scholarly journals Pioneering easy-to-use forestry data with Forest Explorer

Semantic Web ◽  
2021 ◽  
pp. 1-14
Author(s):  
Guillermo Vega-Gorgojo ◽  
José M. Giménez-García ◽  
Cristóbal Ordóñez ◽  
Felipe Bravo
Keyword(s):  
User Study ◽  
Open Data ◽  
Data Resource ◽  
Domain Experts ◽  
Area Of Interest ◽  
Land Cover Map ◽  
Data Manager ◽  
The Cross ◽  
Interactive Map ◽  
Future Work

Forest Explorer is a web tool that can be used to easily browse the contents of the Cross-Forest dataset, a Linked Open Data resource containing the forestry inventory and land cover map from Spain. The tool is purposed for domain experts and lay users to facilitate the exploration of forestry data. Since these two groups are not knowledgable on Semantic Web, the user interface is designed to hide the complexity of RDF, OWL or SPARQL. An interactive map is provided for this purpose, allowing users to navigate to the area of interest and presenting forestry data with different levels of detail according to the zoom level. Forest Explorer offers different filter controls and is localized to English and Spanish. All the data is retrieved from the Cross-Forest and DBpedia endpoints through the Data manager. This component feeds the different Feature managers with the data needed to be displayed in the map. The Data manager uses a reduced set of SPARQL templates to accommodate any data request of the Feature managers. Caching and smart geographic querying are employed to limit data exchanges with the endpoint. A live version of the tool is freely available for everybody that wants to try it – any device with a modern browser should be sufficient to test it. Since December 2019, more than 3,200 users have employed Forest Explorer and it has appeared 12 times in the Spanish media. Results from a user study with 28 participants (mainly domain experts) show that Forest Explorer can be used to easily navigate the contents of the Cross-Forest dataset. No important limitations were found, only feature requests such as the integration of new datasets from other countries that are part of our future work.

Enhancing transparency through open government data: the case of data portals and their features and capabilities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Martin Lněnička ◽  
Renata Machova ◽  
Jolana Volejníková ◽  
Veronika Linhartová ◽  
Radka Knezackova ◽  
...  
Keyword(s):  
Open Data ◽  
Open Government ◽  
Data Sets ◽  
Web Content ◽  
Content Type ◽  
Domain Experts ◽  
Computer Mediated ◽  
Open Government Data ◽  
Decision Making Processes ◽  
Government Data

PurposeThe purpose of this paper was to draw on evidence from computer-mediated transparency and examine the argument that open government data and national data infrastructures represented by open data portals can help in enhancing transparency by providing various relevant features and capabilities for stakeholders' interactions.Design/methodology/approachThe developed methodology consisted of a two-step strategy to investigate research questions. First, a web content analysis was conducted to identify the most common features and capabilities provided by existing national open data portals. The second step involved performing the Delphi process by surveying domain experts to measure the diversity of their opinions on this topic.FindingsIdentified features and capabilities were classified into categories and ranked according to their importance. By formalizing these feature-related transparency mechanisms through which stakeholders work with data sets we provided recommendations on how to incorporate them into designing and developing open data portals.Social implicationsThe creation of appropriate open data portals aims to fulfil the principles of open government and enables stakeholders to effectively engage in the policy and decision-making processes.Originality/valueBy analyzing existing national open data portals and validating the feature-related transparency mechanisms, this paper fills this gap in existing literature on designing and developing open data portals for transparency efforts.

scholarly journals Resources available for autism research in the big data era: a systematic review

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2880 ◽  
Author(s):  
Reem Al-jawahiri ◽  
Elizabeth Milne
Keyword(s):  
Human Brain ◽  
Brain Connectivity ◽  
Open Data ◽  
Autism Spectrum ◽  
Data Types ◽  
Participant Recruitment ◽  
Data Resource ◽  
Autism Research ◽  
Great Heterogeneity ◽  
Google Search

Recently, there has been a move encouraged by many stakeholders towards generating big, open data in many areas of research. One area where big, open data is particularly valuable is in research relating to complex heterogeneous disorders such as Autism Spectrum Disorder (ASD). The inconsistencies of findings and the great heterogeneity of ASD necessitate the use of big and open data to tackle important challenges such as understanding and defining the heterogeneity and potential subtypes of ASD. To this end, a number of initiatives have been established that aim to develop big and/or open data resources for autism research. In order to provide a useful data reference for autism researchers, a systematic search for ASD data resources was conducted using the Scopus database, the Google search engine, and the pages on ‘recommended repositories’ by key journals, and the findings were translated into a comprehensive list focused on ASD data. The aim of this review is to systematically search for all available ASD data resources providing the following data types: phenotypic, neuroimaging, human brain connectivity matrices, human brain statistical maps, biospecimens, and ASD participant recruitment. A total of 33 resources were found containing different types of data from varying numbers of participants. Description of the data available from each data resource, and links to each resource is provided. Moreover, key implications are addressed and underrepresented areas of data are identified.

Blue, BlueJ, Greenfoot

2018 ◽  
pp. 42-87 ◽  
Author(s):  
Michael Kölling
Keyword(s):  
Future Development ◽  
Age Groups ◽  
Educational Systems ◽  
Diverse Group ◽  
Educational Programming ◽  
Area Of Interest ◽  
Younger Age ◽  
Programming Systems ◽  
Block Based ◽  
Future Work

Educational programming systems are booming. More systems of this kind have been published in the last few years than ever before, and interest in this area is growing. With the rise of programming as a school subject in ever-younger age groups, the importance of dedicated educational systems for programming education is increasing. In the past, professional environments were often used in programming teaching; with the shift to younger age groups, this is no longer tenable. New educational systems are currently being designed by a diverse group of developing teams, in industry, in academia, and by hobbyists. In this chapter, the authors describe their experiences with the design of three systems—Blue, BlueJ, and Greenfoot—and extract lessons that they hope may be useful for designers of future systems. The authors also discuss current developments, and suggest an area of interest where future work might be profitable for many users: the combination of aspects from block-based and text-based programming. They present their work in this area—frame-based editing—and suggest possible future development options.

User Interaction with Linked Data

2016 ◽  
Vol 7 (1) ◽  
pp. 79-91 ◽  
Author(s):  
Dhavalkumar Thakker ◽  
Fan Yang-Turner ◽  
Dimoklis Despotakis
Keyword(s):  
Linked Data ◽  
User Study ◽  
User Interaction ◽  
Heterogeneous Data ◽  
User Studies ◽  
Sensor Data ◽  
Exploratory Search ◽  
Search Tasks ◽  
Algorithmic Support ◽  
Future Work

It is becoming increasingly popular to expose government and citywide sensor data as linked data. Linked data appears to offer a great potential for exploratory search in supporting smart city goals of helping users to learn and make sense of complex and heterogeneous data. However, there are no systematic user studies to provide an insight of how browsing through linked data can support exploratory search. This paper presents a user study that draws on methodological and empirical underpinning from relevant exploratory search studies. The authors have developed a linked data browser that provides an interface for user browsing through several datasets linked via domain ontologies. In a systematic study that is qualitative and exploratory in nature, they have been able to get an insight on central issues related to exploratory search and browsing through linked data. The study identifies obstacles and challenges related to exploratory search using linked data and draws heuristics for future improvements. The authors also report main problems experienced by users while conducting exploratory search tasks, based on which requirements for algorithmic support to address the observed issues are elicited. The approach and lessons learnt can facilitate future work in browsing of linked data, and points at further issues that have to be addressed.

scholarly journals A Study on Visual Representations for Active Plant Wall Data Analysis

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 74 ◽  
Author(s):  
Kahin Akram Hassan ◽  
Yu Liu ◽  
Lonni Besançon ◽  
Jimmy Johansson ◽  
Niklas Rönnberg
Keyword(s):  
User Study ◽  
Line Graph ◽  
Well Being ◽  
Future Research ◽  
Temporal Data ◽  
Indoor Climate ◽  
Structured Interviews ◽  
Future Directions ◽  
Domain Experts ◽  
The Status

The indoor climate is closely related to human health, well-being, and comfort. Thus, an understanding of the indoor climate is vital. One way to improve the indoor climates is to place an aesthetically pleasing active plant wall in the environment. By collecting data using sensors placed in and around the plant wall both the indoor climate and the status of the plant wall can be monitored and analyzed. This manuscript presents a user study with domain experts in this field with a focus on the representation of such data. The experts explored this data with a Line graph, a Horizon graph, and a Stacked area graph to better understand the status of the active plant wall and the indoor climate. Qualitative measures were collected with Think-aloud protocol and semi-structured interviews. The study resulted in four categories of analysis tasks: Overview, Detail, Perception, and Complexity. The Line graph was found to be preferred for use in providing an overview, and the Horizon graph for detailed analysis, revealing patterns and showing discernible trends, while the Stacked area graph was generally not preferred. Based on these findings, directions for future research are discussed and formulated. The results and future directions of this research can facilitate the analysis of multivariate temporal data, both for domain users and visualization researchers.

Network Anomaly Analysis using the Microsoft HoloLens

2018 ◽  
Vol 62 (1) ◽  
pp. 2094-2098
Author(s):  
Steve Beitzel ◽  
Josiah Dykstra ◽  
Paul Toliver ◽  
Jason Youzwak
Keyword(s):  
User Study ◽  
Mixed Reality ◽  
Eye Gaze ◽  
Lessons Learned ◽  
Time Dimension ◽  
Desktop Computer ◽  
Novel Approach ◽  
Anomaly Analysis ◽  
Microsoft Hololens ◽  
Future Work

We investigate the feasibility of using Microsoft HoloLens, a mixed reality device, to visually analyze network capture data and locate anomalies. We developed MINER, a prototype application to visualize details from network packet captures as 3D stereogram charts. MINER employs a novel approach to time-series visualization that extends the time dimension across two axes, thereby taking advantage of the immersive 3D space available via the HoloLens. Users navigate the application through eye gaze and hand gestures to view summary and detailed bar graphs. Callouts display additional detail based on the user’s immediate gaze. In a user study, volunteers used MINER to locate network attacks in a dataset from the 2013 VAST Challenge. We compared the time and effort with a similar test using traditional tools on a desktop computer. Our findings suggest that network anomaly analysis with the HoloLens achieved comparable effectiveness, efficiency and satisfaction. We describe user metrics and feedback collected from these experiments; lessons learned and suggested future work.

scholarly journals Artificial intelligence in oral and maxillofacial radiology: what is currently possible?

2020 ◽  
pp. 20200375
Author(s):  
Min-Suk Heo ◽  
Jo-Eun Kim ◽  
Jae-Joon Hwang ◽  
Sang-Sun Han ◽  
Jin-Soo Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Open Data ◽  
Data Sets ◽  
Radiographic Images ◽  
Data Set ◽  
Actual Clinical Practice ◽  
Area Of Interest ◽  
The Future ◽  
Treatment Plans ◽  
Image Quality Improvement

Artificial intelligence, which has been actively applied in a broad range of industries in recent years, is an active area of interest for many researchers. Dentistry is no exception to this trend, and the applications of artificial intelligence are particularly promising in the field of oral and maxillofacial (OMF) radiology. Recent researches on artificial intelligence in OMF radiology have mainly used convolutional neural networks, which can perform image classification, detection, segmentation, registration, generation, and refinement. Artificial intelligence systems in this field have been developed for the purposes of radiographic diagnosis, image analysis, forensic dentistry, and image quality improvement. Tremendous amounts of data are needed to achieve good results, and involvement of OMF radiologist is essential for making accurate and consistent data sets, which is a time-consuming task. In order to widely use artificial intelligence in actual clinical practice in the future, there are lots of problems to be solved, such as building up a huge amount of fine-labeled open data set, understanding of the judgment criteria of artificial intelligence, and DICOM hacking threats using artificial intelligence. If solutions to these problems are presented with the development of artificial intelligence, artificial intelligence will develop further in the future and is expected to play an important role in the development of automatic diagnosis systems, the establishment of treatment plans, and the fabrication of treatment tools. OMF radiologists, as professionals who thoroughly understand the characteristics of radiographic images, will play a very important role in the development of artificial intelligence applications in this field.

scholarly journals The Potential of Open Data to Automatically Create Learning Resources for Smart Learning Environments

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 61 ◽  
Author(s):  
Adolfo Ruiz-Calleja ◽  
Miguel Bote-Lorenzo ◽  
Guillermo Vega-Gorgojo ◽  
Sergio Serrano-Iglesias ◽  
Juan Asensio-Pérez ◽  
...  
Keyword(s):  
Formal Education ◽  
Learning Experience ◽  
Open Data ◽  
Learning Resources ◽  
Smart Learning Environment ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Local Municipality ◽  
Contextualized Learning ◽  
Future Work

Smart Education requires bridging formal and informal learning experience. However, how to create contextualized learning resources that support this bridging remains a problem. In this paper, we propose to exploit the open data available in the Web to automatically create contextualized learning resources. Our preliminary results are promising, as our system creates thousands of learning resources related to formal education concepts and physical locations in the student’s local municipality. As part of our future work, we will explore how to integrate these resources into a Smart Learning Environment.

scholarly journals An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

2021 ◽  
Vol 6 ◽  
pp. 42
Author(s):  
◽  
Ambroise Ahouidi ◽  
Mozam Ali ◽  
Jacob Almagro-Garcia ◽  
Alfred Amambua-Ngwa ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Copy Number Variants ◽  
Open Data ◽  
Nucleotide Polymorphisms ◽  
Parasite Life Cycle ◽  
Genome Variation ◽  
Data Resource ◽  
Genomic Epidemiology ◽  
Accelerate Development ◽  
Almost All

MalariaGEN is a data-sharing network that enables groups around the world to work together on the genomic epidemiology of malaria. Here we describe a new release of curated genome variation data on 7,000 Plasmodium falciparum samples from MalariaGEN partner studies in 28 malaria-endemic countries. High-quality genotype calls on 3 million single nucleotide polymorphisms (SNPs) and short indels were produced using a standardised analysis pipeline. Copy number variants associated with drug resistance and structural variants that cause failure of rapid diagnostic tests were also analysed.  Almost all samples showed genetic evidence of resistance to at least one antimalarial drug, and some samples from Southeast Asia carried markers of resistance to six commonly-used drugs. Genes expressed during the mosquito stage of the parasite life-cycle are prominent among loci that show strong geographic differentiation. By continuing to enlarge this open data resource we aim to facilitate research into the evolutionary processes affecting malaria control and to accelerate development of the surveillance toolkit required for malaria elimination.

scholarly journals Generating RDF resources from web open data portals

2019 ◽  
Vol 16 (3) ◽  
pp. 1521
Author(s):  
Khalid Saleh Aloufi
Keyword(s):  
Semantic Web ◽  
Web Application ◽  
Open Data ◽  
Public Institutions ◽  
Data Transformations ◽  
Web Standards ◽  
Private And Public ◽  
Data Portal ◽  
Future Work ◽  
The Web

<span>Open data are available from various private and public institutions in different resource formats. There are already great number of open data that are published using open data portals, where datasets and resources are mainly presented in tabular or sheet formats. However, such formats have some barriers with application developments and web standards. One of the web recommenced standards for semantic web application is RDF. There are various research efforts have been focused on presenting open data in RDF formats. However, no framework has transformed tabular open data into RDFs considering the HTML tags and properties of the resources and datasets. Therefore, a methodology is required to generate RDF resources from this type of open data resources. This methodology applies data transformations of open data from a tabular format to RDF files for the Saudi Open Data Portal. The methodology successfully transforms open data resources in sheet format into RDF resources. Recommendations and future work are given to enhance the development of building open data.</span>

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