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jrc data visualisation  (Joint Research Center)

 
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    Joint Research Center jrc data visualisation
    Jrc Data Visualisation, supplied by Joint Research Center, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/visualises+data/jrc+eu+times+model/pm39047988-601-0-19
    Average 90 stars, based on 1 article reviews
    jrc data visualisation - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    other:

    Article Title: Comparing and contrasting the coverage of publicly available structural alerts for protein binding
    Article Snippet: The Asturiol et al [ 5 ] dataset will be referred to as the “JRC dataset” throughout this manuscript, where JRC refers to Asturiol’s affiliation at the European Commission’s Joint Research Centre.

    Article Title: Knowledge infrastructure for integrated data management and analysis supporting new approach methods in predictive toxicology and risk assessment.
    Article Snippet: JRC data visualisation: interactive visualisation of data coming from the viability and cytotoxicity assays that were performed by the Joint Research Centre (JRC), one of the partners inside EU-ToxRisk project.

    Article Title: Getting (ECO)Ready: Does EU Legislation Integrate Up-to-Date Scientific Data for Food Security and Biodiversity Preservation Under Climate Change?
    Article Snippet: The European Commission’s Joint Research Center (JRC) provided access to datasets and all publicly available data in the JRC data catalog, enhancing the ability to scrutinize and comprehend the intricate dynamics of the subject matter.

    Article Title: Monitoring long-term water storage of lakes and reservoirs in arid ungauged basin based on underwater topography derived from multi-source satellite data.
    Article Snippet: • Using ICESat-2 data to obtain underwater elevation points of lakes and reservoirs.. • Establishing an underwater elevation inversion model • Superimposing multi-temporal inversion results to obtain underwater topographic maps.. • Analyzing the storage changes of 14 lakes and reservoirs in the Tarim River Basin A R T I C L E I N F O Editor: Damià Barceló

    Article Title: Trend analysis of COVID-19 mis/disinformation narratives–A 3-year study
    Article Snippet: The direct link for public access to the data is: https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/COVID-MANUSCRIPT/ The data are publicly available from the JRC (Joint Research Centre, European Commission) data catalogue: https://data.jrc.ec.europa.eu/ .

    Article Title: A comparison of global flood models using Sentinel-1 and a change detection approach
    Article Snippet: The outputs from four fluvial flood models were validated in this study: The Joint Research Centre (JRC) model (Dottori et al. 2016), the Global Assessment Report (GAR) model (Rudari et al. 2015), the Catchment-Based Macro‐scale Floodplain (CaMa-flood) model (Yamazaki et al. 2011; Zhou et al. 2021), and the Fathom Global Ltd. (Fathom) model (Sampson et al. 2015).

    Article Title: An overview of the expected public values arising from blockchain adoption in the European public sector
    Article Snippet: Purpose – This study aims to systematically explore the anticipated realisation of public values through blockchain technology (BCT)within theEuropean public sector.. Its purpose is to offer a comprehensive analysis of BCT implementations, focusing on the various expected public values and understanding how these expectations shape the adoption of BCT in public administration across Europe.. Design/methodology/approach – This research involves a qualitative analysis of 165 BCT use cases across European governments at the national, regional and local levels.

    Article Title: The impact of methane leakage on the role of natural gas in the European energy transition
    Article Snippet: The JRC-EU-TIMES model is one of the models currently pursued and developed in the Joint Research Centre (JRC) of the European Commission under the auspices of the JRC Modelling Taskforce.



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    Image Search Results


    Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with Grafana and Alertmanager for visualisation and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate data and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.

    Journal: Sensors (Basel, Switzerland)

    Article Title: AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular Approach Using Sensor Data Integration

    doi: 10.3390/s25196248

    Figure Lengend Snippet: Overall architecture of the proposed AI-integrated monitoring system. The framework comprises two layers: (i) the Knowledge Plane, which consists of metric collection via Prometheus exporters for observation, embedding AI models for predictive analytics, and integration with Grafana and Alertmanager for visualisation and alert routing; and (ii) the Infrastructure Plane, which consists of network devices that generate data and require monitoring and maintenance. Optimisation denotes actions derived from AI predictions, such as early fault alerts and ticket prioritisation, enabling network engineers to optimise network performance after making changes based on these alerts. Directional arrows indicate data and control flows across the components.

    Article Snippet: The integration of this data into the data visualisation tool Grafana facilitates department-wise usage aggregation.

    Techniques: Derivative Assay, Control

    Modular architecture of decentralised monitoring platform. Data flow of the monitoring pipeline work as the metric data is collected from exporters and processed through Prometheus instances hosted on Raspberry Pi edge nodes. Aggregated data is forwarded to the Mimir time-series database for long-term storage and AI model training. Trained models are containerised and deployed either centrally or at the edge for inference. Predictions are exposed as Prometheus metrics and visualised in Grafana dashboards. This architecture enables hybrid edge–cloud analytics, real-time alerting, and scalable tenant isolation.

    Journal: Sensors (Basel, Switzerland)

    Article Title: AI and IoT-Driven Monitoring and Visualisation for Optimising MSP Operations in Multi-Tenant Networks: A Modular Approach Using Sensor Data Integration

    doi: 10.3390/s25196248

    Figure Lengend Snippet: Modular architecture of decentralised monitoring platform. Data flow of the monitoring pipeline work as the metric data is collected from exporters and processed through Prometheus instances hosted on Raspberry Pi edge nodes. Aggregated data is forwarded to the Mimir time-series database for long-term storage and AI model training. Trained models are containerised and deployed either centrally or at the edge for inference. Predictions are exposed as Prometheus metrics and visualised in Grafana dashboards. This architecture enables hybrid edge–cloud analytics, real-time alerting, and scalable tenant isolation.

    Article Snippet: The integration of this data into the data visualisation tool Grafana facilitates department-wise usage aggregation.

    Techniques: Isolation

    RT-qPCR validation of RNA-sequencing. Top 6 downregulated (lower expression in blood-exposed samples) and top 6 upregulated (higher expression in blood-exposed samples). All statistical analyses were performed with unpaired t-tests. ns = not significant, ns = not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, N = 3, α = 0.05.

    Journal: bioRxiv

    Article Title: Adult organotypic brain slice cultures recapitulate extracellular matrix remodelling in haemorrhagic stroke

    doi: 10.1101/2025.07.15.665021

    Figure Lengend Snippet: RT-qPCR validation of RNA-sequencing. Top 6 downregulated (lower expression in blood-exposed samples) and top 6 upregulated (higher expression in blood-exposed samples). All statistical analyses were performed with unpaired t-tests. ns = not significant, ns = not significant, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, N = 3, α = 0.05.

    Article Snippet: All visualisation of RNA- sequencing data was performed using GraphPad Prism 10.3.

    Techniques: Quantitative RT-PCR, Biomarker Discovery, RNA Sequencing, Expressing