cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8094225
title: MNIST-Federated-Learning

description: Please find below the descriptions of the three configurations for partitioning <strong>the MNIST Train dataset into 10 clients and the MNIST Train data: </strong><br> <strong>Balanced Distribution:</strong> In the first configuration, the MNIST dataset is partitioned among 10 clients in a balanced manner. This means that the data samples from each class are evenly distributed among the clients. Each client receives a roughly equal number of images from each digit class, ensuring that the distribution of samples across clients is proportional and representative of the overall dataset. <strong> [ Config 1]</strong> <strong>Heterogeneous Distribution (One Class per Client)</strong>: In the second configuration, the MNIST dataset is partitioned in a heterogeneous manner, where each client is assigned a single digit class exclusively. This means that one client will only receive images of the digit '0', another client will receive images of the digit '1', and so on. In this setup, each client becomes an expert in classifying a specific digit, allowing for specialized training and evaluation. <strong>[ Config 2]</strong> <strong>Mixed Distribution:</strong> In the third configuration, the MNIST dataset is partitioned using a mixed distribution approach. This means that the data samples from all digit classes are distributed among the 10 clients, but the distribution is not necessarily balanced. The number of samples assigned to each client may vary for different digit classes, resulting in an uneven distribution across the clients. This configuration aims to capture both the overall diversity of the dataset and the varying difficulty levels of classifying different digits. <strong>[ Config 3 ]</strong> <strong>The structure of "Mnist-dataset" folder is </strong><em><strong>:</strong></em><br> Mnist-dataset/<br> ├── config1/<br> │ ├── client-1/<br> │ │ └── client_1_config1.csv<br> │ ├── client-2/<br> │ │ └── client_2_config1.csv<br> │ ├── client-3/<br> │ │ └── client_3_config1.csv<br> │ └── ...<br> ├── config2/<br> │ ├── client-1/<br> │ │ └── client_1_config2.csv<br> │ ├── client-2/<br> │ │ └── client_2_config2.csv<br> │ ├── client-3/<br> │ │ └── client_3_config2.csv<br> │ └── ...<br> ├── config3/<br> │ ├── client-1/<br> │ │ └── client_1_config3.csv<br> │ ├── client-2/<br> │ │ └── client_2_config3.csv<br> │ ├── client-3/<br> │ │ └── client_3_config3.csv<br> │ └── ...<br> └── mnist_test.csv<br>
description: {"references": ["DENG, Li. The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE signal processing magazine, 2012, vol. 29, no 6, p. 141-142.", "http://yann.lecun.com/exdb/mnist/"]}
DOI: 10.5281/zenodo.7935038
title: Datos de las nuevas especies de árboles descubiertas en Costa Rica entre 2000 y 2022 - Versión Definitiva

description: Base de datos del artículo: Aguilar-Sandí, D. (2023). <strong>Recuento de las nuevas especies de árboles descubiertas en Costa Rica entre 2000 y 2022</strong>. <em>Biocenosis</em>, <em>34</em>(1), 81–91. https://doi.org/10.22458/rb.v34i1.4827 Versión final.
DOI: 10.5281/zenodo.7935037
title: Datos de las nuevas especies de árboles descubiertas en Costa Rica entre 2000 y 2022 - Versión Definitiva

description: Base de datos del artículo: Aguilar-Sandí, D. (2023). <strong>Recuento de las nuevas especies de árboles descubiertas en Costa Rica entre 2000 y 2022</strong>. <em>Biocenosis</em>, <em>34</em>(1), 81–91. https://doi.org/10.22458/rb.v34i1.4827 Versión final.
DOI: 10.5281/zenodo.8104424
title: rhodymenia_pacifica_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8104423
title: rhodymenia_pacifica_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22691275.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691236.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691179.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691167.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691155.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691152.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691086.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691428
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691419
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691413
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691410
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.5281/zenodo.7775016
title: The GNSS time series along the northern coastline of Java, Indonesia

description: This repository contains the GNSS time series along the northern coastline of Java, both in .rneu and the GAGE's .pos format (https://www.unavco.org/data/gps-gnss/derived-products/docs/NOTICE-TO-DATA-PRODUCT-USERS-GPS-2013-03-15.pdf). The repository also contains the stations' coordinates. Notes:<br> In the .rneu format of the GNSS time series:<br> 1. The outliers have been removed.<br> 2. The offsets due to instrument changes at CGON in mid-2016 and at CSIT in late 2015 have been corrected. Please refer to:<br> Susilo, S., Salman, R., Hermawan, W. <em>et al.</em> GNSS land subsidence observations along the northern coastline of Java, Indonesia. <em>Sci Data</em> <strong>10</strong>, 421 (2023). https://doi.org/10.1038/s41597-023-02274-0
DOI: 10.5281/zenodo.7775015
title: The GNSS time series along the northern coastline of Java, Indonesia

description: This repository contains the GNSS time series along the northern coastline of Java, both in .rneu and the GAGE's .pos format (https://www.unavco.org/data/gps-gnss/derived-products/docs/NOTICE-TO-DATA-PRODUCT-USERS-GPS-2013-03-15.pdf). The repository also contains the stations' coordinates. Notes:<br> In the .rneu format of the GNSS time series:<br> 1. The outliers have been removed.<br> 2. The offsets due to instrument changes at CGON in mid-2016 and at CSIT in late 2015 have been corrected. Please refer to:<br> Susilo, S., Salman, R., Hermawan, W. <em>et al.</em> GNSS land subsidence observations along the northern coastline of Java, Indonesia. <em>Sci Data</em> <strong>10</strong>, 421 (2023). https://doi.org/10.1038/s41597-023-02274-0
DOI: 10.21227/vvbh-jm94
title: Commonsense Reasoning Question Generation and Its Applications

description: Each data instance consists of a paragraph (context), a question, and 4 candidate answers. The goal of each system is to determine the most plausible answer to the question by reading the paragraph.## Expected Output to LeaderboardIf you intend to submit to the leaderboard [here](https://leaderboard.allenai.org/cosmosqa/submissions/get-started), please follow the data format described on the that page. The prediction file should contain one label per line. Since the tasks of QG and QA are complementary, we conducted experiments on two typical data sets in the field of commonsense reasoning QA, including \emph{Cosmos QA}~\cite{DBLP:conf/emnlp/HuangBBC19} and \emph{MCScript 2.0}~\cite{DBLP:conf/lrec/0002MRTP18}. These data sets were split as train/dev/test sets with the size of 25.6k/3k/7k and 14.2k/2.0k/3.6k samples, respectively. The samples mostly required multi-hop reasoning over complex context and commonsense understanding. They were more suitable than other data sets like \emph{CommonsenseQA}~\cite{DBLP:conf/naacl/TalmorHLB19} which provided no text context, \emph{SQuAD}~\cite{DBLP:conf/emnlp/RajpurkarZLL16} did not need multi-hop deduction, and \emph{LogiQA}~\cite{DBLP:conf/ijcai/LiuCLHWZ20} with general questions such as ``\emph{Which one is true?}'' that can be yielded by rules.
DOI: 10.15156/bio/sh0814862.10fu
title: SH0814862.10FU

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/sh0814863.10fu
title: SH0814863.10FU

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/sh0814864.10fu
title: SH0814864.10FU

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: 10.3886/9swn-q357
title: Jordan Compact - Water Smart Homes: Final Analysis Report
DOI: 10.15156/bio/sh0814865.10fu
title: SH0814865.10FU

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/sh0814866.10fu
title: SH0814866.10FU

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/sh0814867.10fu
title: SH0814867.10FU

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.