cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7395030
title: Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 12 of 14

description: This dataset is part of the work https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full. This is the twelfth part of 14 parts of the full dataset (12/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 30ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at https://zenodo.org/record/7294916 The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data https://www.humanconnectome.org/study/hcp-young-adult and randomly select 500 brains as anatomical references. Other details of this dataset can be found at https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: https://zenodo.org/record/7294916 Part 2: https://zenodo.org/record/7389550 Part 3: https://zenodo.org/record/7390382 Part 4: https://zenodo.org/record/7390741 Part 5: https://zenodo.org/record/7391205 Part 6: https://zenodo.org/record/7393060 Part 7: https://zenodo.org/record/7393174 Part 8: https://zenodo.org/record/7393347 Part 9: https://zenodo.org/record/7394250 Part 10: https://zenodo.org/record/7394667 Part 11: https://zenodo.org/record/7394939 Part 12: https://zenodo.org/record/7395031 Part 13: https://zenodo.org/record/7395620 Part 14: https://zenodo.org/record/7395622
DOI: 10.5281/zenodo.7395620
title: Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 13 of 14

description: This dataset is part of the work https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full. This is the thirteenth part of 14 parts of the full dataset (13/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 35ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at https://zenodo.org/record/7294916 The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data https://www.humanconnectome.org/study/hcp-young-adult and randomly select 500 brains as anatomical references. Other details of this dataset can be found at https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: https://zenodo.org/record/7294916 Part 2: https://zenodo.org/record/7389550 Part 3: https://zenodo.org/record/7390382 Part 4: https://zenodo.org/record/7390741 Part 5: https://zenodo.org/record/7391205 Part 6: https://zenodo.org/record/7393060 Part 7: https://zenodo.org/record/7393174 Part 8: https://zenodo.org/record/7393347 Part 9: https://zenodo.org/record/7394250 Part 10: https://zenodo.org/record/7394667 Part 11: https://zenodo.org/record/7394939 Part 12: https://zenodo.org/record/7395031 Part 13: https://zenodo.org/record/7395620 Part 14: https://zenodo.org/record/7395622
DOI: 10.5281/zenodo.7395619
title: Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 13 of 14

description: This dataset is part of the work https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full. This is the thirteenth part of 14 parts of the full dataset (13/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 35ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at https://zenodo.org/record/7294916 The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data https://www.humanconnectome.org/study/hcp-young-adult and randomly select 500 brains as anatomical references. Other details of this dataset can be found at https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: https://zenodo.org/record/7294916 Part 2: https://zenodo.org/record/7389550 Part 3: https://zenodo.org/record/7390382 Part 4: https://zenodo.org/record/7390741 Part 5: https://zenodo.org/record/7391205 Part 6: https://zenodo.org/record/7393060 Part 7: https://zenodo.org/record/7393174 Part 8: https://zenodo.org/record/7393347 Part 9: https://zenodo.org/record/7394250 Part 10: https://zenodo.org/record/7394667 Part 11: https://zenodo.org/record/7394939 Part 12: https://zenodo.org/record/7395031 Part 13: https://zenodo.org/record/7395620 Part 14: https://zenodo.org/record/7395622
DOI: 10.5281/zenodo.7395622
title: Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 14 of 14

description: This dataset is part of the work https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full. This is the fourteenth and final part of 14 parts of the full dataset (14/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 40ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at https://zenodo.org/record/7294916 The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data https://www.humanconnectome.org/study/hcp-young-adult and randomly select 500 brains as anatomical references. Other details of this dataset can be found at https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: https://zenodo.org/record/7294916 Part 2: https://zenodo.org/record/7389550 Part 3: https://zenodo.org/record/7390382 Part 4: https://zenodo.org/record/7390741 Part 5: https://zenodo.org/record/7391205 Part 6: https://zenodo.org/record/7393060 Part 7: https://zenodo.org/record/7393174 Part 8: https://zenodo.org/record/7393347 Part 9: https://zenodo.org/record/7394250 Part 10: https://zenodo.org/record/7394667 Part 11: https://zenodo.org/record/7394939 Part 12: https://zenodo.org/record/7395031 Part 13: https://zenodo.org/record/7395620 Part 14: https://zenodo.org/record/7395622
DOI: 10.5281/zenodo.7395621
title: Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 14 of 14

description: This dataset is part of the work https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full. This is the fourteenth and final part of 14 parts of the full dataset (14/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 40ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at https://zenodo.org/record/7294916 The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data https://www.humanconnectome.org/study/hcp-young-adult and randomly select 500 brains as anatomical references. Other details of this dataset can be found at https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: https://zenodo.org/record/7294916 Part 2: https://zenodo.org/record/7389550 Part 3: https://zenodo.org/record/7390382 Part 4: https://zenodo.org/record/7390741 Part 5: https://zenodo.org/record/7391205 Part 6: https://zenodo.org/record/7393060 Part 7: https://zenodo.org/record/7393174 Part 8: https://zenodo.org/record/7393347 Part 9: https://zenodo.org/record/7394250 Part 10: https://zenodo.org/record/7394667 Part 11: https://zenodo.org/record/7394939 Part 12: https://zenodo.org/record/7395031 Part 13: https://zenodo.org/record/7395620 Part 14: https://zenodo.org/record/7395622
DOI: 10.6084/m9.figshare.21688058
title: MRIO 2008-2018

description: Inter country input output table including 10 sectors and 272 NUTS 2 regions of 28 countries<br>
DOI: 10.6084/m9.figshare.14680506
title: Dataset: Arousal, subjective significance and the origin of neutrally valenced words in the processing of an emotional categorisation task (E6)

description: An emotional categorisation task allows us to study how emotionality is understood and how emotional factors influence decisions. Of particular interest is the processing of emotionality in neutrally valenced words, because emotionality is not only the valence but is also composed of activation (arousal and subjective significance) and the type of process needed to produce emotion (origin). We predicted that increasing activation levels should lead to increased classification of stimuli as emotional, with a focus on the late processing stages, when explicit word processing occurs, which on the electrophysiological level corresponds to P300, N450 and LPC components. The behavioural results showed that the emotionality of words increased with increasing levels of arousal and subjective significance. Automatically originated words were assessed as more emotional than reflective ones. The amplitude of the N450 component revealed dissociation for subjective significance and origin effects, showing that these two dimensions ascribe distinct properties of emotionality. Finally, the LPC component was susceptible to all affective dimensions used in manipulation. Our study showed that arousal, subjective significance and origin are dimensions of affect that shape the processing of words’ emotionality in neutrally valenced stimuli.
DOI: 10.6084/m9.figshare.14680506.v1
title: Dataset: Arousal, subjective significance and the origin of neutrally valenced words in the processing of an emotional categorisation task (E6)

description: An emotional categorisation task allows us to study how emotionality is understood and how emotional factors influence decisions. Of particular interest is the processing of emotionality in neutrally valenced words, because emotionality is not only the valence but is also composed of activation (arousal and subjective significance) and the type of process needed to produce emotion (origin). We predicted that increasing activation levels should lead to increased classification of stimuli as emotional, with a focus on the late processing stages, when explicit word processing occurs, which on the electrophysiological level corresponds to P300, N450 and LPC components. The behavioural results showed that the emotionality of words increased with increasing levels of arousal and subjective significance. Automatically originated words were assessed as more emotional than reflective ones. The amplitude of the N450 component revealed dissociation for subjective significance and origin effects, showing that these two dimensions ascribe distinct properties of emotionality. Finally, the LPC component was susceptible to all affective dimensions used in manipulation. Our study showed that arousal, subjective significance and origin are dimensions of affect that shape the processing of words’ emotionality in neutrally valenced stimuli.
DOI: 10.34740/kaggle/dsv/4680114
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.6826285
title: Dyadic encounter unfamiliar female pairs over 2 days – 16p11.2 Del mouse strain – Del/+-Del/+ - 5257_5180

description: We evaluated the social interactions and communication between unfamiliar individuals in pairs. For these recordings of social behaviour and ultrasonic communication, we focused on pairs of individuals since we currently cannot identify the emitter of USVs when animals were interacting closely. Therefore, we recorded undisturbed dyadic interactions between two unfamiliar individuals (from two different housing cages) of the same age (14-20 weeks of age) and genotype for 47h (two days and nights). For that purpose, we coupled the Live Mouse Tracker system (plugin 931) with one CM16/CMPA microphone (Avisoft Bioacoustics, Glienecke, Germany) connected to the Avisoft Ultrasound Gate 416 (300 kHz sampling rate, 16-bit format; trigger: level of this channel; pre-trigger: 1 s; hold time: 1 s; duration &gt; 0.005 s; trigger event: 2 % energy in 25-125 kHz with entropy &lt; 50%; Avisoft Bioacoustics, Glienecke, Germany). LMT and Avisoft systems were synchronised based on the protocol described in (de Chaumont et al., 2021). Each upload includes the sqlite database from LMT (processed, with USV synchronised) and a zip file with all USVs sequences automatically recorded by Avisoft.
DOI: 10.5281/zenodo.6826284
title: Dyadic encounter unfamiliar female pairs over 2 days – 16p11.2 Del mouse strain – Del/+-Del/+ - 5257_5180

description: We evaluated the social interactions and communication between unfamiliar individuals in pairs. For these recordings of social behaviour and ultrasonic communication, we focused on pairs of individuals since we currently cannot identify the emitter of USVs when animals were interacting closely. Therefore, we recorded undisturbed dyadic interactions between two unfamiliar individuals (from two different housing cages) of the same age (14-20 weeks of age) and genotype for 47h (two days and nights). For that purpose, we coupled the Live Mouse Tracker system (plugin 931) with one CM16/CMPA microphone (Avisoft Bioacoustics, Glienecke, Germany) connected to the Avisoft Ultrasound Gate 416 (300 kHz sampling rate, 16-bit format; trigger: level of this channel; pre-trigger: 1 s; hold time: 1 s; duration &gt; 0.005 s; trigger event: 2 % energy in 25-125 kHz with entropy &lt; 50%; Avisoft Bioacoustics, Glienecke, Germany). LMT and Avisoft systems were synchronised based on the protocol described in (de Chaumont et al., 2021). Each upload includes the sqlite database from LMT (processed, with USV synchronised) and a zip file with all USVs sequences automatically recorded by Avisoft.
DOI: 10.5281/zenodo.7409410
title: Dyadic encounter unfamiliar female pairs over 2 days – 16p11.2 Del mouse strain – Del/+-Del/+ - 5257_5180

description: We evaluated the social interactions and communication between unfamiliar individuals in pairs. For these recordings of social behaviour and ultrasonic communication, we focused on pairs of individuals since we currently cannot identify the emitter of USVs when animals were interacting closely. Therefore, we recorded undisturbed dyadic interactions between two unfamiliar individuals (from two different housing cages) of the same age (14-20 weeks of age) and genotype for 47h (two days and nights). For that purpose, we coupled the Live Mouse Tracker system (plugin 931) with one CM16/CMPA microphone (Avisoft Bioacoustics, Glienecke, Germany) connected to the Avisoft Ultrasound Gate 416 (300 kHz sampling rate, 16-bit format; trigger: level of this channel; pre-trigger: 1 s; hold time: 1 s; duration &gt; 0.005 s; trigger event: 2 % energy in 25-125 kHz with entropy &lt; 50%; Avisoft Bioacoustics, Glienecke, Germany). LMT and Avisoft systems were synchronised based on the protocol described in (de Chaumont et al., 2021). Each upload includes the sqlite database from LMT (processed, with USV synchronised) and a zip file with all USVs sequences automatically recorded by Avisoft.
DOI: 10.6084/m9.figshare.21687629.v1
title: Regional employment

description: Regional empolyment in NUTS2 regions
DOI: 10.6084/m9.figshare.21687629
title: Regional employment

description: Regional empolyment in NUTS2 regions
DOI: 10.6084/m9.figshare.21687926
title: EUREGIO

description: PBL European regional IO table during 2000-2010
DOI: 10.6084/m9.figshare.21687968
title: Regional road freight flows

description: This data article describes a dataset on European road freight traffic. The dataset includes truck traffic flows between 1675 regions all over Europe. In addition to the road freight flows in tons as well as number of vehicles, the dataset also contains the shortest path between the respective regions on the European highway network (E-roads). Fifteen columns provide the following information for each pair of regions: (1) ID origin region, (2) name origin region, (3) ID destination region, (4) name destination region, (5) path in the E-road network, (6) distance from origin region to the E-road network, (7) distance within the E-road network, (8) distance from the E-road network to the destination region, (9) total distance, (10) road freight flow in tons for 2010, (11) road freight flow in tons for 2019, (12) road freight flow in tons for 2030, (13) truck traffic flow in number of vehicles for 2010, (14) truck traffic flow in number of vehicles for 2019, (15) truck traffic flow in number of vehicles for 2030. In addition, a table of nodes and a table of edges of the modelled E-road network is available. Finally, a list with supplementary information on the regions under consideration is given. In 2010, the ETISplus project collected Europe-wide freight volumes from various EU sources as well as from the EU countries and calibrated the resulting origin-destination matrices with measured traffic flows. For the dataset described here, the road freight volume was updated using Eurostat data and a forecast up to 2030 was added. The freight volume was converted into vehicles travelling. Subsequently, the highway network relevant for trucks was extracted from the ETISplus project and manually updated with the current E-road network. Finally, each origin-destination freight volume was allocated to the network using Dijkstra's algorithm. This provides a synthetically generated road freight traffic volume for each road section. The generated data provide an extremely relevant basis for the design of future road infrastructure in Europe, for example hydrogen refuelling stations or charging stations for electric trucks. Thus, the data are not only relevant for traffic science studies, but also of high importance for planners in practice.
DOI: 10.6084/m9.figshare.21687554
title: Regional households' income

description: Regional income in NUTS2 regions
DOI: 10.6084/m9.figshare.21687470
title: Inter Country Input Output table from OECD

description: Inter Country Input Output table from OECD
DOI: 10.6084/m9.figshare.21687515
title: Regional gross value added

description: Regional gross value added
DOI: 10.6084/m9.figshare.21687551
title: Regional gross capital formation

description: REgional gross capital formation in NUTS2 regions
DOI: 10.6084/m9.figshare.21687551.v1
title: Regional gross capital formation

description: REgional gross capital formation in NUTS2 regions
DOI: 10.6084/m9.figshare.21687554.v1
title: Regional households' income

description: Regional income in NUTS2 regions
DOI: 10.5281/zenodo.7298585
title: NGS results of metastatic colorectal cancer patients.

description: Anonymized examples of complete NGS results of 6 metastatic colorectal cancer patients included in a prognostic analysis focused on p53 mutational status.
DOI: 10.5281/zenodo.7298584
title: NGS results of metastatic colorectal cancer patients.

description: Anonymized examples of complete NGS results of 6 metastatic colorectal cancer patients included in a prognostic analysis focused on p53 mutational status.
DOI: 10.25430/researchdata.cab.unipd.it.00000790
title: Death Education for Palliative Psychology

description: Death Education for Palliative Psychology (DE4PP) is a 3-year Erasmus+ project funded by the European community which aimed to create an online course to spread the importance of end-of-life psychology in Europe and to acquire the necessary specific skills to students of the health professions who will work in this field. The countries involved in this project are: Italy both with the University of Padua which is the project leader, represented by Prof. Testoni of the FISPPA department and with Servizio Nazionale Tumori (ANT) Italia Onlus. The other partner countries of the project are: Israel (University of Haifa); Austria (Alpen-Adria-Universität Klagenfurt); Romania (University of Sibiu) and Poland (John Paul II Catholic University of Lublin). In each of the 5 countries, about 20 psychology university students took part in a course on death education, palliative psychology and the use of art therapies at the end of life, consisting of 9 video lessons of about half an hour each. Furthermore, they took part in 4 workshops with students who will provide a practical demonstration of the use of arts therapies in the end of life, with photovoice, psychodrama and intermodal arts therapies. Quantitative surveys were made before and after the course on the group of students who participated and on a control group for each country. Here the data of these surveys are collected.
DOI: 10.6084/m9.figshare.21581233
title: Phylogenetic results dataset 2 from The problematic Cambrian arthropod <i>Tuzoia</i> and the origin of mandibulates revisited

description: Phylogenetic tree, dataset2