Intelligent Systems for Geosciences: An Essential Research Agenda

A research agenda for intelligent systems that will result in fundamental new capabilities for understanding the Earth system. Many aspects of geosciences pose novel problems for intelligent systems research. Geoscience data is challenging because it tends to be uncertain, intermittent, sparse, multiresolution, and multiscale. Geosciences processes and objects often have amorphous spatiotemporal boundaries. The lack of ground truth makes model evaluation, testing, and comparison difficult. Overcoming these challenges requires breakthroughs that would significantly transform intelligent systems, while greatly benefitting the geosciences in turn

Ontologies and the Semantic Web for Digital Investigation Tool Selection

The nascent field of digital forensics is heavily influenced by practice. Much digital forensics research involves the use, evaluation, and categorization of the multitude of tools available to researchers and practitioners. As technology evolves at an increasingly rapid pace, the digital forensics field must constantly adapt by creating and evaluating new tools and techniques to perform forensic analysis on many disparate systems such as desktops, notebook computers, mobile devices, cloud, and personal wearable sensor devices, among many others. While researchers have attempted to use ontologies to classify the digital forensics domain on various dimensions, no ontology of digital forensic tools has been developed that defines the capabilities and relationships among the various digital forensic tools. To address this gap, this work develops an ontology using Resource Description Framework (RDF) and Ontology Web Language (OWL) which is searchable via SP ARQL ( an RDF query language) and catalogues common digital forensic tools. Following the concept of ontology design patterns, our ontology has a modular design to promote integration with existing ontologies. Furthermore, we progress to a semantic web application that employs reasoning in order to aid digital investigators with selecting an appropriate tool. This work serves as an important step towards building the knowledge of digital forensics tools. Additionally, this research sets the preliminary stage to bringing semantic web technology to the digital forensics domain as well as facilitates expanding the developed ontology to other tools and features, relationships, and forensic techniques

Use of Semantic Technology to Create Curated Data Albums

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discovery tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out non-relevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science

Use of Semantic Technology to Create Curated Data Albums

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discover tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out nonrelevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science

Assessing FAIRness of Citizen Science Data in the Context of the Green Deal Data Space

As part of the European Data Strategy, the European Commission is working on common European data spaces, including a Green Deal Data Space (GDDS) that covers issues such as climate change, circular economy, pollution, biodiversity, and deforestation. The successful development of the EU GDDS will depend on the availability of FAIR (findable, accessible, interoperable, and reusable) data sources, including FAIR citizen science data. While the importance of FAIR principles is increasingly acknowledged within the field of citizen science, sources of FAIR data outside the biodiversity domain are generally scarce. This is contributed by the lack of end-to-end technical solutions, readily available semantic resources to support data interoperability, and centralised data repositories suited for citizen science data. To investigate the current state of play with citizen science data FAIR compliance, we conducted a review to elicit platforms, tools and standards either used by or indicated as suitable for facilitating stages of the citizen science project lifecycle. We report on the results of our review and discuss gaps that still exist to achieve citizen science data FAIRness. We also examine three data aggregation platforms identified in our review which closely align with FAIR, namely: the Global Biodiversity Information Facility, OpenStreetMap, and Sensor.Community

Generación y publicación de Linked Data para el monitoreo de la calidad ambiental del agua

El agua dulce resulta un recurso de vital importancia para la salud humana, la sostenibilidad del medio ambiente y la prosperidad económica. Por tanto, hace necesario el desarrollo de sistemas de información que permitan el monitoreo de este recurso, teniendo en cuenta aspectos como cantidad y calidad. Para facilitar su monitoreo se propone adoptar Linked Data, que consiste en un conjunto de buenas prácticas que no solo buscan la publicación de información estructurada en la Web, sino que también apuesta por la interconexión entre distintas fuentes de datos, estandarización e interoperabilidad. La adopción de estas buenas prácticas (Linked Data), también involucran el uso de vocabularios (ontologías), para permitir dar significado a la información contenida y que ésta se convierta en conocimiento una vez se ha puesto en contexto. De igual manera, la adopción de estas buenas prácticas brinda soporte para la interoperabilidad, especialmente semántica, de los sistemas con información geoespacial de tipo ambiental (contaminación hídrica). Este trabajo propone una solución que permita poner dentro del contexto legal vigente información correspondiente a características físico-químicas y microbiológicas de cuerpos hídricos y, al mismo tiempo, facilitar la conexión de estos datos con otras fuentes de información mediante los principios de Linked Data. Asimismo, esta propuesta busca promover el aprovechamiento de ontologías existentes e, incluso, de recursos no ontológicos, conforme a las recomendaciones presentes en el estado del arte, para llevar a cabo la definición del tipo de agua, datos y usos asociados, lo que conducirá al desarrollo de un caso de estudio para la interpretación eficiente de los datos hídricos de la cuenca del río Bogotá, indicando la peligrosidad y el potencial del uso del agua de acuerdo a la legislación que le aplique en el contexto requerido y su uso.Abstract: Fresh water is a resource of vital importance for human health, the sustainability of the environment and economic prosperity. Therefore, it is necessary to develop information systems that allow the monitoring of this resource, taking into account aspects such as quantity and quality. In order to facilitate its monitoring, we propose to adopt Linked Data, which consists of a set of good practices that not only seek the publication of structured information on the Web, but also achieves interconnection between different data sources, standardization and interoperability. The adoption of these good practices (Linked Data), also involves the use of vocabularies (ontologies), to allow giving meaning to the contained information and that this becomes knowledge once it has been put into context. In the same way, the adoption of these good practices provides support for the interoperability, especially semantics, of the systems with geospatial information of environmental type (water pollution). This work proposes a solution that allows putting within the current legal context information corresponding to physical-chemical and microbiological characteristics of water bodies and, at the same time, facilitate the connection of these data with other sources of information through the guidelines of Linked Data. In addition, this proposal seeks to promote the use of existing ontologies and even non-ontological resources, in accordance with the recommendations present in the state of the art, to carry out the definition of the type of water, data and associated uses. This scenario is developed in a case study for the efficient interpretation of water data of the Bogotá river basin, indicating the danger and potential of water use according to the regulation that applies to it in the required context and its use.Maestrí

Technologies for a FAIRer use of Ocean Best Practices

The publication and dissemination of best practices in ocean observing is pivotal for multiple aspects of modern marine science, including cross-disciplinary interoperability, improved reproducibility of observations and analyses, and training of new practitioners. Often, best practices are not published in a scientific journal and may not even be formally documented, residing solely within the minds of individuals who pass the information along through direct instruction. Naturally, documenting best practices is essential to accelerate high-quality marine science; however, documentation in a drawer has little impact. To enhance the application and development of best practices, we must leverage contemporary document handling technologies to make best practices discoverable, accessible, and interlinked, echoing the logic of the FAIR data principles [1]