Publication of INSPIRE-based agricultural linked dataRaul Palma
Results of the publication of linked data from the agriculture sector within DATABio project, based on the agriculture data model developed in FOODIE project
This document discusses exploitation opportunities from the DataBio project, which has received funding from the European Union's Horizon 2020 research program. It outlines how big data technology providers, components, and pipelines created by DataBio can be enhanced and used for new applications. DataBio's big data solutions for agriculture, forestry, and fishery allow for reduced costs through efficiencies, increased revenues by improving yields and catches, and new business models. The document encourages collaboration across the big data value chain and exploiting DataBio's assets and pilot results.
Tomas Reznik (Lesprojekt) presented the achievements of the FOODIE project, in particular the FOODIE data model based on the INSPIRE data specifications. This data model is the backbone for precision farming including fertilisation planning, livestock tracking and many other analytics.
This document discusses a project that has received funding from the European Union's Horizon 2020 research and innovation programme. It describes a data sharing and management platform called Wuudis that allows easy sharing of forestry data. Wuudis enables real-time monitoring, reporting and communication from forest fields using mobile devices and integrates satellite imagery data. The project aims to facilitate data sharing among forestry stakeholders and authorities.
06 standards based application deployment & executionplan4all
Marie-Francoise Voidrot (Open Geospatial Consortium) explained the standardisation efforts leading to better and easier development of apps in agriculture, e.g. forest change detection, through OGC web services.
This document discusses the DataBio project, which has received funding from the European Union's Horizon 2020 research and innovation program. The project involves collecting and analyzing big data from various sources in the agriculture, forestry, fishery, and raw materials sectors. It describes how the project is matching existing software and datasets from partners to pilots and using components to help with different phases of data processing. Pipelines of components are being used in pilots like precision agriculture to detect crop diseases. The goal is to develop a platform that brings together actors and allows components to be deployed with relevant data and interfaces to fulfill pilot requirements.
BDV Webinar Series - Caj - Big Data Breakthroughs for Global Bio-economy Busi...Big Data Value Association
The main goal of the DataBio project is to show the benefits of Big Data technologies in the raw material production from agriculture, forestry and fishery/aquaculture for the bio-economy industry to produce food, energy and bio-materials responsibly and sustainably. DataBio proposes to deploy a state of the art, big data platform on top of the existing partners’ infrastructure and solutions – the Big DATABIO Platform. Achieved impacts are measured against anticipations. In this webinar, we present the impact of the DataBio project and of its big data platform after three years of implementation, and we illustrate some of the novel breakthroughs on practical cases of artificial intelligence applications that are meaningfully boosting crop monitoring businesses with a global potential.
Publication of INSPIRE-based agricultural linked dataRaul Palma
Results of the publication of linked data from the agriculture sector within DATABio project, based on the agriculture data model developed in FOODIE project
This document discusses exploitation opportunities from the DataBio project, which has received funding from the European Union's Horizon 2020 research program. It outlines how big data technology providers, components, and pipelines created by DataBio can be enhanced and used for new applications. DataBio's big data solutions for agriculture, forestry, and fishery allow for reduced costs through efficiencies, increased revenues by improving yields and catches, and new business models. The document encourages collaboration across the big data value chain and exploiting DataBio's assets and pilot results.
Tomas Reznik (Lesprojekt) presented the achievements of the FOODIE project, in particular the FOODIE data model based on the INSPIRE data specifications. This data model is the backbone for precision farming including fertilisation planning, livestock tracking and many other analytics.
This document discusses a project that has received funding from the European Union's Horizon 2020 research and innovation programme. It describes a data sharing and management platform called Wuudis that allows easy sharing of forestry data. Wuudis enables real-time monitoring, reporting and communication from forest fields using mobile devices and integrates satellite imagery data. The project aims to facilitate data sharing among forestry stakeholders and authorities.
06 standards based application deployment & executionplan4all
Marie-Francoise Voidrot (Open Geospatial Consortium) explained the standardisation efforts leading to better and easier development of apps in agriculture, e.g. forest change detection, through OGC web services.
This document discusses the DataBio project, which has received funding from the European Union's Horizon 2020 research and innovation program. The project involves collecting and analyzing big data from various sources in the agriculture, forestry, fishery, and raw materials sectors. It describes how the project is matching existing software and datasets from partners to pilots and using components to help with different phases of data processing. Pipelines of components are being used in pilots like precision agriculture to detect crop diseases. The goal is to develop a platform that brings together actors and allows components to be deployed with relevant data and interfaces to fulfill pilot requirements.
BDV Webinar Series - Caj - Big Data Breakthroughs for Global Bio-economy Busi...Big Data Value Association
The main goal of the DataBio project is to show the benefits of Big Data technologies in the raw material production from agriculture, forestry and fishery/aquaculture for the bio-economy industry to produce food, energy and bio-materials responsibly and sustainably. DataBio proposes to deploy a state of the art, big data platform on top of the existing partners’ infrastructure and solutions – the Big DATABIO Platform. Achieved impacts are measured against anticipations. In this webinar, we present the impact of the DataBio project and of its big data platform after three years of implementation, and we illustrate some of the novel breakthroughs on practical cases of artificial intelligence applications that are meaningfully boosting crop monitoring businesses with a global potential.
This document is part of a project called DataBio that received funding from the European Union to develop big data analytics solutions. It discusses fishery pilot scenarios and elements that are part of Work Package 3 of the DataBio project. The document also outlines expected outcomes, including motivation and business views, and describes the main impact areas of the fishery pilot demonstrators as optimizing fishery operations and planning as well as sustainability and revenue maximization.
This document describes pilot B1.4 which takes place in Rostenice, Czech Republic. The pilot aims to develop a platform for mapping crop vigor using earth observation (EO) data to help with variable rate application of fertilizers and pesticides. Key performance indicators include the area of processed EO data, accuracy of delineated management zones, and increased fertilizer use efficiency. The pilot will use data from Landsat and Sentinel satellites to identify crop status, spatial variability, and management zones to help farmers apply inputs more precisely.
This document summarizes the DataBio project, which received funding from the European Union's Horizon 2020 program. The project aims to boost bioeconomy industries by showing how big data technologies can increase performance and productivity in raw material production from agriculture, forestry, and fisheries. It will build a platform for handling distributed, heterogeneous data from these domains and provide analytics capabilities. The platform will be tested through pilots focused on precision agriculture, horticulture, arable farming, and subsidies/insurance. The project involves 48 partners, including several that provide relevant technologies and solutions.
This document summarizes the DataBio project, which received funding from the European Union's Horizon 2020 program. The project aims to boost bioeconomy industries by showing how big data technologies can increase performance and productivity in raw material production from agriculture, forestry, and fisheries. It will build a platform for handling distributed, heterogeneous data from these domains and provide analytics capabilities. The platform will be tested through pilots focused on precision agriculture, horticulture, arable farming, and subsidies/insurance. The project involves 48 partners, including several that provide relevant technologies and solutions.
This document summarizes the agenda and objectives of the DataBio first stakeholder event workshop in Rome on September 25th, 2017. The workshop aimed to boost networking, stimulate synergies and international cooperation, and foster understanding of DataBio objectives and expected outcomes. The agenda included presentations on the DataBio project, platform, and pilot scenarios in agriculture, forestry, and fisheries. It also included sessions on stakeholder analysis, exploitation opportunities, and interviews with stakeholders.
EDF2013: Selected Talk, Ghislain Atemezing: Towards Interoperable Visualizati...European Data Forum
Selected talk of Ghislain Atemezing at the European Data Forum 2013, 10 April 2013 in Dublin, Ireland: Towards Interoperable Visualization Applications Over Linked Data
This document discusses implementing linked data publication pipelines for agricultural use cases. It describes identifying relevant datasets from various sources for three use cases, including farm data, open EU/national datasets, and sensor data. Various ontologies are selected for representing the different data types and sources in RDF. The goal is to define automated processes to transform heterogeneous data into linked open data in order to integrate and provide access to agricultural data.
This document summarizes funding opportunities for ICT projects in the Horizon 2020 framework program for 2014-2015. It outlines calls for big data, open data, and language technologies projects, including innovation actions to develop new solutions, research projects to advance technologies, and coordination actions. The goals are to help companies build innovative data products, address barriers to data reuse, and crack the language barrier in Europe to facilitate multilingual communication.
EDF2014: Piek Vossen, Professor Computational Lexicology, VU University Amste...European Data Forum
Invited Talk of Piek Vossen, Professor Computational Lexicology, VU University Amsterdam, Netherlands at the European Data Forum 2014, 19 March 2014 in Athens, Greece: NewsReader: recording history by processing massive streams of daily news
Odile Hologne's presentation at the eROSA Workshop “Towards Open Science in Agriculture & Food”, a side event to High Level conference on FOOD 2030, Plovdiv, Bulgaria (13/6/2018)
Data management plans – EUDAT Best practices and case study | www.eudat.euEUDAT
| www.eudat.eu | Presentation given by Stéphane Coutin during the PRACE 2017 Spring School joint training event with the EU H2020 VI-SEEM project (https://vi-seem.eu/) organised by CaSToRC at The Cyprus Institute. Science and more specifically projects using HPC is facing a digital data explosion. Instruments and simulations are producing more and more volume; data can be shared, mined, cited, preserved… They are a great asset, but they are facing risks: we can miss storage, we can lose them, they can be misused,… To start this session, we will review why it is important to manage research data and how to do this by maintaining a Data Management Plan. This will be based on the best practices from EUDAT H2020 project and European Commission recommendation. During the second part we will interactively draft a DMP for a given use case.
DMP exercise: linking data management activities to services - EUDAT Summer ...EUDAT
Now that you’ve heard about several kinds of data services, how could they fit in your workflow? In this hands-on hour Sarah and Marjan invite you to flesh out your Data Management Plan by linking to services. They will also introduce tools to link publications and data: you know how they are related, but is this transparent for others who may be interested in your work?
Visit: https://www.eudat.eu/eudat-summer-school
BDE-SC1 Webinar: OpenPHACTS Re-engineered with Big Data EuropeBigData_Europe
Watch this webinar on YouTube: https://youtu.be/MwG0yhrctDs
Slides for the latest update on our Big Data Europe pilot in Societal Challenge 1: Health, Demographic Change and Wellbeing.
Last year we successfully completed the first phase of this pilot, replicating the functionality of the Open PHACTS Discovery Platform on the BDE infrastructure. The Open PHACTS Discovery Platform brings together pharmacological data resources in an integrated, interoperable infrastructure, and has been developed to reduce barriers to drug discovery for industry, academia, and small businesses.
Learn more about the progress we’ve made, and what’s coming next.
1. General overview of the Big Data Europe project and Societal Challenges it addresses (Ronald Siebes, VU Amsterdam)
2. The Big Data Europe infrastructure, generic components that are being developed, and their flexibility for different applications (Hajira Jabeen, University of Bonn)
3. Latest details of the current state of the Open PHACTS architecture in BDE, and ongoing work (Nick Lynch, CTO, Open PHACTS Foundation)
Project Description of the Linked Open Data (LOD) PILOT Austria - presented at the PiLOD event at VU Amsterdam (Netherlands) on 29.01. 2014 (see: http://www.pilod.nl/) by Martin Kaltenböck of Semantic Web Company.
State of Play presentation at the LOD2 Plenary Vienna 2012: WP9A - LOD for a Distributed Marketplace for Public Sector Contracts by Vojtěch Svátek (UEP)
InfoWeek Digitization Day - The e-ROSA projecte-ROSA
- eROSA is an 18-month project funded by the European Union's Horizon 2020 programme to develop a roadmap for an e-infrastructure to support open science in agriculture.
- The objectives are to build a community of researchers and ICT specialists around this issue, improve understanding of relevant existing infrastructures and projects, and develop a shared vision and roadmap.
- There is a need for such a roadmap as the agricultural research domain currently lacks a coherent research infrastructure, and faces increasing data volume, variety and flows that require improved data sharing, interoperability and analysis capabilities.
Linked data publication pipelines for agri-related use casesRaul Palma
This document discusses implementing linked data publication pipelines for agricultural data. It describes three use cases: 1) publishing farm data from a cereals and biomass crops pilot as linked data, 2) publishing open EU and national agricultural datasets as linked data, and 3) publishing sensor data as linked data from a machinery management pilot. It identifies relevant datasets for the use cases and discusses principles for defining automated pipelines to transform and publish different datasets as linked data.
DataBio Architecture for Big Data and Big Data Visualisationplan4all
This document describes a project called DataBio that received funding from the European Union's Horizon 2020 research and innovation programme. DataBio created a platform and tools for handling big data in the agriculture, forestry, and fishery sectors. The document discusses DataBio's pilots, architecture, reports, use cases involving linking and visualizing data, and an approach for unifying data and metadata using various APIs.
This document is part of a project called DataBio that received funding from the European Union to develop big data analytics solutions. It discusses fishery pilot scenarios and elements that are part of Work Package 3 of the DataBio project. The document also outlines expected outcomes, including motivation and business views, and describes the main impact areas of the fishery pilot demonstrators as optimizing fishery operations and planning as well as sustainability and revenue maximization.
This document describes pilot B1.4 which takes place in Rostenice, Czech Republic. The pilot aims to develop a platform for mapping crop vigor using earth observation (EO) data to help with variable rate application of fertilizers and pesticides. Key performance indicators include the area of processed EO data, accuracy of delineated management zones, and increased fertilizer use efficiency. The pilot will use data from Landsat and Sentinel satellites to identify crop status, spatial variability, and management zones to help farmers apply inputs more precisely.
This document summarizes the DataBio project, which received funding from the European Union's Horizon 2020 program. The project aims to boost bioeconomy industries by showing how big data technologies can increase performance and productivity in raw material production from agriculture, forestry, and fisheries. It will build a platform for handling distributed, heterogeneous data from these domains and provide analytics capabilities. The platform will be tested through pilots focused on precision agriculture, horticulture, arable farming, and subsidies/insurance. The project involves 48 partners, including several that provide relevant technologies and solutions.
This document summarizes the DataBio project, which received funding from the European Union's Horizon 2020 program. The project aims to boost bioeconomy industries by showing how big data technologies can increase performance and productivity in raw material production from agriculture, forestry, and fisheries. It will build a platform for handling distributed, heterogeneous data from these domains and provide analytics capabilities. The platform will be tested through pilots focused on precision agriculture, horticulture, arable farming, and subsidies/insurance. The project involves 48 partners, including several that provide relevant technologies and solutions.
This document summarizes the agenda and objectives of the DataBio first stakeholder event workshop in Rome on September 25th, 2017. The workshop aimed to boost networking, stimulate synergies and international cooperation, and foster understanding of DataBio objectives and expected outcomes. The agenda included presentations on the DataBio project, platform, and pilot scenarios in agriculture, forestry, and fisheries. It also included sessions on stakeholder analysis, exploitation opportunities, and interviews with stakeholders.
EDF2013: Selected Talk, Ghislain Atemezing: Towards Interoperable Visualizati...European Data Forum
Selected talk of Ghislain Atemezing at the European Data Forum 2013, 10 April 2013 in Dublin, Ireland: Towards Interoperable Visualization Applications Over Linked Data
This document discusses implementing linked data publication pipelines for agricultural use cases. It describes identifying relevant datasets from various sources for three use cases, including farm data, open EU/national datasets, and sensor data. Various ontologies are selected for representing the different data types and sources in RDF. The goal is to define automated processes to transform heterogeneous data into linked open data in order to integrate and provide access to agricultural data.
This document summarizes funding opportunities for ICT projects in the Horizon 2020 framework program for 2014-2015. It outlines calls for big data, open data, and language technologies projects, including innovation actions to develop new solutions, research projects to advance technologies, and coordination actions. The goals are to help companies build innovative data products, address barriers to data reuse, and crack the language barrier in Europe to facilitate multilingual communication.
EDF2014: Piek Vossen, Professor Computational Lexicology, VU University Amste...European Data Forum
Invited Talk of Piek Vossen, Professor Computational Lexicology, VU University Amsterdam, Netherlands at the European Data Forum 2014, 19 March 2014 in Athens, Greece: NewsReader: recording history by processing massive streams of daily news
Odile Hologne's presentation at the eROSA Workshop “Towards Open Science in Agriculture & Food”, a side event to High Level conference on FOOD 2030, Plovdiv, Bulgaria (13/6/2018)
Data management plans – EUDAT Best practices and case study | www.eudat.euEUDAT
| www.eudat.eu | Presentation given by Stéphane Coutin during the PRACE 2017 Spring School joint training event with the EU H2020 VI-SEEM project (https://vi-seem.eu/) organised by CaSToRC at The Cyprus Institute. Science and more specifically projects using HPC is facing a digital data explosion. Instruments and simulations are producing more and more volume; data can be shared, mined, cited, preserved… They are a great asset, but they are facing risks: we can miss storage, we can lose them, they can be misused,… To start this session, we will review why it is important to manage research data and how to do this by maintaining a Data Management Plan. This will be based on the best practices from EUDAT H2020 project and European Commission recommendation. During the second part we will interactively draft a DMP for a given use case.
DMP exercise: linking data management activities to services - EUDAT Summer ...EUDAT
Now that you’ve heard about several kinds of data services, how could they fit in your workflow? In this hands-on hour Sarah and Marjan invite you to flesh out your Data Management Plan by linking to services. They will also introduce tools to link publications and data: you know how they are related, but is this transparent for others who may be interested in your work?
Visit: https://www.eudat.eu/eudat-summer-school
BDE-SC1 Webinar: OpenPHACTS Re-engineered with Big Data EuropeBigData_Europe
Watch this webinar on YouTube: https://youtu.be/MwG0yhrctDs
Slides for the latest update on our Big Data Europe pilot in Societal Challenge 1: Health, Demographic Change and Wellbeing.
Last year we successfully completed the first phase of this pilot, replicating the functionality of the Open PHACTS Discovery Platform on the BDE infrastructure. The Open PHACTS Discovery Platform brings together pharmacological data resources in an integrated, interoperable infrastructure, and has been developed to reduce barriers to drug discovery for industry, academia, and small businesses.
Learn more about the progress we’ve made, and what’s coming next.
1. General overview of the Big Data Europe project and Societal Challenges it addresses (Ronald Siebes, VU Amsterdam)
2. The Big Data Europe infrastructure, generic components that are being developed, and their flexibility for different applications (Hajira Jabeen, University of Bonn)
3. Latest details of the current state of the Open PHACTS architecture in BDE, and ongoing work (Nick Lynch, CTO, Open PHACTS Foundation)
Project Description of the Linked Open Data (LOD) PILOT Austria - presented at the PiLOD event at VU Amsterdam (Netherlands) on 29.01. 2014 (see: http://www.pilod.nl/) by Martin Kaltenböck of Semantic Web Company.
State of Play presentation at the LOD2 Plenary Vienna 2012: WP9A - LOD for a Distributed Marketplace for Public Sector Contracts by Vojtěch Svátek (UEP)
InfoWeek Digitization Day - The e-ROSA projecte-ROSA
- eROSA is an 18-month project funded by the European Union's Horizon 2020 programme to develop a roadmap for an e-infrastructure to support open science in agriculture.
- The objectives are to build a community of researchers and ICT specialists around this issue, improve understanding of relevant existing infrastructures and projects, and develop a shared vision and roadmap.
- There is a need for such a roadmap as the agricultural research domain currently lacks a coherent research infrastructure, and faces increasing data volume, variety and flows that require improved data sharing, interoperability and analysis capabilities.
Linked data publication pipelines for agri-related use casesRaul Palma
This document discusses implementing linked data publication pipelines for agricultural data. It describes three use cases: 1) publishing farm data from a cereals and biomass crops pilot as linked data, 2) publishing open EU and national agricultural datasets as linked data, and 3) publishing sensor data as linked data from a machinery management pilot. It identifies relevant datasets for the use cases and discusses principles for defining automated pipelines to transform and publish different datasets as linked data.
DataBio Architecture for Big Data and Big Data Visualisationplan4all
This document describes a project called DataBio that received funding from the European Union's Horizon 2020 research and innovation programme. DataBio created a platform and tools for handling big data in the agriculture, forestry, and fishery sectors. The document discusses DataBio's pilots, architecture, reports, use cases involving linking and visualizing data, and an approach for unifying data and metadata using various APIs.
This document summarizes the DataBio project, which received funding from the European Union's Horizon 2020 program. The project aims to boost bioeconomy industries by showing how big data technologies can increase performance and productivity in raw material production from agriculture, forestry, and fisheries. It will build a platform for handling distributed, heterogeneous data from these domains and provide analytics capabilities. The platform will be tested through pilots focused on precision agriculture, horticulture, arable farming, and subsidies/insurance. The project involves 48 partners, including several that provide relevant technologies and solutions.
This document summarizes the results of the DataBio project which received funding from the European Union's Horizon 2020 research and innovation programme. It describes 27 pilots conducted across agriculture, forestry, and fisheries that utilized big data sources and analytics. Key results included 95 technology components, 38 datasets, 15 data pipelines developed, over 180 events attended, and 31 exploitable results identified. Specific pilots demonstrated reduced costs and increased yields from precision agriculture and optimized vessel operations in fisheries.
BDV Webinar Series - Thanasis - Big Data Breakthroughs for Global Bio-economy...Big Data Value Association
The main goal of the DataBio project is to show the benefits of Big Data technologies in the raw material production from agriculture, forestry and fishery/aquaculture for the bio-economy industry to produce food, energy and bio-materials responsibly and sustainably. DataBio proposes to deploy a state of the art, big data platform on top of the existing partners’ infrastructure and solutions – the Big DATABIO Platform. Achieved impacts are measured against anticipations. In this webinar, we present the impact of the DataBio project and of its big data platform after three years of implementation, and we illustrate some of the novel breakthroughs on practical cases of artificial intelligence applications that are meaningfully boosting crop monitoring businesses with a global potential.
MEASURE H2020 project presented at OW2con'19, June 12-13, 2019, Paris. OW2
How to MEASURE The Enterprise Architecture Models Quality With The MEASURE Platform, The H2020 Databio Project Success Story. This presentation is given by Alessandra Bagnato, Research Scientist and Head of the Research Unit at Softeam.
02 agriculture challenges, existing standardisation efforts and data bio agri...plan4all
Karel Charvat (Lesprojekt) presented the current challenges in agriculture. Karel mentioned the standardisation in agriculture as one of the main challenges that needs much more attention.
BDV Webinar Series - Ephrem - Big Data Breakthroughs for Global Bio-economy B...Big Data Value Association
The main goal of the DataBio project is to show the benefits of Big Data technologies in the raw material production from agriculture, forestry and fishery/aquaculture for the bio-economy industry to produce food, energy and bio-materials responsibly and sustainably. DataBio proposes to deploy a state of the art, big data platform on top of the existing partners’ infrastructure and solutions – the Big DATABIO Platform. Achieved impacts are measured against anticipations. In this webinar, we present the impact of the DataBio project and of its big data platform after three years of implementation, and we illustrate some of the novel breakthroughs on practical cases of artificial intelligence applications that are meaningfully boosting crop monitoring businesses with a global potential.
Exposing EO Linked (meta-)Data from OpenSearch CatalogueRaul Palma
This document discusses exposing Earth observation (EO) linked (meta-)data from OpenSearch catalogues. It provides background on linked data principles and publishing EO data as linked data. It proposes an approach to generate linked data from EO product metadata by implementing wrappers around APIs to transform requests and results into RDF in real-time. This allows querying REST APIs with SPARQL and exposing the results through a SPARQL endpoint without needing to store the data physically. The FedEO system provides a specific use case, federating access to multiple EO catalogues through its OpenSearch interface.
This document discusses the DataBio project, which received Horizon 2020 funding. The project aims to boost the bioeconomy industry by applying big data technologies to data collected from farms, fisheries, and forestry. Large amounts of sensor data could enhance knowledge of raw material production if combined and analyzed at scale. The project will showcase big data benefits through pilots in agriculture, including precision horticulture, arable farming, and insurance/subsidies. It will integrate data from various sources and use analytics for descriptive, predictive and other purposes. The document also describes cooperation between SensLog and CEP Proton for sensor data management and complex event processing.
This document summarizes a project that applied big data techniques in precision agriculture in Greece. The project collected data from sensors, satellites, and farmers to generate advisory services helping small farmers optimize irrigation, fertilization, and pest/disease management. This reduced costs and increased yields for olive, grape, and peach farmers. The project established a business model providing the services by subscription to support small farmers with no upfront investment. Results included reduced costs, optimized fertilizer use, and increased profits for participating farmers.
This document summarizes a workshop on new ways to tackle agriculture challenges. The workshop is part of the DataBio project, which has received funding from the European Union to develop big data solutions for the bioeconomy sector. The DataBio project involves 48 partners from 17 countries working over 3 years with a total budget of 16.2 million euros. The workshop agenda includes presentations on agriculture challenges, DataBio agriculture pilots, standardization efforts, exploitation platforms for agriculture monitoring, and demonstrations of web-based visualization tools for open agriculture data. The goal is to discuss how big data and new technologies can help increase sustainability and productivity in the agriculture sector.
This document discusses 4 forest monitoring products (SF1-SF4) that use Copernicus data to provide information for improved forest management. SF1 identifies vegetation changes. SF2 creates vegetation and deciduous/coniferous maps. SF3 identifies major tree species. SF4 provides detailed forest inventories at the stand scale. The products aim to offer cost-effective regional and local information to forest owners and authorities. Automated processing using the Geospatial Exploitation Platform allows recurrent production and distribution of results.
What is an RDA Europe Node and how to get involved. Webinar by RDA Europe 4.0 to introduce the network of RDA Europe nodes and the process to become a new node.
PaNOSC and Research Data Management / Battery2030+ Initiative Workshop / 12 M...PaNOSC
On March 12th, 2021, PaNOSC coordinator, Andy Götz, attended with an invited talk the 2nd online workshop of the Battery2030+ Initiative, focused on the benefits of research data management (RDM) and guidelines, through the showcase of best practice examples, including PaNOSC.
The document discusses the Once-Only Principle Project (TOOP), an EU-funded project aimed at establishing a digital single market in Europe. The project seeks to (1) bridge data silos and reduce duplication by enabling information to be accessed once and reused many times across borders and sectors; (2) be user-centric and bring most-used citizen services online with cross-border access; and (3) decrease administrative burdens through increased data sharing between public agencies. The TOOP project has run pilots involving 20 EU member states and over 50 partners from public administrations, universities, and businesses to test the feasibility of the Once-Only Principle across Europe.
Big Data PPP Industrial Data Platforms - Towards cross-sectorial optimization and traceability
To start identifying synergies and to learn how different projects will address key data collection, sharing, integration, and exploitation challenges, a series of webinars have been organized under the umbrella of this Big Data Value PPP. These webinars are also organized by BDVA, BDVe project, and other projects which are part of this PPP.
Similar to Integration of Open Land Use, Smart Point of Interest and Open Transport Map using RDF (20)
Agrihub INSPIRE Hackathon 2021: Challenge #7: Analysis, processing and standa...plan4all
This is a presentation of results of Challenge #7: Analysis, processing and standardisation of data from agriculture machinery for easier utilization by farmers of the Agrihub INSPIRE Hackathon 2021.
Challenge #3 agro environmental services final presentationplan4all
This document discusses 3 use cases for agro-environmental services. Use case 1 aims to improve access and sharing of geo data by addressing challenges with data inventory, publishing, and communication. Use case 2 examines structural changes to water canals by mapping a canal in Slovakia and analyzing subsidized land nearby. Use case 3 identifies dynamic landscape changes over time through vulnerability analysis and analyzing landscape structure changes using data like Corine Land Cover and NDVI indexes. The team behind this work includes representatives from universities and government organizations in Slovakia.
The document discusses a pilot project that will implement a new soil methodology in 7 locations in the EU and 4 locations in China. It will create an integrated platform using advanced sensing tools, land monitoring, and data fusion to achieve sustainable land management. The platform aims to maximize land productivity while minimizing environmental impacts. The project partners will run pilots of this new methodology in Greece, Spain, Belgium, Czech Republic, and several locations in China.
Calculation of agro climatic factors from global climatic dataplan4all
Authors: Pavel Hájek,
Raitis Berzins , Jiří Valeš, Martin Pitoňák , Vincent
Onckelet , Tomáš Andrš, Veronika Osmiková , Ronald
Ssembajwe , Amit Kirschenbaum , Jörg Schliesser , Michal Kepka & Karel Jedlička
Digitalization of indigenous knowledge in African agriculture for fostering f...plan4all
Authors:
Antoine Kantiza, AKANTIZA CONSULT, Burundi
Didier Muyiramye, Swedish University of Agricultural Sciences, Rwanda
Elias Cherenet Weldemariam, HARAMAYA UNIVERSITY, Ethiopia
Petr Horak, WIRELESSINFO, Czech Republic
Robert Sabimana, Frutus Fresco Ltd, Uganda
Pavel Hajek, West Bohemia University, Czech Republic
Tuula Löytty, Smart & Lean Hub Oy, Finland
Demet Osmancelebioglu, Smart & Lean Hub Oy, Finland
This document summarizes social innovations in rural areas to address challenges from the Covid-19 pandemic. It describes projects in Finland and Spain that raised awareness of local food and businesses, supported community centers, repurposed rural accommodations, and improved rural digital infrastructure and connectivity. The atlas of best practices aims to provide rural stakeholders with ideas to support farmers and communities, integrate new residents, promote local tourism, and improve business practices as the collection expands.
The EUXDAT project, which received Horizon 2020 funding, has come to an end after three years of collaboration and development. The project developed an e-infrastructure platform to address UN Sustainable Development Goals and the European Green Deal. Key deliverables of the project included the final specification of the infrastructure platform (D4.5), definition of three pilots and scenarios (D5.6), description of the end users' platform (End Users’ Platform v3), and description of the infrastructure platform and services (D4.6, D5.7). Webinars were held in October 2020 to present results of the pilots and infrastructure.
Karel charvat map-compositions-format-intro-presentation-by-karel (1)plan4all
Karel Charvat on behalf of Plan4all, Lesprojekt, BOSC and Asplan Viak gave a presentation about the project to create a Google Docs-like map application and map composition format.
Karel charvat map-whiteboard-collaborative-map-making-breakout-sessionplan4all
Karel Charvat on behalf of Plan4all, Lesprojekt, BOSC and Asplan Viak gave a presentation about the project to create a Google Docs-like map application and map composition format.
This document discusses codes of conduct for farm data sharing. It notes the challenges in balancing farmers' rights to protect business data while also needing to share data for precision agriculture and services. Existing codes focus on consent, disclosure and transparency. Challenges include ensuring proper representation, independent oversight, and alignment with legal frameworks. Success requires adoption, credibility, clarity and balancing interests. Codes can help build trust but self-regulation has limitations; cooperation across stakeholders is important.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Integration of Open Land Use, Smart Point of Interest and Open Transport Map using RDF
1. 1
Tomáš Řezník, Karel Charvát, Raúl Palma, Dmitrij Kozuch, Otakar
Čerba, Karel Jedlička, Raitis Berzins and Runar Bergheim
„Linked Open Data in Agriculture“
MACS-G20 Workshop in Berlin (Germany), 27 – 28 September 2017
This project has received funding from the European
Union’s Horizon 2020 research and innovation
programme under grant agreement No 732064.
2. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
1. Motivations
2. Projects introduction
3. Data stores introduction
4. Datasets integration
5. Business model
6. Conclusions
3. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
Sir, we have data with whole
country coverage for you at
very special price.
Are your data available also for
rural areas?
Well,...
4. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
And if you find rural data…
…up to 80% of all costs for establishing
a Geographic Information System (GIS)
relates to data acquisition…
…so why not to re-use outputs from
past European projects?
5. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
EU FP7, ICT CIP, 2014- 2017
Open Land Use (OLU)
EU FP7, ICT CIP, 2014- 2017
Open Transport Map (OTM)
EU FP7, ICT CIP, 2014- 2017
Smart Points of Interest (SPOI)
Horizon 2020 ICT call on Big Data
48 partners from 16 countries
Funded between 2017 and 2020
Overall budget 16 mil. €
EU contribution 13 mil. €
6. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
Seamless coverage of the most of
Europe (around 45 millions objects)
Re-use of INSPIRE (2007/2/EC)
principles
◦ Data model based on INSPIRE Existing
Land Use object data model (Annex III
Land Use Spatial data theme)
◦ Reliable local sources of data are used
which have certain quality, certain update
period, persistent identifier where possible
◦ Detailed object-based metadata
7. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
8. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
http://sdi4apps.eu/open_land_use/
SHP RDF
9. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
http://sdi4apps.eu/open_land_use/download/
Query: SELECT ?o ?p ?s FROM <http://www.sdi4apps.eu/olu.rdf>
WHERE { ?o
<http://sdi4apps.eu/open_land_use/rdf#municipalEurostatCode
> ?filter_categ. FILTER (str(?filter_categ)='BE244021'). ?o
?p ?s. } ORDER BY ?o LIMIT 10
10. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
http://opentransportmap.info/
11. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
Seamless coverage of Europe (around
45 millions objects)
Re-use of INSPIRE (2007/2/EC)
principles
◦ Data model based on INSPIRE Road
Transport Networks object data model
(Annex I Transport Networks Spatial data
theme)
◦ OpenStreetMap used as a primary source
of information, machinery monitoring
◦ Routable
12. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
http://opentransportmap.info/
SHP GML
RDF
13. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
<rdf:Description
rdf:about="http://opentransportmap.info/rdf/Vol
ume16618393">
<otm:roadlink
rdf:resource="http://opentransportmap.info/rdf/
11836027"/>
<otm:trafficvolume>5</otm:trafficvolume>
<otm:column>333</otm:column>
<otm:fro
mtime>2016-04-04 00:00:00</otm:fromtime>
<otm:totime>2016-04-04 01:00:00</otm:totime>
</rdf:Description>
14. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
Seamless and open Points of Interest
database
More than 27 million objects
Europe, Africa and South East Asia
15. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
http://sdi4apps.eu/spoi
16. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
OWL ontology
SPARQL endpoint
17. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
Massive data transformation into RDF
Linking of datasets
Loading datasets in Virtuoso
Query building
Data visualization
18. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
Used software:
◦ D2RQ for transforming Relational Databases as
Virtual RDF Graphs
◦ RDF for the representation of data
◦ Ontologies providing the underlying vocabulary and
relations
◦ Virtuoso for storing the semantic datasets
◦ Sparql for querying semantic data
◦ Silk for discovery of links
◦ Hslayers NG for visualisation of data
◦ Metaphactory for visualisation of data
19. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
Dataset Name Graph in FOODIE endpoint Source Triples
OLU** http://w3id.org/foodie/olu# Transformed from PostgreSQL 127,925,971
SPOI http://www.sdi4apps.eu/poi.rdf Source provided by WRLS,
modified and fixed before
loading
381,393,555
NUTS http://nuts.geovocab.org/ Open Source 316,238
OTM*** http://w3id.org/foodie/otm# Transformed from PostgreSQL 154,340,611
Dataset Name Graph in FOODIE endpoint Source Triples
Hilucs
classification
http://w3id.org/foodie/hilucs# Transformed from PostgreSQL 397
Urban Atlas* http://w3id.org/foodie/atlas# Transformed from PostgreSQL 19,606,025
Corine* http://w3id.org/foodie/corine# Transformed from PostgreSQL 16,777,533
Eurovoc http://foodie-cloud.org/eurovoc Open Source 425,667
Emergel http://foodie-cloud.org/emergel CTIC 256,239
20. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
Sparql endpoint: https://www.foodie-
cloud.org/sparql
21. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
◦ Faceted search browser: http://www.foodie-
cloud.org/fct/
22. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
◦ Map visualisation:
http://ng.hslayers.org/examples/olu_spoi/?hs_panel=info&hs_x=160779
9.902082933&hs_y=6462976.717926565&hs_z=16&visible_layers=Base%
20layer;Land%20use%20parcels
23. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
◦ Map visualisation:
http://ng.hslayers.org/examples/olu_spoi/?hs_panel=info&hs_x=160779
9.902082933&hs_y=6462976.717926565&hs_z=16&visible_layers=Base%
20layer;Land%20use%20parcels
24. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
Data already once co-financed from European
budget
Data available free of charge
Further analyses, data processing, live data
etc. is a subject of payment
25. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
DataBio adds value through application logic
on top of presented data.
26. This document is part of a project that has received funding from the European Union’s Horizon 2020 programme under grant agreement No 732064. It is the property of
the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. See www.databio.eu.
Tomáš Řezník
Lesprojekt / Masaryk University
tomas.reznik@sci.muni.cz