A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

        1 2 3 | Last   [Sort by number of followers]   [Restore default list]

  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 426 journals)
Showing 1 - 200 of 265 Journals sorted alphabetically
AAS Open Research     Open Access   (Followers: 2)
ABC Journal of Advanced Research     Open Access  
Academic Voices : A Multidisciplinary Journal     Open Access   (Followers: 2)
Accountability in Research: Policies and Quality Assurance     Hybrid Journal   (Followers: 20)
Acta Materialia Transilvanica     Open Access  
Acta Nova     Open Access   (Followers: 1)
Acta Scientifica Malaysia     Open Access   (Followers: 1)
Acta Scientifica Naturalis     Open Access   (Followers: 3)
Adıyaman University Journal of Science     Open Access  
Advanced Science     Open Access   (Followers: 13)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 10)
Advanced Theory and Simulations     Hybrid Journal   (Followers: 5)
Advances in Research     Open Access  
Advances in Science and Technology     Full-text available via subscription   (Followers: 17)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 8)
Afrique Science : Revue Internationale des Sciences et Technologie     Open Access   (Followers: 2)
AFRREV STECH : An International Journal of Science and Technology     Open Access   (Followers: 4)
American Academic & Scholarly Research Journal     Open Access   (Followers: 6)
American Journal of Applied Sciences     Open Access   (Followers: 27)
American Journal of Humanities and Social Sciences     Open Access   (Followers: 14)
ANALES de la Universidad Central del Ecuador     Open Access   (Followers: 3)
Anales del Instituto de la Patagonia     Open Access  
Applied Mathematics and Nonlinear Sciences     Open Access   (Followers: 1)
Apuntes de Ciencia & Sociedad     Open Access  
Arab Journal of Basic and Applied Sciences     Open Access  
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives Internationales d'Histoire des Sciences     Partially Free   (Followers: 6)
Archives of Current Research International     Open Access  
ARO. The Scientific Journal of Koya University     Open Access  
ARPHA Conference Abstracts     Open Access   (Followers: 6)
ARPHA Proceedings     Open Access   (Followers: 4)
ArtefaCToS : Revista de estudios sobre la ciencia y la tecnología     Open Access   (Followers: 1)
Asia-Pacific Journal of Science and Technology     Open Access  
Asian Journal of Advanced Research and Reports     Open Access   (Followers: 2)
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 2)
Asian Journal of Scientific Research     Open Access   (Followers: 3)
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 7)
Australian Field Ornithology     Full-text available via subscription   (Followers: 4)
Australian Journal of Social Issues     Hybrid Journal   (Followers: 7)
Avances en Ciencias e Ingeniería     Open Access  
AZimuth     Full-text available via subscription   (Followers: 2)
Bangladesh Journal of Scientific Research     Open Access   (Followers: 1)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 3)
Berichte Zur Wissenschaftsgeschichte     Hybrid Journal   (Followers: 10)
Berkeley Scientific Journal     Full-text available via subscription  
BIBECHANA     Open Access   (Followers: 2)
BibNum     Open Access  
Bilge International Journal of Science and Technology Research     Open Access   (Followers: 1)
Bioethics Research Notes     Full-text available via subscription   (Followers: 16)
Bistua : Revista de la Facultad de Ciencias Básicas     Open Access  
BJHS Themes     Open Access  
Black Sea Journal of Engineering and Science     Open Access  
Borneo Journal of Resource Science and Technology     Open Access  
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Bulletin de la Société Royale des Sciences de Liège     Open Access  
Bulletin of the National Research Centre     Open Access  
Butlletí de la Institució Catalana d'Història Natural     Open Access  
Central European Journal of Clinical Research     Open Access  
Chain Reaction     Full-text available via subscription  
Ciencia & Natura     Open Access   (Followers: 1)
Ciencia Amazónica (Iquitos)     Open Access   (Followers: 1)
Ciencia en Desarrollo     Open Access   (Followers: 2)
Ciencia en su PC     Open Access   (Followers: 1)
Ciencia Ergo Sum     Open Access  
Ciência ET Praxis     Open Access  
Ciencia y Tecnología     Open Access  
Ciencia, Docencia y Tecnología     Open Access  
Ciencias Holguin     Open Access   (Followers: 2)
CienciaUAT     Open Access   (Followers: 1)
Citizen Science : Theory and Practice     Open Access   (Followers: 2)
Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering     Open Access  
Communications in Applied Sciences     Open Access  
Comprehensive Therapy     Hybrid Journal   (Followers: 3)
Comunicata Scientiae     Open Access   (Followers: 1)
ConCiencia     Open Access  
Conference Papers in Science     Open Access   (Followers: 2)
Configurations     Full-text available via subscription   (Followers: 10)
COSMOS     Hybrid Journal  
Crea Ciencia Revista Científica     Open Access   (Followers: 2)
Cuadernos de Investigación UNED     Open Access  
Current Issues in Criminal Justice     Hybrid Journal   (Followers: 15)
Current Research in Geoscience     Open Access   (Followers: 8)
Dalat University Journal of Science     Open Access  
Data     Open Access   (Followers: 3)
Data Curation Profiles Directory     Open Access   (Followers: 4)
Dhaka University Journal of Science     Open Access  
Dharmakarya     Open Access  
Diálogos Interdisciplinares     Open Access  
Digithum     Open Access   (Followers: 2)
Discover Sustainability     Open Access   (Followers: 3)
Einstein (São Paulo)     Open Access  
Ekaia : EHUko Zientzia eta Teknologia aldizkaria     Open Access  
Elkawnie : Journal of Islamic Science and Technology     Open Access  
Emergent Scientist     Open Access  
Enhancing Learning in the Social Sciences     Open Access   (Followers: 9)
Enseñanza de las Ciencias : Revista de Investigación y Experiencias Didácticas     Open Access  
Entramado     Open Access  
Entre Ciencia e Ingeniería     Open Access   (Followers: 1)
Epiphany     Open Access   (Followers: 4)
Episteme Transversalis     Open Access  
Ergo     Open Access  
Estação Científica (UNIFAP)     Open Access   (Followers: 1)
Ethiopian Journal of Education and Sciences     Open Access   (Followers: 6)
Ethiopian Journal of Science and Technology     Open Access  
Ethiopian Journal of Sciences and Sustainable Development     Open Access   (Followers: 6)
European Online Journal of Natural and Social Sciences     Open Access   (Followers: 12)
European Scientific Journal     Open Access   (Followers: 10)
Evidência - Ciência e Biotecnologia - Interdisciplinar     Open Access  
Exchanges : the Warwick Research Journal     Open Access   (Followers: 2)
Experimental Results     Open Access  
Extensionismo, Innovación y Transferencia Tecnológica     Open Access   (Followers: 3)
Facets     Open Access  
Fides et Ratio : Revista de Difusión Cultural y Científica     Open Access   (Followers: 1)
Fırat University Turkish Journal of Science & Technology     Open Access  
Fontanus     Open Access  
Forensic Science Policy & Management: An International Journal     Hybrid Journal   (Followers: 372)
Frontiers for Young Minds     Open Access  
Frontiers in Climate     Open Access   (Followers: 3)
Frontiers in Science     Open Access   (Followers: 1)
Futures & Foresight Science     Hybrid Journal   (Followers: 4)
Gaudium Sciendi     Open Access   (Followers: 1)
Gazi University Journal of Science     Open Access  
Ghana Studies     Full-text available via subscription   (Followers: 15)
Global Journal of Pure and Applied Sciences     Full-text available via subscription  
Global Journal of Science Frontier Research     Open Access   (Followers: 2)
Globe, The     Full-text available via subscription   (Followers: 4)
HardwareX     Open Access  
Heidelberger Jahrbücher Online     Open Access  
Heliyon     Open Access  
Himalayan Journal of Science and Technology     Open Access   (Followers: 1)
History of Science and Technology     Open Access  
Hoosier Science Teacher     Open Access  
Iberoamerican Journal of Science Measurement and Communication     Open Access  
Impact     Open Access   (Followers: 2)
Indian Journal of History of Science     Hybrid Journal  
Indonesian Journal of Fundamental Sciences     Open Access  
Indonesian Journal of Science and Mathematics Education     Open Access   (Followers: 4)
Indonesian Journal of Science and Technology     Open Access  
Ingenieria y Ciencia     Open Access   (Followers: 1)
Innovare : Revista de ciencia y tecnología     Open Access  
Instruments     Open Access  
Integrated Research Advances     Open Access  
Interciencia     Open Access   (Followers: 1)
Interface Focus     Full-text available via subscription  
International Annals of Science     Open Access  
International Archives of Science and Technology     Open Access  
International Journal of Academic Research in Business, Arts & Science     Open Access   (Followers: 2)
International Journal of Advanced Multidisciplinary Research and Review     Open Access  
International Journal of Advancement in Education and Social Sciences     Open Access   (Followers: 1)
International Journal of Advances in Engineering, Science and Technology     Open Access   (Followers: 3)
International Journal of Applied Science     Open Access  
International Journal of Basic and Applied Sciences     Open Access   (Followers: 4)
International Journal of Computational and Experimental Science and Engineering (IJCESEN)     Open Access  
International Journal of Culture and Modernity     Open Access  
International Journal of Engineering, Science and Technology     Open Access  
International Journal of Innovation and Applied Studies     Open Access   (Followers: 12)
International Journal of Innovative Research and Scientific Studies     Open Access   (Followers: 6)
International Journal of Innovative Research in Social and Natural Sciences     Open Access   (Followers: 2)
International Journal of Network Science     Hybrid Journal   (Followers: 3)
International Journal of Recent Contributions from Engineering, Science & IT     Open Access   (Followers: 1)
International Journal of Research in Science     Open Access   (Followers: 2)
International Journal of Science & Emerging Technologies     Open Access   (Followers: 1)
International Journal of Sciences : Basic and Applied Research     Open Access  
International Journal of Social Sciences and Management     Open Access   (Followers: 3)
International Journal of Technology Policy and Law     Hybrid Journal   (Followers: 7)
International Letters of Social and Humanistic Sciences     Open Access   (Followers: 1)
International Review of Applied Sciences     Open Access  
InterSciencePlace     Open Access   (Followers: 1)
Investiga : TEC     Open Access  
Investigación Joven     Open Access  
Investigación Valdizana     Open Access  
Investigacion y Ciencia     Open Access   (Followers: 1)
Iranian Journal of Science and Technology, Transactions A : Science     Hybrid Journal  
iScience     Open Access   (Followers: 1)
Issues in Science & Technology     Free   (Followers: 7)
Istituto Lombardo - Accademia di Scienze e Lettere - Rendiconti di Scienze     Open Access  
Ithaca : Viaggio nella Scienza     Open Access  
J : Multidisciplinary Scientific Journal     Open Access  
Jaunujų mokslininkų darbai     Open Access  
Journal de la Recherche Scientifique de l'Universite de Lome     Full-text available via subscription   (Followers: 2)
Journal for New Generation Sciences     Open Access   (Followers: 3)
Journal of Chromatography & Separation Techniques     Open Access   (Followers: 12)
Journal of Advanced Research     Open Access   (Followers: 3)
Journal of Al-Qadisiyah for Pure Science     Open Access   (Followers: 1)
Journal of Alasmarya University     Open Access  
Journal of Analytical Science & Technology     Open Access   (Followers: 6)
Journal of Applied Science and Technology     Full-text available via subscription   (Followers: 1)
Journal of Applied Sciences and Environmental Management     Open Access   (Followers: 3)
Journal of Big History     Open Access   (Followers: 3)
Journal of Composites Science     Open Access   (Followers: 3)
Journal of Critical Thought and Praxis     Open Access   (Followers: 2)
Journal of Deliberative Mechanisms in Science     Open Access  
Journal of Diversity Management     Open Access   (Followers: 6)
Journal of Indian Council of Philosophical Research     Hybrid Journal  
Journal of Institute of Science and Technology     Open Access  
Journal of Integrated Science and Technology     Open Access  
Journal of Interaction Science     Open Access   (Followers: 1)
Journal of Kerbala University     Open Access   (Followers: 1)
Journal of King Saud University - Science     Open Access   (Followers: 1)
Journal of Law, Information and Science     Full-text available via subscription   (Followers: 20)

        1 2 3 | Last   [Sort by number of followers]   [Restore default list]

Similar Journals
Journal Cover
Data
Number of Followers: 3  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2306-5729
Published by MDPI Homepage  [233 journals]
  • Data, Vol. 6, Pages 54: Data on the Quantification of Aspartate, GABA and
           Glutamine Levels in the Spinal Cord of Larval Sea Lampreys after a
           Complete Spinal Cord Injury

    • Authors: Blanca Fernández-López, Natividad Pereiro, Anunciación Lafuente, María Celina Rodicio, Antón Barreiro-Iglesias
      First page: 54
      Abstract: We used high-performance liquid chromatography (HPLC) methods to quantify aspartate, GABA, and glutamine levels in the spinal cord of larval sea lampreys following a complete spinal cord injury. Mature larval sea lampreys recover spontaneously from a complete spinal cord transection and the changes in neurotransmitter systems after spinal cord injury might be related to their amazing regenerative capabilities. The data presented here show the concentration of the aminoacidergic neurotransmitters GABA (and its precursor glutamine) and aspartate in the spinal cord of control (non-injured) and 2-, 4-, and 10-week post-lesion animals. Statistical analyses showed that GABA and aspartate levels significantly increase in the spinal cord four weeks after a complete spinal cord injury and that glutamine levels decrease 10 weeks after injury as compared to controls. These data might be of interest to those studying the role of neurotransmitters and neuromodulators in recovery from spinal cord injury in vertebrates.
      Citation: Data
      PubDate: 2021-05-24
      DOI: 10.3390/data6060054
      Issue No: Vol. 6, No. 6 (2021)
       
  • Data, Vol. 6, Pages 55: Machine Learning-Based Algorithms to Knowledge
           Extraction from Time Series Data: A Review

    • Authors: Giuseppe Ciaburro, Gino Iannace
      First page: 55
      Abstract: To predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time series represents a time sequence of numerical values observed in the past at a measurable variable. The values are sampled at equidistant time intervals, according to an appropriate granular frequency, such as the day, week, or month, and measured according to physical units of measurement. In machine learning-based algorithms, the information underlying the knowledge is extracted from the data themselves, which are explored and analyzed in search of recurring patterns or to discover hidden causal associations or relationships. The prediction model extracts knowledge through an inductive process: the input is the data and, possibly, a first example of the expected output, the machine will then learn the algorithm to follow to obtain the same result. This paper reviews the most recent work that has used machine learning-based techniques to extract knowledge from time series data.
      Citation: Data
      PubDate: 2021-05-25
      DOI: 10.3390/data6060055
      Issue No: Vol. 6, No. 6 (2021)
       
  • Data, Vol. 6, Pages 56: Automation of Work Processes and Night Work

    • Authors: Urška Kosem, Mirko Markič, Annmarie Gorenc Zoran
      First page: 56
      Abstract: Background: Automation of production processes is not just a simple replacement of a person in production, but it should lead to the success of an organization and contribute to the sustainable development of society and the natural environment. The aim of our study was to find out whether the level of automation of production processes affects the proportion of night work hours of production workers and whether employers are willing to automate production processes to achieve a lower number of night work hours. Methods: We used a quantitative approach to collect primary data through the survey method. The questionnaire was completed by 502 large and medium-sized manufacturing companies in Slovenia. Results: We found no statistically significant correlation between the level of automation of production processes and the percentage of night work hours of production workers. We also found that the reduction of the proportion of night work does not appear to be the main motivator for the introduction of automation of production processes. Conclusions: Based on the results, we rejected the assumption that automation of production processes has a direct impact on the proportion of night work. Moreover, our study will benefit all those who are concerned with the automation of production processes and night work.
      Citation: Data
      PubDate: 2021-05-26
      DOI: 10.3390/data6060056
      Issue No: Vol. 6, No. 6 (2021)
       
  • Data, Vol. 6, Pages 57: Dataset for the Solar Incident Radiation and
           Electricity Production BIPV/BAPV System on the Northern/Southern Façade
           in Dense Urban Areas

    • Authors: Hassan Gholami, Harald Nils Røstvik
      First page: 57
      Abstract: The prosperous implementation of Building Integrated Photovoltaics (BIPV), as well as Building Attached Photovoltaics (BAPV), needs an accurate and detailed assessment of the potential of solar irradiation and electricity production of various commercialised technologies in different orientations on the outer skins of the building. This article presents a dataset for the solar incident radiation and electricity production of PV systems in the north and south orientations in a dense urban area (in the northern hemisphere). The solar incident radiation and the electricity production of two back-to-back PV panels with a ten-centimetre gap for one year are monitored and logged as primary data sources. Using Microsoft Excel, both panels’ efficiency is also presented as a secondary source of data. The implemented PV panels are composed of polycrystalline silicon cells with an efficiency of 16.9%. The results depicted that the actual efficiency of the south-facing panel (13–15%) is always closer to the standard efficiency of the panel compared to the actual efficiency of the north-facing panel (8–12%). Moreover, although the efficiency of the south-facing panel on sunny days of the year is almost constant, the efficiency of the north-facing panel decreases significantly in winter. This phenomenon might be linked to the spectral response of the polycrystalline silicon cells and different incident solar radiation spectrum on the panels. While the monitored data cover the radiation and system electricity production in various air conditions, the analysis is mainly conducted for sunny days, and more investigation is needed to analyse the system performance in other weather conditions (like cloudy and overcast skies). The presented database could be used to analyse the performance of polycrystalline silicon PV panels and their operational efficiency in a dense urban area and for different orientations.
      Citation: Data
      PubDate: 2021-05-26
      DOI: 10.3390/data6060057
      Issue No: Vol. 6, No. 6 (2021)
       
  • Data, Vol. 6, Pages 58: A Large-Scale Dataset of Barley, Maize and Sorghum
           Variety Identification Using DNA Fingerprinting in Ethiopia

    • Authors: Frederic Kosmowski, Alemayehu Ambel, Asmelash Tsegay, Alemayehu Negawo, Jason Carling, Andrzej Kilian, The Central Statistics Agency
      First page: 58
      Abstract: The data described in this paper were part of a large-scale nationally representative household survey, the Ethiopian Socioeconomic Survey (ESS 2018/19). Grain samples of barley, maize and sorghum were collected in six regions in Ethiopia. Variety identification was assessed by matching samples to a reference library composed of released improved materials, using approximately 50,000 markers from DArTseq platforms. This data were part of a study documenting the reach of CGIAR-related germplasms in Ethiopia. These objective measures of crop varietal adoption, unique in the public domain, can be analyzed along with a large set of variables related to agro-ecologies, household characteristics and plot management practices, available in the Ethiopian Socioeconomic Survey 2018/19.
      Citation: Data
      PubDate: 2021-06-03
      DOI: 10.3390/data6060058
      Issue No: Vol. 6, No. 6 (2021)
       
  • Data, Vol. 6, Pages 59: APIs for EU Governments: A Landscape Analysis on
           Policy Instruments, Standards, Strategies and Best Practices

    • Authors: Lorenzino Vaccari, Monica Posada, Mark Boyd, Mattia Santoro
      First page: 59
      Abstract: Application Programming Interfaces (APIs) could greatly facilitate the exchange of data and functionalities between software applications in a flexible, controlled and secure way, especially on the web. Private companies, from startups to enterprises, have been using APIs for several years now, but it is only recently that APIs have seen increased interest in the public sector. API adoption in the public sector faces organisational, technical, legal and economic obstacles, and to overcome these barriers, proposed methods from the private sector and early adopters in the public sector provide a way forward. The available documentation is often sparse, difficult to find and to reuse for new contexts. No past efforts to collect and analyse these resources have been made. To address this shortcoming, this paper describes a landscape analysis in four areas: the main European Commission policy instruments on the adoption of APIs, the available web API standards, a set of European government API strategies and cases, and a list of government proposed methods distilled from more than 3900 documents. Our results reveal that European policy legislation and associated instruments promote, and in some cases mandate, the use of APIs, and that governments’ API strategies in the European Union are rather young but also that there are well known web APIs standards and proposed methods ready to support the digital transformation of governments through rapid, harmonised and successful adoption of APIs.
      Citation: Data
      PubDate: 2021-06-08
      DOI: 10.3390/data6060059
      Issue No: Vol. 6, No. 6 (2021)
       
  • Data, Vol. 6, Pages 60: Information Quality Assessment for Data Fusion
           Systems

    • Authors: Miguel A. Becerra, Catalina Tobón, Andrés Eduardo Castro-Ospina, Diego H. Peluffo-Ordóñez
      First page: 60
      Abstract: This paper provides a comprehensive description of the current literature on data fusion, with an emphasis on Information Quality (IQ) and performance evaluation. This literature review highlights recent studies that reveal existing gaps, the need to find a synergy between data fusion and IQ, several research issues, and the challenges and pitfalls in this field. First, the main models, frameworks, architectures, algorithms, solutions, problems, and requirements are analyzed. Second, a general data fusion engineering process is presented to show how complex it is to design a framework for a specific application. Third, an IQ approach, as well as the different methodologies and frameworks used to assess IQ in information systems are addressed; in addition, data fusion systems are presented along with their related criteria. Furthermore, information on the context in data fusion systems and its IQ assessment are discussed. Subsequently, the issue of data fusion systems’ performance is reviewed. Finally, some key aspects and concluding remarks are outlined, and some future lines of work are gathered.
      Citation: Data
      PubDate: 2021-06-08
      DOI: 10.3390/data6060060
      Issue No: Vol. 6, No. 6 (2021)
       
  • Data, Vol. 6, Pages 61: A Framework Using Contrastive Learning for
           Classification with Noisy Labels

    • Authors: Madalina Ciortan, Romain Dupuis, Thomas Peel
      First page: 61
      Abstract: We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies, such as pseudo-labeling, sample selection with Gaussian Mixture models, and weighted supervised contrastive learning have, been combined into a fine-tuning phase following the pre-training. In this paper, we provide an extensive empirical study showing that a preliminary contrastive learning step brings a significant gain in performance when using different loss functions: non robust, robust, and early-learning regularized. Our experiments performed on standard benchmarks and real-world datasets demonstrate that: (i) the contrastive pre-training increases the robustness of any loss function to noisy labels and (ii) the additional fine-tuning phase can further improve accuracy, but at the cost of additional complexity.
      Citation: Data
      PubDate: 2021-06-09
      DOI: 10.3390/data6060061
      Issue No: Vol. 6, No. 6 (2021)
       
  • Data, Vol. 6, Pages 62: Measurements of LoRaWAN Technology in Urban
           Scenarios: A Data Descriptor

    • Authors: Pavel Masek, Martin Stusek , Ekaterina Svertoka , Jan Pospisil, Radim Burget, Elena Simona Lohan, Ion Marghescu , Jiri Hosek, Aleksandr Ometov
      First page: 62
      Abstract: This work is a data descriptor paper for measurements related to various operational aspects of LoRaWAN communication technology collected in Brno, Czech Republic. This paper also provides data characterizing the long-term behavior of the LoRaWAN channel collected during the two-month measurement campaign. It covers two measurement locations, one at the university premises, and the second situated near the city center. The dataset’s primary goal is to provide the researchers lacking LoRaWAN devices with an opportunity to compare and analyze the information obtained from 303 different outdoor test locations transmitting to up to 20 gateways operating in the 868 MHz band in a varying metropolitan landscape. To collect the data, we developed a prototype equipped with a Microchip RN2483 Low-Power Wide-Area Network (LPWAN) LoRaWAN technology transceiver module for the field measurements. As an example of data utilization, we showed the Signal-to-noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) in relation to the closest gateway distance.
      Citation: Data
      PubDate: 2021-06-10
      DOI: 10.3390/data6060062
      Issue No: Vol. 6, No. 6 (2021)
       
  • Data, Vol. 6, Pages 43: Data for Interaction Diagrams of Geopolymer FRC
           Slender Columns with Double-Layer GFRP and Steel Reinforcement

    • Authors: Mohammad AlHamaydeh, Fouad Amin
      First page: 43
      Abstract: This article provides data of axial load-bending moment capacities of plain and fiber-reinforced geopolymer concrete (GPC, FRGPC) columns. The columns were reinforced by double layers of longitudinal and transverse reinforcement using steel and/or glass-fiber-reinforced polymer (GFRP) bars. The concrete fiber-reinforcing materials included steel and synthetic fibers. The columns data included different parameters like the longitudinal reinforcement ratio, the applied load eccentricity, and the columns’ slenderness ratio. The data was collected from different analysis output files then sorted and tabulated in usable formatted tables. The data can support the development of design axial load-bending moment interactions. In addition, further processing of the data can yield analytical strength curves which are useful in determining the columns stability under different structural loading configurations. Researchers and educators can make use of these data for illustrations and prospective new research suggestions.
      Citation: Data
      PubDate: 2021-04-26
      DOI: 10.3390/data6050043
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 44: Collection of a Bacterial Community Reconstructed
           from Marine Metagenomes Derived from Jinhae Bay, South Korea

    • Authors: Jae-Hyun Lim, Il-Nam Kim
      First page: 44
      Abstract: Marine bacteria are known to play significant roles in marine biogeochemical cycles regarding the decomposition of organic matter. Despite the increasing attention paid to the study of marine bacteria, research has been too limited to fully elucidate the complex interaction between marine bacterial communities and environmental variables. Jinhae Bay, the study area in this work, is the most anthropogenically eutrophied coastal bay in South Korea, and while its physical and biogeochemical characteristics are well described, less is known about the associated changes in microbial communities. In the present study, we reconstructed a metagenomics data based on the 16S rRNA gene to investigate temporal and vertical changes in microbial communities at three depths (surface, middle, and bottom) during a seven-month period from June to December 2016 at one sampling site (J1) in Jinhae Bay. Of all the bacterial data, Proteobacteria, Bacteroidetes, and Cyanobacteria were predominant from June to November, whereas Firmicutes were predominant in December, especially at the middle and bottom depths. These results show that the composition of the microbial community is strongly associated with temporal changes. Furthermore, the community compositions were markedly different between the surface, middle, and bottom depths in summer, when water column stratification and bottom water hypoxia (low dissolved oxygen level) were strongly developed. Metagenomics data contribute to improving our understanding of important relationships between environmental characteristics and microbial community change in eutrophication-induced and deoxygenated coastal areas.
      Citation: Data
      PubDate: 2021-04-26
      DOI: 10.3390/data6050044
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 45: Data from Smartphones and Wearables

    • Authors: Joaquín Torres-Sospedra, Aleksandr Ometov
      First page: 45
      Abstract: Wearables are wireless devices that we “wear” on our bodies [...]
      Citation: Data
      PubDate: 2021-04-28
      DOI: 10.3390/data6050045
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 46: IntelliRehabDS (IRDS)—A Dataset of Physical
           Rehabilitation Movements

    • Authors: Alina Miron, Noureddin Sadawi, Waidah Ismail, Hafez Hussain, Crina Grosan
      First page: 46
      Abstract: In this article, we present a dataset that comprises different physical rehabilitation movements. The dataset was captured as part of a research project intended to provide automatic feedback on the execution of rehabilitation exercises, even in the absence of a physiotherapist. A Kinect motion sensor camera was used to record gestures. The dataset contains repetitions of nine gestures performed by 29 subjects, out of which 15 were patients and 14 were healthy controls. The data are presented in an easily accessible format, provided as 3D coordinates of 25 body joints along with the corresponding depth map for each frame. Each movement was annotated with the gesture type, the position of the person performing the gesture (sitting or standing) as well as a correctness label. The data are publicly available and were released with to provide a comprehensive dataset that can be used for assessing the performance of different patients while performing simple movements in a rehabilitation setting and for comparing these movements with a control group of healthy individuals.
      Citation: Data
      PubDate: 2021-04-30
      DOI: 10.3390/data6050046
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 47: Industry 4.0 and Proactive Works Council Members

    • Authors: Mari Božič, Annmarie Gorenc Zoran, Matej Jevšček
      First page: 47
      Abstract: Background: Integrating Industry 4.0 technologies in organizations affects employees’ workplaces and working conditions. Works Council members play an essential role in this because as intermediaries of information between employees and management, they increase mutual trust and help introduce changes in the work environment. This article discusses the Works Council members’ autopoietic endowments that are necessary for their proactive activity, which we discuss as building blocks for creating constructive relationships with management and quality energy in an organization. As such, we were interested in examining whether the autopoietic endowments of Works Council members influenced the type of relationship with the Works Council and management, and whether this relationship affected Works Council members’ organizational energy. Methods: A questionnaire was developed, piloted and distributed to Works Council Members, and 220 completed questionnaires were returned. Results: We found that the higher the level of self-awareness, the better the relationship between Works Council members and management. Moreover, poor energy represented poor relationships, and poor relationships signified a higher degree of resigned inertia and corrosive energy. Conclusions: Our research provides managements with insights into the relationship between employees and management, and the quality of their organizational energy.
      Citation: Data
      PubDate: 2021-04-30
      DOI: 10.3390/data6050047
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 48: Designing Knowledge Sharing System for Statistical
           Activities in BPS-Statistics Indonesia

    • Authors: Dana Indra Sensuse, Viktor Suwiyanto, Sofian Lusa, Arfive Gandhi, Muhammad Mishbah, Damayanti Elisabeth
      First page: 48
      Abstract: Statistics of Indonesia’s (BPS) performance are not optimal since there is a lack of integration among business processes. This has resulted in unsynchronized data, unstandardized business processes, and inefficient IT investment. To encourage more qualified and integrated business processes, BPS should optimize the knowledge sharing process (KSP) among government employees in statistical areas. This study designed a Knowledge Sharing System (KSS) to facilitate KSP in BPS towards knowledge sharing improvement. The KSS manifested a hypothesis that the design of qualified knowledge management can facilitate an organization to overcome the lack of integration among business processes. Hence, BPS can avoid repetitive mistakes, improve work efficiency, and reduce the risk of failure. This study generated a business process-oriented KSS by combining soft system methodology with the B-KIDE (Business process-oriented Knowledge Infrastructure Development) Framework. It delivered research artifacts (a rich picture, CATWOE analysis (costumer, actor, transformation, weltanschauung, owner, environment), and conceptual model) to capture eight mechanisms of knowledge, map them into the knowledge process, and define the applicable technology. The KSS model has perceived a score of 0.40 using the Kappa formula that indicates the stakeholders’ acceptance. Therefore, BPS can leverage a qualified KSS towards the integrated business processes statistically while the hypothesis was accepted.
      Citation: Data
      PubDate: 2021-05-12
      DOI: 10.3390/data6050048
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 49: Factors That Affect E-Learning Platforms after the
           Spread of COVID-19: Post Acceptance Study

    • Authors: Rana Saeed Al-Maroof, Khadija Alhumaid, Iman Akour, Said Salloum
      First page: 49
      Abstract: The fear of vaccines has led to population rejection due to various reasons. Students have had their own inquiries towards the effectiveness of the vaccination, which leads to vaccination hesitancy. Vaccination hesitancy can affect students’ perception, hence, acceptance of e-learning platforms. Therefore, this research attempts to explore the post-acceptance of e-learning platforms based on a conceptual model that has various variables. Each variable contributes differently to the post-acceptance of the e-learning platform. The research investigates the moderating role of vaccination fear on the post-acceptance of e-learning platforms among students. Thus, the study aims at exploring students’ perceptions about their post-acceptance of e-learning platforms where vaccination fear functions as a moderator. The current study depends on an online questionnaire that is composed of 29 items. The total number of respondents is 630. The collected data was implemented to test the study model and the proposed constructs and hypotheses depending on the Smart PLS Software. Fear of vaccination has a significant impact on the acceptance of e-learning platforms, and it is a strong mediator in the conceptual model. The findings indicate a positive effect of the fear of vaccination as a mediator in the variables: perceived ease of use and usefulness, perceived daily routine, perceived critical mass and perceived self-efficiency. The implication gives a deep insight to take effective steps in reducing the level of fear of vaccination, supporting the vaccination confidence among educators, teachers and students who will, in turn, affect the society as a whole.
      Citation: Data
      PubDate: 2021-05-12
      DOI: 10.3390/data6050049
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 50: Dataset on the Effects of Anti-Insect Nets of
           Different Porosity on Mineral and Organic Acids Profile of Cucurbita pepo
           L. Fruits and Leaves

    • Authors: Luigi Formisano, Michele Ciriello, Christophe El-Nakhel, Stefania De Pascale, Youssef Rouphael
      First page: 50
      Abstract: The growing interest in healthy foods has driven the agricultural sector towards eco-friendly implementation to manage biotic and abiotic factors in protected environments. In this perspective, anti-insect nets are an effective tool for controlling harmful insect populations concomitantly with reducing chemicals’ interference. However, the low porosity of nets necessary to ensure high exclusion efficiency for a designated insect leads to reduced airflow, impacting the productivity and quality attributes of vegetables. The evidence presented in this dataset pertains to the content of total nitrogen, minerals (i.e., NO3, K, PO4, SO4, Ca, Mg, Cl, and Na), and organic acids (i.e., malate and citrate) of zucchini squash (Cucurbita pepo L. cv. Zufolo F1) in leaves and fruits grown with two anti-insect nets with different porosities (Biorete® 50 mesh and Biorete® 50 mesh AirPlus), is and analyzed by the Kjeldahl method and ion chromatography (ICS3000), respectively. Data of total nitrogen concentration, macronutrients, and organic acids provide in-depth information about plants’ physiological response to microclimate changes induced by anti-insect nets.
      Citation: Data
      PubDate: 2021-05-13
      DOI: 10.3390/data6050050
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 51: LeLePhid: An Image Dataset for Aphid Detection and
           Infestation Severity on Lemon Leaves

    • Authors: Jorge Parraga-Alava, Roberth Alcivar-Cevallos, Jéssica Morales Carrillo, Magdalena Castro, Shabely Avellán, Aaron Loor, Fernando Mendoza
      First page: 51
      Abstract: Aphids are small insects that feed on plant sap, and they belong to a superfamily called Aphoidea. They are among the major pests causing damage to citrus crops in most parts of the world. Precise and automatic identification of aphids is needed to understand citrus pest dynamics and management. This article presents a dataset that contains 665 healthy and unhealthy lemon leaf images. The latter are leaves with the presence of aphids, and visible white spots characterize them. Moreover, each image includes a set of annotations that identify the leaf, its health state, and the infestation severity according to the percentage of the affected area on it. Images were collected manually in real-world conditions in a lemon plant field in Junín, Manabí, Ecuador, during the winter, by using a smartphone camera. The dataset is called LeLePhid: lemon (Le) leaf (Le) image dataset for aphid (Phid) detection and infestation severity. The data can facilitate evaluating models for image segmentation, detection, and classification problems related to plant disease recognition.
      Citation: Data
      PubDate: 2021-05-17
      DOI: 10.3390/data6050051
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 52: The Modern Greek Language on the Social Web: A
           Survey of Data Sets and Mining Applications

    • Authors: Maria Nefeli Nikiforos, Yorghos Voutos, Anthi Drougani, Phivos Mylonas, Katia Lida Kermanidis
      First page: 52
      Abstract: Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.
      Citation: Data
      PubDate: 2021-05-17
      DOI: 10.3390/data6050052
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 53: Recursive Genetic Micro-Aggregation Technique:
           Information Loss, Disclosure Risk and Scoring Index

    • Authors: Ebaa Fayyoumi, Omar Alhuniti
      First page: 53
      Abstract: This research investigates the micro-aggregation problem in secure statistical databases by integrating the divide and conquer concept with a genetic algorithm. This is achieved by recursively dividing a micro-data set into two subsets based on the proximity distance similarity. On each subset the genetic operation “crossover” is performed until the convergence condition is satisfied. The recursion will be terminated if the size of the generated subset is satisfied. Eventually, the genetic operation “mutation” will be performed over all generated subsets that satisfied the variable group size constraint in order to maximize the objective function. Experimentally, the proposed micro-aggregation technique was applied to recommended real-life data sets. Results demonstrated a remarkable reduction in the computational time, which sometimes exceeded 70% compared to the state-of-the-art. Furthermore, a good equilibrium value of the Scoring Index (SI) was achieved by involving a linear combination of the General Information Loss (GIL) and the General Disclosure Risk (GDR).
      Citation: Data
      PubDate: 2021-05-20
      DOI: 10.3390/data6050053
      Issue No: Vol. 6, No. 5 (2021)
       
  • Data, Vol. 6, Pages 35: A Sentinel-2 Dataset for Uganda

    • Authors: Jonas Ardö
      First page: 35
      Abstract: Earth observation data provide useful information for the monitoring and management of vegetation- and land-related resources. The Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) was used to download, process and composite Sentinel-2 data from 2018–2020 for Uganda. Over 16,500 Sentinel-2 data granules were downloaded and processed from top of the atmosphere reflectance to bottom of the atmosphere reflectance and higher-level products, totalling > 9 TB of input data. The output data include the number of clear sky observations per year, the best available pixel composite per year and vegetation indices (mean of EVI and NDVI) per quarter. The study intention was to provide analysis-ready data for all of Uganda from Sentinel-2 at 10 m spatial resolution, allowing users to bypass some basic processing and, hence, facilitate environmental monitoring.
      Citation: Data
      PubDate: 2021-03-30
      DOI: 10.3390/data6040035
      Issue No: Vol. 6, No. 4 (2021)
       
  • Data, Vol. 6, Pages 36: Targeted Chemometrics Investigations of Source-,
           Age- and Gender-Dependencies of Oral Cavity Malodorous Volatile Sulphur
           Compounds

    • Authors: Kerry L. Grootveld, Victor Ruiz-Rodado, Martin Grootveld
      First page: 36
      Abstract: Halitosis is a highly distressing, socially unaesthetic condition, with a very high incidence amongst the adult population. It predominantly arises from excessive oral cavity volatile sulphur compound (VSC) concentrations, which have either oral or extra-oral etiologies (90–95% and 5–10% of cases, respectively). However, reports concerning age- and gender-related influences on the patterns and concentrations of these malodorous agents remain sparse; therefore, this study’s first objective was to explore the significance and impact of these potential predictor variables on the oral cavity levels of these malodorants. Moreover, because non-oral etiologies for halitosis may represent avatars of serious extra-oral diseases, the second objective was to distinguish between etiology- (source-) dependent patterns of oral cavity VSCs. Oral cavity VSC determinations were performed on 116 healthy human participants using a non-stationary gas chromatographic facility, and following a 4 h period of abstention from all non-respiratory oral activities. Participants were grouped according to ages or age bands, and gender. Statistical analyses of VSC level data acquired featured both univariate/correlation and multivariate (MV) approaches. Factorial analysis-of-variance and MV analyses revealed that the levels of all VSCs monitored were independent of both age and gender. Principal component analysis (PCA) and a range of further MV analysis techniques, together with an agglomerative hierarchal clustering strategy, demonstrated that VSC predictor variables were partitioned into two components, the first arising from orally-sourced H2S and CH3SH, the second from extra-orally-sourced (CH3)2S alone (about 55% and 30% of total variance respectively). In conclusion, oral cavity VSC concentrations appear not to be significantly influenced by age and gender. Furthermore, (CH3)2S may serve as a valuable biomarker for selected extra-oral conditions.
      Citation: Data
      PubDate: 2021-04-06
      DOI: 10.3390/data6040036
      Issue No: Vol. 6, No. 4 (2021)
       
  • Data, Vol. 6, Pages 37: FastFix Albatross Data: Snapshots of Raw GPS L-1
           Data from Southern Royal Albatross

    • Authors: Timothy C. A. Molteno, Keith W. Payne
      First page: 37
      Abstract: This dataset contains 4-millisecond snapshots of the GPS radio spectrum stored by wildlife tracking tags deployed on adult Southern Royal Albatross (Diomedea epomophora) in New Zealand. Approximately 60,000 snapshots were recovered from nine birds over two southern-hemisphere summers in 2012 and 2013. The data can be post-processed using snapshot positioning algorithms, and are made available as a test dataset for further development of these algorithms. Included are post-processed position estimates for reference, as well as test data from stationary tags positioned under various test conditions for the purposes of characterizing tag performance.
      Citation: Data
      PubDate: 2021-04-07
      DOI: 10.3390/data6040037
      Issue No: Vol. 6, No. 4 (2021)
       
  • Data, Vol. 6, Pages 38: Hand-Washing Video Dataset Annotated According to
           the World Health Organization’s Hand-Washing Guidelines

    • Authors: Martins Lulla, Aleksejs Rutkovskis, Andreta Slavinska, Aija Vilde, Anastasija Gromova, Maksims Ivanovs, Ansis Skadins, Roberts Kadikis, Atis Elsts
      First page: 38
      Abstract: Washing hands is one of the most important ways to prevent infectious diseases, including COVID-19. The World Health Organization (WHO) has published hand-washing guidelines. This paper presents a large real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Pauls Stradins Clinical University Hospital. There are 3185 hand-washing episodes in total, each of which is annotated by up to seven different persons. The annotations classify the washing movements according to the WHO guidelines by marking each frame in each video with a certain movement code. The intention of this “in-the-wild” dataset is two-fold: to serve as a basis for training machine-learning classifiers for automated hand-washing movement recognition and quality control, and to allow to investigation of the real-world quality of washing performed by working medical staff. We demonstrate how the data can be used to train a machine-learning classifier that achieves classification accuracy of 0.7511 on a test dataset.
      Citation: Data
      PubDate: 2021-04-07
      DOI: 10.3390/data6040038
      Issue No: Vol. 6, No. 4 (2021)
       
  • Data, Vol. 6, Pages 39: Exploring Inner-City Residents’ and
           Foreigners’ Commitment to Improving Air Pollution: Evidence from a Field
           Survey in Hanoi, Vietnam

    • Authors: Quan-Hoang Vuong, Tri Vu Phu, Tuyet-Anh T. Le, Quy Van Khuc
      First page: 39
      Abstract: Solutions for mitigating and reducing environmental pollution are important priorities for many developed and developing countries. This study was conducted to better understand the degree to which inner-city citizens and foreigners perceive air pollution and respond to it, particularly how much they willingly contribute to improving air quality in Vietnam, a lower-middle-income nation in Southeast Asia. During mid-December 2019, a stratified random sampling technique and a contingent valuation method (CVM) were employed to survey 199 inhabitants and 75 foreigners who reside and travel within the inner-city of Hanoi. The data comprises four major groups of information on: (1) perception of air pollution and its impacts, (2) preventive measures used to mitigate polluted air, (3) commitments on willingness-to-pay (WTP) for reducing air pollution alongside reasons for the yes-or-no-WTP decision, and (4) demographic information of interviewees. The findings and data of this study could offer many policy implications for better environmental management in the study area and beyond.
      Citation: Data
      PubDate: 2021-04-10
      DOI: 10.3390/data6040039
      Issue No: Vol. 6, No. 4 (2021)
       
  • Data, Vol. 6, Pages 40: Isolation of Microsatellite Markers from De Novo
           Whole Genome Sequences of Coptotermes gestroi (Wasmann) (Blattodea:
           Rhinotermitidae)

    • Authors: Li Yang Lim, Shawn Cheng, Abdul Hafiz Ab Majid
      First page: 40
      Abstract: Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae) is a subterranean termite species from Southeast Asia which has been unintentionally introduced to many parts of the world through commerce and modern transportation. Known for causing extensive damage to timber used in the built environment, the termite also has a habit of nesting in carton nests in wood and wooden structures in buildings. As so little is known of its breeding system, colony, and genetic structure, we initiated work to sequence its genome with an Illumina HiSeq™ 2000 sequencer. In this publication, we announce our paired-end sequencing data and report the isolation of 119,190 microsatellite markers from our DNA assembly. The microsatellite marker reported in this publication can be used to elucidate the mating system and genetic structure of this highly invasive termite species. Additionally, in this announcement the study authors make the Bio Project sequence accession number SRR13105492 accessible from the Sequence Read Archive database.
      Citation: Data
      PubDate: 2021-04-10
      DOI: 10.3390/data6040040
      Issue No: Vol. 6, No. 4 (2021)
       
  • Data, Vol. 6, Pages 41: AMAΛΘΕΙA: A Dish-Driven
           Ontology in the Food Domain

    • Authors: Stella Markantonatou, Katerina Toraki, Panagiotis Minos, Anna Vacalopoulou, Vivian Stamou, George Pavlidis
      First page: 41
      Abstract: We present AΜAΛΘΕΙA (AMALTHIA), an application ontology that models the domain of dishes as they are presented in 112 menus collected from restaurants/taverns/patisseries in East Macedonia and Thrace in Northern Greece. AΜAΛΘΕΙA supports a tourist mobile application offering multilingual translation of menus, dietary and cultural information about the dishes and their ingredients, as well as information about the geographical dispersion of the dishes. In this document, we focus on the food/dish dimension that constitutes the ontology’s backbone. Its dish-oriented perspective differentiates AΜAΛΘΕΙA from other food ontologies and thesauri, such as Langual, enabling it to codify information about the dishes served, particularly considering the fact that they are subject to wide variation due to the inevitable evolution of recipes over time, to geographical and cultural dispersion, and to the chef’s creativity. We argue for the adopted design decisions by drawing on semantic information retrieved from the menus, as well as other social and commercial facts, and compare AMAΛΘΕΙA with other important taxonomies in the food field. To the best of our knowledge, AΜAΛΘΕΙA is the first ontology modeling (i) dish variation and (ii) Greek (commercial) cuisine (a component of the Mediterranean diet).
      Citation: Data
      PubDate: 2021-04-14
      DOI: 10.3390/data6040041
      Issue No: Vol. 6, No. 4 (2021)
       
  • Data, Vol. 6, Pages 42: BOOSTR: A Dataset for Accelerator Control Systems

    • Authors: Diana Kafkes, Jason St. John
      First page: 42
      Abstract: The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab’s Rapid-Cycling Synchrotron (RCS) operating at 15 Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster’s gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.
      Citation: Data
      PubDate: 2021-04-16
      DOI: 10.3390/data6040042
      Issue No: Vol. 6, No. 4 (2021)
       
  • Data, Vol. 6, Pages 23: Information System for Selection of Conditions and
           Equipment for Mammalian Cell Cultivation

    • Authors: Natalia Menshutina, Elena Guseva, Diana Batyrgazieva, Igor Mitrofanov
      First page: 23
      Abstract: Over the past few decades, animal cell culture technology has advanced significantly. It is now considered a reliable, functional, and relatively well-developed technology. At present, biotherapeutic drugs are synthesized using cell culture techniques by large manufacturing enterprises that produce products for commercial use and clinical research. The reliable implementation of mammalian cell culture technology requires the optimization of a number of variables, including the culture environment and bioreactor conditions, suitable cell lines, operating costs, efficient process management and, most importantly, quality. Successful implementation also requires an appropriate process development strategy, industrial scale, and characteristics, as well as the certification of sustainable procedures that meet the requirements of current regulations. All of this has led to a trend of increasing research in the field of biotechnology and, as a result, to a great accumulation of scientific information which, however, remains fragmentary and non-systematic. The development of information and network technologies allow us to solve this problem. Information system creation allows for implementation of the modern concept of integrating various structured and unstructured data, as well as the collection of information from internal and external sources. We propose and develop an information system which contains the conditions and various parameters of cultivation processes. The associated ranking system is the result of the set of recommendations—both from technological and hardware solutions—which allow for choosing the optimal conditions for the cultivation of mammalian cells at the stage of scientific research, thereby significantly reducing the time and cost of work. The proposed information system allows for the accumulation of experience regarding existing technologies for the cultivation of mammalian cells, along with application to the development of new technologies. The main goal of the present work is to discuss information systems, the organizational support of scientific research in the field of mammalian cell cultivation, and to provide a detailed description of the developed system and its main modules, including the conceptual and logical scheme of the database.
      Citation: Data
      PubDate: 2021-02-25
      DOI: 10.3390/data6030023
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 24: A Data Resource for Sulfuric Acid Reactivity of
           Organic Chemicals

    • Authors: William Bains, Janusz Jurand Petkowski, Sara Seager
      First page: 24
      Abstract: We describe a dataset of the quantitative reactivity of organic chemicals with concentrated sulfuric acid. As well as being a key industrial chemical, sulfuric acid is of environmental and planetary importance. In the absence of measured reaction kinetics, the reaction rate of a chemical with sulfuric acid can be estimated from the reaction rate of structurally related chemicals. To allow an approximate prediction, we have collected 589 sets of kinetic data on the reaction of organic chemicals with sulfuric acid from 262 literature sources and used a functional group-based approach to build a model of how the functional groups would react in any sulfuric acid concentration from 60–100%, and between −20 °C and 100 °C. The data set provides the original reference data and kinetic measurements, parameters, intermediate computation steps, and a set of first-order rate constants for the functional groups across the range of conditions −20 °C–100 °C and 60–100% sulfuric acid. The dataset will be useful for a range of studies in chemistry and atmospheric sciences where the reaction rate of a chemical with sulfuric acid is needed but has not been measured.
      Citation: Data
      PubDate: 2021-02-25
      DOI: 10.3390/data6030024
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 25: FIKWaste: A Waste Generation Dataset from Three
           Restaurant Kitchens in Portugal

    • Authors: Lucas Pereira, Vitor Aguiar, Fábio Vasconcelos
      First page: 25
      Abstract: In the era of big data and artificial intelligence, public datasets are becoming increasingly important for researchers to build and evaluate their models. This paper presents the FIKWaste dataset, which contains time series data for the volume of waste produced in three restaurant kitchens in Portugal. Organic (undifferentiated) and inorganic (glass, paper, and plastic) waste bins were monitored for a consecutive period of four weeks. In addition to the time series measurements, the FIKWaste dataset contains labels for waste disposal events, i.e., when the waste bins are emptied, and technical and non-technical details of the monitored kitchens.
      Citation: Data
      PubDate: 2021-02-26
      DOI: 10.3390/data6030025
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 26: FIKWater: A Water Consumption Dataset from Three
           Restaurant Kitchens in Portugal

    • Authors: Lucas Pereira, Vitor Aguiar, Fábio Vasconcelos
      First page: 26
      Abstract: With the advent of the IoT and low-cost sensing technologies, the availability of data has reached levels never imagined before by the research community. However, independently of their size, data are only as valuable as the ability to have access to them. This paper presents the FIKWater dataset, which contains time series data for hot and cold water demand collected from three restaurant kitchens in Portugal for consecutive periods between two and four weeks. The measurements were taken using ultrasonic flow meters, at a sampling frequency of 0.2 Hz. Additionally, some details of the monitored spaces are also provided.
      Citation: Data
      PubDate: 2021-03-02
      DOI: 10.3390/data6030026
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 27: Collection of Environmental Variables and
           Bacterial Community Compositions in Marian Cove, Antarctica, during Summer
           2018

    • Authors: Kim, Lim, Kim, Kim
      First page: 27
      Abstract: Marine bacteria, which are known as key drivers for marine biogeochemical cycles and Earth’s climate system, are mainly responsible for the decomposition of organic matter and production of climate-relevant gases (i.e., CO₂, N₂O, and CH₄). However, research is still required to fully understand the correlation between environmental variables and bacteria community composition. Marine bacteria living in the Marian Cove, where the inflow of freshwater has been rapidly increasing due to substantial glacial retreat, must be undergoing significant environmental changes. During the summer of 2018, we conducted a hydrographic survey to collect environmental variables and bacterial community composition data at three different layers (i.e., the seawater surface, middle, and bottom layers) from 15 stations. Of all the bacterial data, 17 different phylum level bacteria and 21 different class level bacteria were found and Proteobacteria occupy 50.3% at phylum level following Bacteroidetes. Gammaproteobacteria and Alphaproteobacteria, which belong to Proteobacteria, are the highest proportion at the class level. Gammaproteobacteria showed the highest relative abundance in all three seawater layers. The collection of environmental variables and bacterial composition data contributes to improving our understanding of the significant relationships between marine Antarctic regions and marine bacteria that lives in the Antarctic.
      Citation: Data
      PubDate: 2021-03-05
      DOI: 10.3390/data6030027
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 28: Stark Width Data for Tb II, Tb III and Tb IV
           Spectral Lines

    • Authors: Milan S. Dimitrijević
      First page: 28
      Abstract: A dataset of Stark widths for Tb II, Tb III and Tb IV is presented. To data obtained before, the results of new calculations for 62 Tb III lines from 5d to 6pj(6,j)o, a transition array, have been added. Calculations have been performed by using the simplified modified semiempirical method for temperatures from 5000 to 80,000 K for an electron density of 1017 cm−3. The results were also used to discuss the regularities within multiplets and a supermultiplet.
      Citation: Data
      PubDate: 2021-03-08
      DOI: 10.3390/data6030028
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 29: LeafLive-DB: Classification and Data Storage of
           Botanical Studies

    • Authors: Jorge Rodolfo Beingolea, Diego Ramos-Pires, Jorge Rendulich, Milagros Zegarra, Juan Borja-Murillo, Simone A. Siqueira da Fonseca
      First page: 29
      Abstract: The development of studies, projects, and technologies that contribute to the understanding and preservation of plant biodiversity is becoming highly necessary, as well as tools and software platforms that enable the storage and classification of information resulting from studies on biodiversity. This work presents LeafLive-DB, a software platform that helps map and characterize species from the Brazilian plant biodiversity, offering the possibility of worldwide distribution. Developed by Brazilian and Peruvians researchers, this platform, which is available in its first version, features some functions for consulting and registering plant species and their taxonomy, among other information, through intuitive interfaces and an environment that promotes collaboration and data and research sharing. The platform innovates in data processing, functionality, and development architecture. It has ten thousand registers, and it should start to be distributed in partnership with schools and higher education institutions.
      Citation: Data
      PubDate: 2021-03-09
      DOI: 10.3390/data6030029
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 30: Dataset of the Optimization of a Low Power
           Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical
           Failure Analysis

    • Authors: Gaiardo, Novel, Scattolo, Bucciarelli, Bellutti, Pepponi
      First page: 30
      Abstract: Over the last few years, employment of the standard silicon microfabrication techniques for the gas sensor technology has allowed for the development of ever-small, low-cost, and low-power consumption devices. Specifically, the development of silicon microheaters (MHs) has become well established to produce MOS gas sensors. Therefore, the development of predictive models that help to define a priori the optimal design and layout of the device have become crucial, in order to achieve both low power consumption and high mechanical stability. In this research dataset, we present the experimental data collected to develop a specific and useful predictive thermal-mechanical model for high performing silicon MHs. To this aim, three MH layouts over three different membrane sizes were developed by using the standard silicon microfabrication process. Thermal and mechanical performances of the produced devices were experimentally evaluated, by using probe stations and mechanical failure analysis, respectively. The measured thermal curves were used to develop the predictive thermal model towards low power consumption. Moreover, a statistical analysis was finally introduced to cross-correlate the mechanical failure results and the thermal predictive model, aiming at MH design optimization for gas sensing applications. All the data collected in this investigation are shown.
      Citation: Data
      PubDate: 2021-03-09
      DOI: 10.3390/data6030030
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 31: KazNewsDataset: Single Country Overall Digital
           Mass Media Publication Corpus

    • Authors: Kirill Yakunin, Maksat Kalimoldayev, Ravil I. Mukhamediev, Rustam Mussabayev, Vladimir Barakhnin, Yan Kuchin, Sanzhar Murzakhmetov, Timur Buldybayev, Ulzhan Ospanova, Marina Yelis, Akylbek Zhumabayev, Viktors Gopejenko, Zhazirakhanym Meirambekkyzy, Alibek Abdurazakov
      First page: 31
      Abstract: Mass media is one of the most important elements influencing the information environment of society. The mass media is not only a source of information about what is happening but is often the authority that shapes the information agenda, the boundaries, and forms of discussion on socially relevant topics. A multifaceted and, where possible, quantitative assessment of mass media performance is crucial for understanding their objectivity, tone, thematic focus and, quality. The paper presents a corpus of Kazakhstan media, which contains over 4 million publications from 36 primary sources (which has at least 500 publications). The corpus also includes more than 2 million texts of Russian media for comparative analysis of publication activity of the countries, also about 4000 sections of state policy documents. The paper briefly describes the natural language processing and multiple-criteria decision-making methods, which are the algorithmic basis of the text and mass media evaluation method, and describes the results of several research cases, such as identification of propaganda, assessment of the tone of publications, calculation of the level of socially relevant negativity, comparative analysis of publication activity in the field of renewable energy. Experiments confirm the general possibility of evaluating the socially significant news, identifying texts with propagandistic content, evaluating the sentiment of publications using the topic model of the text corpus since the area under receiver operating characteristics curve (ROC AUC) values of 0.81, 0.73 and 0.93 were achieved on abovementioned tasks. The described cases do not exhaust the possibilities of thematic, tonal, dynamic, etc., analysis of the considered corpus of texts. The corpus will be interesting to researchers considering both multiple publications and mass media analysis, including comparative analysis and identification of common patterns inherent in the media of different countries.
      Citation: Data
      PubDate: 2021-03-14
      DOI: 10.3390/data6030031
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 32: A High-Accuracy GNSS Dataset of Ground Truth
           Points Collected within Îles-de-Boucherville National Park, Quebec,
           Canada

    • Authors: Kathryn Elmer, Margaret Kalacska
      First page: 32
      Abstract: A new ground truth dataset generated with high-accuracy Global Navigation Satellite Systems (GNSS) positional data of the invasive reed Phragmites australis subsp. australis within Îles-de-Boucherville National Park (Quebec, Canada) is described. The park is one of five study sites for the Canadian Airborne Biodiversity Observatory (CABO) and has stands of invasive P. australis spread throughout the park. Previously, within the context of CABO, no ground truth data had been collected within the park consolidating the locations of P. australis. This dataset was collected to serve as training and validation data for CABO airborne hyperspectral imagery acquired in 2019 to assist with the detection and mapping of P. australis. The locations of the ground truth points were found to be accurate within one pixel of the hyperspectral imagery. Overall, 320 ground truth points were collected, representing 158 locations where P. australis was present and 162 locations where it was absent. Auxiliary data includes field photographs and digitized field notes that provide context for each point.
      Citation: Data
      PubDate: 2021-03-14
      DOI: 10.3390/data6030032
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 33: Tools for Remote Exploration: A Lithium (Li)
           Dedicated Spectral Library of the Fregeneda–Almendra Aplite–Pegmatite
           Field

    • Authors: Joana Cardoso-Fernandes, João Silva, Filipa Dias, Alexandre Lima, Ana C. Teodoro, Odile Barrès, Jean Cauzid, Mônica Perrotta, Encarnación Roda-Robles, Maria Anjos Ribeiro
      First page: 33
      Abstract: The existence of diagnostic features in the visible and infrared regions makes it possible to use reflectance spectra not only to identify mineral assemblages but also for calibration and classification of satellite images, considering lithological and/or mineral mapping. For this purpose, a consistent spectral library with the target spectra of minerals and rocks is needed. Currently, there is big market pressure for raw materials including lithium (Li) that has driven new satellite image applications for Li exploration. However, there are no reference spectra for petalite (a Li mineral) in large, open spectral datasets. In this work, a spectral library was built exclusively dedicated to Li minerals and Li pegmatite exploration through satellite remote sensing. The database includes field and laboratory spectra collected in the Fregeneda–Almendra region (Spain–Portugal) from (i) distinct Li minerals (spodumene, petalite, lepidolite); (ii) several Li pegmatites and other outcropping lithologies to allow satellite-based lithological mapping; (iii) areas previously misclassified as Li pegmatites using machine learning algorithms to allow comparisons between these regions and the target areas. Ancillary data include (i) sample location and coordinates, (ii) sample conditions, (iii) sample color, (iv) type of face measured, (v) equipment used, and for the laboratory spectra, (vi) sample photographs, (vii) continuum removed spectra files, and (viii) statistics on the main absorption features automatically extracted. The potential future uses of this spectral library are reinforced by its major advantages: (i) data is provided in a universal file format; (ii) it allows users to compare field and laboratory spectra; (iii) a large number of complementary data allow the comparison of shape, asymmetry, and depth of the absorption features of the distinct Li minerals.
      Citation: Data
      PubDate: 2021-03-16
      DOI: 10.3390/data6030033
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 34: A Data Descriptor for Black Tea Fermentation
           Dataset

    • Authors: Gibson Kimutai, Alexander Ngenzi, Rutabayiro Ngoga Said, Rose C. Ramkat, Anna Förster
      First page: 34
      Abstract: Tea is currently the most popular beverage after water. Tea contributes to the livelihood of more than 10 million people globally. There are several categories of tea, but black tea is the most popular, accounting for about 78% of total tea consumption. Processing of black tea involves the following steps: plucking, withering, crushing, tearing and curling, fermentation, drying, sorting, and packaging. Fermentation is the most important step in determining the final quality of the processed tea. Fermentation is a time-bound process and it must take place under certain temperature and humidity conditions. During fermentation, tea color changes from green to coppery brown to signify the attainment of optimum fermentation levels. These parameters are currently manually monitored. At present, there is only one existing dataset on tea fermentation images. This study makes a tea fermentation dataset available, composed of tea fermentation conditions and tea fermentation images.
      Citation: Data
      PubDate: 2021-03-19
      DOI: 10.3390/data6030034
      Issue No: Vol. 6, No. 3 (2021)
       
  • Data, Vol. 6, Pages 7: Data for Sustainable Platform Economy: Connections
           between Platform Models and Sustainable Development Goals

    • Authors: Mayo Fuster Morell, Ricard Espelt, Enric Senabre Hidalgo
      First page: 7
      Abstract: In recent years, the platform economy has been recognised by researchers and governments around the world for its potential to contribute to the sustainable development of society. Yet, platform economy cases such as Uber, Airbnb, and Deliveroo have created a huge controversy over their socioeconomic impact, while other alternative models have been associated with a new form of cooperativism. In parallel, the United Nations are advocating global sustainable development by promoting Sustainable Development Goals (SDGs), considering elements such as decent work, inclusive and sustainable economic growth, and fostering innovation. In any case, the SDGs have been also criticised for the lack of digital perspective. This dataset draws from two 2020 European projects’ (DECODE and PLUS) data collections and presents the possibility to compare different platform economy models and their connections with the SDGs.
      Citation: Data
      PubDate: 2021-01-20
      DOI: 10.3390/data6020007
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 8: Characteristics of Recent Aftershocks Sequences
           (2014, 2015, 2018) Derived from New Seismological and Geodetic Data on the
           Ionian Islands, Greece

    • Authors: Moshou, Argyrakis, Konstantaras, Daverona, Sagias
      First page: 8
      Abstract: In 2014–2018, four strong earthquakes occurred in the Ionian Sea, Greece. After these events, a rich aftershock sequence followed. More analytically, according to the manual solutions of the National Observatory of Athens, the first event occurred on 26 January 2014 in Cephalonia Island with magnitude ML = 5.8, followed by another in the same region on 3 February 2014 with magnitude ML = 5.7. The third event occurred on 17 November 2015, ML = 6.0 in Lefkas Island and the last on 25 October 2018, ML = 6.6 in Zakynthos Island. The first three of these earthquakes caused moderate structural damages, mainly in houses and produced particular unrest to the local population. This work determines a seismic moment tensor for both large and intermediate magnitude earthquakes (M > 4.0). Geodetic data from permanent GPS stations were analyzed to investigate the displacement due to the earthquakes.
      Citation: Data
      PubDate: 2021-01-20
      DOI: 10.3390/data6020008
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 9: On Linear and Circular Approach to GPS Data
           Processing: Analyses of the Horizontal Positioning Deviations Based on the
           Adriatic Region IGS Observables

    • Authors: Davor Šakan, Serdjo Kos, Biserka Drascic Ban, David Brčić
      First page: 9
      Abstract: Global and regional positional accuracy assessment is of the highest importance for any satellite navigation system, including the Global Positioning System (GPS). Although positioning error can be expressed as a vector quantity with direction and magnitude, most of the research focuses on error magnitude only. The positional accuracy can be evaluated in terms of navigational quadrants as further refinement of error distribution, as it was shown here. This research was conducted in the wider area of the Northern Adriatic Region, employing the International Global Navigation Satellite Systems (GNSS) Service (IGS) data and products. Similarities of positional accuracy and deviations distributions for Single Point Positioning (SPP) were addressed in terms of magnitudes. Data were analyzed during the 11-day period. Linear and circular statistical methods were used to quantify regional positional accuracy and error behavior. This was conducted in terms of both scalar and vector values, with assessment of the underlying probability distributions. Navigational quadrantal positioning error subset analysis was carried out. Similarity in the positional accuracy and positioning deviations behavior, with uneven positional distribution between quadrants, indicated the directionality of the total positioning error. The underlying distributions for latitude and longitude deviations followed approximately normal distributions, while the radius was approximated by the Rayleigh distribution. The Weibull and gamma distributions were considered, as well. Possible causes of the analyzed positioning deviations were not investigated, but the ultimate positioning products were obtained as in standard, single-frequency positioning scenarios.
      Citation: Data
      PubDate: 2021-01-21
      DOI: 10.3390/data6020009
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 10: Balancing Plurality and Educational Essence:
           Higher Education Between Data-Competent Professionals and Data
           Self-Empowered Citizens

    • Authors: Nils Hachmeister, Katharina Weiß, Juliane Theiß, Reinhold Decker
      First page: 10
      Abstract: Data are increasingly important in central facets of modern life: academics, professions, and society at large. Educating aspiring minds to meet highest standards in these facets is the mandate of institutions of higher education. This, naturally, includes the preparation for excelling in today’s data-driven world. In recent years, an intensive academic discussion has resulted in the distinction between two different modes of data related education: data science and data literacy education. As a large number of study programs and offers is emerging around the world, data literacy in higher education is a particular focus of this paper. These programs, despite sharing the same name, differ substantially in their educational content, i.e., a high plurality can be observed. This paper explores this plurality, comments on the role it might play and suggests ways it can be dealt with by maintaining a high degree of adaptiveness and plurality while simultaneously establishing a consistent educational “essence”. It identifies a skill set, data self-empowerment, as a potential part of this essence. Data science and literacy education are still experiencing changeability in their emergence as fields of study, while additionally being stirred up by rapid developments, bringing about a need for flexibility and dialectic.
      Citation: Data
      PubDate: 2021-01-21
      DOI: 10.3390/data6020010
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 11: The Effect of Preprocessing Techniques, Applied to
           Numeric Features, on Classification Algorithms’ Performance

    • Authors: Esra’a Alshdaifat, Doa’a Alshdaifat, Ayoub Alsarhan, Fairouz Hussein, Subhieh Moh’d Faraj S. El-Salhi
      First page: 11
      Abstract: It is recognized that the performance of any prediction model is a function of several factors. One of the most significant factors is the adopted preprocessing techniques. In other words, preprocessing is an essential process to generate an effective and efficient classification model. This paper investigates the impact of the most widely used preprocessing techniques, with respect to numerical features, on the performance of classification algorithms. The effect of combining various normalization techniques and handling missing values strategies is assessed on eighteen benchmark datasets using two well-known classification algorithms and adopting different performance evaluation metrics and statistical significance tests. According to the reported experimental results, the impact of the adopted preprocessing techniques varies from one classification algorithm to another. In addition, a statistically significant difference between the considered data preprocessing techniques is demonstrated.
      Citation: Data
      PubDate: 2021-01-21
      DOI: 10.3390/data6020011
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 12: A Systematic Survey of ML Datasets for Prime CV
           Research Areas—Media and Metadata

    • Authors: Helder F. Castro, Jaime S. Cardoso, Maria T. Andrade
      First page: 12
      Abstract: The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.
      Citation: Data
      PubDate: 2021-01-22
      DOI: 10.3390/data6020012
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 13: Acknowledgment to Reviewers of Data in 2020

    • Authors: Data Editorial Office Data Editorial Office
      First page: 13
      Abstract: Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Data maintains its standards for the high quality of its published papers [...]
      Citation: Data
      PubDate: 2021-02-01
      DOI: 10.3390/data6020013
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 14: Retinal Fundus Multi-Disease Image Dataset
           (RFMiD): A Dataset for Multi-Disease Detection Research

    • Authors: Samiksha Pachade, Prasanna Porwal, Dhanshree Thulkar, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, Luca Giancardo, Gwenolé Quellec, Fabrice Mériaudeau
      First page: 14
      Abstract: The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment. One challenge that limits the adoption of computer-aided diagnosis tool by ophthalmologists is the number of sight-threatening rare pathologies, such as central retinal artery occlusion or anterior ischemic optic neuropathy, and others are usually ignored. In the past two decades, many publicly available datasets of color fundus images have been collected with a primary focus on diabetic retinopathy, glaucoma, age-related macular degeneration and few other frequent pathologies. To enable development of methods for automatic ocular disease classification of frequent diseases along with the rare pathologies, we have created a new Retinal Fundus Multi-disease Image Dataset (RFMiD). It consists of 3200 fundus images captured using three different fundus cameras with 46 conditions annotated through adjudicated consensus of two senior retinal experts. To the best of our knowledge, our dataset, RFMiD, is the only publicly available dataset that constitutes such a wide variety of diseases that appear in routine clinical settings. This dataset will enable the development of generalizable models for retinal screening.
      Citation: Data
      PubDate: 2021-02-03
      DOI: 10.3390/data6020014
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 15: Repository Approaches to Improving Quality of
           Shared Data and Code

    • Authors: Ana Trisovic, Katherine Mika, Ceilyn Boyd, Sebastian Feger, Mercè Crosas
      First page: 15
      Abstract: Sharing data and code for reuse have become increasingly important in scientific work over the past decade. However, in practice, shared data and code may be unusable, or published results obtained from them may be irreproducible. Data repository features and services contribute significantly to the quality, longevity, and reusability of datasets. This paper presents a combination of original and secondary data analysis studies focusing on computational reproducibility, data curation, and gamified design elements that can be employed to indicate and improve the quality of shared data and code. The findings of these studies are sorted into three approaches that can be valuable to data repositories, archives, and other research dissemination platforms.
      Citation: Data
      PubDate: 2021-02-03
      DOI: 10.3390/data6020015
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 16: Investigating the Adoption of Big Data Management
           in Healthcare in Jordan

    • Authors: Hani Bani-Salameh, Mona Al-Qawaqneh, Salah Taamneh
      First page: 16
      Abstract: Software developers and data scientists use and deal with big data to easily discover useful knowledge and find better solutions to improve healthcare services and patient safety. Big data analytics (BDA) is getting attention due to its role in decision-making across the healthcare field. Therefore, this article examines the adoption mechanism of big data analytics and management in healthcare organizations in Jordan. Additionally, it discusses health big data’s characteristics and the challenges, and limitations for health big data analytics and management in Jordan. This article proposes a conceptual framework that allows utilizing health big data. The proposed conceptual framework suggests a way to merge the existing health information system with the National Health Information Exchange (HIE), which might play a role in extracting insights from our massive datasets, increases the data availability and reduces waste in resources. When applying the framework, the collected data are processed to develop knowledge and support decision-making, which helps improve the health care quality for both the community and individuals by improving diagnosis, treatment, and other services.
      Citation: Data
      PubDate: 2021-02-06
      DOI: 10.3390/data6020016
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 17: Agricultural Crop Change in the Willamette Valley,
           Oregon, from 2004 to 2017

    • Authors: Bogdan M. Strimbu, George Mueller-Warrant, Kristin Trippe
      First page: 17
      Abstract: The Willamette Valley, bounded to the west by the Coast Range and to the east by the Cascade Mountains, is the largest river valley completely confined to Oregon. The fertile valley soils combined with a temperate, marine climate create ideal agronomic conditions for seed production. Historically, seed cropping systems in the Willamette Valley have focused on the production of grass and forage seeds. In addition to growing over two-thirds of the nation’s cool-season grass seed, cropping systems in the Willamette Valley include a diverse rotation of over 250 commodities for forage, seed, food, and cover cropping applications. Tracking the sequence of crop rotations that are grown in the Willamette Valley is paramount to answering a broad spectrum of agronomic, environmental, and economical questions. Landsat imagery covering approximately 25,303 km2 were used to identify agricultural crops in production from 2004 to 2017. The agricultural crops were distinguished by classifying images primarily acquired by three platforms: Landsat 5 (2003–2013), Landsat 7 (2003–2017), and Landsat 8 (2013–2017). Before conducting maximum likelihood remote sensing classification, the images acquired by the Landsat 7 were pre-processed to reduce the impact of the scan line corrector failure. The corrected images were subsequently used to classify 35 different land-use classes and 137 unique two-year-long sequences of 57 classes of non-urban and non-forested land-use categories from 2004 through 2014. Our final data product uses new and previously published results to classify the western Oregon landscape into 61 different land use classes, including four majority-rule-over-time super-classes and 57 regular classes of annually disturbed agricultural crops (19 classes), perennial crops (20 classes), forests (13 classes), and urban developments (5 classes). These publicly available data can be used to inform and support environmental and agricultural land-use studies.
      Citation: Data
      PubDate: 2021-02-07
      DOI: 10.3390/data6020017
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 18: The State of the Art in Methodologies of Course
           Recommender Systems—A Review of Recent Research

    • Authors: Deepani B. Guruge, Rajan Kadel, Sharly J. Halder
      First page: 18
      Abstract: In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.
      Citation: Data
      PubDate: 2021-02-11
      DOI: 10.3390/data6020018
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 19: High-to-Low (Regional) Fertility Transitions in a
           Peripheral European Country: The Contribution of Exploratory Time Series
           Analysis

    • Authors: Jesus Rodrigo-Comino, Gianluca Egidi, Luca Salvati, Giovanni Quaranta, Rosanna Salvia, Antonio Gimenez-Morera
      First page: 19
      Abstract: Diachronic variations in demographic rates have frequently reflected social transformations and a (more or less evident) impact of sequential economic downturns. By assessing changes over time in Total Fertility Rate (TFR) at the regional scale in Italy, our study investigates the long-term transition (1952–2019) characteristic of Mediterranean fertility, showing a continuous decline of births since the late 1970s and marked disparities between high- and low-fertility regions along the latitude gradient. Together with a rapid decline in the country TFR, the spatiotemporal evolution of regional fertility in Italy—illustrated through an exploratory time series statistical approach—outlines the marked divide between (wealthier) Northern regions and (economically disadvantaged) Southern regions. Non-linear fertility trends and increasing spatial heterogeneity in more recent times indicate the role of individual behaviors leveraging a generalized decline in marriage and childbearing propensity. Assuming differential responses of regional fertility to changing socioeconomic contexts, these trends are more evident in Southern Italy than in Northern Italy. Reasons at the base of such fertility patterns were extensively discussed focusing—among others—on the distinctive contribution of internal and international migrations to regional fertility rates. Based on these findings, Southern Italy, an economically disadvantaged, peripheral region in Mediterranean Europe, is taken as a paradigmatic case of demographic shrinkage—whose causes and consequences can be generalized to wider contexts in (and outside) Europe.
      Citation: Data
      PubDate: 2021-02-16
      DOI: 10.3390/data6020019
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 20: Dataset of Two-Dimensional Gel Electrophoresis
           Images of Acute Myeloid Leukemia Patients before and after Induction
           Therapy

    • Authors: Juan E. Urrea, Luisa F. Restrepo, Jeanette Prada-Arismendy, Erwing Castillo, Manuel M. Goez, Maria C. Torres-Madronero, Edilson Delgado-Trejos, Sarah Röthlisberger
      First page: 20
      Abstract: Acute myeloid leukemia (AML) is a malignant disorder of the hematopoietic stem and progenitor cells, which results in the build-up of immature blasts in the bone marrow and eventually in the peripheral blood of affected patients. Accurately assessing a patient´s prognosis is very important for clinical management of the disease, which is why there are several prognostic factors such as age, performance status at diagnosis, platelet count, serum creatinine and albumin that are taken into account by the clinician when deciding the course of treatment. However, proteomic changes related to treatment response in this patient group have not been widely explored. Here, we make available a set of 22 two-dimensional gel electrophoresis (2DGE) images obtained from the peripheral blood samples of 11 patients with AML, taken at the time of diagnosis and after induction therapy (approximately 21–28 days after starting treatment). The same set of 2DGE images is also made available after a preprocessing stage (an additional 22 2DGE pre-processed images), which was performed using algorithms developed in Python, in order to improve the visualization of characteristic spots and facilitate proteomic analysis of this type of images.
      Citation: Data
      PubDate: 2021-02-18
      DOI: 10.3390/data6020020
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 21: An Open GMNS Dataset of a Dynamic Multi-modal
           Transportation Network Model of Melbourne, Australia

    • Authors: Nourmohammadi, Mansourianfar, Shafiei, Gu, Saberi
      First page: 21
      Abstract: Simulation-based dynamic traffic assignment models are increasingly used in urban transportation systems analysis and planning. They replicate traffic dynamics across transportation networks by capturing the complex interactions between travel demand and supply. However, their applications particularly for large-scale networks have been hindered by the challenges associated with the collection, parsing, development, and sharing of data-intensive inputs. In this paper, we develop and share an open dataset for reproduction of a dynamic multi-modal transportation network model of Melbourne, Australia. The dataset is developed consistently with the General Modeling Network Specification (GMNS), enabling software-agnostic human and machine readability. GMNS is a standard readable format for sharing routable transportation network data that is designed to be used in multimodal static and dynamic transportation operations and planning models.
      Citation: Data
      PubDate: 2021-02-19
      DOI: 10.3390/data6020021
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 22: A Long-Term, Real-Life Parkinson Monitoring
           Database Combining Unscripted Objective and Subjective Recordings

    • Authors: Jeroen G. V. Habets, Margot Heijmans, Albert F. G. Leentjens, Claudia J. P. Simons, Yasin Temel, Mark L. Kuijf, Pieter L. Kubben, Christian Herff
      First page: 22
      Abstract: Accurate real-life monitoring of motor and non-motor symptoms is a challenge in Parkinson’s disease (PD). The unobtrusive capturing of symptoms and their naturalistic fluctuations within or between days can improve evaluation and titration of therapy. First-generation commercial PD motion sensors are promising to augment clinical decision-making in general neurological consultation, but concerns remain regarding their short-term validity, and long-term real-life usability. In addition, tools monitoring real-life subjective experiences of motor and non-motor symptoms are lacking. The dataset presented in this paper constitutes a combination of objective kinematic data and subjective experiential data, recorded parallel to each other in a naturalistic, long-term real-life setting. The objective data consists of accelerometer and gyroscope data, and the subjective data consists of data from ecological momentary assessments. Twenty PD patients were monitored without daily life restrictions for fourteen consecutive days. The two types of data can be used to address hypotheses on naturalistic motor and/or non-motor symptomatology in PD.
      Citation: Data
      PubDate: 2021-02-23
      DOI: 10.3390/data6020022
      Issue No: Vol. 6, No. 2 (2021)
       
  • Data, Vol. 6, Pages 2: The Use of National Strategic Reference Framework
           Data in Knowledge Graphs and Data Mining to Identify Red Flags

    • Authors: Charalampos Bratsas, Evangelos Chondrokostas, Kleanthis Koupidis, Ioannis Antoniou
      First page: 2
      Abstract: Red Flags in fiscal projects are warning signs that may indicate underlying problems with their implementation. In this paper, we present how National Strategic Reference Framework Open Data can be used to take full advantage of semantic web technologies and data mining techniques to build a knowledge-based system that identifies Red Flags. We collected the data from the Open Data API provided by the Greek Ministry of Economy and Finance. Data modeling consist of two ontologies; the Vocabulary of Fiscal Projects, describing the fiscal projects and the National Strategic Reference Framework Greece Vocabulary, illustrating the Greek National Strategic Reference Framework data. We transformed the data into RDF triples and uploaded them onto an OpenLink Virtuoso Server, so that we could retrieve them via SPARQL queries. Performance indicators were defined to assess the state of the project and Density-Based Spatial Clustering of Applications with Noise, (DBSCAN) was used to identify Red Flags. User’s demands is that rejected projects should raise Red Flags, to avoid project failure and assist the auditor to organize the monitoring process efficiently, by avoiding to examine most of the non-problematic projects. We performed a use case scenario in which an auditor has to examine NSRF projects, approximately 12 months before the end of the programming period. The system retrieved the fiscal information, calculated the performance indicators and identified the Red Flags. The last update of the projects status after the end of the programming period was retrieved and extracted the number of rejected projects, to test whether the user requirements are satisfied. Rejected projects consist of 3.8% of the total projects. The results of the use case scenario show that RedFlags platform is more likely to identify project failures and not raise Red Flags on not rejected projects. Therefore, the RedFlags platform using open data, assists the auditor to organize the monitoring process better.
      Citation: Data
      PubDate: 2021-01-04
      DOI: 10.3390/data6010002
      Issue No: Vol. 6, No. 1 (2021)
       
  • Data, Vol. 6, Pages 3: Drugs, Active Ingredients and Diseases Database in
           Spanish. Augmenting the Resources for Analyses on Drug–Illness
           Interactions

    • Authors: Irene López-Rodríguez, César F. Reyes-Manzano, Israel Reyes-Ramírez, Tania J. Contreras-Uribe, Lev Guzmán-Vargas
      First page: 3
      Abstract: Quantitative and qualitative data on active-ingredient drug composition are essential information for characterizing near-field exposure of consumers to product-related chemicals, among other things. Equally as important is the characterization of the relationship between one or many active ingredients in terms of the diseases they are prescribed for. Such evaluations, however, require quantitative information at different anatomical levels. To complement the available sources of information on active substances and diseases, we have designed a database with enough versatility to potentially be used in a variety of analyzes. By using information provided by a well-established online pharmacological dictionary, we present a database with 11 tables which are easy to access and manipulate. Specifically, we present datasets containing the details of 12,827 marketed drug products, 40,164 diseases, 6231 active pharmaceutical ingredients and 4093 side effects. We exemplify the usefulness of our database with three simple visualizations, which confirm the importance of the data for quantifying the complexity in the associations among active substances, diseases and side effects. Although there are databases with detailed information on active substances and diseases, none of them can be found in Spanish. Our work presents an option that contributes substantially to obtaining well classified information in order to evaluate the roles of active pharmaceutical ingredients, diseases and side effects. These datasets also provide information about clinical and pharmacological groupings which may be useful for clinical and academic researchers. The database will be regularly updated and extended with the newly available Virtual Medicinal Products.
      Citation: Data
      PubDate: 2021-01-09
      DOI: 10.3390/data6010003
      Issue No: Vol. 6, No. 1 (2021)
       
  • Data, Vol. 6, Pages 4: No-z Model for Magnetic Fields of Different
           Astrophysical Objects and Stability of the Solutions

    • Authors: Evgeny Mikhailov, Daniela Boneva, Maria Pashentseva
      First page: 4
      Abstract: A wide range of astrophysical objects, such as the Sun, galaxies, stars, planets, accretion discs etc., have large-scale magnetic fields. Their generation is often based on the dynamo mechanism, which is connected with joint action of the alpha-effect and differential rotation. They compete with the turbulent diffusion. If the dynamo is intensive enough, the magnetic field grows, else it decays. The magnetic field evolution is described by Steenbeck—Krause—Raedler equations, which are quite difficult to be solved. So, for different objects, specific two-dimensional models are used. As for thin discs (this shape corresponds to galaxies and accretion discs), usually, no-z approximation is used. Some of the partial derivatives are changed by the algebraic expressions, and the solenoidality condition is taken into account as well. The field generation is restricted by the equipartition value and saturates if the field becomes comparable with it. From the point of view of mathematical physics, they can be characterized as stable points of the equations. The field can come to these values monotonously or have oscillations. It depends on the type of the stability of these points, whether it is a node or focus. Here, we study the stability of such points and give examples for astrophysical applications.
      Citation: Data
      PubDate: 2021-01-10
      DOI: 10.3390/data6010004
      Issue No: Vol. 6, No. 1 (2021)
       
  • Data, Vol. 6, Pages 5: Aircraft Engine Run-to-Failure Dataset under Real
           Flight Conditions for Prognostics and Diagnostics

    • Authors: Manuel Arias Chao, Chetan Kulkarni, Kai Goebel, Olga Fink
      First page: 5
      Abstract: A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health, respectively, fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.
      Citation: Data
      PubDate: 2021-01-13
      DOI: 10.3390/data6010005
      Issue No: Vol. 6, No. 1 (2021)
       
  • Data, Vol. 6, Pages 6: The Hierarchical Classifier for COVID-19 Resistance
           Evaluation

    • Authors: Nataliya Shakhovska, Ivan Izonin, Nataliia Melnykova
      First page: 6
      Abstract: Finding dependencies in the data requires the analysis of relations between dozens of parameters of the studied process and hundreds of possible sources of influence on this process. Dependencies are nondeterministic and therefore modeling requires the use of statistical methods for analyzing random processes. Part of the information is often hidden from observation or not monitored. That is why many difficulties have arisen in the process of analyzing the collected information. The paper aims to find frequent patterns and parameters affected by COVID-19. The novelty of the paper is hierarchical architecture comprises supervised and unsupervised methods. It allows the development of an ensemble of the methods based on k-means clustering and classification. The best classifiers from the ensemble are random forest with 500 trees and XGBoost. Classification for separated clusters gives us higher accuracy on 4% in comparison with dataset analysis. The proposed approach can be used also for personalized medicine decision support in other domains. The features selection allows us to analyze the following features with the highest impact on COVID-19: age, sex, blood group, had influenza.
      Citation: Data
      PubDate: 2021-01-15
      DOI: 10.3390/data6010006
      Issue No: Vol. 6, No. 1 (2021)
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 35.173.234.169
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-