Find research datasets worth reusing

ID market related datasets for HLU9 of the CROSSBOW project

Datasets for HLU9 of the CROSSBOW project organised by countries individually. What is included in the dataset? <ol> <li>Cross-Zonal Capacities - unformatted dataset published as received from the TSOs <ul> <li>CZC data for the years 2018 & 2019 were gathered in HLU9. The CZC values were for the timeframe before the ID market took place. Parameters included: Date & Time, CZC value to and from the delivering LFC area. CZC data was collected for borders of TSOs from the CROSSBOW project consortium.</li> </ul> </li> <li>ID market data - unformatted dataset published as received from the TSOs <ul> <li>ID related data for the years 2018 & 2019 were gathered in HLU9. The parameters that were included in the data were: Date & Time, Volume Weighted Average Price, Traded volume, Location… ID market related data was collected for countries of CROSSBOW TSOs, where such data was available.</li> </ul> </li> </ol>

Hyperspectral imagery Research Products - Toulouse urban area 2015 (French ANR HYEP project)

The HYEP project (ANR 14-CE22-0016-01) main goal was to propose a panel of methods and processes designed for hyperspectral imaging, which specificity makes a weighty auxiliary for the monitoring of the elements of the urban area.  The main results of the project can be found at <ul> <li><a href="http://doi.org/10.1080/01431161.2017.1410247">G. Roussel, C. Weber, X. Briottet and X. Ceamanos, "Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution", International Journal of Remote Sensing, vol. 39(5), pp. 1593-1614, 2018.</a></li> <li><a href="http://doi.org/10.1109/ECMSM.2017.7945884">F. Z. Benhalouche, M. S. Karoui, Y. Deville, I. Boukerch, A. Ouamri, ``Multi-sharpening hyperspectral remote sensing data by multiplicative joint-criterion linear-quadratic nonnegative matrix factorization'', Proceedings of the 2017 IEEE International Workshop on Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM 2017), May 24-26, 2017, Donostia - San Sebastian</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01903469">Gintautas Mozgeris, Vytaut ̇e Juodkien ̇e, Donatas Jonikaviˇcius, Lina Straigyt ̇e, S ́ebastien Gadal, and Walid Ouerghemmi. Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment. Remote Sensing, 10(10), October 2018.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-02281003">Christiane Weber, Thomas Houet, S ́ebastien Gadal, Rahim Aguejdad, Grzegorz Skupinski, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Cl ́ement Mallet, and Arnaud Le Bris. HYEP HYperspectral imagery for Environmental urban Planning : principaux résultats. In 7ème colloque scientifique du groupe SFPT-GH, Toulouse, France, July 2019. ONERA - SFTP.</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01852844">Christiane Weber, Rahim Aguejdad, Xavier Briottet, Josselin Aval, Sophie Fabre, Jean Demuynck, Emmanuel Zenou, Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra, Sébastien Gadal, Walid Ouerghemmi, Clément Mallet, Arnaud Le Bris, and Nesrine CHEHATA. Hyperspectral Imagery for Environmental Urban Planning. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2018, pages 1628–1631, Valencia, Spain, July 2018a. IEEE.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01854904">Christiane Weber, Rahim Aguejdad, X Briottet, J Avala, S. Fabre, J Demuynck, E Zenou, Y. Deville, M. Karoui, F Z Benhalouche, S Gadal, W Ourghemmi, C. Mallet, A. Le Bris, and N. Chehata. HYPERSPECTRAL IMAGERY FOR ENVIRONMENTAL URBAN PLANNING. In IGARSS 2018, Valencia, Spain, 2018b. </a></li> <li><a href="http://doi.org/10.5194/isprs-archives-XLII-1-W1-167-201">W. Ouerghemmi, A. Le Bris, Nesrine CHEHATA, and Clément Mallet. A two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, volume XLII-1/W1, pages 167–174, Hanover, Germany, May 2017. Copernicus GmbH (Copernicus Publications).</a></li> <li><a href="https://hal.inria.fr/hal-02384455">Christiane Weber, Sébastien GADAL, Xavier Briottet, and Clément Mallet. Apport de l’imagerie hyperspectrale pour la planification urbaine. In Karine Emsellem, Diego Moreno, Christine Voiron-Canicio, and Didier Josselin, editors, SAGEO 2016 - Spatial Analysis and Geomatics, Actes de la conférence SAGEO’2016 - Spatial Analysis and GEOmatics, pages 454–462, Nice, France, December 2016. </a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01359643">Gintautas Mozgeris, S ́ebastien Gadal, Donatas Jonikaviˇcius, Lina Straigyte, Walid Ouerghemmi, and Vytaut ̇e Juodkiene. Hyperspectral and color-infrared imaging from ultra-light aircraft: Potential to recognize tree species in urban environments. In University of California Los Angeles, editor, 8th Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pages 542–546, Los Angeles, United States, August 2016.</a></li> <li><a href="https://hal.inria.fr/hal-02384458">Alexandre Hervieu, Arnaud Le Bris, and Cl ́ement Mallet. Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas. In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, volume III-3, pages 457–464, Prague, Czech Republic, July 2016.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01888126">Christiane Weber, Thomas Houet, Sebastien GADAL, Rahim Aguejdad, Grzegorz Skupinski, Aziz Serradj, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Clément Mallet, and Arnaud Le Bris. ANR HYEP ANR 14-CE22-0016-01Hyperspectral imagery for Environmental urban Planning HyepProgramme Mobilité et systèmes urbains 2014. Research report, CNRS UMR TETIS, ESPACE, LETG ; ONERA ; GIPSA-lab ; IRAP ; IGN, October 2018c. </a></li> <li><a href="https://doi.org/10.1080/01431161.2019.1579937">Josselin Aval, Sophie Fabre, Emmanuel Zenou, David Sheeren, Mathieu Fauvel & Xavier Briottet (2019) Object-based fusion for urban tree species classification from hyperspectral, panchromatic and nDSM data, International Journal of Remote Sensing, 40:14, 5339-5365, DOI: 10.1080/01431161.2019.1579937 </a></li> <li><a href="https://doi.org/10.3390/rs11111269">Charlotte Brabant, Emilien Alvarez-Vanhard, Achour Laribi, Gwenaël Morin, Kim Thanh Nguyen et al. Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification Remote Sensing, MDPI, 2019, 11 (11), pp.1269. &lang;10.3390/rs11111269&rang; hal-02191084v1 </a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191363v1">C. Brabant, Emilien Alvarez-Vanhard, Gwenaël Morin, Thanh Ngoc Nguyen, Achour Laribi et al. Evaluation of dimensional reduction methods on urban vegetation classification performance using hyperspectral data IGARSS 2018, Jul 2018, Valencia, Spain halshs-02191363v1</a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191097v1">Charlotte Brabant, Emilien Alvarez-Vanhard, Thomas Houet. Improving the classification of urban tree diversity from Very High Spatial Resolution hyperspectral images: comparison of multiples techniques Joint Urban Remote Sensing Event (JURSE 2019), May 2019, Vannes, France halshs-02191097v1</a></li> </ul> This Dataset contains five research outputs of this project that were produced on the basis of Hyperspectral data obtained during an acquisition campaign led on Toulouse (France) urban area on July 2015 using Hyspex instrument which provides 408 spectral bands spread over 0.4 – 2.5 μ. Flight altitude lead to 2 m spatial resolution images. <ul> <li>Fields_samples.7z:  ESRI Shape Format.  Supervised SVN classification results for 600 urban trees according to a 3 level nomenclature: leaf type (5 classes), family (12 & 19 classes) and species (14 & 27 classes). The number of classes differ for the two latter as they depend on the minimum number of individuals considered (4 and 10 individuals per class respectively). Trees positions have been acquired using differential GPS and are given with centimetric to decimetric precision. A randomly selected subset of these trees has been used to train machine SVM and Random Forest classification algorithms. Those algorithms were applied to hyperspectral images using a number of classes for family (12 & 19 classes) and species (14 & 27 classes) levels defined according to the minimum number of individuals considered during training/validation process (4 and 10 individuals per class, respectively). Global classification precision for several training subsets is given by Brabant et al, 2019 (<a href="https://www.mdpi.com/470202">https://www.mdpi.com/470202</a>) in terms of averaged overall accuracy (AOA) and averaged kappa index of agreement (AKIA).</li> <li>HySPex-2m.7z: full hyperspectral VNIR-SWIR ENVI standard image obtained from the coregistration of both VNIR and SWIR ones through a signal aggregation process that allowed to obtain a synthetic VNIR 1.6 m spatial resolution image, with pixels exactly corresponding to natif SWIR image ones. First, a spatially resampled 1.6 m VNIR image was built, where output pixel values were calculated as the average of the VNIR 0.8 m pixel values that spatially contribute to it. Then, ground control points (GCP) were selected over both images and SWIR one was tied to the VNIR 1.6 m image using a bilinear resampling method using ENVI tool. This lead to a 1.6 m spatial resolution full VNIR-SWIR image.</li> <li>HYPXIM-4m.7z,  HYPXIM-8m.7z,  Sentinel2-10m.7z: hyperspectral ENVI standard simulated images. Spatial and spectral configurations generated correspond to ESA SENTINEL-2 instrument that was lunched on 2015, and HYPXIM sensor which was under study at that time. </li> </ul>

Dataset of crop yield and management in CS02 from Diverfarming project

Auxiliary data related to crop management and data of different crop yields in mandarin monoculture and mandarin diversified treatments during three crop cycles in case study 02 from Diverfarming project

UWB-IODA project: Datasets associated to WP2

The signal acquisition is carried out using an IR-UWB sensor module Xethru X4, from Novelda, Norway, having the following parameters: Output power                                  −12.6 dBm Center frequency                            8.748 GHz Pulse repetition frequency                  400 MHz Bandwidth (–10 dB)                           2.95 GHz Range resolution                                  6.4 mm Beamwidth                                            65° Staggered PRF sequence length          220 cycles No. of antenna arrays per radar chip    1 Tx & 1 Rx The transmitted signals reflected by the human back contain the information related to the motion caused by the human heart beats. The backscattered signals are then stored in the PC through a micro-USB cable. The distance of the human to the radar sensor is kept between 0.5 and 1m due to the short distance between the driver and the seat. During the experiments, the subjects were resting for most of the time and also talking and moving slightly their bodies for certain time intervals. The five data files corresponds to the five subjects involved in the experiments, having the following characteristics: Gender    Age (years)    Height (cm)    Weight (kg)     M                 33               182               72    M                 21               173               73    M                 28               174               77    F                  26               170               60    M                 24               165               62 All of these individuals were healthy and without any special health conditions or disabilities.

GERONTE H2020 project - GERDAT006 - Dataset of symptoms and proms for specific cancer types and gender

This dataset describes a series of symptoms, potentially indicative of treatment-related complications, destabilised comorbidity or functional decline, to be used in the Geronte project for symptoms monitoring in older patients with multimorbidity during and after their cancer treatment

GERONTE H2020 project - GERDAT004 - Dataset of self-management recommendations

The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of self-management recommendations that can be used in the care for older patients with cancer and multimorbidity. GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities. Patient empowerment by supporting self-management is an important component of the Geronte care pathway. As patients will be monitoring themselves at home, to register side-effects of treatment, decompensation of comorbidities and signs of functional decline, they will also be faced with questions about how to deal with the issues that they are having. While one important component of the care pathway is early signalling of complications to allow for early intervention by health care professionals, there is also a lot that patients can do for themselves at home to enhance their life-style, decrease burden of signs and symptoms or to improve outcomes. This led to the composition of a dataset to be included in the GERONTE care pathway, which is presented here.

GERONTE H2020 project - GERDAT003 - Core intrinsic capacity dataset

The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of intrinsic capacity/frailty data to be included and assessed in the evaluation of older patients with cancer and multimorbidity. GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities. An important component of the GerOnTe care pathway was to determine which intrinsic capacity/frailty information is need for optimizing treatment decision making and the subsequent care trajectory. We developed a list of comorbidities and intrinsic capacity/frailty itmes from literature and subsequently asked an expert panel to determine which of these were relevant for oncologic decision making and care. This led to the composition of a dataset to be included in the GERONTE care pathway, which is shown in this dataset. These data can to be included in the evaluation of older patients with cancer and multimorbidity to determine the feasibility of treatment and additional care needs

GERONTE H2020 Project - GERDAT002 - Composition of the health care professional consortium

The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of core health care professionals that should be involvoed in the evaluation and treatment trajectories of older patients with cancer and multimorbidity. GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities. An important component of the GerOnTe care pathway was to determine which health care professionals should be included in the health care professional consortium (HPC) providing care for the patient. Beforehand, we had considered the option of four core members and at least eight other participants depending on the patient’s specificities or profile. Based on clinical experience, we developed a list of 15 potential participants, including general practitioner, one or more oncology specialists (such as surgeons, medical oncologists, radiotherapists), geriatrician, oncology nurse, social worker, clinical pharmacist, physiotherapist, anaesthesiologist, home care nurse, dietician, occupational therapist, spiritual helpers/clerics, psychologist/psychiatrist, palliative care specialist, organ-specific physician(s) such as cardiologist, pulmonologist, nephrologist, rheumatologist etc. This list was presented to the expert panel, and they were asked to determine whether or not these participants should be involved in decision-making and/or the subsequent oncologic care trajectory; experts could specify if these participants should be involved for all patients, only in specific situations/profiles, or did not need to be involved. The results of this expert panel survey and the subsequent composition of the health care professional consortium forms the basis of this dataset.

Experimental Results of the REWIRE FED4FIRE+ Open Call (OC) 9 Project

This repository contains the detailed experimental results of the REWIRE "Experimenting with SDN-based Adaptable Non-IP Protocol Stacks in Smart-City Environments" project. This work has received funding from the EU's Horizon 2020 research and innovation programme through the 9th open call scheme of the FED4FIRE+ (grant agr. no 732638)

SOON Project presentation at CHIST-ERA Projects Seminar 2022 (March 28-30 2022)

Short video presentation proposed for CHIST-ERA Projects Seminar 2022 Video contest event - March 28-30 2022..

opencc-by-4.0May 2022View details →

773782 COASTAL EU Project: stream data from SW Messinia, Greece

In the framework of COASTAL Project (<a href="https://h2020-coastal.eu/">https://h2020-coastal.eu/</a>) , the Institute of Oceanography/HCMR coordinates the Multi-Actor Lab for the SW Messinia case study. The water quality of six small rivers was studied and evaluated to assess the environmental status of area. This kind of evaluation follows the European Water Framework Directive 2000/60/EU, which includes biotic and environmental characteristics, with emphasis on the Biological Quality Elements. Sampling and measurements were collected in seven periods, including macro-invertebrate fauna, physicochemical parameters and diatoms. The first one took place in October 2018, the second one in December 2018, the third in April 2019, the fourth in August 2019, the fifth in November - December 2019, the sixth in December 2020 and the last one in December 2021.

opencc-by-4.0May 2022View details →

773782 COASTAL EU Project: stream data from SW Messinia, Greece

opencc-by-4.0May 2022View details →

Data of Female members of National Federations Sport Governing Boards. Database GESPORT Project.

This database has been built by the authors. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain and the United Kingdom in 2018. With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP

opencc-by-4.0Jul 2021View details →

1000 Genomes Project Cleaned Dataset

The first four authors performed standard quality control analysis on the 1000 Genomes (1KG) Project genotypes that were generated on the Illumina Omni2.5M chip, at the Broad and Sanger Institutes. The datasets were then posted on the website of The Centre for Applied Genomics at Sick Kids Hospital at https://www.tcag.ca/tools/1000genomes.html. The last two authors then looked for overlap between those datasets and the Hapmap3 datasets that had gene expression for Endoplasmic Reticulum Aminopeptidase 2 (ERAP2), and chose the Yoruban from Ibadan, Nigeria (YRI) and Utah residents with Northern and Western European ancestry (CEU) subpopulations. These two subpopulations had the largest overlap between the 1KG and HapMap3 datasets, with 91 YRI and 104 CEU samples. The text files provided in this repository contain the IDs of all invidividuals and phenotypes for the labelled populations e.g. ERAP2_CEU_YRI_phenotypes.txt has phenotypes for both populations. The two *_pc_outliers.txt contain the IDs of the individuals excluded from analysis due to extraneous principal components. In summary, 88 YRI and 102 CEU individuals were included in the analysis.

Bathymetry 2015 - 1m - HBC Project

Digital Bathymetry data set., cell size 1 m×1m.

Optical Cluster set definitions associated with CERTO project deliverable 4.2

This set of files consists of pickle and csv files that describe the optical water class sets computed as part of the CERTO project ( https://certo-project.org ).  A written description and discussion of these clusters is provided in Deliverable 4.2 from the CERTO project.

Project Information Model resulting from the on-site survey

The On-site Analysis and Verification Service (ODAVS) is a service developed within the Encore project. It allows users to check any constructability issues regarding renovation projects of residential buildings by means of surveys facilitated by a mixed reality tool.  This dataset includes two IFC files of the renovation studies developed by Univpm and JEA (both partners of Encore project) at JEA experimental building in Caceres, and assessed on-site on 2021 December 14th and 15th. The dataset also includes an XML file containing the list of 33 URLS pointing to audio files previously published on a different Zenodo dataset [1]. Note that the access to the Zenodo dataset [1] is restricted. The interested users must ask the dataset authors for permission to access the dataset itself. In the XML file, for each comment, the GUID of the IFC object referred by the audio comment itself is given. References [1] "Pictures and Videos Collected During ODAVS Activity", by Carbonari A. and Vaccarini M., DOI 10.5281/zenodo.6531860, URL: https://doi.org/10.5281/zenodo.6531860

CSI-COP Dataset of Organisations to Approach in Citizen Science Projects

This dataset complements CSI-COP project deliverable D2.3 report: 'Framework for Engaging Citizen Scientists'. The D2.3 deliverable was produced in 2020 by partners in CSI-COP work package 2 led by Professor Olga Stepankova of Czech Technical University, Prague (CTU). The Stepankova et al. (2020) report is available on this Zenodo platform here: <a href="https://zenodo.org/record/4066515#.Yrx9DezMLb0">https://zenodo.org/record/4066515#.Yrx9DezMLb0</a>

D2.2 Open data concerning social inclusion provided on the project homepage - Emerging findings

Authors to the case posters and contributors from consortium partners are described in the deliverable.  The H2020 YouCount project runs from February 2021 to January 2024 and the consortium consists of 11 partners from nine European countries. Multiple case studies—consisting of 10 co-creative Y-CSS projects with young citizen scientists (YCS) aged between about 13-29 years old across nine countries in Europe—will provide knowledge about the positive drivers of social inclusion in general. The cases will further produce knowledge as well as innovations in relation to social participation, social belonging, and citizenship. In line with YouCount’s commitment to Open Science and Data Management based on the FAIR Principles, D2.2 provides a sample of open data concerning social inclusion from the research and innovation activities during the implementation period. The open data is based on informed consent and includes the following files included in the report: 1.      File 1 Homepage 30-06-22, Case descriptions. 2.      File 2 Case posters 08-06-2022, Experiences with inclusive co-creative Y-CSS in multiple case study.        3.      File 3 Narrative text 26-06-22, Experiences with developing the YouCount app toolkit, methodology. 4.      File 4 Quotes 25-06-22, Views and experiences with social inclusion of youths, YouCount/ECSA WG EIE webinars 2021 and             YouCount newsletters 2022. 5.      File 5 Links to YouCount app toolkit, 28-06-22, Youths’ views and experiences with social inclusion opportunities, observations. Notably, the open data are based on a co-creative and flexible research design and comes in an early phase of the case studies. They can thus only be used as emerging data and preliminary findings. Still, the data contain valuable information of the research experiences and voices from young people found in the early phase of conducting hands on co-creative Y-CSS. More systematic open social inclusion data will be provided later in the project.    The open data can also be found at the project website <a href="https://www.youcountproject.eu/">Home - YouCount - Social Citizen Science (youcountproject.eu)</a>. Note! They are shared under CC-BY (text) and CC-BY-ND (images case posters) due to confidentiality issues.