cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25405/data.ncl.24574729.v2
title: Language Dataset

description: Dataset containing the images and labels for the Language data used in the CVPR NAS workshop Unseen-data challenge under the codename "LaMelo"The Language dataset is a constructed dataset using words from aspell dictionaries. The intention of this dataset is to require machine learning models to not only perform image classification but also linguistic analysis to figure out which letter frequency is associated with each language. For each Language image we selected four six-letter words using the standard latin alphabet and removed any words with letters that used diacritics (such as ́e or ̈u) or included ‘y’ or ‘z’.We encode these words on a graph with one axis representing the index of the 24 character long string (the four words joined together) and the other representing the letter (going A-X).The data is in a channels-first format with a shape of (n, 1, 24, 24) where n is the number of samples in the corresponding set (50,000 for training, 10,000 for validation, and 10,000 for testing).There are ten classes in the dataset, with 7,000 examples of each, distributed evenly between the three subsets.The ten classes and corresponding numerical label are as follows:English: 0,<br>Dutch: 1,<br>German: 2,<br>Spanish: 3,<br>French: 4,<br>Portuguese: 5,<br>Swahili: 6,<br>Zulu: 7,<br>Finnish: 8,<br>Swedish: 9
DOI: 10.25405/data.ncl.24574729.v1
title: Language Dataset

description: Dataset containing the images and labels for the Language data used in the CVPR NAS workshop Unseen-data challenge under the codename "LaMelo"The Language dataset is a constructed dataset using words from aspell dictionaries. The intention of this dataset is to require machine learning models to not only perform image classification but also linguistic analysis to figure out which letter frequency is associated with each language. For each Language image we selected four six-letter words using the standard latin alphabet and removed any words with letters that used diacritics (such as ́e or ̈u) or included ‘y’ or ‘z’.We encode these words on a graph with one axis representing the index of the 24 character long string (the four words joined together) and the other representing the letter (going A-X).The data is in a channels-first format with a shape of (n, 1, 24, 24) where n is the number of samples in the corresponding set (50,000 for training, 10,000 for validation, and 10,000 for testing).There are ten classes in the dataset, with 7,000 examples of each, distributed evenly between the three subsets.The ten classes and corresponding numerical label are as follows:English: 0,<br>Dutch: 1,<br>German: 2,<br>Spanish: 3,<br>French: 4,<br>Portuguese: 5,<br>Swahili: 6,<br>Zulu: 7,<br>Finnish: 8,<br>Swedish: 9
DOI: 10.25405/data.ncl.24574678
title: MultNIST Dataset

description: Dataset containing the images and labels for the MultNIST data used in the CVPR NAS workshop Unseen-data challenge under the codename "Mateo"The MultNIST dataset is a constructed dataset from MNIST Images. The intention of this dataset is to require machine learning models to do more than just image classification but also perform a calculation, in this case multiplaction followed by a mod operation. For each image, three MNIST Images were randomly chosen and combined together through the colour channels, resulting in a three colour-channel image so each MNIST image represents one colour channel. The data is in a channels-first format with a shape of (n, 3, 28, 28) where n is the number of samples in the corresponding set (50,000 for training, 10,000 for validation, and 10,000 for testing).There are ten classes in the dataset, with 7,000 examples of each, distributed evenly between the three subsets.The label of each image is generated using the formula "(r * b * g) % 10" where r, g, and b are the red, green, and blue colour channels respectively. An example of a MultNIST Image would be a rgb configuation of 3, 7, and 4 respectively, which would result in a label of 4 ((3 * 7 * 4) % 10).
DOI: 10.25405/data.ncl.24574678.v1
title: MultNIST Dataset

description: Dataset containing the images and labels for the MultNIST data used in the CVPR NAS workshop Unseen-data challenge under the codename "Mateo"The MultNIST dataset is a constructed dataset from MNIST Images. The intention of this dataset is to require machine learning models to do more than just image classification but also perform a calculation, in this case multiplaction followed by a mod operation. For each image, three MNIST Images were randomly chosen and combined together through the colour channels, resulting in a three colour-channel image so each MNIST image represents one colour channel. The data is in a channels-first format with a shape of (n, 3, 28, 28) where n is the number of samples in the corresponding set (50,000 for training, 10,000 for validation, and 10,000 for testing).There are ten classes in the dataset, with 7,000 examples of each, distributed evenly between the three subsets.The label of each image is generated using the formula "(r * b * g) % 10" where r, g, and b are the red, green, and blue colour channels respectively. An example of a MultNIST Image would be a rgb configuation of 3, 7, and 4 respectively, which would result in a label of 4 ((3 * 7 * 4) % 10).
DOI: 10.25405/data.ncl.24574678.v2
title: MultNIST Dataset

description: Dataset containing the images and labels for the MultNIST data used in the CVPR NAS workshop Unseen-data challenge under the codename "Mateo"The MultNIST dataset is a constructed dataset from MNIST Images. The intention of this dataset is to require machine learning models to do more than just image classification but also perform a calculation, in this case multiplaction followed by a mod operation. For each image, three MNIST Images were randomly chosen and combined together through the colour channels, resulting in a three colour-channel image so each MNIST image represents one colour channel. The data is in a channels-first format with a shape of (n, 3, 28, 28) where n is the number of samples in the corresponding set (50,000 for training, 10,000 for validation, and 10,000 for testing).There are ten classes in the dataset, with 7,000 examples of each, distributed evenly between the three subsets.The label of each image is generated using the formula "(r * b * g) % 10" where r, g, and b are the red, green, and blue colour channels respectively. An example of a MultNIST Image would be a rgb configuation of 3, 7, and 4 respectively, which would result in a label of 4 ((3 * 7 * 4) % 10).
DOI: 10.3886/e244963v1
title: More than just work: The Earned Income Tax Credit and job quality

description: The data and do files here create the tables and figures from "More than just work: The Earned Income Tax Credit and Job Quality", by Katherine Michelmore and Natasha Pilkauskas. The paper uses a simulated benefit strategy to examine how expansions to the EITC from the 1990s-2010s affected the quality of jobs among unmarried, less-educated mothers.
description: Unmarried mothers with less than a college degree with at least one child under the age of 19 present in the household.
DOI: 10.25405/data.ncl.24574354
title: AddNIST Dataset

description: Dataset containing the images and labels for the AddNIST data used in the CVPR NAS workshop Unseen-data challenge under the codename "Adaline"The AddNIST dataset is a constructed dataset from MNIST Images. The intention of this dataset is to require machine learning models to do more than just image classification but also perform a calculation, in this case addition. For each image, three MNIST Images were randomly chosen and combined together through the colour channels, resulting in a three colour-channel image so each MNIST image represents one colour channel. The data is in a channels-first format with a shape of (n, 3, 28, 28) where n is the number of samples in the corresponding set (45,000 for training, 15,000 for validation, and 10,000 for testing).There are twenty classes in the dataset, with 3,500 examples of each, distributed evenly between the three subsets.The label of each image is generated using the formula "(r + b + g) - 1" where r, g, and b are the red, green, and blue colour channels respectively. An example of an AddNIST Image would be a rgb configuation of 3, 7, and 4 respectively, which would result in a label of 13 ((3 + 7 + 4) - 1).
DOI: 10.25405/data.ncl.24574354.v1
title: AddNIST Dataset

description: Dataset containing the images and labels for the AddNIST data used in the CVPR NAS workshop Unseen-data challenge under the codename "Adaline"The AddNIST dataset is a constructed dataset from MNIST Images. The intention of this dataset is to require machine learning models to do more than just image classification but also perform a calculation, in this case addition. For each image, three MNIST Images were randomly chosen and combined together through the colour channels, resulting in a three colour-channel image so each MNIST image represents one colour channel. The data is in a channels-first format with a shape of (n, 3, 28, 28) where n is the number of samples in the corresponding set (45,000 for training, 15,000 for validation, and 10,000 for testing).There are twenty classes in the dataset, with 3,500 examples of each, distributed evenly between the three subsets.The label of each image is generated using the formula "(r + b + g) - 1" where r, g, and b are the red, green, and blue colour channels respectively. An example of an AddNIST Image would be a rgb configuation of 3, 7, and 4 respectively, which would result in a label of 13 ((3 + 7 + 4) - 1).
DOI: 10.25405/data.ncl.24574354.v2
title: AddNIST Dataset

description: Dataset containing the images and labels for the AddNIST data used in the CVPR NAS workshop Unseen-data challenge under the codename "Adaline"The AddNIST dataset is a constructed dataset from MNIST Images. The intention of this dataset is to require machine learning models to do more than just image classification but also perform a calculation, in this case addition. For each image, three MNIST Images were randomly chosen and combined together through the colour channels, resulting in a three colour-channel image so each MNIST image represents one colour channel. The data is in a channels-first format with a shape of (n, 3, 28, 28) where n is the number of samples in the corresponding set (45,000 for training, 15,000 for validation, and 10,000 for testing).There are twenty classes in the dataset, with 3,500 examples of each, distributed evenly between the three subsets.The label of each image is generated using the formula "(r + b + g) - 1" where r, g, and b are the red, green, and blue colour channels respectively. An example of an AddNIST Image would be a rgb configuation of 3, 7, and 4 respectively, which would result in a label of 13 ((3 + 7 + 4) - 1).
DOI: 10.15468/dl.wvap9b
title: Occurrence Download

description: A dataset containing 140 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mollinedia oligantha",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 140 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014185-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18495578
title: SuperDARN data in netCDF format (2014-Jan)

description: 2014-Jan SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.25405/data.ncl.24551539
title: CIFARTile Dataset

description: The CIFARTile dataset is constructed using batches of image from the popular CIFAR-10 dataset (available at https://www.cs.toronto.edu/~kriz/cifar.html)Each CIFARTile image is a joining of four CIFAR-10 Images in a grid pattern. The solution to this dataset is for a model to identify the number of classes from the original CIFAR-10 that appear in the CIFARTile image. The original CIFAR-10 has 10 classes: Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, and Truck. A example CIFARTile image might include two images of horses, a car, and a bird.The data is in a channels-first format with a shape of (n, 3, 64, 64) where n is the number of samples in the corresponding set (45,000 for training, 15,000 for validation, and 10,000 for testing).There are four classes in the dataset, with 17,500 examples of each distributed as evenly as possible between the three subsets.Each images label represents the number of CIFAR-10 classes that appear minus one, for zero-indexing. This means the above example of two horse images, one car, and one bird would have a final label of 2, as there are three different classes.0: All sub-images belong to the same CIFAR-10 class.<br>1: There are two CIFAR-10 classes among the sub-images.<br>2: There are three CIFAR-10 classes among the sub-images<br>3: All usb-images belong to different CIFAR-10 classes.
DOI: 10.25405/data.ncl.24551539.v2
title: CIFARTile Dataset

description: The CIFARTile dataset is constructed using batches of image from the popular CIFAR-10 dataset (available at https://www.cs.toronto.edu/~kriz/cifar.html)Each CIFARTile image is a joining of four CIFAR-10 Images in a grid pattern. The solution to this dataset is for a model to identify the number of classes from the original CIFAR-10 that appear in the CIFARTile image. The original CIFAR-10 has 10 classes: Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, and Truck. A example CIFARTile image might include two images of horses, a car, and a bird.The data is in a channels-first format with a shape of (n, 3, 64, 64) where n is the number of samples in the corresponding set (45,000 for training, 15,000 for validation, and 10,000 for testing).There are four classes in the dataset, with 17,500 examples of each distributed as evenly as possible between the three subsets.Each images label represents the number of CIFAR-10 classes that appear minus one, for zero-indexing. This means the above example of two horse images, one car, and one bird would have a final label of 2, as there are three different classes.0: All sub-images belong to the same CIFAR-10 class.<br>1: There are two CIFAR-10 classes among the sub-images.<br>2: There are three CIFAR-10 classes among the sub-images<br>3: All usb-images belong to different CIFAR-10 classes.
DOI: 10.5281/zenodo.6478185
title: SuperDARN data in netCDF format (2014-Jan)

description: 2014-Jan SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.25405/data.ncl.24551539.v1
title: CIFARTile Dataset

description: The CIFARTile dataset is constructed using batches of image from the popular CIFAR-10 dataset (available at https://www.cs.toronto.edu/~kriz/cifar.html)Each CIFARTile image is a joining of four CIFAR-10 Images in a grid pattern. The solution to this dataset is for a model to identify the number of classes from the original CIFAR-10 that appear in the CIFARTile image. The original CIFAR-10 has 10 classes: Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, and Truck. A example CIFARTile image might include two images of horses, a car, and a bird.The data is in a channels-first format with a shape of (n, 3, 64, 64) where n is the number of samples in the corresponding set (45,000 for training, 15,000 for validation, and 10,000 for testing).There are four classes in the dataset, with 17,500 examples of each distributed as evenly as possible between the three subsets.Each images label represents the number of CIFAR-10 classes that appear minus one, for zero-indexing. This means the above example of two horse images, one car, and one bird would have a final label of 2, as there are three different classes.0: All sub-images belong to the same CIFAR-10 class.<br>1: There are two CIFAR-10 classes among the sub-images.<br>2: There are three CIFAR-10 classes among the sub-images<br>3: All usb-images belong to different CIFAR-10 classes.
DOI: 10.15468/dl.b35pd8
title: Occurrence Download

description: A dataset containing 1514570 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Perca fluviatilis, Sander lucioperca, Salvelinus fontinalis, Sparus aurata, Acipenser gueldenstaedtii, Argyrosomus regius, Mugil cephalus, Crenimugil seheli, Lates calcarifer, Clarias gariepinus, Rhabdosargus sarba)",
 "HasCoordinate is true",
 "DecimalLatitude is less than 90",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is less than 180",
 "DecimalLongitude is greater than -180"
 ]
}

 The dataset includes 1514570 records from 1221 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014145-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25405/data.ncl.24118743
title: Chesseract Dataset

description: A dataset containing the images and labels for the Chesseract data used in the CVPR NAS workshop Unseen-data challenge under the codename "Chester"The Chesseract dataset is constructed from publicly accessible Chess games played by Grandmasters: Bobby Fischer, Garry Kasparov, Magnus Carlsen, Viswanathan Anand, Hikaru Nakamura, Anatoly Karpov, Fabiano Caruana, and Mikhail Tal.We extracted the final 15% of board states where the nth board state refers to the position after N moves.Chesseract uses 12 channels to encode the information rather than the traditional 2 or 3 for grayscale and RGB images respectively. Each channel represents a singular piece type and colour combination. In essence, this has allowed us to represent the pieces and their position on the board in a one-hot encoded format.A rendering of one of the board states has been included in this archive to demonstrate how the machine perceives the dataset. A 2D traditional of the same board state is also included. Please note in the 3D rendering the white pieces have been rendered as gray to be more visible.The data is in a channels-first format with a shape of (n, 12, 8, 8) where n is the number of samples in the corresponding set (49,998 for training, 10,001 for validation, and 10,001 for testing).There are three classes in the dataset, with labels 23,333 examples of each distributed as evenly as possible between the three subsets.The three classes and corresponding numerical labels are as follows:<br>White wins: 0,<br>Draw: 1,<br>Black wins: 2<br>
DOI: 10.25405/data.ncl.24118743.v2
title: Chesseract Dataset

description: A dataset containing the images and labels for the Chesseract data used in the CVPR NAS workshop Unseen-data challenge under the codename "Chester"
The Chesseract dataset is constructed from publicly accessible Chess games played by Grandmasters: Bobby Fischer, Garry Kasparov, Magnus Carlsen, Viswanathan Anand, Hikaru Nakamura, Anatoly Karpov, Fabiano Caruana, and Mikhail Tal.
We extracted the final 15% of board states where the nth board state refers to the position after N moves.
Chesseract uses 12 channels to encode the information rather than the traditional 2 or 3 for grayscale and RGB images respectively. Each channel represents a singular piece type and colour combination. In essence, this has allowed us to represent the pieces and their position on the board in a one-hot encoded format.
A rendering of one of the board states has been included in this archive to demonstrate how the machine perceives the dataset. A 2D traditional of the same board state is also included. Please note in the 3D rendering the white pieces have been rendered as gray to be more visible.
The data is in a channels-first format with a shape of (n, 12, 8, 8) where n is the number of samples in the corresponding set (49,998 for training, 10,001 for validation, and 10,001 for testing).
There are three classes in the dataset, with labels 23,333 examples of each distributed as evenly as possible between the three subsets.
The three classes and corresponding numerical labels are as follows:<br>
White wins: 0,<br>
Draw: 1,<br>
Black ins: 2<br>
DOI: 10.25405/data.ncl.24118743.v3
title: Chesseract Dataset

description: A dataset containing the images and labels for the Chesseract data used in the CVPR NAS workshop Unseen-data challenge under the codename "Chester"The Chesseract dataset is constructed from publicly accessible Chess games played by Grandmasters: Bobby Fischer, Garry Kasparov, Magnus Carlsen, Viswanathan Anand, Hikaru Nakamura, Anatoly Karpov, Fabiano Caruana, and Mikhail Tal.We extracted the final 15% of board states where the nth board state refers to the position after N moves.Chesseract uses 12 channels to encode the information rather than the traditional 2 or 3 for grayscale and RGB images respectively. Each channel represents a singular piece type and colour combination. In essence, this has allowed us to represent the pieces and their position on the board in a one-hot encoded format.A rendering of one of the board states has been included in this archive to demonstrate how the machine perceives the dataset. A 2D traditional of the same board state is also included. Please note in the 3D rendering the white pieces have been rendered as gray to be more visible.The data is in a channels-first format with a shape of (n, 12, 8, 8) where n is the number of samples in the corresponding set (49,998 for training, 10,001 for validation, and 10,001 for testing).There are three classes in the dataset, with labels 23,333 examples of each distributed as evenly as possible between the three subsets.The three classes and corresponding numerical labels are as follows:<br>White wins: 0,<br>Draw: 1,<br>Black wins: 2<br>