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?"

description: Authors

Matteo Esposito, University of Oulu, Finland Francesco Palagiano, Multitel di Lerede Alessandro & C. s.a.s. Valentina Lenarduzzi, University of Oulu, Finland Davide Taibi, University of Oulu, Finland

Content Overview

This replication package contains the following materials:



Data: RAW Questionnaire dataset.

Contact Information


For any issues, questions, or further assistance, please do not hesitate to contact the paper's authors. We are here to help!
DOI: 10.5281/zenodo.14748640
title: Replication Package for "On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?"

description: Authors

Matteo Esposito, University of Oulu, Finland Francesco Palagiano, Multitel di Lerede Alessandro & C. s.a.s. Valentina Lenarduzzi, University of Oulu, Finland Davide Taibi, University of Oulu, Finland

Content Overview

This replication package contains the following materials:



Data: RAW Questionnaire dataset.

Contact Information


For any issues, questions, or further assistance, please do not hesitate to contact the paper's authors. We are here to help!
DOI: 10.5281/zenodo.14909961
title: Replication Package for "On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?"

description:  

Authors

Matteo Esposito, University of Oulu, Finland Francesco Palagiano, Multitel di Lerede Alessandro & C. s.a.s. Valentina Lenarduzzi, University of Oulu, Finland Davide Taibi, University of Oulu, Finland

Content Overview

This replication package contains the following materials:



Data: RAW Questionnaire dataset.

Data Description: We have now included a new file detailing the columns of the raw excel files and their connections with the Research Questions of our work.

Contact Information


For any issues, questions, or further assistance, please do not hesitate to contact the paper's authors. We are here to help!
DOI: 10.5281/zenodo.14909984
title: Replication Package for "On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?"

description:  

Authors

Matteo Esposito, University of Oulu, Finland Francesco Palagiano, Multitel di Lerede Alessandro & C. s.a.s. Valentina Lenarduzzi, University of Oulu, Finland Davide Taibi, University of Oulu, Finland

Content Overview

This replication package contains the following materials:



Data: RAW Questionnaire dataset.

Data Description: We have now included a new file detailing the columns of the raw excel files and their connections with the Research Questions of our work.

Contact Information


For any issues, questions, or further assistance, please do not hesitate to contact the paper's authors. We are here to help!
DOI: 10.34740/kaggle/dsv/10821069
title: UK Property Price official data (Monthly Update)
DOI: 10.15468/dl.gxu8fv
title: Occurrence Download

description: A dataset containing 227344 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ochrophyta",
 "Year 2012-2012"
 ]
}

 The dataset includes 227344 records from 305 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001082-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.g9ywoatx
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (7 records)
DOI: 10.15468/dl.n2ng2h
title: Occurrence Download

description: A dataset containing 200353 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ochrophyta",
 "Year 2013-2013"
 ]
}

 The dataset includes 200353 records from 313 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001084-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/faxymw
title: Some findings of common, protected, and invasive species in Ukraine recorded in 2016-2023

description: The dataset is made of collected records of nationally, regionally and internationally protected species, common wildlife species of Ukraine and invasive species (like red-eared slider, Trachemys scripta). The dataset also contains numerous records of invasive plant species. Some of the records can contribute to the understanding of the ntural ranges of some species.
DOI: 10.15468/d8e725
title: The interaction between distribution of papyrus endemic bird species and vegetation characteristics in a highly degraded key bird area

description: The data set contains occurrence records of
five papyrus endemic birds sampled from Nyamuliro wetland between August and
November 2023.
DOI: 10.5281/zenodo.14849718
title: Field observation of river dryness and wetness

description: Field work observation of river dryness and wetness
DOI: 10.5281/zenodo.14849719
title: Field observation of river dryness and wetness

description: Field work observation of river dryness and wetness
DOI: 10.34740/kaggle/dsv/10821779
title: Clinics for Physiotherapy in Region Midt
DOI: 10.5281/zenodo.14373971
title: BioSR for LLS-SIM: Dataset of Fibrillarin

description: This is LLS-SIM dataset of Fibrillarin, which is a part of BioSR for LLS-SIM dataset (https://doi.org/10.5281/zenodo.14322457).

BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy.

The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models.
DOI: 10.5281/zenodo.14373972
title: BioSR for LLS-SIM: Dataset of Fibrillarin

description: This is LLS-SIM dataset of Fibrillarin, which is a part of BioSR for LLS-SIM dataset (https://doi.org/10.5281/zenodo.14322457).

BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy.

The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models.
DOI: 10.5281/zenodo.14909576
title: BioSR for LLS-SIM: Dataset of Fibrillarin

description: This is LLS-SIM dataset of Fibrillarin, which is a part of BioSR for LLS-SIM dataset (https://doi.org/10.5281/zenodo.14322457).

BioSR for LLS-SIM is a biological image dataset acquired using our home-built lattice light-sheet structured illumination microscopy (LLS-SIM). It currently includes paired diffraction-limited LLSM and LLS-SIM images of a variety of biology structures, constituting a high-quality volumetric SR dataset. The BioSR for LLS-SIM dataset originates from our paper [Qiao, C., Li, Z., Wang, Z., Lin, Y., ... & Li, D. (2024). Fast adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging. bioRxiv, 2024-05], serving as the supplementary data to execute meta-training or other proposed deep-learning-based algorithms, which provides sources for the community to try our methods, replicate our results, and develop their own methods for super-resolution lattice light-sheet microscopy.

The BioSR for LLS-SIM dataset includes 10 diverse task datasets from 10 distinct biological specimens (granular component, chromosomes, fibrillar center, fibrillarin, Lyso, MTs, F-actin in pollen tubes, inner mitochondrial membrane, ER in adherent Cos-7 cells and ER in mitotic Hela cells during metaphase). For each type of samples, we acquired raw LLS-SIM images from about 30-50 ROIs. For each ROI, five different levels of light intensity ranging from low to high fluorescence levels were acquired, and the images of the highest fluorescence level (i.e. the GT raw images) were reconstructed into high-quality GT LLS-SIM images via the conventional LLS-SIM reconstruction algorithm, which could be used as the groud truth in the training phase of deep-learning models.
DOI: 10.6084/m9.figshare.28463780
title: 1data.xlsx

description: Antifibrotic therapy in silicosis mice<br>
DOI: 10.6084/m9.figshare.28463780.v1
title: 1data.xlsx

description: Antifibrotic therapy in silicosis mice<br>
DOI: 10.5281/zenodo.14910071
title: GCfix: A Fast and Accurate Fragment Length-Specific Method for Correcting GC Bias in Cell-Free DNA
DOI: 10.5281/zenodo.14910072
title: GCfix: A Fast and Accurate Fragment Length-Specific Method for Correcting GC Bias in Cell-Free DNA
DOI: 10.15468/dl.zv6bne
title: Occurrence Download

description: A dataset containing 191348 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is NOAA National Coral Reef Monitoring Program Coral Demographic Data"
 ]
}

 The dataset includes 191348 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001086-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/10822102
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.15468/dl.4p5e65
title: Occurrence Download

description: A dataset containing 3521 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ascarina lucida Hook.fil."
 ]
}

 The dataset includes 3521 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001087-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5s6d73
title: Occurrence Download

description: A dataset containing 266 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Paraguay",
 "TaxonKey is Peltophorum dubium (Spreng.) Taub."
 ]
}

 The dataset includes 266 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001088-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/10822154
title: Full TMDB Movies Dataset 2024 (1M Movies)
DOI: 10.5281/zenodo.14910119
title: cardamine_flexuosa_spades_01.fasta.gz
DOI: 10.5281/zenodo.14910120
title: cardamine_flexuosa_spades_01.fasta.gz
DOI: 10.5281/zenodo.14910140
title: coptodon_congicus_spades_01.fasta.gz
DOI: 10.5281/zenodo.14910141
title: coptodon_congicus_spades_01.fasta.gz
DOI: 10.5281/zenodo.14910152
title: macrostomum_karlingi_spades_01.fasta.gz
DOI: 10.5281/zenodo.14910153
title: macrostomum_karlingi_spades_01.fasta.gz
DOI: 10.5281/zenodo.14910101
title: MPI-ESM-HR seasonal hindcast

description: Upwelling processes bring nutrient-rich waters from the deep ocean to the surface. Areas of upwelling are often associated with high productivity, offering great economic value in terms of fisheries. Thus, predictive skill for regional oceanographic conditions is highly desirable. Recently, seasonal predictability of coastal upwelling systems has received attention with potential skill excepted at  the southern edge of the Canary Upwelling System: the Senegalo-Mauritanian Upwelling System (SMUS), in spite of its primary importance to the functioning of the ecosystem and its crucial relevance for local populations. On this aspect, we provide here an overview of the  seasonal  Sea Surface Temperature (SST) prediction in the SMUS with the latest version of MPI-ESM1.2 hindcasts. In the SMUS region, seasonal variations of SST are predictable 1 to 4 months in advance during boreal winter, consistent with variable wind forcing being the dominant driver of SST predictability particularly  during the strong phases of the Atlantic Meridional Mode (AMM)  and El Nino Southern Oscillation (ENSO) events. However the  AMM plays a major role in enhancing the predictability of SST in the SMUS, while the remote influence  from Pacific ENSO shows only a limited contribution to SST predictability. Predicting SST of coastal upwelling systems may have direct implications on fisheries management strategies.
DOI: 10.5281/zenodo.14910102
title: MPI-ESM-HR seasonal hindcast

description: Upwelling processes bring nutrient-rich waters from the deep ocean to the surface. Areas of upwelling are often associated with high productivity, offering great economic value in terms of fisheries. Thus, predictive skill for regional oceanographic conditions is highly desirable. Recently, seasonal predictability of coastal upwelling systems has received attention with potential skill excepted at  the southern edge of the Canary Upwelling System: the Senegalo-Mauritanian Upwelling System (SMUS), in spite of its primary importance to the functioning of the ecosystem and its crucial relevance for local populations. On this aspect, we provide here an overview of the  seasonal  Sea Surface Temperature (SST) prediction in the SMUS with the latest version of MPI-ESM1.2 hindcasts. In the SMUS region, seasonal variations of SST are predictable 1 to 4 months in advance during boreal winter, consistent with variable wind forcing being the dominant driver of SST predictability particularly  during the strong phases of the Atlantic Meridional Mode (AMM)  and El Nino Southern Oscillation (ENSO) events. However the  AMM plays a major role in enhancing the predictability of SST in the SMUS, while the remote influence  from Pacific ENSO shows only a limited contribution to SST predictability. Predicting SST of coastal upwelling systems may have direct implications on fisheries management strategies.
DOI: 10.5281/zenodo.14910148
title: Coffee Roasting Agtron Dataset

description: Description:







To ensure uniformity in coffee roasting grades, the Specialty Coffee Roasting Agtron Dataset Association of America (SCAA) and Agtron Inc. have developed a globally accepted scale. This scale ranges from 0 to 100, with higher values indicating lighter roasts and lower values signifying darker roasts.

Dataset Content

The dataset includes images of coffee beans roasted at various Agtron levels, captured using multiple mobile devices. Each image set is accompanied by a reference photo representing the lighting conditions, ensuring standardization across all samples.







Download Dataset









Data Capture Conditions



Device Variability: Coffee bean images are taken using different mobile devices to account for varying camera qualities and resolutions.

Lighting Reference: Alongside the coffee images, a photo of a blank white sheet is included to serve as a lighting reference. This allows for normalization of lighting conditions, making the dataset reliable for visual analysis.


Agtron Scale and Its Importance

The Agtron scale is the industry standard for measuring the degree of coffee roasting. As the roast becomes darker, the value on the scale decreases. This scale is crucial in both quality control and research, allowing coffee professionals to consistently classify and compare roast levels.

Applications

This dataset is useful for several applications, including:



Roast Level Analysis: Training machine learning models to identify and classify coffee roast levels based on visual inputs.

Quality Control: Automating the analysis of roast consistency in coffee production.

Research & Development: Coffee researchers and companies can utilize the dataset to innovate new roasting techniques or improve product consistency.


Potential Use in AI and Machine Learning

The dataset serves as a foundation for AI development, specifically in image recognition. With the reference lighting condition and varied device capture, this dataset is ideal for developing models that can automate the classification of coffee roast levels based on the Agtron scale.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14910149
title: Coffee Roasting Agtron Dataset

description: Description:







To ensure uniformity in coffee roasting grades, the Specialty Coffee Roasting Agtron Dataset Association of America (SCAA) and Agtron Inc. have developed a globally accepted scale. This scale ranges from 0 to 100, with higher values indicating lighter roasts and lower values signifying darker roasts.

Dataset Content

The dataset includes images of coffee beans roasted at various Agtron levels, captured using multiple mobile devices. Each image set is accompanied by a reference photo representing the lighting conditions, ensuring standardization across all samples.







Download Dataset









Data Capture Conditions



Device Variability: Coffee bean images are taken using different mobile devices to account for varying camera qualities and resolutions.

Lighting Reference: Alongside the coffee images, a photo of a blank white sheet is included to serve as a lighting reference. This allows for normalization of lighting conditions, making the dataset reliable for visual analysis.


Agtron Scale and Its Importance

The Agtron scale is the industry standard for measuring the degree of coffee roasting. As the roast becomes darker, the value on the scale decreases. This scale is crucial in both quality control and research, allowing coffee professionals to consistently classify and compare roast levels.

Applications

This dataset is useful for several applications, including:



Roast Level Analysis: Training machine learning models to identify and classify coffee roast levels based on visual inputs.

Quality Control: Automating the analysis of roast consistency in coffee production.

Research & Development: Coffee researchers and companies can utilize the dataset to innovate new roasting techniques or improve product consistency.


Potential Use in AI and Machine Learning

The dataset serves as a foundation for AI development, specifically in image recognition. With the reference lighting condition and varied device capture, this dataset is ideal for developing models that can automate the classification of coffee roast levels based on the Agtron scale.

This dataset is sourced from Kaggle.