Polar Data Forum IV – An Ocean of Opportunities

Biodiversity is a data-intensive science and relies on data from a large number of disciplines in order to build up a coherent picture of the extent and trajectory of life on earth (Bowker 2000). The ability to integrate such data from different disciplines, geographic regions and scales is crucial for making better decisions towards sustainable development. As the Biodiversity Information Standards (TDWG) community tackles standards development and adoption beyond its initial emphases on taxonomy and species distributions, expanding its impact and engaging a wider audience becomes increasingly important. Biological interactions data (e.g., predator-prey, host-parasite, plant-pollinator) have been a topic of interest within TDWG for many years and a Biological Interaction Data Interest Group (IG) was established in 2016 to address that issue. The IG has been working on the complexity of representing interactions data and surveying how Darwin Core (DwC, Wieczorek 2012) is being used to represent them (Salim 2022). The importance of cross-disciplinary science and data inspired the recently funded WorldFAIR project—Global cooperation on FAIR data policy and practice—coordinated by the Committee on Data of the International Science Council (CODATA), with the Research Data Alliance (RDA) as a major partner. WorldFAIR will work with a set of case studies to advance implementation of the FAIR data principles (Fig. 1). The FAIR data principles promote good practices in data management, by making data and metadata Findable, Accessible, Interoperable, and Reusable (Wilkinson 2016). Interoperability will be a particular focus to facilitate cross-disciplinary research. A set of recommendations and a framework for FAIR assessment in a set of disciplines will be developed (Molloy 2022). One of WorldFAIR's case studies is related to plant-pollinator interactions data. Its starting point is the model and schema proposed by Salim (2022) based on the DwC standard, which adheres to the diversifying GBIF data model strategy and on the Plant-Pollinator vocabulary described by Salim (2021). The case study on plant-pollinator interactions originated in the TDWG Biological Interaction Data Interest Group (IG) and within the RDA Improving Global Agricultural Data (IGAD) Community of Practice. IGAD is a forum for sharing experiences and providing visibility to research and work in food and agricultural data and has become a space for networking and blending ideas related to data management and interoperability. This topic was chosen because interoperability of plant-pollinator data is needed for better monitoring of pollination services, understanding the impacts of cultivated plants on wild pollinators and quantifying the contribution of wild pollinators to cultivated crops, understanding the impact of domesticated bees on wild ecosystems, and understanding the behaviour of these organisms and how this influences their effectiveness as pollinators. In addition to the ecological importance of these data, pollination is economically important for food production. In Brazil, the economic value of the pollination service was estimated at US$ 12 billion in 2018 (Wolowski 2019). All eleven case studies within the WorldFAIR project are working on FAIR Implementation Profiles (FIPs), which capture comprehensive sets of FAIR principle implementation choices made by communities of practice and which can accelerate convergence and facilitate cross-collaboration between disciplines (Schultes 2020). The FIPs are published through the FIP Wizard, which allows the creation of FAIR Enabling Resources. The FIPs creation will be repeated by the end of the project and capture results obtained from each case study in order to advance data interoperability. In the first FIP, resources from the Global Biodiversity Information Facility (GBIF) and Global Biotic Interactions (GloBI) were catalogued by the Plant-Pollinator Case Study team, and we expect to expand the existing FAIR Enabling Resources by the end of the project and contribute to plant-pollinator data interoperability and reuse. To tackle the challenge of promoting FAIR data for plant-pollinator interactions within the broad scope of the several disciplines and subdisciplines that generate and use them, we will conduct a survey of existing initiatives handling plant-pollinator interactions data and summarise the current status of best practices in the community. Once the survey is concluded, we will choose at least five agriculture-specific plant-pollination initiatives from our partners, to serve as targets for standards adoption. For data to be interoperable and reusable, it is essential that standards and best practices are community-developed to ensure adoption by the tool builders and data scientists across the globe. TDWG plays an important role in this scenario and we expect to engage the IG and other interested parties in that discussion.

PurposeThe purpose of this article is to give an overview of the development of a Virtual Research Environment (VRE) conceptual model for the management of research data at a South African university.Design/methodology/approachThe research design of this article consists of empirical and non-empirical research. The non-empirical part consists of a critical literature review to synthesise the strengths, weaknesses (limitations) and omissions of identified VRE models as found in literature to develop a conceptual VRE model. As part of the critical literature review concepts were clarified and possible applications of VREs in research lifecycles and research data lifecycles were explored. The empirical part focused on the practical application of this model. This part of the article follows an interpretivist paradigm, and a qualitative research approach, using case studies as inquiry method. Case studies with a positivist perspective were selected through purposive sampling, and inferences were drawn from the sample to design and test a conceptual VRE model, and to investigate the management of research data through a VRE. Investigation was done through a process of participatory action research (PAR) and included semi-structured interviews and participant observation data collection techniques. Evaluation of findings was done through formative and summative evaluation.FindingsThe article presents a VRE conceptual model, with identified generic component layers and components that could potentially be applied and used in different research settings/disciplines. The article also reveals the role that VREs play in the successful management of research data throughout the research lifecycle. Guidelines for setting up a conceptual VRE model are offered.Practical implicationsThis article assisted in clarifying and validating the various components of a conceptual VRE model that could be used in different research settings and disciplines for research data management.Originality/valueThis article confirms/validates generic layers and components that would be needed in a VRE by synthesising these in a conceptual model in the context of a research lifecycle and presents guidelines for setting up a conceptual VRE model.

Best management practices (BMPs) are the tools which are adopted to run any activity while inflicting least negative impacts on the surrounding environment. Since, water pollution is one of the most important issues in today's world; it is indispensable to discuss the activities which are deteriorating its quality. Although, a number of treatment technologies and methods are in place for treating the point sources of pollution, there is a need to address nonpoint sources of pollution. There are many successful practices which are capable of curtailing the impacts of nonpoint source pollution, if adopted. In this paper an attempt has been made to evaluate the performance and trade‐offs of various management practices to address the issues of agricultural nonpoint source pollution. Nevertheless, there is a dearth of guiding principles which can pave a way for adoption of these practices. Therefore, an attempt has also been made to explore the aspects of policy formulations along with emphasizing the factors which can lead to adoption/ non‐adoption of such practices.

The Cancer Genome Atlas (TCGA) community is a joint effort to characterize cancer driver genes in 33 different cancer types from nearly 10,000 exomes [1]. In such an effort, several methods for predicting cancer genes from genomic data are constantly being developed and improved. OpenEBench (https://openebench.bsc.es), is the ELIXIR benchmarking and technical monitoring platform for bioinformatics tools, web servers and workflows. OpenEBench is part of the ELIXIR tools platform ecosystem (https://www.elixir-europe.org/platforms/tools). Its development is led by the INB/ELIXIR-ES (https://inb-elixir.es) Central node at the Barcelona Supercomputing Center (BSC) in collaboration with different partners across Europe. It is focused on providing an infrastructure where end-users can learn from different available options and select the one best fitting their scientific needs; bioinformatics software developers can find relevant datasets and meaningful scientific challenges to evaluate their own developments, and communities interested in a particular scientific domain can easily define which datasets and metrics are relevant for developers to work on, which in turn will allow the field to move ahead. TCGA, as a engaged community in OpenEbench, provides an ideal use-case to demonstrate the latest developments at OpenEBench including the deployment of use of a Virtual Research Environment (VRE). End-users can have access to the software tools who has participated in the TCGA effort to identify cancer driver genes across 33 different cancer types while developers have the opportunity to evaluate their latest developments by submitting their predictions to the OpenEBench VRE. A predefined workflow including format and data validation, metrics computation, results consolidation and visualization is executed for each submission. Results can be visualized at the VRE, downloaded and/or extended by using a number of scripts provided via the OpenEBench repository [2]. In the case of TCGA, users can test their performance against 8 different tools. A list of gene identifiers should be submitted for doing so. They are then compared against several reference datasets defined by the TCGA community depending on the different cancer types, which results in two assessment metrics (true positive rate and precision). This allows the submitted predictions to be benchmarked against the rest of the participants currently present within the TCGA community. The benchmark results are provided in a compressed file (containing the assessment data for all participants and an SVG image with the visualization) and can also be visualized in a scatter-plot where the user can then apply several classification methods implemented in OpenEBench in order to transform the results to table format, which are easier to interpret by non-expert users. [1] Bailey, M. H., Tokheim, C., Porta-Pardo, E., Sengupta, S., Bertrand, D., Weerasinghe, A., … Mariamidze, A. (2018). Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell, 173(2), 371–385.e18. [2] https://github.com/inab/TCGA_visualizer

From 2005 to 2006, the U.S. Geological Survey worked cooperatively with the South Carolina Department of Transportation in Beaufort and Colleton Counties, South Carolina, to assess the performance of four different structural devices that served as best management practices (BMPs). These structural devices were installed to mitigate the effects of stormwater runoff on waterways near State roads. The South Carolina Department of Transportation is required to address the quality of stormwater runoff from State-maintained roadways as part of the National Pollutant Discharge Elimination System stormwater program mandated in the Clean Water Act. The performance assessment of the four structural best management practices was based on stormflow measurements and chemical analyses of stormwater-quality samples collected during a 20-month period from March 2005 through October 2006, which represented a range of seasons and rainfall intensities. A total of 49 sample sets that included stormwater from the inlet and outlet of each of the four structural devices were collected as flow-weighted composites to provide event-mean concentrations of suspended sediment, nutrients, and trace metals. In addition, each set included grab samples that were collected to provide the first flush concentrations of oil and grease and fecal-indicator bacteria. A tiered statistical approach was used in the data analysis. Performances of the four structural BMPs were assessed individually based on how well the BMPs were able to reduce the selected constituents. Descriptive statistics and nonparametric Wilcoxon signed rank tests were applied to event-mean concentrations and loads in the water entering the inlet and in the water leaving the outlet of each BMP for each constituent to identify if significant reductions occurred. If significant reductions occurred, the BMP was considered efficient at reducing the constituent. To quantify efficiency, a simplistic approach was applied to compute mean and geometric mean efficiency ratios for the significantly reduced constituents in each BMP. Each BMP performance was ranked based on its computed efficiency ratios, however, the computed efficiency ratios were not sufficient to determine if statistical differences occurred among the performances of the four BMPs. Consequently, a more complex approach was used to apply statistical comparison tests to reduction percentages computed for individual storms (a modified removal efficiency of individual storm-load approach) to determine if differences in event-mean concentrations, loads, and reduction percentages for significantly reduced constituents occurred among the four structural BMPs. Overall, the four BMPs were efficient in reducing suspended-sediment event-mean concentrations and loads in the stormwater entering the inlets of the BMPs to significantly lower event-mean concentrations before discharging the stormwater from the outlets. The cumulative suspended-sediment event-mean load in stormwater entering the BMPs from the storms sampled during the data-collection period was 1,026 kilograms (1.13 tons). The BMPs removed a cumulative suspended-sediment load of 558 kilograms (0.62 ton). The BMPs tended to preferentially trap the sand-size fraction of the sediment, thereby releasing a greater percentage of fine-grained (silt and clay) sediment in the water discharging from the outlet. The preferential trapping of fine-grained sediment by the BMPs could explain, at least in part, why the BMPs were not successful at significantly reducing these constituents. In general, the four BMPs were not successful at significantly reducing fecal bacteria, nutrients, and total organic carbon (including associated properties of biochemical oxygen demand and chemical oxygen demand). Three of the four BMPs significantly lowered oil and grease concentrations before the stormwater discharged from the outlet. Additionally, only one BMP was effective at reducing all total and particulate

ABSTRACTOperational stream crossings on forest roads and skid trails are potential sediment delivery areas from timber harvests that can negatively affect water quality if best management practices (BMPs) are not sufficiently implemented. Virginia has three major physiographic regions (Mountains, Piedmont, Coastal Plain), and the diverse topographies require some specificity of BMP guidelines for water quality protection. Furthermore, truck roads and skid trails utilize different drainage and erosion control BMPs to maintain sufficient working conditions and access to timber. As a result, differing BMP approaches and implementation levels for roads and trails inherently have different associated costs. We evaluated BMP implementation levels on 154 stream crossing approaches (3 regions × 2 road types × a minimum of 25 stream crossings). We categorized the crossings as BMP− (failed to meet recommended BMPs), BMP-standard (met recommended guidelines), or BMP+ (exceeded recommended guidelines). We estimated hypothetical costs for elevating BMP− stream crossings to BMP-standard and BMP+ and costs of elevating BMP-standard to BMP+. Average BMP implementation audit scores for stream crossings were 88% on skid trails and 82% on truck roads. To upgrade from a BMP− to BMP-standard, the cost-benefit ratio ($/Mg of sediment prevented) averaged $248.58/Mg for skid trails and $670.33/Mg for truck roads. Enhancement from BMP-standard to BMP+ level is less economically efficient and BMP implementation efficacy at stream crossings appears to plateau at the BMP-standard level.

Collaborative Virtual Research Environment To Support Integration & Steering of Multi-site Experiments

This chapter examines virtual environments which usually represent computer simulated environments, otherwise called virtual worlds or virtual reality and its application in physical therapy. Virtual environments present computer-generated three-dimensional (3D) representation of a physical presence of places in the real world as well as in the imaginary world. Users mainly interact with virtual environments using virtual reality headsets. Many of the current virtual reality environments are primarily visual experiences display either on a computer screens or through other stereoscopic displays. Applications of virtual environments are mainly focused on artificial reality that projects users into a 3D space generated by the computer with some sort of tracking devices which may be the virtual reality headsets worn or another 3D tracking device attached to the body of the user such as the electromagnetic tracker system. Research in virtual environments has made significant impact in the entertainment industry and in the training field such as the flight simulator (Stern, 1995).

The EC-funded iMagine project aims to revolutionise aquatic science research by providing open access to AI-powered image analysis tools and resources. Focused on the theme of "Healthy Oceans, Seas, Coastal and Inland Waters," iMagine addresses the growing need for efficient analysis of vast amounts of image data generated from diverse sources like underwater cameras, drones, microscopes, and satellites.iMagine operates the iMagine AI Platform", a computational platform built upon the AI4OS framework and supported by AI4EOSC. Hosted on OpenStack clouds within the EGI e-Infrastructure Federation, the platform offers significant GPU and storage capacity to handle the dynamic needs of various research projects. It provides a complete suite of tools for the entire machine learning lifecycle, including image annotation, preprocessing, a deep learning model catalogue, model training and evaluation, and model inference for scientific end-users. This comprehensive approach facilitates collaboration and knowledge sharing between AI experts and aquatic science researchers.The platform's core functionalities include:A generic computational platform supporting the development of AI-based image analysis services for the aquatic science community. Development of AI-based image analysis services addressing various scientific challenges within aquatic research. Provision of labelled image datasets, enabling AI models' training and retraining. Sharing of best practices, disseminating knowledge related to imaging data and AI-driven image analysis in aquatic sciences. iMagine supports a diverse range of use cases, demonstrating the power of AI for image analysis in aquatic research. These use cases tackle critical issues including floating litter classification and quantification, plankton taxonomic identification, ecosystem statistics generation, oil spill movement and spread prediction, underwater audio data analysis to track vessel activity, and coral reef health monitoring.The iMagine Competence Centre, consisting of AI experts, domain scientists, and image data owners, facilitates collaboration between use cases and platform providers. The Competence Centre organises regular meetings, training sessions, and feedback collection to refine AI models and ensure the development of robust online services for end-users.To further enhance data quality, reproducibility, and scientific progress, iMagine adheres to best practices in data management, quality control, and model development. All use cases contribute to publicly available image datasets on Zenodo, allowing for model validation, retraining, and the development of new models. iMagine actively collaborates with other prominent projects like EOSC, AI4EU, and Blue-Cloud 2026 to maximise its impact and promote the broader adoption of AI-powered solutions within the aquatic science community.

Forestry Best Management Practices (BMPs) are used for protection of water quality at forest stream crossings, yet effects and costs for gradients of BMPs are not well documented. We evaluated forty-two truck road and skid trail stream crossings using three surrogates of BMP adequacy: (1) potential erosion rates for stream crossing approaches; (2) adequacy of stream crossing BMPs; and (3) overall BMP rating (BMP−, BMP-standard, and BMP+). Subsequently, BMP upgrades were recommended for enhancing BMP− or BMP-standard stream crossings. Costs for BMP upgrades were estimated using an existing road and skid trail cost method. The majority of truck road stream crossings were culverts, while skid trail stream crossings were primarily portable bridges. Potential erosion estimates, BMP audit scores, and BMP ratings all indicated that skid crossings have lower BMP implementation than truck road crossings. BMP improvements commonly identified for skid trail and truck crossings included addition of cover and water control structures. Improved BMPs at skid trail crossings were less expensive than those at truck road crossings. Current BMP guidelines provide economical and effective techniques for reducing erosion, and BMP upgrades have the potential to reduce erosion rates to similar levels found in undisturbed forests.

The project objective was to conduct a detailed literature review of storm water pollutants and mitigation technologies and synthesize the information so that INDOT can implement project results into standards. Because it is a municipal separate storm sewer system (MS4), INDOT is required to minimize storm water pollution. A literature review was carried-out to gauge pollutants examined by other transportation agencies, the pollutant’s relevance to Indiana roadways, and the effectiveness of storm water pollution minimization best management practices (BMP). A cost benefit analysis was also conducted for a few BMP devices used in Indiana. Results showed that a variety of databases contained BMP testing studies and the same type of BMP may not perform similarly at different sites. Some BMPs can also generate pollutants. Very little BMP design, cost, and performance data were obtained during this study from INDOT and municipalities contacted due to the organizations being unable to access it. Manufacturer self-reported BMP device performance data found was not corroborated by independent device testing data. Reliance on manufacturer reported data greatly overestimated the device’s cost benefit; field validated device performance data are needed. Based on project results INDOT should consider (1) establishing agency-wide procedures to begin collecting pertinent storm water BMP information from ongoing and planned projects, (2) surveying which and how many BMPs are under INDOT control, (3) applying caution when estimating BMP performance based on manufacturer reported data or BMP performance data from other parts of the U.S., (4) conducting a field investigation to determine pollutant removal effectiveness for select BMPs.

To meet the rising demand for comprehensive bioinformatics tools in the life sciences, the German Network for Bioinformatics Infrastructure (de.NBI) was established in 2015 as a national bioinformatics consortium offering high quality bioinformatics services, comprehensive training and powerful computing capacities. The de.NBI Cloud is one of the flagship services for the de.NBI & ELIXIR Germany network with more than 4000 registered users, over 1350 projects, of which more than 620 are currently active, and over 1380 publications citing its use. Eight federated cloud centers adhere to a unified governance structure and utilize the centralized project application and management workflow available through the de.NBI Cloud Portal. Single sign-on and user management are provided via LifeScience Login and eduGAIN. PIs from German universities or research institutes may submit project applications, which are reviewed by the Cloud Access Committee and, upon approval, resources are allocated to one of the cloud centers. Regular staff training, mutual internal audits and external ISMS certification (ISO27001 or BSI C5) ensure secure operations of the data centers. The de.NBI Cloud offers various project types for your computing needs, covering different levels of expertise and experience in cloud computing: From the simple and fast start of pre-configured VMs with easy access (SimpleVM), to the use of bioinformatics tools and workflows (Galaxy), to the possibility of running your own infrastructures and services (OpenStack & Kubernetes). We offer pre-configured virtual research environments (VRE), which can be accessed easily when using our SimpleVM platform. They include integrated development environments, data science notebooks and virtual desktop environments. The VREs can be accessed in the web browser or by secure shell (ssh). VREs and images tailored for specific needs can also be used to provide uniform VMs for trainings and workshops. The cluster module makes it easy to set up a SLURM-based grid cluster for resource-intensive computations. Users can also provide recipes for custom VREs. The de.NBI Cloud provides computational resources for a diverse portfolio of projects within Europe (ELIXIR, EOSC, Horizon Europe) and Germany (NFDI projects), ranging from (web)service development and hosting, to execution of memory and CPU-intensive bioinformatics workflows, GPU resources for machine learning, resources for hackathons, workshops and trainings, as well as resources for proof-of-concept development, and secure environments for sensitive data processing. Regular trainings and workshops teach you how to use the cloud infrastructure effectively for your research. The annual Cloud User Meeting offers users the opportunity for networking and to get in touch with the administrators of the cloud locations and the cloud governance. Researchers present their work in the cloud and training courses expand the range of expertise for experienced cloud users, while also providing a comprehensive introduction for beginners. The de.NBI Cloud offers various project types for your computing needs, covering different levels of expertise and experience in cloud computing: From the simple and fast start of pre-configured VMs with easy access (SimpleVM), to the use of bioinformatics tools and workflows (Galaxy), to the possibility of running your own infrastructures and services (OpenStack & Kubernetes). Selection of training course topics: • VM basics: Linux & command line • SimpleVM & Research Environments • BioConda & BioContainers • CWL, Nextflow, Snakemake • Infrastructure as Code: Ansible, Terraform • Grid computing: BiBiGrid / SLURM

Forest residue utilization for energy has increased in the southeastern United States and additional increases are anticipated. However, such removals generate concerns regarding potential accelerated erosion and nutrient drains, which could affect site productivity and stream water quality. To address erosion and water quality concerns, postharvest erosion rates, operational feature areas, best management practice (BMP) implementation, residual ground cover, soil disturbance, and forest residue concentrations were evaluated on ten biomass and ten conventional clearcut harvests in Virginia’s Coastal Plain. No significant differences in erosion rates (P = 0.4727) were observed between biomass (0.71 tons ac-1 yr-1) and conventional (0.61 tons ac-1 yr-1) harvests. BMP compliance percentages were similar (P = 0.6776) for both biomass (88.54%) and conventional (90.99%) harvests. BMP implementation scores were determined to be a significant predictor of erosion rates (P < 0.0001). A significant reduction (28%) in postharvest residue concentrations was observed for biomass (10.22 tons ac-1) versus conventional harvests (14.24 tons ac-1) (P = 0.0173). Scores for BMP implementation and erosion rates were not significantly different between treatments, indicating that sufficient residual biomass exists for appropriate implementation of BMPs. BMPs provided similar erosion protection on both biomass and conventional clearcut harvests.Study Implications: Forest residues are widely used to cover and stabilize bare soil as best management practices (BMPs) for erosion control. The expanding bioenergy market has raised concerns that overutilization of forest residues may occur. Comparisons of biomass and conventional harvests in the Coastal Plain of Virginia revealed few significant postharvest differences. Implementation of existing water quality BMPs is important for minimizing erosion on biomass harvests; however, the development of biomass harvesting guidelines in addition to current state BMPs appears unnecessary under current harvesting practices. Harvest residue management should be conducted to ensure adequate supplies of woody debris remain for site protection.

A 2001 survey indicated that many growers are reluctant to adopt research-based recommendations because of a perception that it is not practical or applicable to their specific farming operation. Other growers, however, appear to adopt these practices successfully. Highlighting “model” growers is a method that can be used to field-test research findings and facilitate grower adoption. The objectives of this project were to: 1) establish field demonstrations with potato (Solariumtuberosum L.) growers who generally follow researchbased best management practices (BMPs); 2) establish plots within each field to compare BMPs with a high input, maximum yield management (MYM) approach; and 3) enhance grower confidence regarding researchbased BMPs. Fourteen field trials were conducted in the Pacific Northwest during 2002-2005. Five replicates of BMP and MYM plots were established in each field. The BMPs consisted of sampling, scouting, and use of prediction models to aid in determining rate and timing of inputs to maximize returns. In contrast, the MYM approach was based on tradition and calendar timing, with a near zero tolerance for pest and nutrient limitations. The MYM plots had 1.7 to 13.2% more fertilizer and pesticide costs than the BMP plots. The MYM treatments resulted in significant marketable yield increases in three fields and decreases in two fields, with the remaining nine fields and the combined average of all 14 fields being statistically equivalent. When factoring in estimated costs, only two fields resulted in a monetary advantage with MYM treatment. In contrast, the BMP treatment resulted in significant increases in net crop value in five fields, as well as the combined average of all 14 fields ($200 ha-1 or 3.2%). These field demonstrations, along with associated field days and grower meetings, have resulted in many documented changes in grower practices towards BMPs, with many more undocumented changes probable.

For two years, the Marine Metadata Interoperability (MMI) project has focused on improving marine data systems interoperability. Through community organizing and technical development, MMI has broadened awareness of the problems, increased community participation in its solutions, and advanced efforts toward interoperable data systems design. These universal themes match MMI's vision and mission, and demand an open cultural and technical approach. Good practices in data management, metadata, and engineering all represent international ambitions. Although funding sources for the Marine Metadata Interoperability project have been exclusively American, the project has always had international participation and flavor. Our original Steering Committee included international membership (Roy Lowry, of the British Oceanographic Data Centre), and several of our technical contributors are not from the United States. In presentations and meetings in 2005 in Brest, France, and Sardinia, Italy, the Principal Investigator found eager listeners and much agreement on goals and methods