cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14226278
title: Metadata record for the manuscript: Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study

description: <b>Summary</b><br> This metadata record provides details of the data supporting the claims of the related manuscript: “Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study”. The related study present a tissue non-destructive method for automated biochemical recurrence (BCR) prognosis, termed ”Histotyping”, that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Type of data: automated BCR prognosis training and validation data; clinical data; ground truth maps & segmentation results Subject of data: <i>Homo sapiens</i>: computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide Sample size: n=889 patients Population characteristics: patient were digitised on a variety of whole-slide scanners. Recruitment: patients from 6 sources: University of Pennsylvania (UPenn), University Hospitals Cleveland Medical Center (UH), NewYork-Presbyterian Hospital/Weill Cornell Medical Center (WCM), University of Turku (UTurku), The Cancer Genome Atlas (TCGA), and the Icahn School of Medicine at Mount Sinai (MS). <b>Data access</b> The following files are openly available as part of this data record: - ‘training_set_HT_scores.xlsx’ contains the Histotyping scores of each patient in the training set (n=214). - ‘boundary_layer_data.xlsx’ contains the Histotyping scores of each validation set patients during successive boundary layer removals. - ‘HT_UMAP_supporting_data.xlsx’ contains image metrics, Histotyping scores, Histotyping+ scores, and UMAP components for each patient in the validation set. - The folder ‘gland_segmentations.zip’ contains ground truth masks and segmentation results on lumen segmentation model validation set images. Note that output masks are provided before preprocessing. Statistics and features in the related article were derived using postprocessing described in the supplementary information. The patient clinical data are contained in the Excel spreadsheet ‘patient_clinical_info.xlsx’. These data are not publicly available for the following reason: material transfer agreements from source hospitals do not allow public sharing of patient information. However, the data can be made available upon reasonable request to the corresponding author. <b>Corresponding author(s) for this study</b> Dr Anant Madabhushi, Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH, USA. anant.madabhushi@case.edu. <br> <b>Study approval </b> Data collection was approved by institutional review boards at each institution and conducted in accordance with U.S. Common Rule guidelines.
DOI: 10.6084/m9.figshare.14226278.v1
title: Metadata record for the manuscript: Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study

description: <b>Summary</b><br> This metadata record provides details of the data supporting the claims of the related manuscript: “Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study”. The related study present a tissue non-destructive method for automated biochemical recurrence (BCR) prognosis, termed ”Histotyping”, that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Type of data: automated BCR prognosis training and validation data; clinical data; ground truth maps & segmentation results Subject of data: <i>Homo sapiens</i>: computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide Sample size: n=889 patients Population characteristics: patient were digitised on a variety of whole-slide scanners. Recruitment: patients from 6 sources: University of Pennsylvania (UPenn), University Hospitals Cleveland Medical Center (UH), NewYork-Presbyterian Hospital/Weill Cornell Medical Center (WCM), University of Turku (UTurku), The Cancer Genome Atlas (TCGA), and the Icahn School of Medicine at Mount Sinai (MS). <b>Data access</b> The following files are openly available as part of this data record: - ‘training_set_HT_scores.xlsx’ contains the Histotyping scores of each patient in the training set (n=214). - ‘boundary_layer_data.xlsx’ contains the Histotyping scores of each validation set patients during successive boundary layer removals. - ‘HT_UMAP_supporting_data.xlsx’ contains image metrics, Histotyping scores, Histotyping+ scores, and UMAP components for each patient in the validation set. - The folder ‘gland_segmentations.zip’ contains ground truth masks and segmentation results on lumen segmentation model validation set images. Note that output masks are provided before preprocessing. Statistics and features in the related article were derived using postprocessing described in the supplementary information. The patient clinical data are contained in the Excel spreadsheet ‘patient_clinical_info.xlsx’. These data are not publicly available for the following reason: material transfer agreements from source hospitals do not allow public sharing of patient information. However, the data can be made available upon reasonable request to the corresponding author. <b>Corresponding author(s) for this study</b> Dr Anant Madabhushi, Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH, USA. anant.madabhushi@case.edu. <br> <b>Study approval </b> Data collection was approved by institutional review boards at each institution and conducted in accordance with U.S. Common Rule guidelines.
DOI: 10.5281/zenodo.4683132
title: Eukaryotic virome of Belgian mosquitoes

description: De novo assembled viral sequences &gt;1kb from the eukaryotic virome of Belgian mosquitoes caught in 2019 and the Genbank record for the recovered complete viral genomes which correspond to accession numbers MW699041-MW699049.
DOI: 10.5281/zenodo.4569092
title: Eukaryotic virome of Belgian mosquitoes

description: De novo assembled viral sequences &gt;1kb from the eukaryotic virome of Belgian mosquitoes caught in 2019 and the Genbank record for the recovered complete viral genomes which correspond to accession numbers MW699041-MW699049.
DOI: 10.5281/zenodo.4683123
title: Sheba75 white matter tracts streamlines dataset

description: Sheba75 white matter tracts streamlines dataset
DOI: 10.5281/zenodo.4683122
title: Sheba75 white matter tracts streamlines dataset

description: Sheba75 white matter tracts streamlines dataset
DOI: 10.5281/zenodo.4562403
title: Training Effective Neural Networks on Structured Data with Federated Learning

description: Federated Learning decreases privacy risks when training Machine Learning (ML) models on distributed data, as it removes the need for sharing and centralizing sensitive data. However, this learning paradigm can also influence the effectiveness of the obtained prediction models. In this paper, we specifically study Neural Networks, as a powerful and popular ML model, and contrast the impact of Federated Learning on the effectiveness compared to a centralized approach - when data is aggregated at one place before processing - to assess to what extent Federated Learning is suited as a replacement. We also analyze the effect of non-independent and identically distributed (non-iid) data on effectiveness and convergence speed (efficiency) of Federated Learning. Based on this, we show in which scenarios (depending on the dataset, the number of nodes in the setting and data distribution) Federated Learning can be successfully employed.
DOI: 10.5281/zenodo.4562402
title: Training Effective Neural Networks on Structured Data with Federated Learning

description: Federated Learning decreases privacy risks when training Machine Learning (ML) models on distributed data, as it removes the need for sharing and centralizing sensitive data. However, this learning paradigm can also influence the effectiveness of the obtained prediction models. In this paper, we specifically study Neural Networks, as a powerful and popular ML model, and contrast the impact of Federated Learning on the effectiveness compared to a centralized approach - when data is aggregated at one place before processing - to assess to what extent Federated Learning is suited as a replacement. We also analyze the effect of non-independent and identically distributed (non-iid) data on effectiveness and convergence speed (efficiency) of Federated Learning. Based on this, we show in which scenarios (depending on the dataset, the number of nodes in the setting and data distribution) Federated Learning can be successfully employed.
DOI: 10.25646/8320
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.14406665
title: Transcriptomic profile of ataxia telangiectasia cells treated for 30 days with a low dose of dexamethasone

description: Dexamethasone administered by autologous red blood cells (RBCs) is used in clinical trials and as compassionate therapy to alleviate ataxia telangiectasia (AT) symptoms, providing long-term delivery of low doses of the drug. In the present paper, we report a dexamethasone-induced variation in gene expression that differed between healthy and AT primary fibroblasts simulating patient conditions. Gene expression levels were analysed by a microarray platform. The obtained gene-set data were used for biological pathway analysis by Reactome functional network clustering. Over 3000 probes were differentially modulated by dexamethasone in wild-type (WT) and AT fibroblasts. Consequently, the biological pathways induced by dexamethasone treatment also differed between the two samples. Some of the pathways were the same as those normally altered in AT compared to WT cells. Our results were consistent with earlier studies on HDAC4 and DDIT4 dynamics. The 30-day low dose dexamethasone treatment induced differential gene expression in AT and WT cells, leading to the partial recovery of genes that are usually dysregulated in AT. This is probably due to glucocorticoid receptor diversity in AT, which is likely genetically imprinted in AT, thus accounting for the differential response of AT cells to dexamethasone treatment.
DOI: 10.6084/m9.figshare.14406665.v1
title: Transcriptomic profile of ataxia telangiectasia cells treated for 30 days with a low dose of dexamethasone

description: Dexamethasone administered by autologous red blood cells (RBCs) is used in clinical trials and as compassionate therapy to alleviate ataxia telangiectasia (AT) symptoms, providing long-term delivery of low doses of the drug. In the present paper, we report a dexamethasone-induced variation in gene expression that differed between healthy and AT primary fibroblasts simulating patient conditions. Gene expression levels were analysed by a microarray platform. The obtained gene-set data were used for biological pathway analysis by Reactome functional network clustering. Over 3000 probes were differentially modulated by dexamethasone in wild-type (WT) and AT fibroblasts. Consequently, the biological pathways induced by dexamethasone treatment also differed between the two samples. Some of the pathways were the same as those normally altered in AT compared to WT cells. Our results were consistent with earlier studies on HDAC4 and DDIT4 dynamics. The 30-day low dose dexamethasone treatment induced differential gene expression in AT and WT cells, leading to the partial recovery of genes that are usually dysregulated in AT. This is probably due to glucocorticoid receptor diversity in AT, which is likely genetically imprinted in AT, thus accounting for the differential response of AT cells to dexamethasone treatment.
DOI: 10.6084/m9.figshare.14406596.v2
title: Dataset factors predicting hospital readmission

description: Datasets for the study of factors predicting hospital readmission among Thais with post myocardial infarction<br>
DOI: 10.3886/e134442v2
title: Does Giving Support Moderate the Association Between Social Relationships and IL6?

description: This study aimed to examine whether perceived availability to give support to others moderates associations between social relationships and inflammation. The data from the longitudinal follow-up of the National Survey of Midlife Development in the U.S. (MIDUS 2) were used. Results showed that perceived availability to give support moderated the associations between IL-6 and indicators of positive social relationships, including social integration, perceived support from others, and positive relations with others. Indicators of positive social relationships were associated with lower IL-6 among individuals higher, but not lower, in perceived availability to give support. The moderating effects of giving support held after adjusting for socio-demographic and health-related covariates, except for the association between perceived support from others and IL-6.<br>
description: Presence of Common Scales: Documentations for the measures, variables, and scales used in the MIDUS 2 and MIDUS 2: Biomarker Project can be found on the following websites. <br><br>Ryff, Carol, Almeida, David M., Ayanian, John, Carr, Deborah S., Cleary, Paul D., Coe, Christopher, … Williams, David. Midlife in the United States (MIDUS 2), 2004-2006. Inter-university Consortium for Political and Social Research [distributor], 2017-11-20. https://doi.org/10.3886/ICPSR04652.v7<br><br>Ryff, Carol D., Seeman, Teresa, and Weinstein, Maxine. Midlife in the United States (MIDUS 2): Biomarker Project, 2004-2009. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-03-27. https://doi.org/10.3886/ICPSR29282.v9
description: This study used the data from the longitudinal follow-up of the National Survey of Midlife Development in the U.S. (MIDUS 2). The baseline sample of MIDUS 2 consisted of individuals from four subsamples, including a national random digit dialing sample, oversamples from five metropolitan areas, siblings of individuals from the random digit dialing sample, and a national random digit dialing sample of twin pairs.
description: English-speaking adults who reside in the United States.
DOI: 10.15468/59ubk5
title: A new water mite genus from Kazakhstan (Acari, Hydrachnidia, Hydryphantidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Tuzovskij, Petr V., Stolbov, Vitaly A. (2021): A new water mite genus from Kazakhstan (Acari, Hydrachnidia, Hydryphantidae). Zootaxa 4951 (1): 187-192, DOI: https://doi.org/10.11646/zootaxa.4951.1.11
DOI: 10.15468/yqdvz9
title: New Caledonian species misplaced in Horaeomorphus Schaufuss belong in Heterotetramelus Franz (Coleoptera, Staphylinidae, Scydmaeninae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Jałoszyński, Paweł (2021): New Caledonian species misplaced in Horaeomorphus Schaufuss belong in Heterotetramelus Franz (Coleoptera, Staphylinidae, Scydmaeninae). Zootaxa 4951 (1): 130-136, DOI: https://doi.org/10.11646/zootaxa.4951.1.5
DOI: 10.15468/qksdm3
title: New species and new distributional record of the genus Broscosoma Rosenhauer 1846 (Coleoptera, Carabidae, Broscinae) from China

description: This dataset contains the digitized treatments in Plazi based on the original journal article Jiang, Ri-Xin, Liu, Xiao-Fan, Wang, Shuo (2021): New species and new distributional record of the genus Broscosoma Rosenhauer 1846 (Coleoptera, Carabidae, Broscinae) from China. Zootaxa 4951 (1): 137-146, DOI: https://doi.org/10.11646/zootaxa.4951.1.6
DOI: 10.5281/zenodo.4683169
title: Inventory-Shop-Transport Dataset MySQL

description: Inventory-Shop-Transport Dataset MySQL<br> - Inventory System Data - Point of Sale (PoS) software data<br> - Supershop Data<br> - Computer Store Data - Ticket Buy-sell Data etc.
DOI: 10.5281/zenodo.4683168
title: Inventory-Shop-Transport Dataset MySQL

description: Inventory-Shop-Transport Dataset MySQL<br> - Inventory System Data - Point of Sale (PoS) software data<br> - Supershop Data<br> - Computer Store Data - Ticket Buy-sell Data etc.
DOI: 10.5287/bodleian:ermk5jpo7
title: ALSO and BLSO evaluation Thailand Myanmar border
DOI: 10.5517/ccdc.csd.cc24qx0t
title: CCDC 1988216: Experimental Crystal Structure Determination

description: Related Article: Feng-jun Ban, En-hong Sheng, Teng-fei Gong, Cheng-cheng Ye|2012|Hecheng Huaxue|20|486|
DOI: 10.22008/fk2/plnueo/58jwmt
title: basalmelt.mat

description: Basal melt rates as MATLAB file
DOI: 10.22008/fk2/plnueo/j2y2jx
title: basalmelt.nc

description: Basal melt rates as NetCDF file
DOI: 10.22008/fk2/plnueo/poskoc
title: basalmelt.csv

description: Basal melt rates as csv file
DOI: 10.22008/fk2/plnueo/yhu0fv
title: heatsources.nc

description: This file contains the individual heat sources used for calculating the basal melt.
DOI: 10.22008/fk2/plnueo/g5cdai
title: make_figures_karlssonetal2021.py

description: Calculating and plotting the basal melt rates from the heat sources.
DOI: 10.18112/openneuro.ds003612.v1.0.0
title: TODO: name of the dataset
DOI: 10.5066/p9ivqyn5
title: Domoic acid and saxitoxin in seabirds from California and Rhode Island 2015-2017

description: As harmful algal blooms (HABs) increase in magnitude and duration worldwide, they are becoming an expanding threat to marine wildlife. Over the past decade, domoic acid (DA) and saxitoxin (STX) have been increasingly problematic bicoastally in the United States. We investigated pooled seabird mortality data from opportunistic sampling events between 2007-2018, across three states, to examine the patterns and role of DA and STX in seabird mortality events from the continental United States. Patterns in DA and STX levels and affected tissue type were reviewed, and two specific accounts of localized and pervasive event types were examined. There has been increased toxin detection with expanded tissue testing during large mortality events. Additionally, there is new evidence of metabolic processing of HAB toxins in seabirds, and the first known account of coincident HAB intoxication (DA and STX) in seabirds is documented. This report contributes qualitative insight into temporal trends and provides recommendations for future research.
DOI: 10.5281/zenodo.4683174
title: ExtremeEarth Polar Use Case Training Data

description: <strong>This is a set of sea ice, and iceberg, analysis data prepared for the ExtremeEarth project. The intention is that this dataset is useful for training and validating automated methods for processing satellite images, although it could also be applied to ice analyst training. The region chosen for the analysis was Danmarkshavn on the east coast of Greenland. This has a wide variety of sea ice and iceberg conditions, and was continuously monitored by key European Sentinel satellites during 2018. The dataset consists of 12 days, approximately monthly, throughout the year, covering winter and spring, summer melt, and autumn freeze-up. The presence of extensive (land) fast ice in the area ensures that classifications on those areas can be applied to additional dates. The sea ice classification includes primary and secondary ice types, including partial concentrations and form (floe size).</strong> <strong>The initial version (1.0.0) contains basic ice/water classification, and the polygons remain unclassified for sea ice concentrations, ice type and form.</strong> <strong>The updated version (2.0.0) now contains the additional information on sea ice concentrations, ice type and form.</strong>
DOI: 10.5281/zenodo.3695275
title: ExtremeEarth Polar Use Case Training Data

description: <strong>This is a set of sea ice, and iceberg, analysis data prepared for the ExtremeEarth project. The intention is that this dataset is useful for training and validating automated methods for processing satellite images, although it could also be applied to ice analyst training. The region chosen for the analysis was Danmarkshavn on the east coast of Greenland. This has a wide variety of sea ice and iceberg conditions, and was continuously monitored by key European Sentinel satellites during 2018. The dataset consists of 12 days, approximately monthly, throughout the year, covering winter and spring, summer melt, and autumn freeze-up. The presence of extensive (land) fast ice in the area ensures that classifications on those areas can be applied to additional dates. The sea ice classification includes primary and secondary ice types, including partial concentrations and form (floe size).</strong> <strong>The initial version (1.0.0) contains basic ice/water classification, and the polygons remain unclassified for sea ice concentrations, ice type and form.</strong> <strong>The updated version (2.0.0) now contains the additional information on sea ice concentrations, ice type and form.</strong>
DOI: 10.5281/zenodo.3922068
title: Templates for FAIRness evaluation criteria - RDA-SHARC ig

description: The SHARC Interest Group of the Research Data Alliance was established to improve research crediting and rewarding mechanisms for scientists who wish to organise their data (and material resources) for community sharing. This requires that data are findable and accessible on the Web, and comply with shared standards making them interoperable and reusable in alignment with the FAIR principles. It takes considerable time, energy, expertise and motivation. It is imperative to facilitate the processes to encourage scientists to share their data. To that aim, supporting FAIR principles compliance processes and increasing the human understanding of FAIRness criteria – i.e., promoting FAIRness literacy – and not only the machine-readability of the criteria, are critical steps in the data sharing process. Appropriate human-understandable criteria must be the first identified in the FAIRness assessment processes and roadmap. This document is a reusable template that aims to support FAIRification assessment with human understandable criteria. The level of compliance for each criterion can be used to prioritise the most appropriate and sufficient training, support and actions.
DOI: 10.25318/3310032101-eng
title: Reasons business or organization did not access any funding or credit due to the COVID-19 pandemic, by business characteristics, first quarter of 2021

description: Reasons business or organization did not access any funding or credit due to the COVID-19 pandemic, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.