cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.18151709
title: Supplementary Material for: The Global Spread of Severe Obesity in Toddlers, Children and Adolescents – a Systematic Review & Meta Analysis

description: Introduction: Severe obesity among children and adolescents has emerged as a public health concern in multiple places around the world. Methods: We searched the Medline database for articles on severe obesity rates in children published between January 1960 and January 2020. For studies with available prevalence rates for an early and a more recent time period, the relative increase in prevalence was imputed. Results: In total, 874 publications were identified, of which 38 contained relevant epidemiological data. Rates of severe obesity varied significantly according to age, gender, geographic area, and the definition of severe obesity. The highest rates of class II and III obesity in the US according to the Centers of Disease Control cutoff were 9.5% and 4.5%, respectively. Seventeen studies reported prevalence rates in at least two time periods. Data for 9,190,718 individuals showed a 1.71 (95%CI, 1.53-1.90) greater odds for severe obesity in 2006-2017 (N=5,029,584) vs. 1967-2007 (N=4,161,134). In an analysis limited to studies from 1980s’ with a minimum follow-up of 20 years, a 9.16(95%CI, 7.76-10.80) greater odds for severe obesity in recent vs. earlier time was found. An analysis limited to studies from 2000, with a follow-up of 5-15 years, a 1.09 (95%CI, 0.99-1.20) greater odds was noted when comparing (2011-2017; N=4,991,831) vs. (2000-2011; N=4,134,340). Conclusion: Severe pediatric obesity is escalating with a marked increase from the1980’s and a slower rate from 2000.
DOI: 10.48370/ofd/wpeuxu/tc0ndg
title: Periconia byssoides Pers. F-4325 (2).jpg

description: :unav
DOI: 10.48370/ofd/wpeuxu/c0ydyr
title: Periconia byssoides Pers. F-4325.tif

description: :unav
DOI: 10.48370/ofd/wpeuxu/pinwqe
title: Periconia byssoides Pers. F-4325.jpg

description: :unav
DOI: 10.48370/ofd/wpeuxu
title: Periconia byssoides Pers., 4325

description: Fungarium Micromycetes
DOI: 10.48370/ofd/k4x5id/1sj4p0
title: Periconia minutissima Corda F-4035.tif

description: :unav
DOI: 10.48370/ofd/k4x5id
title: Periconia minutissima Corda, 4035

description: Fungarium Micromycetes
DOI: 10.48370/ofd/u3ah0a/awg2en
title: Peronospora agrestis Gäumann F-2272.tif

description: :unav
DOI: 10.48370/ofd/u3ah0a
title: Peronospora agrestis Gäumann, 2272

description: Fungarium Micromycetes
DOI: 10.48370/ofd/eisihp/qzws1l
title: Peronospora agrestis Gäumann F-2797.tif

description: :unav
DOI: 10.48370/ofd/eisihp
title: Peronospora agrestis Gäumann, 2797

description: Fungarium Micromycetes
DOI: 10.48370/ofd/jjmcmi/f31b1g
title: Peronospora agrestis Gäumann F-0195.tif

description: :unav
DOI: 10.48370/ofd/jjmcmi
title: Peronospora agrestis Gäumann, 195

description: Fungarium Micromycetes
DOI: 10.48370/ofd/ftujta/1uqc6a
title: Peronospora agrestis Gäumann F-0196.tif

description: :unav
DOI: 10.48370/ofd/ftujta
title: Peronospora agrestis Gäumann, 196

description: Fungarium Micromycetes
DOI: 10.21227/2bmr-g224
title: Artificial Intelligence Applications in K-12 Education: A Systematic Literature Review

description: None
DOI: 10.48370/ofd/mnysy0/pgdv7i
title: Peronospora agrimoniae H. Sydow F-1971.tif

description: :unav
DOI: 10.48370/ofd/mnysy0
title: Peronospora agrimoniae H. Sydow,, 1971

description: Fungarium Micromycetes
DOI: 10.6084/m9.figshare.19122311
title: Supplementary Material for: Validation of a Market-Approved Artificial Intelligence Mobile Health App for Skin Cancer Screening: A Prospective Multicenter Diagnostic Accuracy Study

description: <b><i>Background:</i></b> Mobile health (mHealth) consumer applications (apps) have been integrated with deep learning for skin cancer risk assessments. However, prospective validation of these apps is lacking. <b><i>Objectives:</i></b> To identify the diagnostic accuracy of an app integrated with a convolutional neural network for the detection of premalignant and malignant skin lesions. <b><i>Methods:</i></b> We performed a prospective multicenter diagnostic accuracy study of a CE-marked mHealth app from January 1 until August 31, 2020, among adult patients with at least one suspicious skin lesion. Skin lesions were assessed by the app on an iOS or Android device after clinical diagnosis and before obtaining histopathology. The app outcome was compared to the histopathological diagnosis, or if not available, the clinical diagnosis by a dermatologist. The primary outcome was the sensitivity and specificity of the app to detect premalignant and malignant skin lesions. Subgroup analyses were conducted for different smartphone types, the lesion’s origin, indication for dermatological consultation, and lesion location. <b><i>Results:</i></b> In total, 785 lesions, including 418 suspicious and 367 benign control lesions, among 372 patients (50.8% women) with a median age of 71 years were included. The app performed at an overall 86.9% (95% CI 82.3–90.7) sensitivity and 70.4% (95% CI 66.2–74.3) specificity. The sensitivity was significantly higher on the iOS device compared to the Android device (91.0 vs. 83.0%; <i>p</i> = 0.02). Specificity calculated on benign control lesions was significantly higher than suspicious skin lesions (80.1 vs. 45.5%; <i>p</i> &lt; 0.001). Sensitivity was higher in skin fold areas compared to smooth skin areas (92.9 vs. 84.2%; <i>p</i> = 0.01), while the specificity was higher for lesions in smooth skin areas (72.0 vs. 56.6%; <i>p</i> = 0.02). <b><i>Conclusion:</i></b> The diagnostic accuracy of the mHealth app is far from perfect, but is potentially promising to empower patients to self-assess skin lesions before consulting a health care professional. An additional prospective validation study, particularly for suspicious pigmented skin lesions, is warranted. Furthermore, studies investigating mHealth implementation in the lay population are needed to demonstrate the impact on health care systems.
DOI: 10.6084/m9.figshare.19122311.v1
title: Supplementary Material for: Validation of a Market-Approved Artificial Intelligence Mobile Health App for Skin Cancer Screening: A Prospective Multicenter Diagnostic Accuracy Study

description: <b><i>Background:</i></b> Mobile health (mHealth) consumer applications (apps) have been integrated with deep learning for skin cancer risk assessments. However, prospective validation of these apps is lacking. <b><i>Objectives:</i></b> To identify the diagnostic accuracy of an app integrated with a convolutional neural network for the detection of premalignant and malignant skin lesions. <b><i>Methods:</i></b> We performed a prospective multicenter diagnostic accuracy study of a CE-marked mHealth app from January 1 until August 31, 2020, among adult patients with at least one suspicious skin lesion. Skin lesions were assessed by the app on an iOS or Android device after clinical diagnosis and before obtaining histopathology. The app outcome was compared to the histopathological diagnosis, or if not available, the clinical diagnosis by a dermatologist. The primary outcome was the sensitivity and specificity of the app to detect premalignant and malignant skin lesions. Subgroup analyses were conducted for different smartphone types, the lesion’s origin, indication for dermatological consultation, and lesion location. <b><i>Results:</i></b> In total, 785 lesions, including 418 suspicious and 367 benign control lesions, among 372 patients (50.8% women) with a median age of 71 years were included. The app performed at an overall 86.9% (95% CI 82.3–90.7) sensitivity and 70.4% (95% CI 66.2–74.3) specificity. The sensitivity was significantly higher on the iOS device compared to the Android device (91.0 vs. 83.0%; <i>p</i> = 0.02). Specificity calculated on benign control lesions was significantly higher than suspicious skin lesions (80.1 vs. 45.5%; <i>p</i> &lt; 0.001). Sensitivity was higher in skin fold areas compared to smooth skin areas (92.9 vs. 84.2%; <i>p</i> = 0.01), while the specificity was higher for lesions in smooth skin areas (72.0 vs. 56.6%; <i>p</i> = 0.02). <b><i>Conclusion:</i></b> The diagnostic accuracy of the mHealth app is far from perfect, but is potentially promising to empower patients to self-assess skin lesions before consulting a health care professional. An additional prospective validation study, particularly for suspicious pigmented skin lesions, is warranted. Furthermore, studies investigating mHealth implementation in the lay population are needed to demonstrate the impact on health care systems.
DOI: 10.48370/ofd/c5drjt/vkbr78
title: Peronospora alsinearum Caspary F-0162.tif

description: :unav
DOI: 10.48370/ofd/c5drjt
title: Peronospora alsinearum Caspary, 162

description: Fungarium Micromycetes
DOI: 10.6084/m9.figshare.19122212
title: Gapless-REMA100: A gapless 100-m Reference Elevation Model of Antarctica with voids filled by multi-source DEMs

description: Here we generate a gapless 100-m reference elevation model of Antarctica (Gapless-REMA100) with the voids filled by combining multi-source DEMs. All the voids in original REMA mosaic were automatically filled from multi-source DEMs e.g., TanDEM-X PolarDEM and other freely available DEM products covering entire or regional Antarctica. The experimental results with the Ice, Cloud and Land elevation Satellite-2 (ICESat-2) laser altimetry data validate that the filled 100-m REMA mosaic of voids can reach an elevation accuracy similar to that of the original REMA dataset for the west and east Antarctica ice sheet as well as the entire continent. In the Antarctic Peninsula area, the vertical accuracy of the filled REMA mosaic by the proposed method is superior to that of the officially released version of the Polar Geospatial Center (PGC). The file 'Gapless-REMA100.tif' is a gapless 100-m reference elevation model of Antarctica (Gapless-REMA100). The Gapless-REMA100 dataset is a newly released product published in our ISPRS P&RS 2022 paper. If this paper or the released Gapless-REMA100 dataset is helpful to your research, please cite it:Yuting Dong, Ji Zhao, Caiyong Li, and Mingsheng Liao, "Gapless-REMA100: A gapless 100-m Reference Elevation Model of Antarctica with voids filled by multi-source DEMs," ISPRS Journal of Photogrammetry and Remote Sensing, 2022.
DOI: 10.6084/m9.figshare.19122212.v1
title: Gapless-REMA100

description: Here we generate a gapless 100-m reference elevation model of Antarctica (Gapless-REMA100) with the voids filled by combining multi-source DEMs. All the voids in original REMA mosaic were automatically filled from multi-source DEMs e.g., TanDEM-X PolarDEM and other freely available DEM products covering entire or regional Antarctica. The experimental results with the Ice, Cloud and Land elevation Satellite-2 (ICESat-2) laser altimetry data validate that the filled 100-m REMA mosaic of voids can reach an elevation accuracy similar to that of the original REMA dataset for the west and east Antarctica ice sheet as well as the entire continent. In the Antarctic Peninsula area, the vertical accuracy of the filled REMA mosaic by the proposed method is superior to that of the officially released version of the Polar Geospatial Center (PGC). The file 'Gapless-REMA100.tif' is a gapless 100-m reference elevation model of Antarctica (Gapless-REMA100). The Gapless-REMA100 dataset is a newly released product published in our ISPRS P&RS 2022 paper. If this paper or the released Gapless-REMA100 dataset is helpful to your research, please cite it:Yuting Dong, Ji Zhao, Caiyong Li, and Mingsheng Liao, "Gapless-REMA100: A gapless 100-m Reference Elevation Model of Antarctica with voids filled by multi-source DEMs," ISPRS Journal of Photogrammetry and Remote Sensing, 2022.
DOI: 10.48370/ofd/095wnd/dong5y
title: Peronospora alta Fuckel F-0005.tif

description: :unav
DOI: 10.48370/ofd/095wnd
title: Peronospora alta Fuckel, 5

description: Fungarium Micromycetes
DOI: 10.48370/ofd/makcoe/intaln
title: Peronospora alta Fuckel F-0028.tif

description: :unav
DOI: 10.48370/ofd/makcoe
title: Peronospora alta Fuckel, 28

description: Fungarium Micromycetes
DOI: 10.48370/ofd/vws8iv/uudlbk
title: Peronospora alta Fuckel F-0191.tif

description: :unav
DOI: 10.48370/ofd/vws8iv
title: Peronospora alta Fuckel, 191

description: Fungarium Micromycetes
DOI: 10.48370/ofd/a7vhdg/j5b8uq
title: Peronospora alta Fuckel F-0190.tif

description: :unav
DOI: 10.48370/ofd/a7vhdg
title: Peronospora alta Fuckel, 190

description: Fungarium Micromycetes
DOI: 10.48370/ofd/sotv03/babozz
title: Peronospora alta Fuckel F-1061.tif

description: :unav
DOI: 10.48370/ofd/sotv03
title: Peronospora alta Fuckel, 1061

description: Fungarium Micromycetes
DOI: 10.48370/ofd/6ufaa3/40z92n
title: Peronospora aparines (de Bary) Gäumann F-1675.tif

description: :unav
DOI: 10.48370/ofd/6ufaa3
title: Peronospora aparines (de Bary) Gäumann, 1675

description: Fungarium Micromycetes
DOI: 10.5281/zenodo.5963396
title: CADD-SV - A framework to score the effects of structural variants in health and disease

description: Required annotation data-set to run the CADD-SV framework; a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Pre-scored variants as well as additional information on used features.<br> A webserver for online scoring as well as data downloads is available at: https://cadd-sv.bihealth.org/<br> Source code for CADD-SV is available at GitHub: https://github.com/kircherlab/CADD-SV
DOI: 10.5281/zenodo.5963395
title: CADD-SV - A framework to score the effects of structural variants in health and disease

description: Required annotation data-set to run the CADD-SV framework; a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Pre-scored variants as well as additional information on used features.<br> A webserver for online scoring as well as data downloads is available at: https://cadd-sv.bihealth.org/<br> Source code for CADD-SV is available at GitHub: https://github.com/kircherlab/CADD-SV
DOI: 10.48370/ofd/pr6ftm/e1kddk
title: Peronospora arborescens (Berkeley) Caspary F-3776.tif

description: :unav
DOI: 10.48370/ofd/pr6ftm
title: Peronospora arborescens (Berkeley) Caspary, 3776

description: Fungarium Micromycetes
DOI: 10.48370/ofd/z40sno/6k5m5v
title: Peronospora arborescens (Berkeley) Caspary F-3770.tif

description: :unav
DOI: 10.48370/ofd/z40sno
title: Peronospora arborescens (Berkeley) Caspary, 3770

description: Fungarium Micromycetes
DOI: 10.48370/ofd/o1uivn/tpswkt
title: Peronospora arborescens (Berkeley) Caspary F-3966.tif

description: :unav
DOI: 10.48370/ofd/o1uivn
title: Peronospora arborescens (Berkeley) Caspary, 3966

description: Fungarium Micromycetes
DOI: 10.48370/ofd/ha9aeo/2pf6ky
title: Peronospora arborescens (Berkeley) Caspary F-3966.tif

description: :unav
DOI: 10.48370/ofd/ha9aeo/s2rhud
title: Peronospora arborescens (Berkeley) Caspary F-3965.tif

description: :unav
DOI: 10.48370/ofd/ha9aeo/p27b12
title: Peronospora arborescens (Berkeley) Caspary F-3965 (2).tif

description: :unav
DOI: 10.48370/ofd/ha9aeo/0njs1k
title: Peronospora arborescens (Berkeley) Caspary F-3965 (3).tif

description: :unav
DOI: 10.48370/ofd/ha9aeo/ujtq6a
title: Peronospora arborescens (Berkeley) Caspary F-3965 (4).tif

description: :unav
DOI: 10.48370/ofd/ha9aeo
title: Peronospora arborescens (Berkeley) Caspary, 3965

description: Fungarium Micromycetes
DOI: 10.34740/kaggle/dsv/3138544
title: COVID Vaccination in World (updated daily)
DOI: 10.48370/ofd/wl5ios/z5bogj
title: Peronospora arenariae (Berkeley) Tulasne F-0161.tif

description: :unav
DOI: 10.48370/ofd/wl5ios
title: Peronospora arenariae (Berkeley) Tulasne, 161

description: Fungarium Micromycetes
DOI: 10.48370/ofd/8qbrso/7cziwi
title: Peronospora arenariae (Berkeley) Tulasne F-2265.tif

description: :unav
DOI: 10.48370/ofd/8qbrso/qwibxf
title: Peronospora arenariae (Berkeley) Tulasne F-2265 (5).tif

description: :unav
DOI: 10.48370/ofd/8qbrso/wrxwsy
title: Peronospora arenariae (Berkeley) Tulasne F-2265 (4).tif

description: :unav
DOI: 10.48370/ofd/8qbrso/5puwa2
title: Peronospora arenariae (Berkeley) Tulasne F-2265 (2).tif

description: :unav
DOI: 10.48370/ofd/8qbrso/tmixsl
title: Peronospora arenariae (Berkeley) Tulasne F-2265 (3).tif

description: :unav
DOI: 10.48370/ofd/8qbrso
title: Peronospora arenariae (Berkeley) Tulasne, 2265

description: Fungarium Micromycetes
DOI: 10.5286/isis.e.rb2210282
title: Exploring Hydrogen Bonding in Ethylene Glycol \2013 Water Mixtures at 3 Different Concentrations

description: Ethylene glycol (EG) is used in the industry as a precursor in polyester fibres production, as a preservative in the cosmetic industry and as a cryoprotectant solvent, when mixed with water. The presence of EG in an aqueous mixture lowers its freezing point and increases its boiling point. Due to the presence of hydroxyl groups in EG structure, the molecules within this liquid form hydrogen bonding. Proposed investigation of these networks will enrich the existing understanding of the structural properties of this industrially important liquid. We also plan to probe the effect of presence of water molecules on EG hydrogen bonding network. Three different concentrations will be tested to gain insight into a correlation between macroscopic properties, which vary with different EG concentration and the local ordering.
DOI: 10.5286/isis.e.rb2210282-1
title: Exploring Hydrogen Bonding in Ethylene Glycol \2013 Water Mixtures at 3 Different Concentrations

description: Ethylene glycol (EG) is used in the industry as a precursor in polyester fibres production, as a preservative in the cosmetic industry and as a cryoprotectant solvent, when mixed with water. The presence of EG in an aqueous mixture lowers its freezing point and increases its boiling point. Due to the presence of hydroxyl groups in EG structure, the molecules within this liquid form hydrogen bonding. Proposed investigation of these networks will enrich the existing understanding of the structural properties of this industrially important liquid. We also plan to probe the effect of presence of water molecules on EG hydrogen bonding network. Three different concentrations will be tested to gain insight into a correlation between macroscopic properties, which vary with different EG concentration and the local ordering.
DOI: 10.6084/m9.figshare.13395470
title: An estimate of the deepest branches of the tree of life from ancient vertically-evolving genes

description: This is the data supplement for the manuscript "An estimate of the deepest branches of the tree of life from ancient vertically-evolving genes"<br><br>Authors: Edmund R. R. Moody1, Tara A. Mahendrarajah2, Nina Dombrowski2, James W. Clark1, Celine Petitjean1, Pierre Offre2, Gergely J. Szöllősi3,4,5, Anja Spang2,6*, Tom A. Williams1*<br>1. School of Biological Sciences, University of Bristol, Bristol BS8 1TH, UK.2. NIOZ, Royal Netherlands Institute for Sea Research, Department of Marine Microbiology and Biogeochemistry; AB Den Burg, The Netherlands3. Dept. of Biological Physics, Eötvös Loránd University, 1117 Budapest, Hungary 4. MTA-ELTE “Lendület” Evolutionary Genomics Research Group, 1117 Budapest, Hungary; 5. Institute of Evolution, Centre for Ecological Research, 1121 Budapest, Hungary6. Department of Cell- and Molecular Biology, Science for Life Laboratory, Uppsala University, SE-75123, Uppsala, Sweden<br>*Co-corresponding authors: tom.a.williams@bristol.ac.uk, anja.spang@nioz.nl<br>This data supplement contains sequence alignments, trees, and python scripts used for analysis as well as tables of results.
DOI: 10.6084/m9.figshare.13395470.v5
title: An estimate of the deepest branches of the tree of life from ancient vertically-evolving genes

description: This is the data supplement for the manuscript "An estimate of the deepest branches of the tree of life from ancient vertically-evolving genes"<br><br>Authors: Edmund R. R. Moody1, Tara A. Mahendrarajah2, Nina Dombrowski2, James W. Clark1, Celine Petitjean1, Pierre Offre2, Gergely J. Szöllősi3,4,5, Anja Spang2,6*, Tom A. Williams1*<br>1. School of Biological Sciences, University of Bristol, Bristol BS8 1TH, UK.2. NIOZ, Royal Netherlands Institute for Sea Research, Department of Marine Microbiology and Biogeochemistry; AB Den Burg, The Netherlands3. Dept. of Biological Physics, Eötvös Loránd University, 1117 Budapest, Hungary 4. MTA-ELTE “Lendület” Evolutionary Genomics Research Group, 1117 Budapest, Hungary; 5. Institute of Evolution, Centre for Ecological Research, 1121 Budapest, Hungary6. Department of Cell- and Molecular Biology, Science for Life Laboratory, Uppsala University, SE-75123, Uppsala, Sweden<br>*Co-corresponding authors: tom.a.williams@bristol.ac.uk, anja.spang@nioz.nl<br>This data supplement contains sequence alignments, trees, and python scripts used for analysis as well as tables of results.
DOI: 10.5519/qd.dqibdnih
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1293 records)
DOI: 10.6084/m9.figshare.19122422
title: Questionnaire - Health professionals’ translation preferences and expectations in biomedical translation

description: <b>Aim of survey study:</b> the goal of this study was to investigate the translation preferences of health professionals concerning instructions for use of medical devices and compare those translation preferences with health professionals’ expectations about what translators should do when they translate texts for these healthcare settings and about the features of translated biomedical texts.<b>Dataset: </b>Questionnaire in European Portuguese designed to elicit translation preferences and expectations in biomedical translation and aimed at health professionals.
DOI: 10.5281/zenodo.5970349
title: Relationship Between Poetic Meter and Meaning in Accentual-Syllabic Verse (data and replication code)

description: <strong>main.py</strong>: script to train both lda and word2vec models <strong>main.ipynb</strong>: Jupyter Notebook containing all the analyses reported in the paper <strong>pos.ipynb</strong>: clustering based on frequencies of parts-of-speech <strong>corpora</strong>: contains original data for Czech, English, and Dutch poetry in JSON (proprietary German and Russian not included) <pre><code>{ &lt;= Each item in the following lists corresponds to particular poem and holds: 'words': [] &lt;= list of lemmata found in the poem 'pos_tags': [] &lt;= their POS-tags (Positional Morphological Tags for Czech, MyStem for Russian, TreeTagger tagsets for other corpora) 'meters': [[]] &lt;= list of meters found in poem 'years': [] &lt;= year when poem published (year when author born in case of English) 'n_words': [] &lt;= number of words 'n_lines': [] &lt;= number of lines 'authors': [] &lt;= author of the poem 'titles': [] &lt;= title of the poem 'schemes': [] &lt;= line-ending schemes } </code></pre> <strong>dicts</strong>: contains Gensim dictionary files for all 5 corpora <strong>fig</strong>: contains all resulting figures <strong>json</strong> <strong>&gt; metadata:</strong> contains all metadata on poems in particular corpora <pre><code>{ &lt;= Each item in the following lists corresponds to particular poem and holds: 'meters': [[]] &lt;= list of meters found in poem 'years': [] &lt;= year when poem published (year when author born in case of English) 'n_words': [] &lt;= number of words 'n_lines': [] &lt;= number of lines 'authors': [] &lt;= author of the poem 'titles': [] &lt;= title of the poem } </code></pre> <strong>json &gt; topics:</strong> contains topic probabilities in particular poems <pre><code>[ &lt;= each item corresponds to particular poem and comprise 100-dimensional dict { 'topic title': its probability in poem } ] </code></pre> <strong>json &gt; pos:</strong> contains POS relative frequencies in particular poems <pre><code>[ &lt;= each item corresponds to particular poem { 'POS': its frequency } ] </code></pre> <strong>json &gt; w2v:</strong> contains mapping of lemmata and their neighbours in word2vec models <strong>models</strong>: contains pretrained lda and word2vec models (Gensim) <strong>regression</strong>: contains data and code to produce S5_table and S6_fig