cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5992286
title: A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography

description: Full dataset of segmentations for both the thoracic artery and the proximal pulmonary arteries (format: standard NIfTI, nii) from Saitta S, Sturla F, Caimi A, Riva A, Palumbo MC, Nano G, Votta E, Corte AD, Glauber M, Chiappino D, Marrocco-Trischitta MM, Redaelli A. A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography. J Digit Imaging. 2022 Jan 26. doi: 10.1007/s10278-021-00535-1. Epub ahead of print. PMID: 35083618. Abstract Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning. For 465 CT scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a CNN with a 3D U-Net architecture. The remaining 70 scans were used for testing. The trained CNN was embedded within computational geometry processing pipeline which provides aortic metrics of interest for TEVAR planning. The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation, and tortuosity. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA). The trained CNN yielded a mean Dice score of 0.95 and was able to generalize to 9 pathological cases of thoracic aortic aneurysm, providing accurate segmentations. CILCA arches were characterized by significantly greater angulation (p = 0.015) and tortuosity (p = 0.048) in PLZ 3 vs. standard arches. For both arch configurations, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p &lt; 0.0001), angulation (p &lt; 0.0001), and tortuosity (p &lt; 0.0001). Our tool allows clinicians to obtain objective and repeatable PLZs mapping, and a range of automatically derived complex aortic metrics.
DOI: 10.5281/zenodo.5959181
title: Impact of runoff temporal distribution on ice dynamics.

description: These are the data related to the study about "Impact of runoff temporal distribution on ice dynamics." <br> by Basile de Fleurian, Richard Davy and Petra M. Langebroek <br> <br> The data are the results of ISSM simulations as described in the publication and saved on a netCDF4 format. <br> Each tar files contain 100 ".nc" files each exported from a single simulation. "TimeshiftXX" identifies each simulation and has its own starting time. "SL" represents season length available in short medium or long. "LR" represent the Lapse Rate value only the standard mid LR is available here as presented in the manuscript. "DT" represents the length of the initiation of the melt season available in short medium or long. "Temp" represents the intensity of the melt season length available in low medium or high. The naming convention is such that : "-SLlow-LRmid-DTmid" replaces "SLlow-TempHigh" "-SLmid-LRmid-DTmid" replaces "SLmid-Tempmid" "-SLhigh-LRmid-DTmid" replaces "SLhigh-Templow" The different variables and their units are described in the netCDF file.<br> If you require further information or some variale that are not present in those files feel free to contact me at: <br> basile.defleurian@uib.no <br>
DOI: 10.5281/zenodo.5959180
title: Impact of runoff temporal distribution on ice dynamics.

description: These are the data related to the study about "Impact of runoff temporal distribution on ice dynamics." <br> by Basile de Fleurian, Richard Davy and Petra M. Langebroek <br> <br> The data are the results of ISSM simulations as described in the publication and saved on a netCDF4 format. <br> Each tar files contain 100 ".nc" files each exported from a single simulation. "TimeshiftXX" identifies each simulation and has its own starting time. "SL" represents season length available in short medium or long. "LR" represent the Lapse Rate value only the standard mid LR is available here as presented in the manuscript. "DT" represents the length of the initiation of the melt season available in short medium or long. "Temp" represents the intensity of the melt season length available in low medium or high. The naming convention is such that : "-SLlow-LRmid-DTmid" replaces "SLlow-TempHigh" "-SLmid-LRmid-DTmid" replaces "SLmid-Tempmid" "-SLhigh-LRmid-DTmid" replaces "SLhigh-Templow" The different variables and their units are described in the netCDF file.<br> If you require further information or some variale that are not present in those files feel free to contact me at: <br> basile.defleurian@uib.no <br>
DOI: 10.14469/hpc/10103
title: Be2Li6 WFN

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10104
title: Be2Li6 NBO C2h

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10105
title: Be2Li6 3C NBO

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10107
title: Be2Li6 CAS(10,13)

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10108
title: Be2Li6 NBO C2h Def2-TZVPPD

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10109
title: Be2Li6 NBO C2h Def2-TZVPPD SCRF=water

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10111
title: Be2 2- wB97XD/Def2-TZVPPD guess=alter freq NBO

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10112
title: Be2 2- CCSD/Def2-TZVPPD NBO freq

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10114
title: Be2 2- CCSD/Def2-TZVPPD SCRF=water

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10115
title: Be2 2-, CCSD(T)/Def2-TZVPPD scrf=water

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10116
title: Mg2 2- wB97XD/Def2-TZVPPD guess=alter freq

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10117
title: Be2Li2 wB97XD/Def2-TZVPPD scrf=water guess=alter, linear

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10118
title: Be2Li2 wB97XD/Def2-TZVPPD scrf=water guess=alter C2h

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10119
title: Be2Li2 wB97XD/Def2-TZVPPD scrf=water guess=alter C2

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10120
title: Be2Li2 wB97XD/Def2-TZVPPD scrf=water guess=alter C2 NBO

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/10121
title: Be2Li2 wB97XD/Def2-TZVPPD scrf=water ground stateG = -44.418631 =&gt; -44.498032 (49.8)

description: Gaussian 16 (C01) calculation
DOI: 10.34740/kaggle/dsv/3150811
title: Omicron daily cases by country (COVID-19 variant)
DOI: 10.17866/rd.salford.16918180.v1
title: Dataset of British English speech recordings for psychoacoustics and speech processing research

description: This paper presents the Clarity Speech Corpus, a publicly available, forty speaker British English speech dataset. The corpus was created for the purpose of running listening tests to gauge speech intelligibility and quality in the Clarity Project, which has the goal of advancing speech signal processing by hearing aids through a series of challenges. The dataset is suitable for machine learning and other uses in speech and hearing technology, acoustics and psychoacoustics. The data comprises recordings of approximately 10,000 sentences drawn from the British National Corpus (BNC) with suitable length, words and grammatical construction for speech intelligibility testing. The collection process involved the selection of a subset of BNC sentences, the recording of these produced by 40 British English speakers, and the processing of these recordings to create individual sentence recordings with associated transcripts and metadata.
DOI: 10.48370/ofd/v2zrep
title: Botryohypochnus isabellinum (Fr.) J.Erikss., 3098

description: Fungarium Macromycetes
DOI: 10.5281/zenodo.5986721
title: Volunteer classifications of images from the Cropland Capture game

description: Each entry represents a single classification of a single image by a volunteer rater. The dataset contains six columns: imgid: The unique identifier for each image used in the Cropland Capture campaign<br> userid: The unique identifier for each volunteer in the Cropland Capture campaign<br> rating: The answer provided; can be only one of the following:<br> 1: yes cropland<br> 2: no cropland<br> 0: maybe <br> date: Timestamp of the rating<br> ratingid: The unique identifier of the rating (this is different for each data row)<br> platform: What interface did the volunteer use to provide this rating?<br> 1: iPhone5<br> 2: iPhone, other models<br> 3: iPad<br> 4: Browser<br> &gt;100: Android; different numbers indicate the screen size in pixels For more information, please see the following publications: Salk, CF, T Sturn, L See, S Fritz (2017). Limitations of majority agreement in crowdsourced image interpretation. <em>Transactions in GIS</em>, 21: 207–223. Salk, CF, T Sturn, L See, S Fritz (2016). Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task. <em>Remote Sensing</em>, 8: 744. Salk, CF, T Sturn, L See, S Fritz and C Perger (2016). Assessing quality of volunteer crowdsourcing contributions: Lessons from the Cropland Capture game. <em>International Journal of Digital Earth</em>, 9(4): 410-426.
description: This dataset is a result of the 'Using Crowdsourcing and Gaming Approaches for EO4SD Services (GAME.EO)' funded by the European Space Agency (contract no. 4000125186/18/1-NB).
DOI: 10.5281/zenodo.5986720
title: Volunteer classifications of images from the Cropland Capture game

description: Each entry represents a single classification of a single image by a volunteer rater. The dataset contains six columns: imgid: The unique identifier for each image used in the Cropland Capture campaign<br> userid: The unique identifier for each volunteer in the Cropland Capture campaign<br> rating: The answer provided; can be only one of the following:<br> 1: yes cropland<br> 2: no cropland<br> 0: maybe <br> date: Timestamp of the rating<br> ratingid: The unique identifier of the rating (this is different for each data row)<br> platform: What interface did the volunteer use to provide this rating?<br> 1: iPhone5<br> 2: iPhone, other models<br> 3: iPad<br> 4: Browser<br> &gt;100: Android; different numbers indicate the screen size in pixels For more information, please see the following publications: Salk, CF, T Sturn, L See, S Fritz (2017). Limitations of majority agreement in crowdsourced image interpretation. <em>Transactions in GIS</em>, 21: 207–223. Salk, CF, T Sturn, L See, S Fritz (2016). Local knowledge and professional background have a minimal impact on volunteer citizen science performance in a land-cover classification task. <em>Remote Sensing</em>, 8: 744. Salk, CF, T Sturn, L See, S Fritz and C Perger (2016). Assessing quality of volunteer crowdsourcing contributions: Lessons from the Cropland Capture game. <em>International Journal of Digital Earth</em>, 9(4): 410-426.
description: This dataset is a result of the 'Using Crowdsourcing and Gaming Approaches for EO4SD Services (GAME.EO)' funded by the European Space Agency (contract no. 4000125186/18/1-NB).
DOI: 10.48370/ofd/hbgv3g
title: Phlebia centrifuga P. Karst., 1093

description: Fungarium Makromycetes
DOI: 10.5281/zenodo.5947547
title: HySN2018v2005ERA5

description: HySN2018v2005ERA5 is a meteorological dataset for Norway and parts of neighbouring countries at 1x1 km2 and daily time steps, covering the period 1960-2020. Variables included are downward short- and longwave radiation (rsds and rlds, respectively), relative humidity (hurs) and surface pressure (ps). The dataset is created based on seNorge2018v20.05 (Lussana et al., 2019) and ERA5 (Hersbach et al., 2020; Bell et al., 2021). HySN2018v2005ERA5 is an updated and extended version of the HySN dataset (Erlandsen et al., 2019). The files are in netCDF4 format, and the map projection is UTM zone33N. When using this dataset, please refer to Huang et al. (2022) and Erlandsen et al. (2019). This research is supported by The Research Council of Norway through the project ‘IMPRINT’ (project number 295128). The results contain modified Copernicus Climate Change Service information. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.
description: {"references": ["Huang, S., S. Eisner, I. Haddeland and Z. Mengistu, 2022, Evaluation of two new-generation global soil databases for large-scale hydrological modelling in Norway, submitted to Journal of Hydrology", "Erlandsen, H.B.,\u202fL.M. Tallaksen and J. Kristiansen, 2019, Merits of novel high-resolution estimates and existing long-term estimates of humidity and incident radiation in a complex domain, Earth Syst. Sci. Data, 11, 797-821, doi: 10.5194/essd-11-797-2019", "Bell et al., 2021, The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. R. Meteorol. Soc., 147:4186-4227, doi: 10.1002/qj.4174", "Hersbach, H. et al., 2020,\u00a0The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 146:1999-2049, doi:10.1002/qj.3803", "Lussana, C.,\u202fO.E. Tveito, A. Dobler\u202fand K. Tunheim, 2019, seNorge_2018, daily precipitation, and temperature datasets over Norway, Earth Syst. Sci. Data, 11, 1531-1551, doi:10.5194/essd-11-1531-2019"]}
DOI: 10.5281/zenodo.5947546
title: HySN2018v2005ERA5

description: HySN2018v2005ERA5 is a meteorological dataset for Norway and parts of neighbouring countries at 1x1 km2 and daily time steps, covering the period 1960-2020. Variables included are downward short- and longwave radiation (rsds and rlds, respectively), relative humidity (hurs) and surface pressure (ps). The dataset is created based on seNorge2018v20.05 (Lussana et al., 2019) and ERA5 (Hersbach et al., 2020; Bell et al., 2021). HySN2018v2005ERA5 is an updated and extended version of the HySN dataset (Erlandsen et al., 2019). The files are in netCDF4 format, and the map projection is UTM zone33N. When using this dataset, please refer to Huang et al. (2022) and Erlandsen et al. (2019). This research is supported by The Research Council of Norway through the project ‘IMPRINT’ (project number 295128). The results contain modified Copernicus Climate Change Service information. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.
description: {"references": ["Huang, S., S. Eisner, I. Haddeland and Z. Mengistu, 2022, Evaluation of two new-generation global soil databases for large-scale hydrological modelling in Norway, submitted to Journal of Hydrology", "Erlandsen, H.B.,\u202fL.M. Tallaksen and J. Kristiansen, 2019, Merits of novel high-resolution estimates and existing long-term estimates of humidity and incident radiation in a complex domain, Earth Syst. Sci. Data, 11, 797-821, doi: 10.5194/essd-11-797-2019", "Bell et al., 2021, The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. R. Meteorol. Soc., 147:4186-4227, doi: 10.1002/qj.4174", "Hersbach, H. et al., 2020,\u00a0The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 146:1999-2049, doi:10.1002/qj.3803", "Lussana, C.,\u202fO.E. Tveito, A. Dobler\u202fand K. Tunheim, 2019, seNorge_2018, daily precipitation, and temperature datasets over Norway, Earth Syst. Sci. Data, 11, 1531-1551, doi:10.5194/essd-11-1531-2019"]}
DOI: 10.4228/zalf.gyk3-tb90
title: Media clipping ‚Mückenatlas‘ 2014-2017

description: The raw data of media reports originated from Argus Data Insights, a media monitoring service that provides information on the media presence of companies, also called media clipping. The company was assigned from 2014 until the end of 2017 by the ZALF to find and consolidate media content for monthly reporting. The service’s engine searched the media types ‘print’, ‘radio’, ‘television’, ‘online’ and ‘news agencies’ (dpa) on a national level by using the keywords ‘Mückenatlas’, alone or in combination with ‘Leibniz-Zentrum für Agrarlandschaftsforschung’.
We have not provided the variable "link", i.e. the link to an online article, if available, due to the already occurred or expected future deletion of the corresponding websites.

Research domain: Ecology of Agricultural Landscapes
DOI: 10.48370/ofd/1kza7f/w1ylr5
title: Peronospora hiemalis Gäumann F-0193.tif

description: :unav
DOI: 10.48370/ofd/1kza7f
title: Peronospora hiemalis Gäumann, 193

description: Fungarium Micromycetes
DOI: 10.48370/ofd/hlw6na/txynzp
title: Peronospora hiemalis Gäumann F-1077.tif

description: :unav
DOI: 10.48370/ofd/hlw6na
title: Peronospora hiemalis Gäumann, 1077

description: Fungarium Micromycetes
DOI: 10.4228/zalf.w6e6-352
title: Mückenatlas Submissions

description: Anonymised submissions to the ‘Mückenatlas’ project between 2014 and 2017. Due to the EU General Data Protection Regulation, we cannot provide the submissions of the geo-coordinates as this could reveal the home addresses of the participants of the 'Mückenatlas' project. This information cannot be requested by the authors or the institution’s data protection officer either, as they are subject to the same restrictions. With this dataset, all analyses except those concerning of the point pattern analysis can be reproduced.

Research domain: Ecology of Agricultural Landscapes

Research question: None
DOI: 10.48370/ofd/10jy1d/ctgyv3
title: Peronospora hiemalis Gäumann F-2196 (2).tif

description: :unav
DOI: 10.48370/ofd/10jy1d/ledox7
title: Peronospora hiemalis Gäumann F-2196 (5).tif

description: :unav
DOI: 10.48370/ofd/10jy1d/i26qwq
title: Peronospora hiemalis Gäumann F-2196.tif

description: :unav
DOI: 10.48370/ofd/10jy1d/eqzqnt
title: Peronospora hiemalis Gäumann F-2196 (3).tif

description: :unav
DOI: 10.48370/ofd/10jy1d/lhrybn
title: Peronospora hiemalis Gäumann F-2196 (4).tif

description: :unav
DOI: 10.48370/ofd/10jy1d
title: Peronospora hiemalis Gäumann, 2196

description: Fungarium Micromycetes
DOI: 10.48370/ofd/bozhtd/swtshf
title: Peronospora hiemalis Gäumann F-2447.tif

description: :unav
DOI: 10.48370/ofd/bozhtd
title: Peronospora hiemalis Gäumann, 2447

description: Fungarium Micromycetes
DOI: 10.21227/a0x0-qj44
title: Optimal Capacity Planning in Renewable Energy Deployment Considering Climate Change with a California Case Study

description: This research studies California’s case on the relationship between climate change and renewable energy generation, and provides a methodology to estimate optimal renewable energy capacity in transitioning capacity from fuel-based energy to renewable energy considering California’s climate condition.
DOI: 10.48370/ofd/nk0llj/6nfl2x
title: Peronospora hiemalis Gäumann F-0001.tif

description: :unav
DOI: 10.48370/ofd/nk0llj
title: Peronospora hiemalis Gäumann, 1

description: Fungarium Micromycetes
DOI: 10.48370/ofd/oghpcc/8kz5pw
title: Peronospora hiemalis Gäumann F-0194.tif

description: :unav
DOI: 10.48370/ofd/oghpcc
title: Peronospora hiemalis Gäumann, 194

description: Fungarium Micromycetes
DOI: 10.48370/ofd/nqkdjv/ju3ykj
title: Peronospora hiemalis Gäumann F-0192.tif

description: :unav
DOI: 10.48370/ofd/nqkdjv
title: Peronospora hiemalis Gäumann, 192

description: Fungarium Micromycetes
DOI: 10.5281/zenodo.5992808
title: Dati estratti da "Epidemia COVID-19", bollettino dell'Istituto Superiore di Sanità (ISS)

description: <br> “EPIDEMIA COVID-19” è un bollettino prodotto dall’Istituto Superiore di Sanità (ISS) che riporta i dati della sorveglianza integrata concernente l’infezione da virus SARS-CoV-2. Il bollettino è redatto ai sensi dell’Ordinanza n. 640 del 27 febbraio 2020. Si legge nella nota introduttiva che “Il conteggio dei casi viene effettuato sommando tutti i casi di infezione da virus SARS-CoV2 confermati tramite test molecolari e antigenici, e segnalati al sistema di sorveglianza attraverso una piattaforma web dedicata. Il database nazionale è quindi la somma dei casi presenti nei 21 sistemi informativi regionali/PPAA costruiti ad hoc per la sorveglianza COVID-19”. Si riportano di seguito grafici di dati estratti dai rapporti, concernenti casistica su persone di età ≥ 12 anni, ricoveri e vittime. Le tabelle indicano anche lo stato vaccinale e la fascia d’età. Si presti attenzione che le classi relative alla vaccinazione sono state ridefinite a seconda dei Decreti Legge del Governo. I dati sono estratti partendo dal bollettino del 10 Novembre 2021 fino a quelli del 2 Febbraio 2022. Si noti che i dati riportati risultano dal conteggio sull’arco temporale di un mese. I bollettini differiscono per lo spostamento di una settimana. L’inizio dell’arco temporale è in ritardo di un mese per i contagi ma di più di un mese per gli altri dati. <br> Il primo bollettino considerato è quello 10-Novembre-2021 che riporta le diagnosi di contagio dal 08/10/2021 al 07/11/2021, il numero delle vittime tra il 17/09/2021 al 17/10/2021, e i dati di ospedalizzazioni e terapie intensive dal 01/10/2021 al 31/10/2021. Il bollettino più recente è quello 02-Febbraio-2022, pubblicato il 5 Febbraio 2022. contiene i dati delle diagnosi di contagio tra il 31 Dicembre 2021 ed il 30 Gennaio 2022, delle vittime tra il 10/12/2021 al 09/01/2022., delle ospedalizzazioni 17/12/2021 al 16/01/2022., delle terapie intensive 17/12/2021 al 16/01/2022.
DOI: 10.48370/ofd/zpgkar/vpi1vz
title: Peronospora lamii A. Braun F-2476.tif

description: :unav
DOI: 10.48370/ofd/zpgkar
title: Peronospora lamii A. Braun, 2476

description: Fungarium Micromycetes
DOI: 10.48370/ofd/l0f2us/4tgfdu
title: Peronospora lamii A. Braun F-0181.tif

description: :unav
DOI: 10.48370/ofd/l0f2us
title: Peronospora lamii A. Braun, 181

description: Fungarium Micromycetes
DOI: 10.48370/ofd/tonb8r/xl5p3x
title: Peronospora lamii A. Braun F-0180.tif

description: :unav
DOI: 10.48370/ofd/tonb8r
title: Peronospora lamii A. Braun, 180

description: Fungarium Micromycetes