cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0215262.10fu
title: SH0215262.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0215263.10fu
title: SH0215263.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0215264.10fu
title: SH0215264.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0215265.10fu
title: SH0215265.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0215266.10fu
title: SH0215266.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0215267.10fu
title: SH0215267.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0215268.10fu
title: SH0215268.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.649z5c
title: Occurrence Download

description: A dataset containing 2297 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is England Otter Survey Database"
 ]
}

 The dataset includes 2297 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002797-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p3pjzs
title: Occurrence Download

description: A dataset containing 12002 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Grindelia squarrosa (Pursh) Dunal"
 ]
}

 The dataset includes 12002 records from 309 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002799-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/gpfsp3gpxv.1
title: Data on Academic Performance and Socioeconomic Status of Engineering Students Across Nine Campuses at the National University of Colombia

description: The dataset contains anonymized academic performance data of undergraduate engineering students from 2011 to 2023 at the nine campuses of the Universidad Nacional de Colombia. Key variables include socioeconomic status, faculty, gender, academic program, age, and cumulative weighted academic average (CWAA). This dataset provides insights into academic outcomes across various demographics, enabling analysis of patterns that may inform educational strategies and improve equity in engineering education.
DOI: 10.17632/b4s434d392.1
title: Disease severity and pruritus treatment outcomes in prurigo nodularis: A systematic review of randomized-controlled trials

description: Prurigo nodularis (PN) is a chronic immune-mediated condition characterized by intensely pruritic indurated nodules. Clinical trials have investigated several therapies for PN, describing the efficacy of trialed agents. This systematic review of randomized controlled trials investigates evidence regarding treatments for PN severity and pruritus.
DOI: 10.17632/vtv3jyy448.1
title: RiverTurb, an IoT water turbidity monitoring system for river applications

description: We designed a low-cost, open-source turbidity sensor for in-situ monitoring of water quality in streams and rivers. This monitoring system, named RiverTurb, is constructed using the widely available SEN0189 sensor. RiverTurb measures turbidity using the optical principle of attenuation, providing an affordable alternative to traditional commercial sensors, which cost typically in the range of thousands of euros. This paper outlines the hardware modification of the sensors, system development and calibration processes, including detailed instructions for assembly, calibration and usage. Hardware modifications were necessary to adapt the sensor for outdoor monitoring, i.e. replacing the original LEDs with ones with daylight filters and adding a new power connection to the sensor. Both these modifications were done to prevent sunlight influence. In field deployment, the monitoring system and associated sensor demonstrated reliable performance, accurately capturing turbidity variations and sediment concentrations. Calibration tests confirmed the sensor's accuracy and precision, making it suitable for long-term environmental monitoring. The sensor's simple design and building requirements make it an ideal choice for continuous turbidity measurement in aquatic environments. By enabling widespread, cost-effective monitoring, this sensor enhances our ability to study sediment dynamics and water quality with high spatial and temporal resolution, benefiting both researchers and environmental management efforts.
DOI: 10.34740/kaggle/dsv/10839147
title: e-IPO Indonesia Data - Investment
DOI: 10.5281/zenodo.14884159
title: ISS Mouse Brain

description: This dataset is identical in content with the ISS (in-situ-sequencing) data published at the BioImage Archive: https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BSST700

Here, the file names have been changed and a decoding code codebook and experimental metadata file have been added.

The purpose of this upload is to make the data readily useable with the ISS analysis code at ...
DOI: 10.5281/zenodo.14884160
title: ISS Mouse Brain

description: This dataset is identical in content with the ISS (in-situ-sequencing) data published at the BioImage Archive: https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BSST700

Here, the file names have been changed and a decoding code codebook and experimental metadata file have been added.

The purpose of this upload is to make the data readily useable with the ISS analysis code at ...
DOI: 10.5281/zenodo.14916448
title: Új kaszálóberendezések kifejlesztése a HIDROT Kft-nél
DOI: 10.5281/zenodo.14916449
title: Új kaszálóberendezések kifejlesztése a HIDROT Kft-nél
DOI: 10.5281/zenodo.14888356
title: NREL Renewable Energy Siting Lab Data

description: This repository contains all data produced by the NREL Renewable Energy Siting Lab. The Siting Lab offers information on solar energy siting regulations and zoning ordinances, as well as supply curve data. Documentation particular to each dataset can be found in the relevant dataset zipfile. Archived from 
https://data.openei.org/siting_lab

This archive contains raw input data for the Public Utility Data Liberation (PUDL)
software developed by Catalyst Cooperative. It is
organized into Frictionless
Data Packages. For additional information about this data and PUDL, see the
following resources:


  
The PUDL Repository on GitHub
  
PUDL Documentation
  
Other Catalyst Cooperative data archives
DOI: 10.5281/zenodo.14888357
title: NREL Renewable Energy Siting Lab Data

description: This repository contains all data produced by the NREL Renewable Energy Siting Lab. The Siting Lab offers information on solar energy siting regulations and zoning ordinances, as well as supply curve data. Documentation particular to each dataset can be found in the relevant dataset zipfile. Archived from 
https://data.openei.org/siting_lab

This archive contains raw input data for the Public Utility Data Liberation (PUDL)
software developed by Catalyst Cooperative. It is
organized into Frictionless
Data Packages. For additional information about this data and PUDL, see the
following resources:


  
The PUDL Repository on GitHub
  
PUDL Documentation
  
Other Catalyst Cooperative data archives
DOI: 10.5281/zenodo.14916479
title: NREL Renewable Energy Siting Lab Data

description: This repository contains all data produced by the NREL Renewable Energy Siting Lab. The Siting Lab offers information on solar energy siting regulations and zoning ordinances, as well as supply curve data. Documentation particular to each dataset can be found in the relevant dataset zipfile. Archived from 
https://data.openei.org/siting_lab

This archive contains raw input data for the Public Utility Data Liberation (PUDL)
software developed by Catalyst Cooperative. It is
organized into Frictionless
Data Packages. For additional information about this data and PUDL, see the
following resources:


  
The PUDL Repository on GitHub
  
PUDL Documentation
  
Other Catalyst Cooperative data archives
DOI: 10.17632/h68fjkrzkz.1
title: Sociodemographic and obstetric data on stillbirths from a Brazilian social program, 2008-2022

description: This dataset presents sociodemographic and clinical information of pregnant women who suffered stillbirth in the state of Pernambuco covering the period from 2008 to 2022. This data was collected from the Mãe Coruja Pernambucana Program (PMCP), a former social program under the Sistema Único de Saúde (SUS). The PMCP operated in the state of Pernambuco, Brazil and it was active in 105 municipalities, primarily in vulnerable areas of the population. This program monitored mothers and children up to 5 years old, providing health support, educational assistance, and social and family care. 

This dataset contains two CSV files: the "dataset.csv" file contains the pre-processed data set, and the "attributes.csv" file contains information about each attribute.
DOI: 10.15468/tf4ejd
title: Database and digitization of bees in Thailand

description: Public species occurrence database such as GBIF provides specimen geographical records for global bee distribution, which is an invaluable resource for researchers studying bee diversity and pollination ecology. However, most records are biased toward specimens collected in North America and Europe. On the contrary, bees from Southeast Asia (SEA) are poorly understood and are not well represented in public databases. The Chulalongkorn University Natural History Museum (CUNHM) in Thailand holds a collection of more than 12,000 bee specimens from 4 families across more than 500 localities in the country's 77 provinces.The initial purpose of this project is to mobilize at least 8,000 Thai bee specimen records deposited at CUNHM and publish in GBIF. Activities include photographing specimens, assigning QR codes, transcribing labels, formatting transcription of the data to enable publication in GBIF.org, mapping species distributions, and holding a workshop to showcase and demonstrate the use of the database.For long-term sustainability of the project, we aim to establish an accurate and reliable digital bee database for the global audience and researchers whose interest are in pollination biology, conservation, bee taxonomy, and biodiversity informatics in Southeast Asia, a lesser-known area of bee diversity. Research fields in climate change, invasive species, and ecology of pollinators will benefit from this work, since information from tropical Asia is often limited and sometimes inaccessible.Beside producing and publishing the database to GBIF, this effort provides a template for hosting other biodiversity information hosted and stored in Thailand by the National Science and Technology Development Agency (NSTDA), a partner that is providing matching funds. The processes and methods of digitization of bee records will be disseminated and shared with the country's other research collections, universities, and institutions through workshop and university lectures. Through these outreach activities, we hope to familiarize and educate audiences on how to utilize the data efficiently—both through the database and GBIF—and to persuade them the importance of pollinators to the public.
DOI: 10.5281/zenodo.14916531
title: Pix2Pix Facades Dataset

description: Description:







The Pix2Pix Facades Dataset is widely used for image-to-image translation tasks, specifically in architectural and structural imagery. It supports the Pix2Pix Generative Adversarial Network (GAN), which excels at translating facade images (buildings) into segmented images. The Pix2Pix model, leveraging a deep convolutional architecture, is capable of generating high-resolution outputs (256×256 pixels and beyond) and is effective in various image-conditional translation tasks, such as style transfer, object rendering, and architectural visualization.

 







Download Dataset

 









Image-Conditional GAN

Pix2Pix uses a specialized GAN architecture, facilitating high-resolution (256×256 pixels) image generation for translation tasks such as facade segmentation.

Applications

This dataset is highly valuable in various fields, primarily in building segmentation, urban planning, and architectural design. It provides essential annotations that help AI models distinguish different elements of building facades, enhancing accuracy in image processing tasks. In urban planning, the dataset aids in creating automated tools for city structure analysis, helping architects and planners visualize potential changes to urban landscapes.

Advanced Use

Beyond architecture, the Pix2Pix Facades dataset extends its utility across a wide range of image-to-image translation tasks. Researchers and developers can leverage this dataset for applications in medical imaging (e.g., converting CT scans into segmented views), satellite imagery (transforming raw satellite data into readable maps), and even fashion (translating sketches into finished designs). Its flexibility in handling various visual translation problems makes it an invaluable tool for advancing AI solutions in fields like autonomous driving, augmented reality, and content generation.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14916532
title: Pix2Pix Facades Dataset

description: Description:







The Pix2Pix Facades Dataset is widely used for image-to-image translation tasks, specifically in architectural and structural imagery. It supports the Pix2Pix Generative Adversarial Network (GAN), which excels at translating facade images (buildings) into segmented images. The Pix2Pix model, leveraging a deep convolutional architecture, is capable of generating high-resolution outputs (256×256 pixels and beyond) and is effective in various image-conditional translation tasks, such as style transfer, object rendering, and architectural visualization.

 







Download Dataset

 









Image-Conditional GAN

Pix2Pix uses a specialized GAN architecture, facilitating high-resolution (256×256 pixels) image generation for translation tasks such as facade segmentation.

Applications

This dataset is highly valuable in various fields, primarily in building segmentation, urban planning, and architectural design. It provides essential annotations that help AI models distinguish different elements of building facades, enhancing accuracy in image processing tasks. In urban planning, the dataset aids in creating automated tools for city structure analysis, helping architects and planners visualize potential changes to urban landscapes.

Advanced Use

Beyond architecture, the Pix2Pix Facades dataset extends its utility across a wide range of image-to-image translation tasks. Researchers and developers can leverage this dataset for applications in medical imaging (e.g., converting CT scans into segmented views), satellite imagery (transforming raw satellite data into readable maps), and even fashion (translating sketches into finished designs). Its flexibility in handling various visual translation problems makes it an invaluable tool for advancing AI solutions in fields like autonomous driving, augmented reality, and content generation.

This dataset is sourced from Kaggle.
DOI: 10.6084/m9.figshare.28468850
title: Supplement files

description: Sofosbuvir has been explored as a promising alternative to Remdesivir, evaluating its efficacy and potential benefits as a therapeutic agent in the fight against viral infections.
DOI: 10.6084/m9.figshare.28468850.v1
title: Supplement files

description: Sofosbuvir has been explored as a promising alternative to Remdesivir, evaluating its efficacy and potential benefits as a therapeutic agent in the fight against viral infections.
DOI: 10.5281/zenodo.14884968
title: Solving the "n_1 × n_2 × … × n_k Dots Puzzle"

description: The aim of this paper is to search to find the exact solution of the NP-complete n_1 × n_2 × … × n_k dots puzzle (an extension of the well-known nine dots puzzle of Samuel Loyd), when k≥3.