BRIDGING THE GAP BETWEEN TECHNOLOGY AND SCIENCE WITH EXAMPLES FROM ECOLOGY AND BIODIVERSITY

Early informatics focused primarily on the application of technology and computer science to a specific domain; modern informatics has broadened to encompass human and knowledge dimensions. Application of technology is but one aspect of informatics. Understanding domain members’ issues, priorities, knowledge, abilities, interactions, tasks and work environments is another aspect, and one that directly impacts application success. Involving domain members in the design and development of technology in their domain is a key factor in bridging the gap between technology and science. This user-centered design (UCD) approach in informatics is presented via an ecoinformatics case study in three areas: collaboration, usability, and education and training

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

WASOS: An Ontology for Modelling Traditional Knowledge of Sustainable Water Stewardship

Recent work and publications concerning sustainable water stewardship in Rajasthan (India) highlight how contemporary challenges are eroding traditional, communal approaches to water stewardship through mechanised extraction beyond the renewable capacities of ecosystems. Our work is focused on developing a formal ontology for modelling the knowledge of traditional water stewardship in India’s drylands by capturing the key constitutional elements of regenerative methods. Our method follows an iterative evolving prototype process for delivering the first version of the Ontology for Sustainable Water Stewardship (WASOS). The ontology contains a moderate number of high-level classes and properties that represent the water management decisionmaking process. By making key relationships visible, we aim to support decision-making in complex catchments particularly where there are contested urban and rural claims on water

The Delft Report: Linked Data and the challenges for geographic information standardization

The addition of Linked Data to the geographic standards may produce effective cost savings in spatial data production and use by improving some issues relevant to Spatial Data Infrastructures (SDI). The combination of Linked Data and SDIs, its benefits and challenges are collected in the report on Linked Data presented at 32nd ISO/TC 211 plenary in Delft. This paper presents a brief summary of the mentioned report, where we focus on the main recommendations in the context and evaluate their potential impact in SDIs

Safeguarding, Integrating and Disseminating Knowledge on Exploited Marine Ecosystems: the Ecoscope

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Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap

Enabling long-term oceanographic research : changing data practices, information management strategies and informatics

Author Posting. © Elsevier B.V., 2008. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 55 (2008): 2132-2142, doi:10.1016/j.dsr2.2008.05.009.Interdisciplinary global ocean science requires new ways of thinking about data and data management. With new data policies and growing technological capabilities, datasets of increasing variety and complexity are being made available digitally and data management is coming to be recognized as an integral part of scientific research. To meet the changing expectations of scientists collecting data and of data reuse by others, collaborative strategies involving diverse teams of information professionals are developing. These changes are stimulating the growth of information infrastructures that support multi-scale sampling, data repositories, and data integration. Two examples of oceanographic projects incorporating data management in partnership with science programs are discussed: the Palmer Station Long-Term Ecological Research program (Palmer LTER) and the United States Joint Global Ocean Flux Study (US JGOFS). Lessons learned from a decade of data management within these communities provide an experience base from which to develop information management strategies – short-term and long-term. Ocean Informatics provides one example of a conceptual framework for managing the complexities inherent to sharing oceanographic data. Elements are introduced that address the economies-of-scale and the complexities-of-scale pertinent to a broader vision of information management and scientific research.Support is provided by NSF OPP-0217282, OCE-0405069, HSD-0433369 and Scripps Institution of Oceanography (K.S.Baker) and by NSF OCE-8814310, OCE-0097291, OCE- 0510046 and OCE-0646353 (C.Chandler)

A methodology for adaptable and robust ecosystem services assessment

Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant one model fits all paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES - both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts

From multiple aspect trajectories to predictive analysis: a case study on fishing vessels in the Northern Adriatic sea

In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch reports (i.e., the quantity and type of fish caught) of the main fishing market of the area. We present all the phases of the database creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. We implement the database by using MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, we perform various analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management. Furthermore, we investigate the use of machine learning methods for predicting the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation in order to drive specific policy design. A variety of prediction methods, taking as input the data in the database and environmental factors such as sea temperature, waves height and Clorophill-a, are put at work in order to assess their prediction ability in this field. To the best of our knowledge, our work represents the first attempt to integrate fishing ships trajectories derived from AIS data, environmental data and catch data for spatio-temporal prediction of CPUE – a challenging task