A growing focus is on optimizing learning outcomes by supporting metacognitive monitoring, enabling learners to self-assess and adjust their learning strategies. Our goal is to support children in enhancing their metacognitive monitoring skills-the ability to precisely assess and regulate their learning processes. Research shows that targeted strategies can help children improve their skills, provided their imprecise metacognitive monitoring performance (MMP) is identified. However, conventional approaches for identifying imprecise MMP often have low performance and fail to provide the precision needed for effective interventions. To address this, we aim to leverage deep learning to analyze facial expressions as a means of estimating MMP. By interpreting subtle facial expressions, our proposed deep learning approach offers a promising alternative to conventional approaches, enabling more precise and timely estimations of MMP. Our provisional findings suggest significant implications for tailoring strategies for learners' metacognitive monitoring.1

Background: A repository of retinal images for research is being established in Scotland. It will permit researchers to validate, tune, and refine artificial intelligence (AI) decision-support algorithms to accelerate safe deployment in Scottish optometry and beyond. Research demonstrates the potential of AI systems in optometry and ophthalmology, though they are not yet widely adopted. Objective: In this study, 18 optometrists were interviewed to (1) identify their expectations and concerns about the national image research repository and their use of AI decision support and (2) gather their suggestions for improving eye health care. The goal was to clarify attitudes among optometrists delivering primary eye care with respect to contributing their patients' images and to using AI assistance. These attitudes are less well studied in primary care contexts. Five ophthalmologists were interviewed to discover their interactions with optometrists. Methods: Between March and August 2021, 23 semistructured interviews were conducted online lasting for 30-60 minutes. Transcribed and pseudonymized recordings were analyzed using thematic analysis. Results: All optometrists supported contributing retinal images to form an extensive and long-running research repository. Our main findings are summarized as follows. Optometrists were willing to share images of their patients' eyes but expressed concern about technical difficulties, lack of standardization, and the effort involved. Those interviewed thought that sharing digital images would improve collaboration between optometrists and ophthalmologists, for example, during referral to secondary health care. Optometrists welcomed an expanded primary care role in diagnosis and management of diseases by exploiting new technologies and anticipated significant health benefits. Optometrists welcomed AI assistance but insisted that it should not reduce their role and responsibilities. Conclusions: Our investigation focusing on optometrists is novel because most similar studies on AI assistance were performed in hospital settings. Our findings are consistent with those of studies with professionals in ophthalmology and other medical disciplines: showing near universal willingness to use AI to improve health care, alongside concerns over training, costs, responsibilities, skill retention, data sharing, and disruptions to professional practices. Our study on optometrists' willingness to contribute images to a research repository introduces a new aspect; they hope that a digital image sharing infrastructure will facilitate service integration.

We present a set of configurable Web service and interactive tools, s-ProvFlow, for managing and exploiting records tracking data lineage during workflow runs. It facilitates detailed analysis of single executions. It helps users manage complex tasks by exposing the relationships between data, people, equipment and workflow runs intended to combine productively. Its logical model extends the PROV standard to precisely record parallel data-streaming applications. Its metadata handling encourages users to capture the application context by specifying how application attributes, often using standard vocabularies, should be added. These metadata records immediately help productivity as the interactive tools support their use in selection and bulk operations. Users rapidly appreciate the power of the encoded semantics as they reap the benefits. This improves the quality of provenance for users and management. Which in turn facilitates analysis of collections of runs, enabling users to manage results and validate procedures. It fosters reuse of data and methods and facilitates diagnostic investigations and optimisations. We present S-ProvFlow's use by scientists, research engineers and managers as part of the DARE hyper-platform as they create, validate and use their data-driven scientific workflows.

Climate impact and adaptation measures are becoming urgent to be put in place and anticipated. During the past years, climate change effects have been producing adverse conditions in many parts of the world, with significant societal and financial impacts. Advanced analysis tools are needed to process ensembles of simulations of the future climate, in order to generate useful and tailored products for end users. An example of a complex analysis tool used in climate research and adaptation studies is a tool to follow storm tracks. In the context of climate change, it is important to know how storm tracks will change in the future, in both their frequency and intensity. Storms can cause significant societal impacts, hence it is important to assess future patterns. Having access to this type of complex analysis tool is very useful, and integrating them with front-ends like the IS-ENES climate4impact (C4I) can enable the use of those tools by a larger number of researchers and end users. Integrating this type of complex tool is not an easy task. It requires significant development effort, especially if one of the objectives is also to adhere to FAIR principles. The DARE Platform enables research developers to faster develop the implementations of scientific workflows more rapidly. This work presents how such a complex analysis tool has been implemented to be easily integrated with the C4I platform. The DARE Platform also provides easy access to e-infrastructure services like EUDAT B2DROP, to store intermediate or final results and powerful provenance-powered tools to help researchers manage their work and data. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements N°824084 and N°777413.

Collaboration across institutional boundaries is widespread and increasing today. It depends on federations sharing data that often have governance rules or external regulations restricting their use. However, the handling of data governance rules (aka. data-use policies) remains manual, time-consuming and error-prone, limiting the rate at which collaborations can form and respond to challenges and opportunities, inhibiting citizen science and reducing data providers' trust in compliance. Using an automated system to facilitate compliance handling reduces substantially the time needed for such non-mission work, thereby accelerating collaboration and improving productivity. We present a framework, Dr.Aid, that helps individuals, organisations and federations comply with data rules, using automation to track which rules are applicable as data is passed between processes and as derived data is generated. It encodes data-governance rules using a formal language and performs reasoning on multi-input-multi-output data-flow graphs in decentralised contexts. We test its power and utility by working with users performing cyclone tracking and earthquake modelling to support mitigation and emergency response. We query standard provenance traces to detach Dr.Aid from details of the tools and systems they are using, as these inevitably vary across members of a federation and through time. We evaluate the model in three aspects by encoding real-life data-use policies from diverse fields, showing its capability for real-world usage and its advantages compared with traditional frameworks. We argue that this approach will lead to more agile, more productive and more trustworthy collaborations and show that the approach can be adopted incrementally. This, in-turn, will allow more appropriate data policies to emerge opening up new forms of collaboration.

Research infrastructures available for researchers in environmental and Earth science are diverse and highly distributed; dedicated research infrastructures exist for atmospheric science, marine science, solid Earth science, biodiversity research, and more. These infrastructures aggregate and curate key research datasets and provide consolidated data services for a target research community, but they also often overlap in scope and ambition, sharing data sources, sometimes even sites, using similar standards, and ultimately all contributing data that will be essential to addressing the societal challenges that face environmental research today. Thus, while their diversity poses a problem for open science and multidisciplinary research, their commonalities mean that they often face similar technical problems and consequently have common requirements when addressing the implementation of best practices in curation, cataloguing, identification and citation, and other related core topics for data science.

Earth and Space Science Open Archive posterOpen AccessYou are viewing the latest version by default [v1]Easy Access to Complex Analysis Tools for Climate Researchers and Climate Data End UsersAuthorsChristianPagéiDMaartenPliegerWimSom de CerffAlessandroSpinusoRosaFilgueiraMalcolmAtkinsonSissyThemeliiDIraklisKlampanosiDVangelisKarkaletsisSee all authors Christian PagéiDCorresponding Author• Submitting AuthorCERFACSiDhttps://orcid.org/0000-0001-7743-9570view email addressThe email was not providedcopy email addressMaarten PliegerRoyal Netherlands Meteorological Instituteview email addressThe email was not providedcopy email addressWim Som de CerffRoyal Netherlands Meteorological Instituteview email addressThe email was not providedcopy email addressAlessandro SpinusoRoyal Netherlands Meteorological Instituteview email addressThe email was not providedcopy email addressRosa FilgueiraUniversity of Edinburghview email addressThe email was not providedcopy email addressMalcolm AtkinsonUniversity of Edinburghview email addressThe email was not providedcopy email addressSissy ThemeliiDNCSR DemokritosiDhttps://orcid.org/0000-0002-6759-4136view email addressThe email was not providedcopy email addressIraklis KlampanosiDNCSR DemokritosiDhttps://orcid.org/0000-0003-0478-4300view email addressThe email was not providedcopy email addressVangelis KarkaletsisNCSR Demokritosview email addressThe email was not providedcopy email address

In recent years, science has relied more than ever on large-scale data as well as on distributed computing and human resources. Scientists and research engineers in fields such as climate science and computational seismology, constantly strive to make good use of remote and largely heterogeneous computing resources (HPC, Cloud, institutional or local resources, etc.), process, archive and analyse results stored in different locations and collaborate effectively with other scientists.

The provenance of research data is of critical importance to the reproducibility of and trust in scientific results. As research infrastructures provide more amalgamated datasets for researchers and more integrated facilities for processing and publishing data, the capture of provenance in a standard, machine-actionable form becomes especially important. Significant progress has already been made in providing standards and tools for provenance tracking, but the integration of these technologies into research infrastructure remains limited in many scientific domains. Further development and collaboration are required to provide frameworks for provenance capture that can be adopted by as widely as possible, facilitating interoperability as well as dataset reuse. In this chapter, we examine the current state of the art for provenance, and the current state of provenance capture in environmental and Earth science research infrastructures in Europe, as surveyed in the course of the ENVRIplus project. We describe a service developed for the upload, dissemination and application of provenance templates that can be used to generate standardised provenance traces from input data in accordance with current best practice and standards. The use of such a service by research infrastructure architects and researchers can expedite both the understanding and use of provenance technologies, and so drive the standard use of provenance capture technologies in future research infrastructure developments.

Accessing and processing large climate data has nowadays become a particularly challenging task for end users, due to the rapidly increasing volumes being produced and made available. Access to climate data is crucial for sustaining research and performing climate change impact assessments. These activities have strong societal impact as climate change affects and requires that almost all economic and social sectors need adapting. The whole climate data archive is expected to reach a volume of 30 PB in 2020 and up to 2000 PB in 2024 (estimated), evolving from 0.03 PB (30 TB) in 2007 and 2 PB in 2014. Data processing and analysis must now take place remotely for the users: users typically have to rely on heterogeneous infrastructures and services between the data and their physical location. Developers of Research Infrastructures have to provide services to those users, hence having to define standards and generic services to fulfil those requirements. It will be shown how the DARE eScience Platform (http://project-dare.eu) will help developers to develop needed services more quickly and transparently for a large range of scientific researchers. The platform is designed for efficient and traceable development of complex experiments and domain-specific services. Most importantly, the DARE Platform integrates the following e-infrastructure services: the climate IS-ENES (https://is.enes.org) Research Infrastructure front-end climate4impact (C4I: https://climate4impact.eu), the EUDAT CDI (https://www.eudat.eu/eudat-collaborative-data-infrastructure-cdi) B2DROP Service, as well as the ESGF (https://esgf.llnl.gov). The DARE Platform itself can be deployed by research communities on local, public or commercial clouds, thanks to its containerized architecture. More specifically, two distinct Use Cases for the climate science domain will be presented. The first will show how an open source software to compute climate indices and indicators (icclim: https://github.com/cerfacs-globc/icclim) is leveraged using the DARE Platform to enable users to build their own workflows. The second Use Case will demonstrate how more complex tools, such as an extra-tropical and tropical cyclone tracking software (https://github.com/cerfacs-globc/cyclone_tracking), can be easily made available to end users by infrastructure and front-end software developers.

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.

Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. Although the scientific community has addressed this challenge from both theoretical and practical approaches, failure prediction, detection, and recovery still raise many research questions. In this paper, we propose an approach inspired by the control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach is inspired on the proportional–integral–derivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, where the controller will react to adjust its output to mitigate faults. PID controllers aim to detect the possibility of a non-steady state far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of large scale data-intensive workflows—data storage overload and memory overflow. We developed a simulator, which implements and evaluates simple standalone PID-inspired controllers to autonomously manage data and memory usage of a data-intensive bioinformatics workflow that consumes/produces over 4.4 TB of data, and requires over 24 TB of memory to run all tasks concurrently. Experimental results obtained via simulation indicate that workflow executions may significantly benefit from the controller-inspired approach, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained when using our proposed method, and faults are detected and mitigated far in advance of their occurrence.

We present a practical approach for provenance capturing in Data-Intensive workflow systems. It provides contextualisation by recording injected domain metadata with the provenance stream. It offers control over lineage precision, combining automation with specified adaptations. We address provenance tasks such as extraction of domain metadata, injection of custom annotations, accuracy and integration of records from multiple independent workflows running in distributed contexts. To allow such flexibility, we introduce the concepts of programmable Provenance Types and Provenance Configuration. Provenance Types handle domain contextualisation and allow developers to model lineage patterns by re-defining API methods, composing easy-to-use extensions. Provenance Configuration, instead, enables users of a Data-Intensive workflow execution o prepare it for provenance capture, by configuring the attribution of Provenance Types to components and by specifying grouping into semantic clusters. This enables better searches over the lineage records. Provenance Types and Provenance Configuration are demonstrated in a system being used by computational seismologists. It is based on an extended provenance model, S-PROV.

With the needs of science and business, data sharing and re-use has become an intensive activity for various areas. In many cases, governance imposes rules concerning data use, but there is no existing computational technique to help data-users comply with such rules. We argue that intelligent systems can be used to improve the situation, by recording provenance records during processing, encoding the rules and performing reasoning. We present our initial work, designing formal models for data rules and flow rules and the reasoning system, as the first step towards helping data providers and data users sustain productive relationships.

The DARE platform enables researchers and their developers to exploit more capabilities to handle complexity and scale in data, computation and collaboration. Today's challenges pose increasing and urgent demands for this combination of capabilities. To meet technical, economic and governance constraints, application communities must use use shared digital infrastructure principally via virtualisation and mapping. This requires precise abstractions that retain their meaning while their implementations and infrastructures change. Giving specialists direct control over these capabilities with detail relevant to each discipline is necessary for adoption. Research agility, improved power and retained return on intellectual investment incentivise that adoption. We report on an architecture for establishing and sustaining the necessary optimised mappings and early evaluations of its feasibility with two application communities.