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Data, Volume 7, Issue 8 (August 2022) – 17 articles

Cover Story (view full-size image): Hyperspectral imaging is an innovative and versatile technology for non-invasive mapping. Robust processing workflows are required to ensure its wide usage. We present an open-source hypercloud dataset that captures the geology of the Black Angel Mountain (Greenland), alongside a detailed and interactive tutorial. This contribution relies on progress made on the correction, interpretation and integration of hyperspectral data. The fusion of hyperspectral scans with 3D point cloud representations opens up new possibilities for the mapping of complex natural targets. Spectroscopic and machine learning tools allow for the rapid and accurate characterization of geological structures in 3D. Users train themselves or test new algorithms with this dataset and the associated tools. View this paper
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5 pages, 686 KiB  
Editorial
A European Approach to the Establishment of Data Spaces
by Marco Minghini, Alexander Kotsev and Carlos Granell
Data 2022, 7(8), 118; https://doi.org/10.3390/data7080118 - 19 Aug 2022
Cited by 3 | Viewed by 2398
Abstract
Within a context defined by the rapid increase in the availability of data, combined with the complexity of data sources, infrastructures, technologies and actors involved in data sharing flows, the European Union (EU) is devising approaches that can reap the benefits of data-driven [...] Read more.
Within a context defined by the rapid increase in the availability of data, combined with the complexity of data sources, infrastructures, technologies and actors involved in data sharing flows, the European Union (EU) is devising approaches that can reap the benefits of data-driven innovation [...] Full article
(This article belongs to the Special Issue A European Approach to the Establishment of Data Spaces)
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12 pages, 3156 KiB  
Data Descriptor
Climate Dataset for South Africa by the Agricultural Research Council
by Mokhele Edmond Moeletsi, Lindumusa Myeni, Ludwig Christian Kaempffer, Derick Vermaak, Gert de Nysschen, Chrisna Henningse, Irene Nel and Dudley Rowswell
Data 2022, 7(8), 117; https://doi.org/10.3390/data7080117 - 17 Aug 2022
Cited by 2 | Viewed by 3247
Abstract
Long-term, reliable, continuous and real-time weather and climatic data are essential for efficient management and sustainable use of natural resources. This paper describes the weather station network (WSN) of the Agricultural Research Council (ARC) of South Africa, including information on instrumentation, data retrieval [...] Read more.
Long-term, reliable, continuous and real-time weather and climatic data are essential for efficient management and sustainable use of natural resources. This paper describes the weather station network (WSN) of the Agricultural Research Council (ARC) of South Africa, including information on instrumentation, data retrieval and processing protocols, calibration and maintenance protocols, as well as applications of the collected data. To this end, the WSN of the ARC consists of over 600 automatic weather stations that are distributed across the country to cover a wide range of agro-climatic zones. At each weather station, air temperature, rainfall, relative humidity, solar irradiance, wind speed and direction are monitored and archived on an hourly basis. The main objective of this WSN is to archive climate information for South Africa as well as supply the agricultural community with weather data to support decision-making. Full article
(This article belongs to the Special Issue 2nd Edition of Data in Astrophysics & Geophysics: Research and Applications)
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16 pages, 30804 KiB  
Data Descriptor
Vertical Jump Data from Inertial and Optical Motion Tracking Systems
by Mateo Rico-Garcia, Juan Botero-Valencia and Ruber Hernández-García
Data 2022, 7(8), 116; https://doi.org/10.3390/data7080116 - 17 Aug 2022
Viewed by 2141
Abstract
Motion capture (MOCAP) is a widely used technique to record human, animal, and object movement for various applications such as animation, biomechanical assessment, and control systems. Different systems have been proposed based on diverse technologies, such as visible light cameras, infrared cameras with [...] Read more.
Motion capture (MOCAP) is a widely used technique to record human, animal, and object movement for various applications such as animation, biomechanical assessment, and control systems. Different systems have been proposed based on diverse technologies, such as visible light cameras, infrared cameras with passive or active markers, inertial systems, or goniometer-based systems. Each system has pros and cons that make it usable in different scenarios. This paper presents a dataset that combines Optical Motion and Inertial Systems, capturing a well-known sports movement as the vertical jump. As a reference system, the optical motion capture consists of six Flex 3 Optitrack cameras with 100 FPS. On the other hand, we developed an inertial system consisting of seven custom-made devices based on the IMU MPU-9250, which includes a three-axis magnetometer, accelerometer and gyroscope, and an embedded Digital Motion Processor (DMP) attached to a microcontroller mounted on a Teensy 3.2 with an ARM Cortex-M4 processor with wireless operation using Bluetooth. The purpose of taking IMU data with a low-cost and customized system is the deployment of applications that can be performed with similar hardware and can be adjusted to different areas. The developed measurement system is flexible, and the acquisition format and enclosure can be customized. The proposed dataset comprises eight jumps recorded from four healthy humans using both systems. Experimental results on the dataset show two usage examples for measuring joint angles and COM position. The proposed dataset is publicly available online and can be used in comparative algorithms, biomechanical studies, skeleton reconstruction, sensor fusion techniques, or machine learning models. Full article
(This article belongs to the Section Information Systems and Data Management)
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10 pages, 3948 KiB  
Data Descriptor
Description and Use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images
by Miguel Vera, Antonio Bravo and Rubén Medina
Data 2022, 7(8), 115; https://doi.org/10.3390/data7080115 - 16 Aug 2022
Cited by 1 | Viewed by 1643
Abstract
The World Health Organization indicates the top cause of death is heart disease. These diseases can be detected using several imaging modalities, especially cardiac computed tomography (CT), whose images have imperfections associated with noise and certain artifacts. To minimize the impact of these [...] Read more.
The World Health Organization indicates the top cause of death is heart disease. These diseases can be detected using several imaging modalities, especially cardiac computed tomography (CT), whose images have imperfections associated with noise and certain artifacts. To minimize the impact of these imperfections on the quality of the CT images, several researchers have developed digital image processing techniques (DPIT) by which the quality is evaluated considering several metrics and databases (DB), both real and simulated. This article describes the processes that made it possible to generate and utilize six three-dimensional synthetic cardiac DBs or voxels-based numerical phantoms. An exhaustive analysis of the most relevant features of images of the left ventricle, belonging to a real CT DB of the human heart, was performed. These features are recreated in the synthetic DBs, generating a reference phantom or ground truth free of imperfections (DB1) and five phantoms, in which Poisson noise (DB2), stair-step artifact (DB3), streak artifact (DB4), both artifacts (DB5) and all imperfections (DB6) are incorporated. These DBs can be used to determine the performance of DPIT, aimed at decreasing the effect of these imperfections on the quality of cardiac images. Full article
(This article belongs to the Topic Complex Data Analytics and Computing with Real-World Applications)
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9 pages, 8316 KiB  
Data Descriptor
An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China
by Lei Li, Chong Xu, Zhiqiang Yang, Zhongjian Zhang and Mingsheng Lv
Data 2022, 7(8), 114; https://doi.org/10.3390/data7080114 - 15 Aug 2022
Cited by 7 | Viewed by 1930
Abstract
Landslides are a typical geological hazard that endangers people’s lives and property in the Loess Plateau. The destructiveness of large-scale landslides, in particular, is incalculable. For example, traffic disruptions, river blockages, and house collapses may all result from landslides. Thus, it is urgent [...] Read more.
Landslides are a typical geological hazard that endangers people’s lives and property in the Loess Plateau. The destructiveness of large-scale landslides, in particular, is incalculable. For example, traffic disruptions, river blockages, and house collapses may all result from landslides. Thus, it is urgent to compile a complete inventory of landslides in a specific region. The investigation object of this study is Baoji City, Shaanxi Province, China. Using the multi-temporal high-resolution remote sensing images from Google Earth, we preliminarily completed the cataloging of large-scale (area > 5000 m2) landslides in the study area through visual interpretation. The inventory was subsequently compared with the existing literature and hazard records for improvement and supplement. We identified 3422 landslides with a total area of 360.7 km2 and an average area of 105,400 m2 for each individual landslide. The largest landslide had an area of 1.71 km2, while the smallest one was 6042 m2. In previous studies, we analyzed these data without describing the data sources in detail. We now provide a shared dataset of each landslide in shp format, containing geographic location, boundary information, etc. The dataset is significantly useful for understanding the distribution characteristics of large-scale landslides in this region. Moreover, it can serve as basic data for the study of paleolandslide resurrection. Full article
(This article belongs to the Special Issue 2nd Edition of Data in Astrophysics & Geophysics: Research and Applications)
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38 pages, 1102 KiB  
Article
Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons
by Asma Dhaouadi, Khadija Bousselmi, Mohamed Mohsen Gammoudi, Sébastien Monnet and Slimane Hammoudi
Data 2022, 7(8), 113; https://doi.org/10.3390/data7080113 - 12 Aug 2022
Cited by 11 | Viewed by 6684
Abstract
The extract, transform, and load (ETL) process is at the core of data warehousing architectures. As such, the success of data warehouse (DW) projects is essentially based on the proper modeling of the ETL process. As there is no standard model for the [...] Read more.
The extract, transform, and load (ETL) process is at the core of data warehousing architectures. As such, the success of data warehouse (DW) projects is essentially based on the proper modeling of the ETL process. As there is no standard model for the representation and design of this process, several researchers have made efforts to propose modeling methods based on different formalisms, such as unified modeling language (UML), ontology, model-driven architecture (MDA), model-driven development (MDD), and graphical flow, which includes business process model notation (BPMN), colored Petri nets (CPN), Yet Another Workflow Language (YAWL), CommonCube, entity modeling diagram (EMD), and so on. With the emergence of Big Data, despite the multitude of relevant approaches proposed for modeling the ETL process in classical environments, part of the community has been motivated to provide new data warehousing methods that support Big Data specifications. In this paper, we present a summary of relevant works related to the modeling of data warehousing approaches, from classical ETL processes to ELT design approaches. A systematic literature review is conducted and a detailed set of comparison criteria are defined in order to allow the reader to better understand the evolution of these processes. Our study paints a complete picture of ETL modeling approaches, from their advent to the era of Big Data, while comparing their main characteristics. This study allows for the identification of the main challenges and issues related to the design of Big Data warehousing systems, mainly involving the lack of a generic design model for data collection, storage, processing, querying, and analysis. Full article
(This article belongs to the Section Information Systems and Data Management)
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8 pages, 5829 KiB  
Data Descriptor
MultimodalGasData: Multimodal Dataset for Gas Detection and Classification
by Parag Narkhede, Rahee Walambe, Pulkit Chandel, Shruti Mandaokar and Ketan Kotecha
Data 2022, 7(8), 112; https://doi.org/10.3390/data7080112 - 12 Aug 2022
Cited by 3 | Viewed by 4425
Abstract
The detection of gas leakages is a crucial aspect to be considered in the chemical industries, coal mines, home applications, etc. Early detection and identification of the type of gas is required to avoid damage to human lives and the environment. The MultimodalGasData [...] Read more.
The detection of gas leakages is a crucial aspect to be considered in the chemical industries, coal mines, home applications, etc. Early detection and identification of the type of gas is required to avoid damage to human lives and the environment. The MultimodalGasData presented in this paper is a novel collection of simultaneous data samples taken using seven different gas-detecting sensors and a thermal imaging camera. The low-cost sensors are generally less sensitive and less reliable; hence, they are unable to detect the gases from a longer distance. A thermal camera that can sense the temperature changes is also used while collecting the present multimodal dataset to overcome the drawback of using only the sensors for detecting gases. This multimodal dataset has a total of 6400 samples, including 1600 samples per class for smoke, perfume, a mixture of smoke and perfume, and a neutral environment. The dataset is helpful for the researchers and system developers to develop and train the state-of-the-art artificial intelligence models and systems. Full article
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7 pages, 2926 KiB  
Data Descriptor
Tropical Wood Species Recognition: A Dataset of Macroscopic Images
by Daniel Alejandro Cano Saenz, Carlos Felipe Ordoñez Urbano, Holman Raul Gaitan Mesa and Rubiel Vargas-Cañas
Data 2022, 7(8), 111; https://doi.org/10.3390/data7080111 - 11 Aug 2022
Cited by 3 | Viewed by 2045
Abstract
Forests are of incalculable value due to the ecosystem services they provide to humanity such as carbon storage, climate regulation and participation in the hydrological cycle. The threat to forests grows as the population increases and the activities that are carried out in [...] Read more.
Forests are of incalculable value due to the ecosystem services they provide to humanity such as carbon storage, climate regulation and participation in the hydrological cycle. The threat to forests grows as the population increases and the activities that are carried out in it, such as: cattle rearing, illegal trafficking, deforestation and harvesting. Moreover, the environmental authorities do not have sufficient capacity to exercise strict control over wood production due to the vast variety of timber species within the countries, the lack of tools to verify timber species in the supply chain and the limited available and labelled digital data of the forest species. This paper presents a set of digital macroscopic images of eleven tropical forest species, which can be used as support at checkpoints, to carry out studies and research based on macroscopic analysis of cross-sectional images of tree species such as: dendrology, forestry, as well as algorithms of artificial intelligence. Images were acquired in wood warehouses with a digital magnifying glass following a protocol used by the Colombian Ministry of Environment, as well as the USA Forest Services and the International Association of Wood Anatomists. The dataset contains more than 8000 images with resolution of 640 × 480 pixels which includes 3.9 microns per pixel, and an area of (2.5 × 1.9) square millimeters where the anatomical features are exposed. The dataset presents great usability for academics and researchers in the forestry sector, wood anatomists and personnel who work with computational models, without neglecting forest surveillance institutions such as regional autonomous corporations and the Ministry of the Environment. Full article
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10 pages, 2979 KiB  
Data Descriptor
Grapevine Plant Image Dataset for Pruning
by Kyriakos D. Apostolidis, Theofanis Kalampokas, Theodore P. Pachidis and Vassilis G. Kaburlasos
Data 2022, 7(8), 110; https://doi.org/10.3390/data7080110 - 09 Aug 2022
Cited by 4 | Viewed by 2648
Abstract
Grapevine pruning is conducted during winter, and it is a very important and expensive task for wine producers managing their vineyard. During grapevine pruning every year, the past year’s canes should be removed and should provide the possibility for new canes to grow [...] Read more.
Grapevine pruning is conducted during winter, and it is a very important and expensive task for wine producers managing their vineyard. During grapevine pruning every year, the past year’s canes should be removed and should provide the possibility for new canes to grow and produce grapes. It is a difficult procedure, and it is not yet fully automated. However, some attempts have been made by the research community. Based on the literature, grapevine pruning automation is approximated with the help of computer vision and image processing methods. Despite the attempts that have been made to automate grapevine pruning, the task remains hard for the abovementioned domains. The reason for this is that several challenges such as cane overlapping or complex backgrounds appear. Additionally, there is no public image dataset for this problem which makes it difficult for the research community to approach it. Motivated by the above facts, an image dataset is proposed for grapevine canes’ segmentation for a pruning task. An experimental analysis is also conducted in the proposed dataset, achieving a 67% IoU and 78% F1 score in grapevine cane semantic segmentation with the U-net model. Full article
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16 pages, 339 KiB  
Data Descriptor
A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave
by Nirmalya Thakur
Data 2022, 7(8), 109; https://doi.org/10.3390/data7080109 - 04 Aug 2022
Cited by 17 | Viewed by 4921
Abstract
The COVID-19 Omicron variant, reported to be the most immune-evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, [...] Read more.
The COVID-19 Omicron variant, reported to be the most immune-evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations related to online learning in the form of tweets. Mining such tweets to develop a dataset can serve as a data resource for different applications and use-cases related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore, this work presents a large-scale, open-access Twitter dataset of conversations about online learning from different parts of the world since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management. The paper also briefly outlines some potential applications in the fields of Big Data, Data Mining, Natural Language Processing, and their related disciplines, with a specific focus on online learning during this Omicron wave that may be studied, explored, and investigated by using this dataset. Full article
(This article belongs to the Special Issue Data Mining and Computational Intelligence for E-learning and Education)
38 pages, 15102 KiB  
Article
Go Wild for a While? A Bibliometric Analysis of Two Themes in Tourism Demand Forecasting from 1980 to 2021: Current Status and Development
by Yuruixian Zhang, Wei Chong Choo, Yuhanis Abdul Aziz, Choy Leong Yee and Jen Sim Ho
Data 2022, 7(8), 108; https://doi.org/10.3390/data7080108 - 31 Jul 2022
Cited by 1 | Viewed by 2239
Abstract
Despite the fact that the concept of forecasting has emerged in the realm of tourism, studies delving into this sector have yet to provide a comprehensive overview of the evolution of tourism forecasting visualization. This research presents an analysis of the current state-of-the-art [...] Read more.
Despite the fact that the concept of forecasting has emerged in the realm of tourism, studies delving into this sector have yet to provide a comprehensive overview of the evolution of tourism forecasting visualization. This research presents an analysis of the current state-of-the-art tourism demand forecasting (TDF) and combined tourism demand forecasting (CTDF) systems. Based on the Web of Science Core Collection database, this study built a framework for bibliometric analysis from these fields in three distinct phases (1980–2021). Furthermore, the VOSviewer analysis software was employed to yield a clearer picture of the current status and developments in tourism forecasting research. Descriptive analysis and comprehensive knowledge network mappings using approaches such as co-citation analysis and cooperation networking were employed to identify trending research topics, the most important countries/regions, institutions, publications, and articles, and the most influential researchers. The results yielded demonstrate that scientific output pertaining to TDF exceeds the output pertaining to CTDF. However, there has been a substantial and exponential increase in both situations over recent years. In addition, the results indicated that tourism forecasting research has become increasingly diversified, with numerous combined methods presented. Furthermore, the most influential papers and writers were evaluated based on their citations, publications, network position, and relevance. The contemporary themes were also analyzed, and obstacles to the expansion of the literature were identified. This is the first study on two topics to demonstrate the ways in which bibliometric visualization can assist researchers in gaining perspectives in the tourism forecasting field by effectively communicating key findings, facilitating data exploration, and providing valuable data for future research. Full article
(This article belongs to the Topic Complex Data Analytics and Computing with Real-World Applications)
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17 pages, 5231 KiB  
Article
Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data
by Nelson Mileu, Nuno M. Costa, Eduarda M. Costa and André Alves
Data 2022, 7(8), 107; https://doi.org/10.3390/data7080107 - 31 Jul 2022
Cited by 6 | Viewed by 2010
Abstract
The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 [...] Read more.
The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobility. Full article
(This article belongs to the Special Issue Health Informatics in the Age of COVID-19)
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13 pages, 2937 KiB  
Article
An Evaluation of the OpenWeatherMap API versus INMET Using Weather Data from Two Brazilian Cities: Recife and Campina Grande
by Anwar Musah, Livia Màrcia Mosso Dutra, Aisha Aldosery, Ella Browning, Tercio Ambrizzi, Iuri Valerio Graciano Borges, Merve Tunali, Selma Başibüyük, Orhan Yenigün, Giselle Machado Magalhaes Moreno, Ana Clara Gomes da Silva, Wellington Pinheiro dos Santos, Clarisse Lins de Lima, Tiago Massoni, Kate Elizabeth Jones, Luiza Cintra Campos and Patty Kostkova
Data 2022, 7(8), 106; https://doi.org/10.3390/data7080106 - 30 Jul 2022
Cited by 8 | Viewed by 2983
Abstract
Certain weather conditions are inadvertently related to increased population of various mosquitoes. In order to predict the burden of mosquito populations in the Global South, it is imperative to integrate weather-related risk factors into such predictive models. There are a lot of online [...] Read more.
Certain weather conditions are inadvertently related to increased population of various mosquitoes. In order to predict the burden of mosquito populations in the Global South, it is imperative to integrate weather-related risk factors into such predictive models. There are a lot of online open-source weather platforms that provide historical, current and future weather forecasts which can be utilised for general predictions, and these electronic sources serve as an alternate option for weather data when physical weather stations are inaccessible (or inactive). Before using data from such online source, it is important to assess the accuracy against some baseline measure. In this paper, we therefore evaluated the accuracy and suitability of weather forecasts of two parameters namely temperature and humidity from the OpenWeatherMap API (an online weather platform) and compared them with actual measurements collected from the Brazilian weather stations (INMET). The evaluation was focused on two Brazilian cites, namely, Recife and Campina Grande. The intention is to prepare an early warning model which will harness data from OpenWeatherMap API for mosquito prediction. Full article
(This article belongs to the Special Issue 2nd Edition of Data in Astrophysics & Geophysics: Research and Applications)
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28 pages, 3029 KiB  
Article
Populating the Data Space for Cultural Heritage with Heritage Digital Twins
by Franco Niccolucci, Achille Felicetti and Sorin Hermon
Data 2022, 7(8), 105; https://doi.org/10.3390/data7080105 - 29 Jul 2022
Cited by 22 | Viewed by 4916
Abstract
The present paper concerns the design of the semantic infrastructure of the data space for cultural heritage as envisaged by the European Commission in its recent documents. Due to the complexity of the cultural heritage data and of their intrinsic inter-relationships, it is [...] Read more.
The present paper concerns the design of the semantic infrastructure of the data space for cultural heritage as envisaged by the European Commission in its recent documents. Due to the complexity of the cultural heritage data and of their intrinsic inter-relationships, it is necessary to introduce a novel ontology, yet compliant with existing standards and interoperable with previous platforms used in this context as Europeana. The data space organization must be tailored to the methods and the theory of cultural heritage, briefly summarized in the introduction. The new ontology is based on the Digital Twin concept, i.e., the digital counterpart of cultural heritage assets incorporating all the digital information pertaining to them. This creates a Knowledge Base on the cultural heritage data space. The paper outlines the main features of the proposed Heritage Digital Twin ontology and provides some examples of its application. Future work will include completing the ontology in all its details and testing it in other real cases and with the various sectors of the cultural heritage community. Full article
(This article belongs to the Special Issue A European Approach to the Establishment of Data Spaces)
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15 pages, 4765 KiB  
Data Descriptor
Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland
by Sandra Lorenz, Sam T. Thiele, Moritz Kirsch, Gabriel Unger, Robert Zimmermann, Pierpaolo Guarnieri, Nigel Baker, Erik Vest Sørensen, Diogo Rosa and Richard Gloaguen
Data 2022, 7(8), 104; https://doi.org/10.3390/data7080104 - 28 Jul 2022
Cited by 3 | Viewed by 2751
Abstract
Hyperspectral imaging is an innovative technology for non-invasive mapping, with increasing applications in many sectors. As with any novel technology, robust processing workflows are required to ensure a wide use. We present an open-source hypercloud dataset capturing the complex but spectacularly well exposed [...] Read more.
Hyperspectral imaging is an innovative technology for non-invasive mapping, with increasing applications in many sectors. As with any novel technology, robust processing workflows are required to ensure a wide use. We present an open-source hypercloud dataset capturing the complex but spectacularly well exposed geology from the Black Angel Mountain in Maarmorilik, West Greenland, alongside a detailed and interactive tutorial documenting relevant processing workflows. This contribution relies on very recent progress made on the correction, interpretation and integration of hyperspectral data in earth sciences. The possibility to fuse hyperspectral scans with 3D point cloud representations (hyperclouds) has opened up new possibilities for the mapping of complex natural targets. Spectroscopic and machine learning tools allow or the rapid and accurate characterization of geological structures in a 3D environment. Potential users can use this exemplary dataset and the associated tools to train themselves or test new algorithms. As the data and the tools have a wide range of application, we expect this contribution to benefit the scientific community at large. Full article
(This article belongs to the Special Issue 2nd Edition of Data in Astrophysics & Geophysics: Research and Applications)
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8 pages, 1173 KiB  
Data Descriptor
Dataset for Estimated Closures of Scallop (Pecten maximus) Production Areas Due to Phycotoxin Contamination along the French Coasts of the Eastern English Channel
by Sarra Chenouf, Mathieu Merzereaud, Pascal Raux and José Antonio Pérez Agúndez
Data 2022, 7(8), 103; https://doi.org/10.3390/data7080103 - 27 Jul 2022
Cited by 1 | Viewed by 1576
Abstract
Commercial bans due to harmful algal blooms (HABs), which are natural events, question the sustainability of human activities in marine and coastal areas. A risk assessment of these bans is important to support decision-making to better manage and mitigate their impacts. However, data [...] Read more.
Commercial bans due to harmful algal blooms (HABs), which are natural events, question the sustainability of human activities in marine and coastal areas. A risk assessment of these bans is important to support decision-making to better manage and mitigate their impacts. However, data are sparse and difficult to collect. The dataset presented in this paper includes “estimated closures of scallop fishing areas” due to HAB toxicity along the French coasts of the English Channel. The closure data were simulated for each scallop (Pecten maximus) fishing area through an algorithm applied to the in situ dataset from the French monitoring network REPHYTOX. The methodology of the production of closure data consists of comparing phycotoxin concentration in scallop to regulatory thresholds of phycotoxins, and then, simulating the number and duration of closures based on the monitoring strategies and closure mechanisms as defined in the regulations. These data only cover closures related to regulatory threshold exceedances of phycotoxins in shellfish. Closures induced by the lack of sampling or other reasons (e.g., failures in toxin analysis) are not included in the dataset because of the lack of information. Data are produced during the scallop fishing season. Facing the non-existence of such a closure database due to the lack of centralized management of local closure decrees, this dataset can be used to analyse the management strategies to deal with HABs and to highlight the governance challenges related to these strategies. It is also useful to study the link between the ecological and the socioeconomic dimensions of HABs, and to describe how toxin concentrations in shellfish translate into socioeconomic impacts and management challenges. This methodology can be applied to other species, other areas and other economic activities. Full article
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8 pages, 1697 KiB  
Data Descriptor
Dataset of Indicators for the Assessment of Ecosystem Services Affected by Agricultural Soil Management
by Carsten Paul, Cenk Donmez, Petra Koeppe, James S. Robinson and Sonja Barnickel
Data 2022, 7(8), 102; https://doi.org/10.3390/data7080102 - 22 Jul 2022
Viewed by 1766
Abstract
Ecosystem services represent an important concept for assessing the sustainability of agricultural management. However, in practical applications, it can be difficult to find indicators suitable for specific services or specific spatial scales. In order to create a toolbox of indicators for assessing the [...] Read more.
Ecosystem services represent an important concept for assessing the sustainability of agricultural management. However, in practical applications, it can be difficult to find indicators suitable for specific services or specific spatial scales. In order to create a toolbox of indicators for assessing the actual or potential supply of ecosystem services in the context of agricultural land and soil management, we conducted a keyword-based literature review in Web of Science Core Collection and SCOPUS, using the terms ecosystem service AND indicator AND agricultur*. The search was performed in January 2019 and was restricted to journal articles written in English. After eliminating duplicates, we identified 180 articles, out of which 121 met our selection criteria. We extracted information on addressed ecosystem services and indicators which used a full-text review. Where studies used ecosystem service definitions other than the Common International Classification of Ecosystem Services (CICES V.5.1), indicators were assigned to the corresponding CICES class or classes. We used the information derived from the review to create factsheets for 37 ecosystem services. Each factsheet provides tables with available indicators applicable at multiple spatial scales that range from field to global, information on the type of input data required, and a reference to the article or articles that the indicator was taken from. The dataset provides a toolbox for researchers to find indicators that fit their respective research needs. Full article
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