cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7713146
title: Rutor glacier fronts footprints

description: Footprints of the various glacial fronts obtained from the elaborated cartographic products: orthophoto from August 2017 satellite imagery, September 2020 aerial orthophoto, July 2021 UAV orthophoto, and September 2021 aerial orthophoto.
description: The presented dataset is the result of the multidisciplinary work of the cc-glacier-lab of the Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino. The Italian Ministry for Education and Research (MIUR), Dipartimento di Eccellenza, funded this research.
description: {"references": ["Corte et al. (2023) submitted"]}
DOI: 10.5281/zenodo.8167743
title: IOW ESM minimal complete setup

description: This a minimal setup for the IOW ESM (compatable to version 1.04.00). The coupled run is performed for 9 days (1959-01-01 to 1959-10-01) in chunks of 3 days. The atmospheric model is CCLM (version 5.00) with 0.22° horizontal resolution for the Eurocordex domain. The oceanic model for the Baltic Sea is the MOM5 model with 3nm horizontal resolution acompanied by the ERGOM ecosystem model. The forcing for CCLM is generated from the ERA5 reanalysis data. The setup is constructed for the HLRN supercomputer and has to be adapted for other target machines. See the related documentation at https://sven-karsten.github.io/iow_esm/intro.html.
DOI: 10.6073/pasta/8547cdc619095555620e7f3573435e72
title: Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Seasonal water table depth data, 2012-2015.
DOI: 10.6084/m9.figshare.21717500.v2
title: Modeling dataset - field network analysis (Esparza & Smith, 2023: Ecosphere)

description: Data used in the analyses for Esparza & Smith, 2023 published in Ecosphere in the Eco-Education track. The file entitled "modeling dataset" contains participant demographic information, personal and professional outstrength/outdegree, survey responses to the science identity portion of the Persistence in the Sciences Survey, and project grades. Students self-reported demographic information on a demographic survey included with the first name generator survey early (week 6) in the semester. Project grades and science identity data were collected late (week 15) in the semester. See Esparza and Smith (In review) Materials and Methods for more information.<br>The file entitled "modeling metadata" includes detailed information on the data included in the "modeling dataset" CSV. file. This includes the ranges/categories and a description of each variable included in the dataset.<br>All data are anonymized so as to protect the identity and privacy of all research participants and their listed contacts. The research has been approved by the Cornell University Institutional Review Board under the exempt protocol #2001009364.
DOI: 10.6084/m9.figshare.21824733.v2
title: Nodelist and Edgelists - field network analysis (Esparza & Smith, 2023: Ecosphere)

description: Data used in the analyses for Esparza & Smith, 2023 published in Ecosphere in the Eco-Education track.<br>The file entitled "node list" contains information on each of the nodes (people) in the field course social system across all timepoints (early, middle, late). This dataset is anonymized, with each node assigned a unique ID in the first column. Additionally, this dataset includes information on the role of each node (e.g., student, family, instructor) and their community type (e.g., personal, professional).<br>The files entitled "edge list" (early, middle, late) each include information on who sent ties to whom and how frequently the interaction(s) occurred within the respective time point.<br>These data were collected using name generators, which typically include two types of questions that probe the names of those with whom an individual interacted (<i>i.e., name generators</i>) and information on those an individual interacted with (<i>i.e., name interpreters</i>). In this study, students completed a name generator survey at weeks six, eleven, and fifteen of the 15-week semester (i.e., timepoint) in a field course with an embedded course-based field research project. See Esparza and Smith (In review) Materials and Methods for more information.<br>The files entitled "node list metadata" and "edge list metadata" include detailed information on the data included in the "node list" and "edge list" CSV. files. These files include the ranges/categories and a description of each variable included in the datasets.All data are anonymized so as to protect the identity and privacy of all research participants and their listed contacts. The research has been approved by the Cornell University Institutional Review Board under the exempt protocol #2001009364.
DOI: 10.6084/m9.figshare.22328218.v1
title: Increased GH/IGF-I axis activity relates with lower hepatic lipids and phosphor metabolism.

description: supplementary figure 1
DOI: 10.6084/m9.figshare.23731071
title: A scoping review of graphene-based biomaterials for in vivo bone tissue engineering. Library and Dataset.

description: <b>Files contains manuscripts/abstracts to be uploaded to Rayyan without duplicates and EndNote Library.</b><br><b>Abstract: </b>The growing demand for more efficient materials for medical applications brought together two previously distinct fields: medicine and engineering. Regenerative medicine has evolved with the engineering contributions to improve materials and devices for medical use. In this regard, graphene is one of the most promising materials for bone tissue engineering and its potential for bone repair has been studied by several research groups. The aim of this study is to conduct a scoping review including articles published in the last 12 years (from 2010 to 2022) that have used graphene and its derivatives (graphene oxide and reduced graphene) in preclinical studies for bone tissue regeneration, searching in PubMed/MEDLINE, Embase, Web of Science, Cochrane Central, and clinicaltrials.gov (to confirm no study has started with clinical trial). Boolean searches were performed using the defined key words “bone” and “graphene”, and manuscript abstracts were uploaded to Rayyan, a web-tool for systematic and scoping reviews. This scoping review was conducted based on Joanna Briggs Institute Manual for Scoping Reviews and the report follows the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Extension for Scoping Reviews (PRISMA-ScR) statement. After the search protocol and application of the inclusion criteria, 77 studies were selected and evaluated by five blinded researchers. Most of the selected studies used composite materials associated with graphene and its derivatives to natural and synthetic polymers, bioglass, and others. Although a variety of graphene materials were analyzed in these studies, they all concluded that graphene, its derivatives, and its composites improve bone repair processes by increasing osteoconductivity, osteoinductivity, new bone formation, and angiogenesis. Thus, this systematic review opens up new opportunities for the development of novel strategies for bone tissue engineering with graphene.
DOI: 10.57841/casdc.0002427
title: 千吨级二氧化碳加氢制甲醇专家现场见证测试数据
DOI: 10.6084/m9.figshare.24160896
title: Supplementary Material for: Deep learning neural network -guided detection of asbestos bodies in bronchoalveolar lavage samples

description: Introduction: Asbestos is a global occupational health hazard and exposure to it by inhalation predisposes to interstitial as well as malignant pulmonary morbidity. Over time, asbestos fibers embedded in lung tissue can become coated with iron-rich proteins and mucopolysaccharides, after which they are called asbestos bodies and can be detected in light microscopy. Bronchoalveolar lavage, a cytological sample from the lower airways, is one of the methods for diagnosing lung asbestosis and related morbidity. Search for asbestos bodies in these samples is generally laborious and time-consuming. We describe a novel diagnostic method, which implements deep-learning neural network technology for the detection of asbestos bodies in bronchoalveolar lavage samples. Methods: Bronchoalveolar lavage samples with suspicion of asbestos exposure were scanned as whole slide images and uploaded to a cloud-based virtual microscopy platform with a neural network training interface. The images were used for training and testing a neural network model capable of recognizing asbestos bodies. To prioritize the model’s sensitivity, we allowed it to also make false-positive suggestions. To test the model, we compared its performance to standard light microscopy diagnostic data as well as the ground truth number of asbestos bodies, which we established by a thorough manual search of the whole slide images. Results: We were able to reach overall sensitivity of 93.4 % (95% CI 90.3 – 95.7 %) in the detection of asbestos bodies in comparison to their ground truth number. Compared to standard light microscopy diagnostic data, our model showed equal to or higher sensitivity in most cases. Conclusion: Our results indicate that deep learning neural network technology offers promising diagnostic tools for routine assessment of bronchoalveolar lavage samples. However, at this stage, a human expert is required to confirm the findings.
DOI: 10.6084/m9.figshare.24160896.v1
title: Supplementary Material for: Deep learning neural network -guided detection of asbestos bodies in bronchoalveolar lavage samples

description: Introduction: Asbestos is a global occupational health hazard and exposure to it by inhalation predisposes to interstitial as well as malignant pulmonary morbidity. Over time, asbestos fibers embedded in lung tissue can become coated with iron-rich proteins and mucopolysaccharides, after which they are called asbestos bodies and can be detected in light microscopy. Bronchoalveolar lavage, a cytological sample from the lower airways, is one of the methods for diagnosing lung asbestosis and related morbidity. Search for asbestos bodies in these samples is generally laborious and time-consuming. We describe a novel diagnostic method, which implements deep-learning neural network technology for the detection of asbestos bodies in bronchoalveolar lavage samples. Methods: Bronchoalveolar lavage samples with suspicion of asbestos exposure were scanned as whole slide images and uploaded to a cloud-based virtual microscopy platform with a neural network training interface. The images were used for training and testing a neural network model capable of recognizing asbestos bodies. To prioritize the model’s sensitivity, we allowed it to also make false-positive suggestions. To test the model, we compared its performance to standard light microscopy diagnostic data as well as the ground truth number of asbestos bodies, which we established by a thorough manual search of the whole slide images. Results: We were able to reach overall sensitivity of 93.4 % (95% CI 90.3 – 95.7 %) in the detection of asbestos bodies in comparison to their ground truth number. Compared to standard light microscopy diagnostic data, our model showed equal to or higher sensitivity in most cases. Conclusion: Our results indicate that deep learning neural network technology offers promising diagnostic tools for routine assessment of bronchoalveolar lavage samples. However, at this stage, a human expert is required to confirm the findings.
DOI: 10.21954/ou.rd.24025410
title: Supplementary materials for PhD thesis "The use of Bayesian networks to determine software inspection process efficiency"

description: Adherence to a defined process or standards is necessary to achieve satisfactory software quality. However, in order to judge whether practices are effective at achieving the required integrity of a software product, a measurement-based approach to the correctness of the software development is required. A defined and measurable process is a requirement for producing safe software productively. In this study the contribution of quality assurance to the software development process, and in particular the contribution that software inspections make to produce satisfactory software products, is addressed.<br><br>I have defined a new model of software inspection effectiveness, which uses a Bayesian Belief Network to combine both subjective and objective data to evaluate the probability of an effective software inspection. Its performance shows an improvement over the existing published models of inspection effectiveness. These previous models made questionable assumptions over the distribution of errors and were essentially static. They could not make use of experience both in terms of process improvement and the increased experience of the inspectors.<br><br>A sensitivity analysis of my model showed that it is consistent with the attributes which were thought important by Michael Fagan in his research into the software inspection method. The performance of my model show that it is an improvement over published models and over a multiple logistic regression model, which was formed using the same calibration data.<br><br>By applying my model of software inspection effectiveness before the inspection takes place, project managers will be able to make better use of inspection resource available. Applying the model using data collected during the inspection will help in estimation of residual errors in a product. Decisions can then be made if further investigations are required to identify errors. The modelling process has been used successfully in an industrial application.
DOI: 10.21954/ou.rd.24025410.v1
title: Supplementary materials for PhD thesis "The use of Bayesian networks to determine software inspection process efficiency"

description: Adherence to a defined process or standards is necessary to achieve satisfactory software quality. However, in order to judge whether practices are effective at achieving the required integrity of a software product, a measurement-based approach to the correctness of the software development is required. A defined and measurable process is a requirement for producing safe software productively. In this study the contribution of quality assurance to the software development process, and in particular the contribution that software inspections make to produce satisfactory software products, is addressed.<br><br>I have defined a new model of software inspection effectiveness, which uses a Bayesian Belief Network to combine both subjective and objective data to evaluate the probability of an effective software inspection. Its performance shows an improvement over the existing published models of inspection effectiveness. These previous models made questionable assumptions over the distribution of errors and were essentially static. They could not make use of experience both in terms of process improvement and the increased experience of the inspectors.<br><br>A sensitivity analysis of my model showed that it is consistent with the attributes which were thought important by Michael Fagan in his research into the software inspection method. The performance of my model show that it is an improvement over published models and over a multiple logistic regression model, which was formed using the same calibration data.<br><br>By applying my model of software inspection effectiveness before the inspection takes place, project managers will be able to make better use of inspection resource available. Applying the model using data collected during the inspection will help in estimation of residual errors in a product. Decisions can then be made if further investigations are required to identify errors. The modelling process has been used successfully in an industrial application.
DOI: 10.5258/soton/d2788
title: Dataset supporting the University of Southampton Doctoral Thesis "Effectiveness of formulation and brief Acceptance and Commitment Therapy for Functional Neurological Disorder".

description: Dataset supporting the University of Southampton Doctoral Thesis "Effectiveness of formulation and brief Acceptance and Commitment Therapy for Functional Neurological Disorder". This data is presented as an Excel file that supports the 2nd Chapter of the above thesis. The data is comparing four different baselines and contains routine data collected every-other day for the length of the study. The data is accessed under CC-BY license.
DOI: 10.57724/jci_20180225_microairpolar_ami
title: MICROAIRPOLAR_Airborne microorganisms captured from air and precipitation 2017-2018
DOI: 10.5281/zenodo.8358831
title: Supplementary data for: "The demographic history of the wild crop relative Brachypodium distachyon is shaped by distinct past and present ecological niches"

description: Supplementary data to https://doi.org/10.1101/2023.06.01.543285
DOI: 10.5281/zenodo.8358830
title: Supplementary data for: "The demographic history of the wild crop relative Brachypodium distachyon is shaped by distinct past and present ecological niches"

description: Supplementary data to https://doi.org/10.1101/2023.06.01.543285
DOI: 10.5281/zenodo.8354940
title: nCNV-seq: nanopore-based CNV analysis tool for brain tumor classification & grading

description: An available glioma test-dataset designed for nCNV-seq analysis and its corresponding database
DOI: 10.5281/zenodo.8354939
title: nCNV-seq: nanopore-based CNV analysis tool for brain tumor classification & grading

description: An available glioma test-dataset designed for nCNV-seq analysis and its corresponding database
DOI: 10.6084/m9.figshare.24160746
title: The net primary productivity with air pollution impacts in Yangtze River Delta region.

description: We developed this dataset to indicate the ozone and aerosol impacts on NPP in Yangtze River Delta region in China. The data file includes four variables:NPP_base: NPP without impacts of ozone and aerosolsNPP_ozone: NPP with impacts of ozone NPP_aerosol: NPP with impacts of aerosolNPP_ozone_aerosol: NPP with impacts of ozone and aerosol
DOI: 10.6084/m9.figshare.24160746.v1
title: The net primary productivity with air pollution impacts in Yangtze River Delta region.

description: We developed this dataset to indicate the ozone and aerosol impacts on NPP in Yangtze River Delta region in China. The data file includes four variables:NPP_base: NPP without impacts of ozone and aerosolsNPP_ozone: NPP with impacts of ozone NPP_aerosol: NPP with impacts of aerosolNPP_ozone_aerosol: NPP with impacts of ozone and aerosol
DOI: 10.5281/zenodo.8358870
title: Capturing Pertinent Symbolic Features for Enhanced Content Based Misinformation Detection

description: Datasets for the paper <em>Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection</em>. The compressed folder contains <strong><em>annotated</em></strong> data carried out using the symbolic models presented in the paper.
DOI: 10.5281/zenodo.8358869
title: Capturing Pertinent Symbolic Features for Enhanced Content Based Misinformation Detection

description: Datasets for the paper <em>Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection</em>. The compressed folder contains <strong><em>annotated</em></strong> data carried out using the symbolic models presented in the paper.
DOI: 10.6084/m9.figshare.24160911
title: Characterizing the mutational burden, DNA methylation landscape and proteome of germ cell tumor-related somatic-type malignancies to identify the tissue-of-origin, mechanisms of therapy resistance and druggable targets

description: <b>Abstract</b>Background:Germ cell tumors (GCT) might undergo transformation into a somatic-type malignancy (STM), resulting in a cell fate switch to tumors usually found in somatic tissues, such as rhabdomyosarcomas or adenocarcinomas. STM is associated with a poor prognosis, but the molecular and epigenetic mechanisms triggering STM are still enigmatic, the tissue-of-origin is under debate and biomarkers are lacking.Methods:To address these questions, we characterized a unique cohort of STM tissues on mutational, epigenetic and protein level using modern and high-throughput methods like TSO assays, 850k DNA methylation arrays and mass spectrometry.Results and Conclusions:For the first time, we show that based on DNA methylation and proteome data carcinoma-related STM more closely resemble yolk-sac tumors, while sarcoma-related STM resemble teratoma. STM harbor mutations in FGF signaling factors (<i>FGF6/23,</i> <i>FGFR1/4)</i>highlighting the corresponding pathway as a therapeutic target. Furthermore, STM utilize signaling pathways, like AKT, FGF, MAPK, and WNT to mediate molecular functions coping with oxidative stress, toxin transport, DNA helicase activity, apoptosis and the cell cycle. Collectively, these data might explain the high therapy resistance of STM. Finally, we identified putative novel biomarkers secreted by STM, like EFEMP1, MIF, and DNA methylation at specific CpG dinucleotides.https://www.nature.com/articles/s41416-023-02425-5<br><br><br>
DOI: 10.6084/m9.figshare.24160911.v2
title: Characterizing the mutational burden, DNA methylation landscape and proteome of germ cell tumor-related somatic-type malignancies to identify the tissue-of-origin, mechanisms of therapy resistance and druggable targets

description: <b>Abstract</b>Background:Germ cell tumors (GCT) might undergo transformation into a somatic-type malignancy (STM), resulting in a cell fate switch to tumors usually found in somatic tissues, such as rhabdomyosarcomas or adenocarcinomas. STM is associated with a poor prognosis, but the molecular and epigenetic mechanisms triggering STM are still enigmatic, the tissue-of-origin is under debate and biomarkers are lacking.Methods:To address these questions, we characterized a unique cohort of STM tissues on mutational, epigenetic and protein level using modern and high-throughput methods like TSO assays, 850k DNA methylation arrays and mass spectrometry.Results and Conclusions:For the first time, we show that based on DNA methylation and proteome data carcinoma-related STM more closely resemble yolk-sac tumors, while sarcoma-related STM resemble teratoma. STM harbor mutations in FGF signaling factors (<i>FGF6/23,</i> <i>FGFR1/4)</i>highlighting the corresponding pathway as a therapeutic target. Furthermore, STM utilize signaling pathways, like AKT, FGF, MAPK, and WNT to mediate molecular functions coping with oxidative stress, toxin transport, DNA helicase activity, apoptosis and the cell cycle. Collectively, these data might explain the high therapy resistance of STM. Finally, we identified putative novel biomarkers secreted by STM, like EFEMP1, MIF, and DNA methylation at specific CpG dinucleotides.https://www.nature.com/articles/s41416-023-02425-5<br><br><br>