cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5121471
title: 7 T MRI rawdata for (part 1): A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry

description: MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 7 T.
DOI: 10.5281/zenodo.5122817
title: MEWE results

description: This file contains the results for MEWE research (link to article will be added later due to current review process).
DOI: 10.5281/zenodo.5122816
title: MEWE results

description: This file contains the results for MEWE research (link to article will be added later due to current review process).
DOI: 10.34808/aqyn-z903
title: Bibliometric data for a research study on scientific productivity of Polish technical universities (Poznań University of Technology 2016-2020) retrieved by InCites benchmarking tool.

description: This dataset was created for the purpose of research on scientific productivity at Polish technical universities.The raw data was retrieved in July 2021 by the InCites benchmarking tool in xlsx format and will be used to create the research profiles of the universities and underlying data of journals articles. The most common definition of research productivity (interchangeable with academic productivity or scientific productivity)  states as research productivity are the number of publications per researchers. However, a more extended definition of scientific productivity, connecting quantity and quality approach involves producing quality research represented by publishing academic papers in reputable international journals, citing these papers, gaining research funding (e.g. national and international grants), and collaborating in scientific teams.
The dataset prepared for the scientific profile of Poznań University of Technology consists records from 2016-2020: Number of Web of Science Documents, Times Cited, % Docs Cited, Rank, Category Normalized Citation Impact, International Collaborations, All Open Access Documents, % International Collaborations, Documents in JIF Journals, Documents in Q1 Journals, Documents in Q2 Journals, Documents in Q3 Journals, Documents in Q4 Journals.
DOI: 10.34808/d6ct-5p32
title: Bibliometric data for a research study on scientific productivity of Polish technical universities (Warsaw University of Technology 2016-2020) retrieved by InCites benchmarking tool.

description: This dataset was created for the purpose of research on scientific productivity at Polish technical universities.The raw data was retrieved in July 2021 by the InCites benchmarking tool in xlsx format and will be used to create the research profiles of the universities and underlying data of journals articles. The most common definition of research productivity (interchangeable with academic productivity or scientific productivity)  states as research productivity are the number of publications per researchers. However, a more extended definition of scientific productivity, connecting quantity and quality approach involves producing quality research represented by publishing academic papers in reputable international journals, citing these papers, gaining research funding (e.g. national and international grants), and collaborating in scientific teams.
The dataset prepared for the scientific profile of Warsaw University of Technology consists records from 2016-2020: Number of Web of Science Documents, Times Cited, % Docs Cited, Rank, Category Normalized Citation Impact, International Collaborations, All Open Access Documents, % International Collaborations, Documents in JIF Journals, Documents in Q1 Journals, Documents in Q2 Journals, Documents in Q3 Journals, Documents in Q4 Journals.
DOI: 10.34808/9x05-gz04
title: Bibliometric data for a research study on scientific productivity of Polish technical universities (Wrocław University of Technology 2016-2020) retrieved by InCites benchmarking tool.

description: This dataset was created for the purpose of research on scientific productivity at Polish technical universities.The raw data was retrieved in July 2021 by the InCites benchmarking tool in xlsx format and will be used to create the research profiles of the universities and underlying data of journals articles. The most common definition of research productivity (interchangeable with academic productivity or scientific productivity)  states as research productivity are the number of publications per researchers. However, a more extended definition of scientific productivity, connecting quantity and quality approach involves producing quality research represented by publishing academic papers in reputable international journals, citing these papers, gaining research funding (e.g. national and international grants), and collaborating in scientific teams.
The dataset prepared for the scientific profile of Wrocław University of Technology consists records from 2016-2020: Number of Web of Science Documents, Times Cited, % Docs Cited, Rank, Category Normalized Citation Impact, International Collaborations, All Open Access Documents, % International Collaborations, Documents in JIF Journals, Documents in Q1 Journals, Documents in Q2 Journals, Documents in Q3 Journals, Documents in Q4 Journals.
DOI: 10.34808/7qvg-v171
title: Bibliometric data for a research study on scientific productivity of Polish technical universities (Technical University Radom 2016-2020) retrieved by InCites benchmarking tool.

description: This dataset was created for the purpose of research on scientific productivity at Polish technical universities.The raw data was retrieved in July 2021 by the InCites benchmarking tool in xlsx format and will be used to create the research profiles of the universities and underlying data of journals articles. The most common definition of research productivity (interchangeable with academic productivity or scientific productivity)  states as research productivity are the number of publications per researchers. However, a more extended definition of scientific productivity, connecting quantity and quality approach involves producing quality research represented by publishing academic papers in reputable international journals, citing these papers, gaining research funding (e.g. national and international grants), and collaborating in scientific teams.
The dataset prepared for the scientific profile of Technical University Radom consists records from 2016-2020: Number of Web of Science Documents, Times Cited, % Docs Cited, Rank, Category Normalized Citation Impact, International Collaborations, All Open Access Documents, % International Collaborations, Documents in JIF Journals, Documents in Q1 Journals, Documents in Q2 Journals, Documents in Q3 Journals, Documents in Q4 Journals.
DOI: 10.34808/g8e6-2y37
title: Bibliometric data for a research study on scientific productivity of Polish technical universities (Opole University of Technology 2016-2020) retrieved by InCites benchmarking tool.

description: This dataset was created for the purpose of research on scientific productivity at Polish technical universities.The raw data was retrieved in July 2021 by the InCites benchmarking tool in xlsx format and will be used to create the research profiles of the universities and underlying data of journals articles. The most common definition of research productivity (interchangeable with academic productivity or scientific productivity)  states as research productivity are the number of publications per researchers. However, a more extended definition of scientific productivity, connecting quantity and quality approach involves producing quality research represented by publishing academic papers in reputable international journals, citing these papers, gaining research funding (e.g. national and international grants), and collaborating in scientific teams.
The dataset prepared for the scientific profile of Opole University of Technology consists records from 2016-2020: Number of Web of Science Documents, Times Cited, % Docs Cited, Rank, Category Normalized Citation Impact, International Collaborations, All Open Access Documents, % International Collaborations, Documents in JIF Journals, Documents in Q1 Journals, Documents in Q2 Journals, Documents in Q3 Journals, Documents in Q4 Journals.
DOI: 10.5281/zenodo.5122825
title: Aleksander Wozniak. Looking at Tokyo Catalogue.

description: The above document was created as a result of a research project entitled<br> <em>Looking at Tokyo - Creative Imaging using Drawing Streams</em> Researcher: Aleksander Woźniak This entry comprises a (3/3) part of documentation of the project. <br> Funded by the National Science Center. Poland<br> Application registration no.: 2017/01 / X / HS2 / 00676
DOI: 10.5281/zenodo.5122824
title: Aleksander Wozniak. Looking at Tokyo Catalogue.

description: The above document was created as a result of a research project entitled<br> <em>Looking at Tokyo - Creative Imaging using Drawing Streams</em> Researcher: Aleksander Woźniak This entry comprises a (3/3) part of documentation of the project. <br> Funded by the National Science Center. Poland<br> Application registration no.: 2017/01 / X / HS2 / 00676
DOI: 10.5281/zenodo.5121916
title: Replication package for: Geography and Agricultural Productivity: Cross-Country Evidence from Micro Plot-Level Data

description: The package contains all the data and code necessary to reproduce the results in Adamopoulos and Restuccia (forthcoming). "Geography and Agricultural Productivity: Cross-Country Evidence from Micro Plot-Level Data." <em>Review of Economic Studies</em>. Detailed instructions on how to use the files are provided in a README file.
DOI: 10.25345/c57z57
title: MassIVE MSV000087865 - Interactive effects Pseudomonas entomophila strain 23S and Clavibacter michiganensis subsp. michiganensis in-culture secretome
title: This research aims at studying the responses of Pseudomonas entomophila strain 23S to Clavibacter michiganensis subsp. michiganensis (Cmm) when grown as pure culture and as mixed culture. The secretome of the cultures were analysed using LC-MS/MS.
DOI: 10.5281/zenodo.5120981
title: University of Manitoba Breast Microwave Imaging Dataset (UM-BMID)

description: <strong>ABSTRACT </strong> Microwave-based breast cancer detection is a growing field that has been investigated as a potential novel method for breast cancer detection. Breast microwave sensing (BMS) systems use low-powered, non-ionizing microwave signals to interrogate the breast tissues. While some BMS systems have been evaluated in clinical trials, many challenges remain before these systems can be used as a viable clinical option, and breast phantoms (breast models) allow for rigorous and controlled experimental investigations. This dataset, the University of Manitoba Breast Microwave Imaging Dataset (UM-BMID), contains S-parameter measurements from experimental scans of MRI-derived breast phantoms, obtained with a pre-clinical breast microwave sensing system operating over 1-8 GHz. The dataset consists of measurements from over 1250 scans of a diverse array of phantoms. The phantom array consists of phantoms of various sizes and breast densities. The .stl files used to produce the 3D-printed phantoms are also included in the dataset. We hope that this dataset can serve as a resource for researchers in breast microwave sensing to evaluate signal processing, image reconstruction, and tumour detection methods. <strong>Inspiration:</strong> This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON" https://cancer.ubrite.org/hackathon-2021/ <strong>Acknowledgements</strong> Tyson Reimer, Jordan Krenkevich, Stephen Pistorius, June 16, 2021, "University of Manitoba Breast Microwave Imaging Dataset (UM-BMID)", IEEE Dataport, doi: https://dx.doi.org/10.21227/1y0z-8t98. https://ieee-dataport.org/open-access/university-manitoba-breast-microwave-imaging-dataset-um-bmid <strong>U-BRITE last update date:</strong> 07/21/2021
description: U-BRITE location: /data/project/ubrite/cancer-hackathon/org/ieee-dataport/university-manitoba-breast-microwave-imaging-dataset-um-bmid
DOI: 10.5281/zenodo.5120974
title: Metascape Results for Prostate Cancer Multiomics Data

description: <strong>ABSTRACT </strong> Large <em>p</em> small <em>n</em> problem is a challenging problem in big data analytics. There are no de facto standard methods available to it. In this study, we propose a tensor decomposition (TD) based unsupervised feature extraction (FE) formalism applied to multiomics datasets, where the number of features is more than 100000 while the number of instances is as small as about 100. The proposed TD based unsupervised FE outperformed other conventional supervised feature selection methods, such as random forest, categorical regression (also known as analysis of variance, ANOVA), and penalized linear discriminant analysis when they are applied to not only multiomics datasets but also synthetic datasets. Genes selected by TD based unsupervised FE were biologically reliable. TD based unsupervised FE turned out to be not only the superior feature selection method but also the method that can select biologically reliable genes. <strong>Instructions: </strong> This is a supplementary file of paper submitted to bigdata2020 <strong>Inspiration:</strong> This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON" https://cancer.ubrite.org/hackathon-2021/ <strong>Acknowledgements</strong> Y-h. Taguchi, July 17, 2020, "Metascape results for Prostate cancer multiomics data", IEEE Dataport, doi: https://dx.doi.org/10.21227/rdmb-jm40. https://ieee-dataport.org/documents/metascape-results-prostate-cancer-multiomics-data <strong>U-BRITE last update date:</strong> 07/21/2021
description: U-BRITE location: /data/project/ubrite/cancer-hackathon/org/ieee-dataport/metascape-results-prostate-cancer-multiomics-data
DOI: 10.5281/zenodo.5120980
title: University of Manitoba Breast Microwave Imaging Dataset (UM-BMID)

description: <strong>ABSTRACT </strong> Microwave-based breast cancer detection is a growing field that has been investigated as a potential novel method for breast cancer detection. Breast microwave sensing (BMS) systems use low-powered, non-ionizing microwave signals to interrogate the breast tissues. While some BMS systems have been evaluated in clinical trials, many challenges remain before these systems can be used as a viable clinical option, and breast phantoms (breast models) allow for rigorous and controlled experimental investigations. This dataset, the University of Manitoba Breast Microwave Imaging Dataset (UM-BMID), contains S-parameter measurements from experimental scans of MRI-derived breast phantoms, obtained with a pre-clinical breast microwave sensing system operating over 1-8 GHz. The dataset consists of measurements from over 1250 scans of a diverse array of phantoms. The phantom array consists of phantoms of various sizes and breast densities. The .stl files used to produce the 3D-printed phantoms are also included in the dataset. We hope that this dataset can serve as a resource for researchers in breast microwave sensing to evaluate signal processing, image reconstruction, and tumour detection methods. <strong>Inspiration:</strong> This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON" https://cancer.ubrite.org/hackathon-2021/ <strong>Acknowledgements</strong> Tyson Reimer, Jordan Krenkevich, Stephen Pistorius, June 16, 2021, "University of Manitoba Breast Microwave Imaging Dataset (UM-BMID)", IEEE Dataport, doi: https://dx.doi.org/10.21227/1y0z-8t98. https://ieee-dataport.org/open-access/university-manitoba-breast-microwave-imaging-dataset-um-bmid <strong>U-BRITE last update date:</strong> 07/21/2021
description: U-BRITE location: /data/project/ubrite/cancer-hackathon/org/ieee-dataport/university-manitoba-breast-microwave-imaging-dataset-um-bmid
DOI: 10.5281/zenodo.5120970
title: Pre-Processed Cancer Multi-Omic Data from TCGA and Synthetic Data

description: <strong>ABSTRACT </strong> It contains the data of four omic profiles (CNV, mRNA, miRNA, and protein) obtained for BRCA, LGG, and LUAD obtained from the TCGA project. In addition, we provide synthetic data for a mixture of isotropic distributions. <strong>Instructions: </strong> Cancer data are identified by cancer type (LGG: low-grade glioma, BRCA: breast cancer, and LUAD: lung cancer). The data are scaled by using the minima and maxima of each column so that the values are between 0 and 1. In these files, the columns are the features and the rows correspond to the patients. The summary data contains only the numerical values. The columns are the features and the rows are the observations. <strong>Inspiration:</strong> This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON" https://cancer.ubrite.org/hackathon-2021/ <strong>Acknowledgements</strong> Diego Salazar, June 20, 2021, "Pre-processed Cancer multi-omic data from TCGA and synthetic data", IEEE Dataport, doi: https://dx.doi.org/10.21227/pjb8-d090. https://ieee-dataport.org/documents/pre-processed-cancer-multi-omic-data-tcga-and-synthetic-data <strong>U-BRITE last update date:</strong> 07/21/2021
description: U-BRITE location: /data/project/ubrite/cancer-hackathon/org/ieee-dataport/pre-processed-cancer-multi-omic-data-tcga-and-synthetic-data
DOI: 10.5281/zenodo.5120973
title: Metascape Results for Prostate Cancer Multiomics Data

description: <strong>ABSTRACT </strong> Large <em>p</em> small <em>n</em> problem is a challenging problem in big data analytics. There are no de facto standard methods available to it. In this study, we propose a tensor decomposition (TD) based unsupervised feature extraction (FE) formalism applied to multiomics datasets, where the number of features is more than 100000 while the number of instances is as small as about 100. The proposed TD based unsupervised FE outperformed other conventional supervised feature selection methods, such as random forest, categorical regression (also known as analysis of variance, ANOVA), and penalized linear discriminant analysis when they are applied to not only multiomics datasets but also synthetic datasets. Genes selected by TD based unsupervised FE were biologically reliable. TD based unsupervised FE turned out to be not only the superior feature selection method but also the method that can select biologically reliable genes. <strong>Instructions: </strong> This is a supplementary file of paper submitted to bigdata2020 <strong>Inspiration:</strong> This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON" https://cancer.ubrite.org/hackathon-2021/ <strong>Acknowledgements</strong> Y-h. Taguchi, July 17, 2020, "Metascape results for Prostate cancer multiomics data", IEEE Dataport, doi: https://dx.doi.org/10.21227/rdmb-jm40. https://ieee-dataport.org/documents/metascape-results-prostate-cancer-multiomics-data <strong>U-BRITE last update date:</strong> 07/21/2021
description: U-BRITE location: /data/project/ubrite/cancer-hackathon/org/ieee-dataport/metascape-results-prostate-cancer-multiomics-data
DOI: 10.5281/zenodo.5120965
title: Mammograms-Breast Cancer Images

description: <strong>ABSTRACT </strong> This is a small dataset as a part of huge dataset of breast cancer images. The images are mammograms. <strong>Instructions: </strong> One can use these images for experimentation on detection and analysis of breast cancer. <strong>Inspiration:</strong> This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON" https://cancer.ubrite.org/hackathon-2021/ <strong>Acknowledgements</strong> G R Sinha, Bhagwati Charan Patel, December 27, 2019, "Mammograms-Breast Cancer Images", IEEE Dataport, doi: https://dx.doi.org/10.21227/9f0p-qx37. https://ieee-dataport.org/documents/mammograms-breast-cancer-images <strong>U-BRITE last update date:</strong> 07/21/2021
description: U-BRITE location: /data/project/ubrite/cancer-hackathon/org/ieee-dataport/mammograms-breast-cancer-images/Mammograms
DOI: 10.5281/zenodo.5120969
title: Pre-Processed Cancer Multi-Omic Data from TCGA and Synthetic Data

description: <strong>ABSTRACT </strong> It contains the data of four omic profiles (CNV, mRNA, miRNA, and protein) obtained for BRCA, LGG, and LUAD obtained from the TCGA project. In addition, we provide synthetic data for a mixture of isotropic distributions. <strong>Instructions: </strong> Cancer data are identified by cancer type (LGG: low-grade glioma, BRCA: breast cancer, and LUAD: lung cancer). The data are scaled by using the minima and maxima of each column so that the values are between 0 and 1. In these files, the columns are the features and the rows correspond to the patients. The summary data contains only the numerical values. The columns are the features and the rows are the observations. <strong>Inspiration:</strong> This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON" https://cancer.ubrite.org/hackathon-2021/ <strong>Acknowledgements</strong> Diego Salazar, June 20, 2021, "Pre-processed Cancer multi-omic data from TCGA and synthetic data", IEEE Dataport, doi: https://dx.doi.org/10.21227/pjb8-d090. https://ieee-dataport.org/documents/pre-processed-cancer-multi-omic-data-tcga-and-synthetic-data <strong>U-BRITE last update date:</strong> 07/21/2021
description: U-BRITE location: /data/project/ubrite/cancer-hackathon/org/ieee-dataport/pre-processed-cancer-multi-omic-data-tcga-and-synthetic-data
DOI: 10.5281/zenodo.5120964
title: Mammograms-Breast Cancer Images

description: <strong>ABSTRACT </strong> This is a small dataset as a part of huge dataset of breast cancer images. The images are mammograms. <strong>Instructions: </strong> One can use these images for experimentation on detection and analysis of breast cancer. <strong>Inspiration:</strong> This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON" https://cancer.ubrite.org/hackathon-2021/ <strong>Acknowledgements</strong> G R Sinha, Bhagwati Charan Patel, December 27, 2019, "Mammograms-Breast Cancer Images", IEEE Dataport, doi: https://dx.doi.org/10.21227/9f0p-qx37. https://ieee-dataport.org/documents/mammograms-breast-cancer-images <strong>U-BRITE last update date:</strong> 07/21/2021
description: U-BRITE location: /data/project/ubrite/cancer-hackathon/org/ieee-dataport/mammograms-breast-cancer-images/Mammograms
DOI: 10.5281/zenodo.5120959
title: Seer Breast Cancer Data

description: <strong>Abstract</strong>: This dataset of breast cancer patients was obtained from the 2017 November update of the SEER Program of the NCI, which provides information on population-based cancer statistics. The dataset involved female patients with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3) diagnosed in 2006-2010. Patients with unknown tumor size, examined regional LNs, regional positive LNs, and patients whose survival months were less than 1 month were excluded; thus, 4024 patients were ultimately included. <strong>Inspiration:</strong> This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON" https://cancer.ubrite.org/hackathon-2021/ <strong>Acknowledgements</strong> JING TENG, January 18, 2019, "SEER Breast Cancer Data", IEEE Dataport, doi: https://dx.doi.org/10.21227/a9qy-ph35. https://ieee-dataport.org/open-access/seer-breast-cancer-data <strong>U-BRITE last update date:</strong> 07/21/2021
description: U-BRITE location: /data/project/ubrite/cancer-hackathon/org/ieee-dataport/seer-breast-cancer-data