This document presents DataBio's Data Management Plan (DMP) as required by the European Commission's Horizon 2020 program. It summarizes the key types and formats of data that will be collected and generated by the DataBio project related to forestry, agriculture, and fisheries. These include structured, semi-structured, unstructured, and machine-generated big data from various sources like sensors, imagery, and machinery. The DMP describes how the data will be managed to ensure it is findable, accessible, interoperable, and reusable as required by the FAIR principles. It also addresses ethical issues, data security, and roles and responsibilities for data management support.
Research engagement in EUDAT| www.eudat.eu | EUDAT
| www.eudat.eu | EUDAT’s vision is to enable European researchers and practitioners from any research discipline to preserve, find, access, and process data in a trusted environment, as part of a Collaborative Data Infrastructure (CDI) conceived as a network of collaborating, cooperating centres, that combine community-specific data repositories with the permanence and persistence of some of Europe’s largest scientific data centres. EUDAT services are community driven solutions. This presentation describes the different ways EUDAT engages with the research communities
An Comprehensive Study of Big Data Environment and its Challenges.ijceronline
Big Data is a data analysis methodology enabled by recent advances in technologies and Architecture. Big data is a massive volume of both structured and unstructured data, which is so large that it's difficult to process with traditional database and software techniques. This paper provides insight to Big data and discusses its nature, definition that include such features as Volume, Velocity, and Variety .This paper also provides insight to source of big data generation, tools available for processing large volume of variety of data, applications of big data and challenges involved in handling big data
The NIH Data Commons - BD2K All Hands Meeting 2015Vivien Bonazzi
Presentation given at the BD2K All Hands meeting in Bethesda, MD, USA in November 2015
https://datascience.nih.gov/bd2k/events/NOV2015-AllHands
Video cast of this presentation:
http://videocast.nih.gov/summary.asp?Live=17480&bhcp=1
talk starts at 2hrs 40min (its about 55mins long) - includes video!
Document describing the Commons : https://datascience.nih.gov/commons
The problem of radicalisation is very high on the European agenda as increasing numbers of young European radicals return from Syria and use the internet to disseminate propaganda. To enable policy makers to design policies to address radicalisation effectively, Policy Cloud consortium will collect data from social media and other sources including the open-source Global Terrorism Database (GTD), the Onion City search engine which accesses data over the TOR dark web sites, and Twitter ( through Firehose). The data will be analysed using sentiment analysis and opinion mining software.
Research engagement in EUDAT| www.eudat.eu | EUDAT
| www.eudat.eu | EUDAT’s vision is to enable European researchers and practitioners from any research discipline to preserve, find, access, and process data in a trusted environment, as part of a Collaborative Data Infrastructure (CDI) conceived as a network of collaborating, cooperating centres, that combine community-specific data repositories with the permanence and persistence of some of Europe’s largest scientific data centres. EUDAT services are community driven solutions. This presentation describes the different ways EUDAT engages with the research communities
An Comprehensive Study of Big Data Environment and its Challenges.ijceronline
Big Data is a data analysis methodology enabled by recent advances in technologies and Architecture. Big data is a massive volume of both structured and unstructured data, which is so large that it's difficult to process with traditional database and software techniques. This paper provides insight to Big data and discusses its nature, definition that include such features as Volume, Velocity, and Variety .This paper also provides insight to source of big data generation, tools available for processing large volume of variety of data, applications of big data and challenges involved in handling big data
The NIH Data Commons - BD2K All Hands Meeting 2015Vivien Bonazzi
Presentation given at the BD2K All Hands meeting in Bethesda, MD, USA in November 2015
https://datascience.nih.gov/bd2k/events/NOV2015-AllHands
Video cast of this presentation:
http://videocast.nih.gov/summary.asp?Live=17480&bhcp=1
talk starts at 2hrs 40min (its about 55mins long) - includes video!
Document describing the Commons : https://datascience.nih.gov/commons
The problem of radicalisation is very high on the European agenda as increasing numbers of young European radicals return from Syria and use the internet to disseminate propaganda. To enable policy makers to design policies to address radicalisation effectively, Policy Cloud consortium will collect data from social media and other sources including the open-source Global Terrorism Database (GTD), the Onion City search engine which accesses data over the TOR dark web sites, and Twitter ( through Firehose). The data will be analysed using sentiment analysis and opinion mining software.
NIH Data Commons - Note: Presentation has animations Vivien Bonazzi
Presented at the Data Commons & Data Science Workshop (University of Chicago - Centre for Data Intensive Science):
NB- there are animations in these slides so static slides might not view well
Moving Toward Big Data: Challenges, Trends and PerspectivesIJRESJOURNAL
Abstract: Big data refers to the organizational data asset that exceeds the volume, velocity, and variety of data typically stored using traditional structured database technologies. This type of data has become the important resource from which organizations can get valuable insightand make business decision by applying predictive analysis. This paper provides a comprehensive view of current status of big data development,starting from the definition and the description of Hadoop and MapReduce – the framework that standardizes the use of cluster of commodity machines to analyze big data. For the organizations that are ready to embrace big data technology, significant adjustments on infrastructure andthe roles played byIT professionals and BI practitioners must be anticipated which is discussed in the challenges of big data section. The landscape of big data development change rapidly which is directly related to the trend of big data. Clearly, a major part of the trend is the result ofthe attempt to deal with the challenges discussed earlier. Lastly the paper includes the most recent job prospective related to big data. The description of several job titles that comprise the workforce in the area of big data are also included.
The effect of technology-organization-environment on adoption decision of bi...IJECEIAES
Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organizationenvironment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
A Comprehensive Study on Big Data Applications and Challengesijcisjournal
Big Data has gained much interest from the academia and the IT industry. In the digital and computing
world, information is generated and collected at a rate that quickly exceeds the boundary range. As
information is transferred and shared at light speed on optic fiber and wireless networks, the volume of
data and the speed of market growth increase. Conversely, the fast growth rate of such large data
generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security.
Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this
study expansively surveys and classifies an assortment of attributes of Big Data, including its nature,
definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a
data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming
model for efficient distributed computing. It works well with semi-structured and unstructured data. A
simple model but good for a lot of applications like Log processing and Web index building.
Real World Application of Big Data In Data Mining Toolsijsrd.com
The main aim of this paper is to make a study on the notion Big data and its application in data mining tools like R, Weka, Rapidminer, Knime,Mahout and etc. We are awash in a flood of data today. In a broad range of application areas, data is being collected at unmatched scale. Decisions that previously were based on surmise, or on painstakingly constructed models of reality, can now be made based on the data itself. Such Big Data analysis now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences. The paper mainly focuses different types of data mining tools and its usage in big data in knowledge discovery.
As we enter the digital economy, it becomes increasingly transparent that the information and data ecosphere will continue to be a complex environment for the foreseeable future, with information being provided from a variety of internal and external sources in the form of files, messages, queries and streams. It would be foolish for any organization to place their bets on any one platform to be their platform of choice because it is incongruent to the thought patterns of the consumers, suppliers, regulators, partners and financiers who will participate in their information ecosphere through data feeds, information requests and a host of other interfaces.
Rather, there is a role of each of these platforms which serve as the conduit for data and the transformation of data into information aligned with the value propositions of the organization. This writing is focused on the big data platform because there are some unique characteristics of the big data environment that require an approach different than many of the legacy environments that exist in organizations. Furthermore, while big data is the one environment that is new and requires these special handling characteristics, there will be future platforms with the same requirements as big data requires today, and hopefully lessons learned will be left to not revisit each of the challenges as the next transformational information ecosphere is made available.
Figure 1 The Fourth Industrial Revolution, World Economic Forum, InfoSight Partners, 2016
This time is different, in that information is the catalyst to achieving value and the platform ideally suited to house information not optimal for storage in the form of rows and columns is the big data environment. Understanding which information is delivered with intended consequences and having the management prowess to tune information shared with customers, prospects, suppliers, partners, regulators and financiers is critical for the digital economy. Additionally, it is specific to understand the challenges each platform housing information bring to the equation. This writing will focus on big data.
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
Auspiciously, big data analytics had made it possible to generate value from immense amounts of raw data. Organizations are able to seek incredible insights which assist them in effective decision making and providing quality of service by establishing innovative strategies to recognize, examine and address the customers’ preferences. However, organizations are reluctant to adopt big data solutions due to several barriers such as data storage and transfer, scalability, data quality, data complexity, timeliness, security, privacy, trust, data ownership, and transparency. Despite the discussion on big data opportunities, in this paper, we present the findings of our in-depth review process that was focused on identifying as well as analyzing the transient and permanent barriers for adopting big data. Although, the transient barriers for big data can be eliminated in the near future with the advent of innovative technical contributions, however, it is challenging to eliminate the permanent barriers enduringly, though their impact could be recurrently reduced with the efficient and effective use of technology, standards, policies, and procedures.
Two-Phase TDS Approach for Data Anonymization To Preserving Bigdata Privacydbpublications
While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. There have been a various different privacy preserving mechanisms developed for protecting privacy at different stages (e.g. data generation, data storage, data processing) of big data life cycle. The goal of this paper is to provide a complete overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms and also we illustrate the infrastructure of big data and state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. This paper focus on the anonymization process, which significantly improve the scalability and efficiency of TDS (top-down-specialization) for data anonymization over existing approaches. Also, we discuss the challenges and future research directions related to preserving privacy in big data.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Hadoop and Big Data Readiness in Africa: A Case of Tanzaniaijsrd.com
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
NIH Data Commons - Note: Presentation has animations Vivien Bonazzi
Presented at the Data Commons & Data Science Workshop (University of Chicago - Centre for Data Intensive Science):
NB- there are animations in these slides so static slides might not view well
Moving Toward Big Data: Challenges, Trends and PerspectivesIJRESJOURNAL
Abstract: Big data refers to the organizational data asset that exceeds the volume, velocity, and variety of data typically stored using traditional structured database technologies. This type of data has become the important resource from which organizations can get valuable insightand make business decision by applying predictive analysis. This paper provides a comprehensive view of current status of big data development,starting from the definition and the description of Hadoop and MapReduce – the framework that standardizes the use of cluster of commodity machines to analyze big data. For the organizations that are ready to embrace big data technology, significant adjustments on infrastructure andthe roles played byIT professionals and BI practitioners must be anticipated which is discussed in the challenges of big data section. The landscape of big data development change rapidly which is directly related to the trend of big data. Clearly, a major part of the trend is the result ofthe attempt to deal with the challenges discussed earlier. Lastly the paper includes the most recent job prospective related to big data. The description of several job titles that comprise the workforce in the area of big data are also included.
The effect of technology-organization-environment on adoption decision of bi...IJECEIAES
Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organizationenvironment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
A Comprehensive Study on Big Data Applications and Challengesijcisjournal
Big Data has gained much interest from the academia and the IT industry. In the digital and computing
world, information is generated and collected at a rate that quickly exceeds the boundary range. As
information is transferred and shared at light speed on optic fiber and wireless networks, the volume of
data and the speed of market growth increase. Conversely, the fast growth rate of such large data
generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security.
Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this
study expansively surveys and classifies an assortment of attributes of Big Data, including its nature,
definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a
data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming
model for efficient distributed computing. It works well with semi-structured and unstructured data. A
simple model but good for a lot of applications like Log processing and Web index building.
Real World Application of Big Data In Data Mining Toolsijsrd.com
The main aim of this paper is to make a study on the notion Big data and its application in data mining tools like R, Weka, Rapidminer, Knime,Mahout and etc. We are awash in a flood of data today. In a broad range of application areas, data is being collected at unmatched scale. Decisions that previously were based on surmise, or on painstakingly constructed models of reality, can now be made based on the data itself. Such Big Data analysis now drives nearly every aspect of our modern society, including mobile services, retail, manufacturing, financial services, life sciences, and physical sciences. The paper mainly focuses different types of data mining tools and its usage in big data in knowledge discovery.
As we enter the digital economy, it becomes increasingly transparent that the information and data ecosphere will continue to be a complex environment for the foreseeable future, with information being provided from a variety of internal and external sources in the form of files, messages, queries and streams. It would be foolish for any organization to place their bets on any one platform to be their platform of choice because it is incongruent to the thought patterns of the consumers, suppliers, regulators, partners and financiers who will participate in their information ecosphere through data feeds, information requests and a host of other interfaces.
Rather, there is a role of each of these platforms which serve as the conduit for data and the transformation of data into information aligned with the value propositions of the organization. This writing is focused on the big data platform because there are some unique characteristics of the big data environment that require an approach different than many of the legacy environments that exist in organizations. Furthermore, while big data is the one environment that is new and requires these special handling characteristics, there will be future platforms with the same requirements as big data requires today, and hopefully lessons learned will be left to not revisit each of the challenges as the next transformational information ecosphere is made available.
Figure 1 The Fourth Industrial Revolution, World Economic Forum, InfoSight Partners, 2016
This time is different, in that information is the catalyst to achieving value and the platform ideally suited to house information not optimal for storage in the form of rows and columns is the big data environment. Understanding which information is delivered with intended consequences and having the management prowess to tune information shared with customers, prospects, suppliers, partners, regulators and financiers is critical for the digital economy. Additionally, it is specific to understand the challenges each platform housing information bring to the equation. This writing will focus on big data.
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
Auspiciously, big data analytics had made it possible to generate value from immense amounts of raw data. Organizations are able to seek incredible insights which assist them in effective decision making and providing quality of service by establishing innovative strategies to recognize, examine and address the customers’ preferences. However, organizations are reluctant to adopt big data solutions due to several barriers such as data storage and transfer, scalability, data quality, data complexity, timeliness, security, privacy, trust, data ownership, and transparency. Despite the discussion on big data opportunities, in this paper, we present the findings of our in-depth review process that was focused on identifying as well as analyzing the transient and permanent barriers for adopting big data. Although, the transient barriers for big data can be eliminated in the near future with the advent of innovative technical contributions, however, it is challenging to eliminate the permanent barriers enduringly, though their impact could be recurrently reduced with the efficient and effective use of technology, standards, policies, and procedures.
Two-Phase TDS Approach for Data Anonymization To Preserving Bigdata Privacydbpublications
While Big Data gradually become a hot topic of research and business and has been everywhere used in many industries, Big Data security and privacy has been increasingly concerned. However, there is an obvious contradiction between Big Data security and privacy and the widespread use of Big Data. There have been a various different privacy preserving mechanisms developed for protecting privacy at different stages (e.g. data generation, data storage, data processing) of big data life cycle. The goal of this paper is to provide a complete overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms and also we illustrate the infrastructure of big data and state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. This paper focus on the anonymization process, which significantly improve the scalability and efficiency of TDS (top-down-specialization) for data anonymization over existing approaches. Also, we discuss the challenges and future research directions related to preserving privacy in big data.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
Hadoop and Big Data Readiness in Africa: A Case of Tanzaniaijsrd.com
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
The software development process is complete for computer project analysis, and it is important to the evaluation of the random project. These practice guidelines are for those who manage big-data and big-data analytics projects or are responsible for the use of data analytics solutions. They are also intended for business leaders and program leaders that are responsible for developing agency capability in the area of big data and big data analytics .
For those agencies currently not using big data or big data analytics, this document may assist strategic planners, business teams and data analysts to consider the value of big data to the current and future programs.
This document is also of relevance to those in industry, research and academia who can work as partners with government on big data analytics projects.
Technical APS personnel who manage big data and/or do big data analytics are invited to join the Data Analytics Centre of Excellence Community of Practice to share information of technical aspects of big data and big data analytics, including achieving best practice with modeling and related requirements. To join the community, send an email to the Data Analytics Centre of Excellence
Linking HPC to Data Management - EUDAT Summer School (Giuseppe Fiameni, CINECA)EUDAT
EUDAT and PRACE joined forces to help research communities gain access to high quality managed e-Infrastructures whose resources can be connected together to enable cross-utilization use cases and make them accessible without any technical barrier. The capability to couple data and compute resources together is considered one of the key factors to accelerate scientific innovation and advance research frontiers. The goal of this session was to present the EUDAT services, the results of the collaboration activity achieved so far and delivers a hands-on on how to write a Data Management Plan or DMP. The DMP is a useful instrument for researchers to reflect on and communicate about the way they will deal with their data. It prompts them to think about how they will generate, analyse and share data during their research project and afterwards.
Visit: https://www.eudat.eu/eudat-summer-school
Big Data is the new technology or science to make the well informed decision in
business or any other science discipline with huge volume of data from new sources of
heterogeneous data. . Such new sources include blogs, online media, social network, sensor network,
image data and other forms of data which vary in volume, structure, format and other factors. Big
Data applications are increasingly adopted in all science and engineering domains, including space
science, biomedical sciences and astronomic and deep space studies. The major challenges of big
data mining are in data accessing and processing, data privacy and mining algorithms. This paper
includes the information about what is big data, data mining with big data, the challenges in big data
mining and what are the currently available solutions to meet those challenges.
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.
Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.
An Investigation on Scalable and Efficient Privacy Preserving Challenges for ...IJERDJOURNAL
ABSTRACT:- Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big data refers to huge amount of digital information collected from multiple and different sources. With the development of application of Internet/Mobile Internet, social networks, Internet of Things, big data has become the hot topic of research across the world, at the same time; big data faces security risks and privacy protection during collecting, storing, analyzing and utilizing. Since a key point of big data is to access data from multiple and different domains security and privacy will play an important role in big data research and technology. Traditional security mechanisms, which are used to secure small scale static data, are inadequate. So the question is which security and privacy technology is adequate for efficient access to big data. This paper introduces the functions of big data, and the security threat faced by big data, then proposes the technology to solve the security threat, finally, discusses the applications of big data in information security. Main expectation from the focused challenges is that it will bring a novel focus on the big data infrastructure.
Big data plays a very crucial role in different fields of the modern world. Big data term is used for the data that is massive, varied and complex structure having the difficulties in collecting, storing, processing, analyzing and visualizing. Research which is to be processed in the direction of revealing the hidden patterns and the correlations between the different types of the data is named as Big Data Analytics or BDA. For the better decision making, for utilizing these useful information or for taking the better insights in the organizations or the company’s big data analytics is used. For this reason the analysis and execution of the big data implementation is needed. This paper aims to provide overview about the contents of the big data, its characteristics, big data analytics phases and the tools and techniques used during the different phases of the analysis. Sakshi Goel | Neeraj Kumar | Saharsh Gera "Big Data: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50641.pdf Paper URL: https://www.ijtsrd.com/computer-science/database/50641/big-data-a-review/sakshi-goel
A presentation given on the Horizon 2020 open data pilot as part of a series of OpenAIRE webinars for Open Access week 2014 - http://www.fosteropenscience.eu/event/openaire-webinars-during-oa-week-2014
The Horizon 2020 Open Data Pilot - OpenAIRE webinar (Oct. 21 2014) by Sarah J...OpenAIRE
Sarah Jones (HATII, Digital Curation Center) will provide more information on the Open Research Data Pilot in H2020: who should participate and how to comply (in collaboration with FOSTER)
Date: Tuesday, October 21 2014
Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbour algorithm is discreetly chosen among them and described along with an example.
Similar to Data bio d6.2-data-management-plan_v1.0_2017-06-30_crea (20)
Hotel management involves overseeing all aspects of a hotel's operations to ensure smooth functioning and exceptional guest experiences. This multifaceted role includes tasks such as managing staff, handling reservations, maintaining facilities, overseeing finances, and implementing marketing strategies to attract guests. Effective hotel management requires strong leadership, communication, organizational, and problem-solving skills to navigate the complexities of the hospitality industry and ensure guest satisfaction while maximizing profitability.
Hamdard Laboratories (India), is a Unani pharmaceutical company in India (following the independence of India from Britain, "Hamdard" Unani branches were established in Bangladesh (erstwhile East Pakistan) and Pakistan). It was established in 1906 by Hakeem Hafiz Abdul Majeed in Delhi, and became
a waqf (non-profitable trust) in 1948. It is associated with Hamdard Foundation, a charitable educational trust.
Hamdard' is a compound word derived from Persian, which combines the words 'hum' (used in the sense of 'companion') and 'dard' (meaning 'pain'). 'Hamdard' thus means 'a companion in pain' and 'sympathizer in suffering'.
The goals of Hamdard were lofty; easing the suffering of the sick with healing herbs. With a simple tenet that no one has ever become poor by giving, Hakeem Abdul Majeed let the whole world find compassion in him.
They had always maintained that working in old, traditional ways would not be entirely fruitful. A broader outlook was essential for a continued and meaningful existence. their effective team at Hamdard helped the system gain its pride of place and thus they made an entry into an expansive world of discovery and research.
Hamdard Laboratories was founded in 1906 in Delhi by Hakeem Hafiz Abdul Majeed and Ansarullah Tabani, a Unani practitioner. The name Hamdard means "companion in suffering" in Urdu language.(itself borrowed from Persian) Hakim Hafiz Abdul Majeed was born in Pilibhit City UP, India in 1883 to Sheikh Rahim Bakhsh. He is said to have learnt the complete Quran Sharif by heart. He also studied the origin of Urdu and Persian languages. Subsequently, he acquired the highest degree in the unani system of medicine.
Hakim Hafiz Abdul Majeed got in touch with Hakim Zamal Khan, who had a keen interest in herbs and was famous for identifying medicinal plants. Having consulted with his wife, Abdul Majeed set up a herbal shop at Hauz Qazi in Delhi in 1906 and started to produce herbal medicine there. In 1920 the small herbal shop turned into a full-fledged production house.
Hamdard Foundation was created in 1964 to disburse the profits of the company to promote the interests of the society. All the profits of the company go to the foundation.
After Abdul Majeed's death, his son Hakeem Abdul Hameed took over the administration of Hamdard Laboratories at the age of fourteen.
Even with humble beginnings, the goals of Hamdard were lofty; easing the suffering of the sick with healing herbs. With a simple tenet that no one has ever become poor by giving, Hakeem Abdul Majeed let the whole world find compassion in him. Unfortunately, he passed away quite early but his wife, Rabia Begum, with the support of her son, Hakeem Abdul Hameed, not only kept the institution in existence but also expanded it. As he grew up, Hakeem Abdul Hameed took on all responsibilities. After helping with his younger brother's upbringing and education, he included him in running the institution. Both brothers Hakeem Abdul Hameed and Hakim Mohammed
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Data bio d6.2-data-management-plan_v1.0_2017-06-30_crea
1. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation
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.
Project Acronym: DataBio
Grant Agreement number: 732064 (H2020-ICT-2016-1 – Innovation Action)
Project Full Title: Data-Driven Bioeconomy
Project Coordinator: INTRASOFT International
DELIVERABLE
D6.2 – Data Management Plan
Dissemination level PU -Public
Type of Document Report
Contractual date of delivery M06 – 30/6/2017
Deliverable Leader CREA
Status - version, date Final – v1.0, 30/6/2017
WP / Task responsible WP6
Keywords: Data management plan, big data, bioeconomy
2. D6.2 – Data Management Plan
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Executive Summary
This document presents DataBio’s D6.2 deliverable, Data Management Plan (DMP), the key
element of good data management. DataBio participates in the European Commission H2020
Program’s extended open research data pilot and hence, a DMP is required. And,
consequently, DataBio project’s datasets will be as open as possible and as closed as
necessary, focusing on sound big data management for the sake of best research practice,
and in order to create value, and foster knowledge and technology out of big datasets for the
good of man. The deliverable describes the data management life cycle for the data to be
collected, processed and/or generated by DataBio project, accounting also for the necessity
to make research data findable, accessible, interoperable and reusable (FAIR).
DataBio’s partners will be encouraged to adhere to sound data management to ensure that
data are well-managed, archived and preserved. Data preservation is synonymous to data
relevance since: (1) data can then be reused by other researchers, (2) data collector can direct
requests for data to the database, rather than address requests individually, (3) preserved
data have the potential to lead to new, unanticipated discoveries, (4) preserved data prevent
duplication of scientific studies that have already been conducted, and (5) archiving data
insures against loss by the data collector. The main issues addressed in this deliverable
include: (1) the purpose of data collection, (2) data type, format, size, velocity, beneficiaries,
and provenance, (3) use of historical data, (4) making data FAIR, (5) data management
support, (6) data security, and (7) ethical aspects.
Doubtless, big data is a new paradigm and is coercing changes in businesses and other
organizations. A few entities in EU are starting to manage the massive data sets and non-
traditional data structures that are typical of big data and/or managing big data by extending
their data management skills and their portfolios of data management software. Big data
management empowers those entities to efficiently automate business operations, operate
closer to real time, and through analytics, add value and learn valuable new facts about
business operations, customers, partners, etc. Within the DataBio framework, big data
management (BDM), is a mixture of conventional and new best practices, skills, teams, data
types, and in-house grown or vendor-built functionality. All of these are being realigned under
DataBio platform built upon partners own experiences and tools. It is anticipated that DataBio
will provide a solution which will assume that datasets will be distributed among different
infrastructures and that their accessibility could be complex, needing to have mechanisms
which facilitate data retrieval, processing, manipulation and visualization as seamlessly as
possible. The infrastructure will open new possibilities for ICT sector, including SMEs to
develop new Bioeconomy 4.0 and will also open new possibilities for companies from the
Earth Observation sector.
Some partners have scaled up pre-existing applications and databases to handle burgeoning
volumes of relational big data, or they have acquired new data management platforms that
3. D6.2 – Data Management Plan
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Page 3
are purpose-built for managing and analyzing multi-structured big data, including streaming
big data. Others are evaluating big data platforms in order to create a brisk market of vendor
products and services for managing and harnessing big data. The Hadoop Distributed File
System (HDFS), MapReduce, various Hadoop tools, complex event processing (for streaming
big data), NoSQL databases (for schema-free big data), in-memory databases (for real-time
analytic processing of big data), private clouds, in-database analytics, and grid computing, will
be some of the software products implemented within the DataBio framework.
During the lifecycle of the DataBio project, big data will be collected that is, very large data
sets (multi-terabyte or larger) consist of a wide range of data types (relational, text, multi-
structured data, etc.) from numerous sources. Most data will come from farm and forestry
machinery, fishing vessels, remote and proximal sensors and imagery, and many other
technologies. DataBio is purposefully collecting big data, specifically:
• Forestry: Big Data methods are expected to bring the possibility to both increase the
value of the forests as well as to decrease the costs within sustainability limits set by
natural growth and ecological aspects. The key technology is to gather more and more
accurate information about the trees from a host of sensors including new generation
of satellites, UAV images, laser scanning, mobile devices through crowdsourcing and
machines operating in the forests.
• Agriculture: Big Data in Agriculture is currently a hot topic. DataBio aims at building a
European vision of Big Data for agriculture. This vision is to offer solution which will
increase role of Big Data role in Agri Food chains in Europe: a perspective, which
prepared recommendation for future big data development in Europe.
• Fisheries: the ambition of this project is to herald and promote the use of Big Data
analytical tools within fisheries applications by initiating several pilots which will
demonstrate benefits of using Big Data in an analytical way for the fisheries, such as
improved analysis of operational data, tools for planning and operational choices,
crowdsourcing methods for fish stock estimation.
This is the first version of DataBio DMP; it will be updated over the course of the project as
warranted by significant changes arising during the project implementation, and the
requirements of the project consortium. At least two updates will be prepared, on Months
18 and 36 of the project.
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Deliverable Leader: Ephrem Habyarimana (CREA)
Contributors:
Jaroslav Šmejkal (ZETOR), Tomas Mildorf (UWB), Bernard
Stevenot (SPACEBEL), Irene Matzakou (INTRASOFT), Ingo
Simonis (OGSE), Christian Zinke (INFAI), Karel Charvat (LESPRO)
Reviewers:
Kyrill Meyer (INFAI), Tomas Mildorf (UWB), Erwin Goor (VITO),
Fabiana Fournier (IBM), Marco Folegani (MEEO)
Approved by: Athanasios Poulakidas (INTRASOFT)
Document History
Version Date Contributor(s) Description
0.1.1-2 12/05/2017
Ephrem
Habyarimana
TOC
0.1.3 22/05/2017
Ephrem
Habyarimana
Reviewed TOC, First assignments
0.2 30/05/2017 Tomas Mildorf Section 4.1 FAIR data costs
0.3 05/06/2017 Bernard Stevenot Section 6 Ethical issues
0.4 09/06/2017
Irene Matzakou,
Athanasios
Poulakidas
Section 5.4 - 5.5 Privacy and sensitive data
management
0.5.1 21/06/2017 Ingo Simonis Section 3.3 and 3.4 added
0.5.2 22/06/2017
Christian Zinke,
Jaroslav Šmejkal
Sections 2.2.4.4 Machine-generated data
and 4.2 added
0.6 23/06/2017
Ephrem
Habyarimana
Added: Executive summary, sections 1.2 &
2.1, and chapter 7
0.7 27/06/2017
Ephrem
Habyarimana
added section 1.3 and made edits
throughout the document.
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0.8 28/06/2017 Tomas Mildorf
Update of Section 2.2.4.3, Section 2.5.4,
Section 2.5.5, Section 3.1.3 and Section
4.1
0.9 30/06/2017
Ephrem
Habyarimana
Included all tables for currently described
DataBio’s datasets; overall edit of entire
document.
1.0 30/06/2017 Athanasios
Poulakidas
Compliance to submission format and
minor changes.
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Table of Contents
EXECUTIVE SUMMARY.....................................................................................................................................2
TABLE OF CONTENTS........................................................................................................................................6
TABLE OF FIGURES ...........................................................................................................................................8
LIST OF TABLES ................................................................................................................................................8
DEFINITIONS, ACRONYMS AND ABBREVIATIONS.............................................................................................9
INTRODUCTION ....................................................................................................................................10
1.1 PROJECT SUMMARY.....................................................................................................................................10
1.2 DOCUMENT SCOPE......................................................................................................................................13
1.3 DOCUMENT STRUCTURE ...............................................................................................................................14
DATA SUMMARY ..................................................................................................................................15
2.1 PURPOSE OF DATA COLLECTION......................................................................................................................15
2.2 DATA TYPES AND FORMATS ...........................................................................................................................17
2.2.1 Structured data.............................................................................................................................17
2.2.2 Semi-structured data ....................................................................................................................17
2.2.3 Unstructured data.........................................................................................................................19
2.2.4 New generation big data ..............................................................................................................19
2.3 HISTORICAL DATA........................................................................................................................................25
2.4 EXPECTED DATA SIZE AND VELOCITY.................................................................................................................26
2.5 DATA BENEFICIARIES ....................................................................................................................................26
2.5.1 Agricultural Sector ........................................................................................................................27
2.5.2 Forestry Sector..............................................................................................................................27
2.5.3 Fishery Sector................................................................................................................................28
2.5.4 Technical Staff...............................................................................................................................28
2.5.5 ICT sector ......................................................................................................................................28
2.5.6 Research and education................................................................................................................30
2.5.7 Policy making bodies.....................................................................................................................30
FAIR DATA ............................................................................................................................................31
3.1 DATA FINDABILITY .......................................................................................................................................31
3.1.1 Data discoverability and metadata provision...............................................................................31
3.1.2 Data identification, naming mechanisms and search keyword approaches.................................33
3.1.3 Data lineage..................................................................................................................................34
3.2 DATA ACCESSIBILITY .....................................................................................................................................37
3.2.1 Open data and closed data...........................................................................................................37
3.2.2 Data access mechanisms, software and tools ..............................................................................38
3.2.3 Big data warehouse architectures and database management systems .....................................38
3.3 DATA INTEROPERABILITY ...............................................................................................................................40
3.3.1 Interoperability mechanisms ........................................................................................................41
3.3.2 Inter-discipline interoperability and ontologies ............................................................................41
3.4 PROMOTING DATA REUSE..............................................................................................................................42
DATA MANAGEMENT SUPPORT............................................................................................................43
4.1 FAIR DATA COSTS........................................................................................................................................43
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4.2 BIG DATA MANAGERS...................................................................................................................................43
4.2.1 Project manager ...........................................................................................................................43
4.2.2 Business Analysts ..........................................................................................................................44
4.2.3 Data Scientists ..............................................................................................................................44
4.2.4 Data Engineer / Architect .............................................................................................................44
4.2.5 Platform architects .......................................................................................................................44
4.2.6 IT/Operation manager..................................................................................................................44
4.2.7 Consultant.....................................................................................................................................45
4.2.8 Business User ................................................................................................................................45
4.2.9 Pilot experts ..................................................................................................................................45
DATA SECURITY ....................................................................................................................................46
5.1 INTRODUCTION...........................................................................................................................................46
5.2 DATA RECOVERY..........................................................................................................................................47
5.3 PRIVACY AND SENSITIVE DATA MANAGEMENT ...................................................................................................48
5.3.1 Introduction ..................................................................................................................................48
5.3.2 Enterprise Data (commercial sensitive data)................................................................................48
5.3.3 Personal Data................................................................................................................................49
5.4 GENERAL PRIVACY CONCERNS ........................................................................................................................50
ETHICAL ISSUES.....................................................................................................................................51
CONCLUSIONS ......................................................................................................................................52
REFERENCES .........................................................................................................................................54
APPENDIX A DATABIO DATASETS ...........................................................................................................55
A.1 SMART POI DATA SET (UWB - D03.01) ....................................................................................................56
A.2 OPEN TRANSPORT MAP (UWB - D03.02) .................................................................................................58
A.3 SENTINELS SCIENTIFIC HUB DATASETS VIA FEDEO GATEWAY (SPACEBEL -D07.01)..........................................60
A.4 NASA CMR LANDSAT DATASETS VIA FEDEO GATEWAY (SPACEBEL - D07.02)...............................................61
A.5 OPEN LAND USE (LESPRO - D02.01) .........................................................................................................62
A.6 FOREST RESOURCE DATA (METSAK - D18.01)............................................................................................64
A.7 CUSTOMER AND FOREST ESTATE DATA (METSAK - D18.02)..........................................................................65
A.8 STORM DAMAGE OBSERVATIONS AND POSSIBLE RISK AREAS (METSAK - D18.03)..............................................67
A.9 QUALITY CONTROL DATA (METSAK - D18.04) ...........................................................................................68
A.10 ONTOLOGY FOR (PRECISION) AGRICULTURE (PSNC - D09.01).......................................................................69
A.11 WUUDIS DATA (MHGS - D20.01)............................................................................................................71
A.12 SIGPAC (TRAGSA - D11.05)....................................................................................................................72
A.13 FIELD DATA - PILOT B2 (TRAGSA - D11.07).................................................................................................74
A.14 IACS (NP - D13.01)..............................................................................................................................75
A.15 SENTINEL DATA......................................................................................................................................76
A.16 TREE SPECIES MAP (FMI - D14.03) ..........................................................................................................76
A.17 STAND AGE MAP (FMI - D14.04) .............................................................................................................77
A.18 CANOPY HEIGHT MAP (FMI - D14.05).......................................................................................................78
A.19 LEAF AREA INDEX (FMI - D14.06).............................................................................................................79
A.20 FOREST DAMAGE (FMI - D14.07).............................................................................................................80
A.21 HYPERSPECTRAL IMAGE ORTHOMOSAIC (SENOP - D44.02) ............................................................................81
A.22 GAIATRONS IOT (DS13.01) ...................................................................................................................81
A.23 PHENOMICS, METABOLOMICS, GENOMICS AND ENVIRONMENTAL DATASETS (CERTH - DS40.01) .........................82
8. D6.2 – Data Management Plan
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Table of Figures
FIGURE 1: DATABIO’S ANALYTICS AND BIG DATA VALUE APPROACH .....................................................................................16
FIGURE 2: THE PROCESSING DATA LIFECYCLE ...................................................................................................................36
FIGURE 3: THE “DISCIPLINARY DATA INTEGRATION PLATFORM: WHERE DO YOU SSIT? (SOURCE: WYBORN)..................................41
FIGURE 4: DATABIO’S DATA MANAGERS.........................................................................................................................45
FIGURE 5: DATA LIFECYCLE ..........................................................................................................................................46
FIGURE 6: THE DATA MODEL OF SMART POINTS OF INTEREST ............................................................................................58
FIGURE 7: THE DATA MODEL OF OPEN TRANSPORT MAP...................................................................................................60
FIGURE 8: FEDEO CLIENT (C07.05) .............................................................................................................................61
List of Tables
TABLE 1: THE DATABIO CONSORTIUM PARTNERS.............................................................................................................10
TABLE 2: SENSOR DATA TOOLS, RESOLUTION AND SPATIAL DENSITY .....................................................................................20
TABLE 3: GEOSPATIAL DATA TOOLS, FORMAT AND ORIGIN .................................................................................................24
TABLE 4: GENOMIC, BIOCHEMICAL AND METABOLOMIC DATA TOOLS, DESCRIPTION AND ACQUISITION........................................25
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Definitions, Acronyms and Abbreviations
Acronym/
Abbreviation
Title
BDVA Big Data Value Association
EC European Commission
EO Earth Observation
ETL Extract Transform Load
DMP Data Management Plan
GSM Global System for Mobile
GSP Global Positioning System
FAIR Findable Accessible Interoperable and Reusable
HDFS Hadoop Distributed File System
ICT Information and Communications Technology
IoT Internet of Things
JDBC Java DataBase Connectivity
JSON JavaScript Object Notation
NoSQL Not Only SQL
OBDC Open Database Connectivity
OEM Object Exchange Model
OGC Open Geospatial Consortium
REST Representational State Transfer
RFID Radio-Frequency IDentification
RPAS Remotely Piloted Aircraft Systems
SME Small-Medium Enterprise
SOAP Simple Object Access Protocol
SQL Structured Query Language
UAV Unmanned Air Vehicle
UI User Interface
WP Work Package
XML eXtensible Markup Language
10. D6.2 – Data Management Plan
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Introduction
1.1 Project Summary
The data intensive target sector on which the
DataBio project focuses is the Data-Driven
Bioeconomy. DataBio focuses on utilizing Big
Data to contribute to the production of the
best possible raw materials from agriculture,
forestry and fishery (aquaculture) for the
bioeconomy industry, as well as their further
processing into food, energy and
biomaterials, while taking into account various accountability and sustainability issues.
DataBio will deploy state-of-the-art big data technologies and existing partners’ infrastructure
and solutions, linked together through the DataBio Platform. These will aggregate Big Data
from the three identified sectors (agriculture, forestry and fishery), intelligently process them
and allow the three sectors to selectively utilize numerous platform components, according
to their requirements. The execution will be through continuous cooperation of end user and
technology provider companies, bioeconomy and technology research institutes, and
stakeholders from the big data value PPP programme.
DataBio is driven by the development, use and evaluation of a large number of pilots in the
three identified sectors, where associated partners and additional stakeholders are also
involved. The selected pilot concepts will be transformed to pilot implementations utilizing
co-innovative methods and tools. The pilots select and utilize the best suitable market-ready
or almost market-ready ICT, Big Data and Earth Observation methods, technologies, tools and
services to be integrated to the common DataBio Platform.
Based on the pilot results and the new DataBio Platform, new solutions and new business
opportunities are expected to emerge. DataBio will organize a series of trainings and
hackathons to support its uptake and to enable developers outside the consortium to design
and develop new tools, services and applications based on and for the DataBio Platform.
The DataBio consortium is listed in Table 1. For more information about the project see [REF-
01].
Table 1: The DataBio consortium partners
Number Name Short name Country
1 (CO) INTRASOFT INTERNATIONAL SA INTRASOFT Belgium
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2 LESPROJEKT SLUZBY SRO LESPRO Czech Republic
3 ZAPADOCESKA UNIVERZITA V PLZNI UWB Czech Republic
4
FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER
ANGEWANDTEN FORSCHUNG E.V. Fraunhofer Germany
5 ATOS SPAIN SA ATOS Spain
6 STIFTELSEN SINTEF SINTEF ICT Norway
7 SPACEBEL SA SPACEBEL Belgium
8
VLAAMSE INSTELLING VOOR TECHNOLOGISCH
ONDERZOEK N.V. VITO Belgium
9
INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ
AKADEMII NAUK PSNC Poland
10 CIAOTECH Srl CiaoT Italy
11 EMPRESA DE TRANSFORMACION AGRARIA SA TRAGSA Spain
12 INSTITUT FUR ANGEWANDTE INFORMATIK (INFAI) EV INFAI Germany
13 NEUROPUBLIC AE PLIROFORIKIS & EPIKOINONION NP Greece
14
Ústav pro hospodářskou úpravu lesů Brandýs nad
Labem UHUL FMI Czech Republic
15 INNOVATION ENGINEERING SRL InnoE Italy
16 Teknologian tutkimuskeskus VTT Oy VTT Finland
17 SINTEF FISKERI OG HAVBRUK AS
SINTEF
Fishery Norway
18 SUOMEN METSAKESKUS-FINLANDS SKOGSCENTRAL METSAK Finland
19 IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD IBM Israel
20 MHG SYSTEMS OY - MHGS MHGS Finland
21 NB ADVIES BV NB Advies Netherlands
22
CONSIGLIO PER LA RICERCA IN AGRICOLTURA E
L'ANALISI DELL'ECONOMIA AGRARIA CREA Italy
23 FUNDACION AZTI - AZTI FUNDAZIOA AZTI Spain
24 KINGS BAY AS KingsBay Norway
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25 EROS AS Eros Norway
26 ERVIK & SAEVIK AS ESAS Norway
27 LIEGRUPPEN FISKERI AS LiegFi Norway
28 E-GEOS SPA e-geos Italy
29 DANMARKS TEKNISKE UNIVERSITET DTU Denmark
30 FEDERUNACOMA SRL UNIPERSONALE Federu Italy
31
CSEM CENTRE SUISSE D'ELECTRONIQUE ET DE
MICROTECHNIQUE SA - RECHERCHE ET
DEVELOPPEMENT CSEM Switzerland
32 UNIVERSITAET ST. GALLEN UStG Switzerland
33 NORGES SILDESALGSLAG SA Sildes Norway
34 EXUS SOFTWARE LTD EXUS
United
Kingdom
35 CYBERNETICA AS CYBER Estonia
36
GAIA EPICHEIREIN ANONYMI ETAIREIA PSIFIAKON
YPIRESION GAIA Greece
37 SOFTEAM Softeam France
38
FUNDACION CITOLIVA, CENTRO DE INNOVACION Y
TECNOLOGIA DEL OLIVAR Y DEL ACEITE CITOLIVA Spain
39 TERRASIGNA SRL TerraS Romania
40
ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS
ANAPTYXIS CERTH Greece
41
METEOROLOGICAL AND ENVIRONMENTAL EARTH
OBSERVATION SRL MEEO Italy
42 ECHEBASTAR FLEET SOCIEDAD LIMITADA ECHEBF Spain
43 NOVAMONT SPA Novam Italy
44 SENOP OY Senop Finland
45
UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO
UNIBERTSITATEA EHU/UPV Spain
46
OPEN GEOSPATIAL CONSORTIUM (EUROPE) LIMITED
LBG OGCE
United
Kingdom
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47 ZETOR TRACTORS AS ZETOR Czech Republic
48
COOPERATIVA AGRICOLA CESENATE SOCIETA
COOPERATIVA AGRICOLA CAC Italy
1.2 Document Scope
This document outlines DataBio’s data management plan (DMP), formally documenting how
data will be handled both during the implementation and upon natural termination of the
project. Many DMP aspects will be considered including metadata generation, data
preservation, data security and ethics, accounting for the FAIR (Findable, Accessible,
Interoperable, Re-usable) data principle. DataBio, Data-driven Bioeconomy project, is an
innovation big data intensive action involving public private partnership to promote
productivity on EU companies in three of the major bioeconomy sectors namely, Agriculture,
forestry and fishery. Experiences from US show that bioeconomy can get a significant boost
from Big Data. In Europe, this sector has until now attracted few large ICT vendors. A central
goal of DataBio is to increase participation of European ICT industry in the development of
Big Data systems for boosting the lagging bioeconomy productivity. As a good case in point,
European agriculture, forestry and fishery can benefit greatly from the European Copernicus
space program which has currently launched its third Sentinel satellite, telemetry IoT, UAVs,
etc.
Farm and forestry machinery, and fishing vessels in use today collect large quantities of data
in unprecedented pattern. Remote and proximal sensors and imagery, and many other
technologies, are all working together to give details about crop and soil properties, marine
environment, weeds and pests, sunlight and shade, and many other primary production
relevant variables. Deploying big data analytics in these data can help the farmers, foresters
and fishers to adjust and improve the productivity of their business operations. On the other
hand, large data sets such as those coming from the Copernicus earth monitoring
infrastructure, are increasingly available on different levels of granularity, but they are
heterogeneous, at times also unstructured, hard to analyze and distributed across various
sectors and different providers. It is here that data management plan comes in. It is
anticipated that DataBio will provide a solution which will assume that datasets will be
distributed among different infrastructures and that their accessibility could be complex,
needing to have mechanisms which facilitate data retrieval, processing, manipulation and
visualization as seamlessly as possible. The infrastructure will open new possibilities for ICT
sector, including SMEs to develop new Bioeconomy 4.0 and will also open new possibilities
for companies from the Earth Observation sector.
This DMP will be updated over the course of DataBio project whenever significant changes
arise. The updates of this document will increasingly provide in-depths on DataBio DMP
strategies with particular interest on the aspects of findability, accessibility, interoperability
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and reusability of the Big Data the project produces. At least two updates will be prepared,
on Month 18 and Month 36 of the project.
1.3 Document Structure
This document is comprised of the following chapters:
Chapter 1 presents an introduction to the project and the document.
Chapter 2 presents the data summary including the purpose of data collection, data size, type
and format, historical data reuse and data beneficiaries.
Chapter 3 outlines DataBio’s FAIR data strategies.
Chapter 4 describes data management support.
Chapter 5 describes data security.
Chapter 6 describes ethical issues.
Chapter 7 presents the concluding remarks.
Appendix A presents the managed data sets.
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Data Summary
2.1 Purpose of data collection
During the lifecycle of the DataBio project, big data will be collected that is, very large data
sets (multi-terabyte or larger) consisting of a wide range of data types (relational, text, multi-
structured data, etc.) from numerous sources, including relatively new generation big data
(machines, sensors, genomics, etc.). The ultimate purpose of data collection is to use the data
as a source of information in the implementation of a variety of big data analytics algorithms,
services and applications DataBio will deploy to create a value, new business facts and insights
with a particular focus on the bioeconomy industry. The big datasets are part of the building
blocks of the DataBio’s big data technology platform (Figure 1) that was designed to help
European companies increase productivity. Big Data experts provide common analytic
technology support for the main common and typical Bioeconomy applications/analytics that
are now emerging through the pilots in the project. Data from the past will be managed and
analyzed, including many different kind of data sources: i.e., descriptive analytics and classical
query/reporting (in need of variety management - and handling and analysis of all of the data
from the past, including performance data, transactional data, attitudinal data, descriptive
data, behavioural data, location-related data, interactional data, from many different
sources). Big data from the present time will be harnessed in the process of monitoring and
real-time analytics - pilot services (in need of velocity processing - and handling of real-time
data from the present) - trigging alarms, actuators etc.
Harnessing big data for the future time include forecasting, prediction and recommendation
analytics - pilot services (in need of volume processing - and processing of large amounts of
data combining knowledge from the past and present, and from models, to provide insight
for the future).
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Figure 1: DataBio’s analytics and big data value approach
Specifically:
• Forestry: Big Data methods are expected to bring the possibility to both increase the
value of the forests as well as to decrease the costs within sustainability limits set by
natural growth and ecological aspects. The key technology is to gather more and more
accurate information about the trees from a host of sensors including new generation
of satellites, UAV images, laser scanning, mobile devices through crowdsourcing and
machines operating in the forests.
• Agriculture: Big Data in Agriculture is currently a hot topic. The DataBio intention is to
build a European vision of Big Data for agriculture. This vision is to offer solutions
which will increase the role of Big Data role in Agri Food chains in Europe: a
perspective, which will prepare recommendation for future big data development in
Europe.
• Fisheries: the ambition is to herald and promote the use of Big Data analytical tools
within fisheries applications by initiating several pilots which will demonstrate
benefits of using Big Data in an analytical way for the fisheries, such as improved
analysis of operational data, tools for planning and operational choices,
crowdsourcing methods for fish stock estimation.
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• The use of Big data analytics will bring about innovation. It will generate significant
economic value, extend the relevant market sectors, and herald novel
business/organizational models. The cross-cutting character of the geo-spatial Big
Data solutions allows the straightforward extension of the scope of applications
beyond the bio-economy sectors. Such extensions of the market for the Big Data
technologies are foreseen in economic sectors, such as: Urban planning, Water
quality, Public safety (incl. technological and natural hazards), Protection of critical
infrastructures, Waste management. On the other hand, the Big Data technologies
revolutionize the business approach in the geospatial market and foster the
emergence of innovative business/organizational models; indeed, to achieve the cost
effectiveness of the services to the customers, it is necessary to organize the offer to
the market on a territorial/local basis, as the users share the same geospatial sources
of data and are best served by local players (service providers). This can be illustrated
by a network of European services providers, developing proximity relationships with
their customers and sharing their knowledge through the network.
2.2 Data types and formats
The DataBio specific data types, formats and sources are listed in detail in Appendix A; below
are described key features of the data used in the project.
2.2.1 Structured data
Structured data refers to any data that resides in a fixed field within a record or file. This
includes data contained in relational databases, spreadsheets, and data in forms of events
such as sensor data. Structured data first depends on creating a data model – a model of the
types of business data that will be recorded and how they will be stored, processed and
accessed. This includes defining what fields of data will be stored and how that data will be
stored: data type (numeric, currency, alphabetic, name, date, address) and any restrictions
on the data input (number of characters; restricted to certain terms such as Mr., Ms. or Dr.;
M or F).
2.2.2 Semi-structured data
Semi-structured data is a cross between structured and unstructured data. It is a type of
structured data, but lacks the strict data model structure. With semi-structured data, tags or
other types of markers are used to identify certain elements within the data, but the data
doesn't have a rigid structure. For example, word processing software now can include
metadata showing the author's name and the date created, with the bulk of the document
just being unstructured text. Emails have the sender, recipient, date, time and other fixed
fields added to the unstructured data of the email message content and any attachments.
Photos or other graphics can be tagged with keywords such as the creator, date, location and
keywords, making it possible to organize and locate graphics. XML and other markup
languages are often used to manage semi-structured data. Semi-structured data is therefore
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a form of structured data that does not conform with the formal structure of data models
associated with relational databases or other forms of data tables, but nonetheless contains
tags or other markers to separate semantic elements and enforce hierarchies of records and
fields within the data. Therefore, it is also known as self-describing structure. In semi-
structured data, the entities belonging to the same class may have different attributes even
though they are grouped together, and the attributes' order is not important. Semi-structured
data are increasingly occurring since the advent of the Internet where full-text documents
and databases are not the only forms of data anymore, and different applications need a
medium for exchanging information. In object-oriented databases, one often finds semi-
structured data.
XML and other markup languages, email, and EDI are all forms of semi-structured data. OEM
(Object Exchange Model) was created prior to XML as a means of self-describing a data
structure. XML has been popularized by web services that are developed utilizing SOAP
principles. Some types of data described here as "semi-structured", especially XML, suffer
from the impression that they are incapable of structural rigor at the same functional level as
Relational Tables and Rows. Indeed, the view of XML as inherently semi-structured
(previously, it was referred to as "unstructured") has handicapped its use for a widening range
of data-centric applications. Even documents, normally thought of as the epitome of semi-
structure, can be designed with virtually the same rigor as database schema, enforced by the
XML schema and processed by both commercial and custom software programs without
reducing their usability by human readers.
In view of this fact, XML might be referred to as having "flexible structure" capable of human-
centric flow and hierarchy as well as highly rigorous element structure and data typing. The
concept of XML as "human-readable", however, can only be taken so far. Some
implementations/dialects of XML, such as the XML representation of the contents of a
Microsoft Word document, as implemented in Office 2007 and later versions, utilize dozens
or even hundreds of different kinds of tags that reflect a particular problem domain - in
Word's case, formatting at the character and paragraph and document level, definitions of
styles, inclusion of citations, etc. - which are nested within each other in complex ways.
Understanding even a portion of such an XML document by reading it, let alone catching
errors in its structure, is impossible without a very deep prior understanding of the specific
XML implementation, along with assistance by software that understands the XML schema
that has been employed. Such text is not "human-understandable" any more than a book
written in Swahili (which uses the Latin alphabet) would be to an American or Western
European who does not know a word of that language: the tags are symbols that are
meaningless to a person unfamiliar with the domain.
JSON or JavaScript Object Notation, is an open standard format that uses human-readable
text to transmit data objects consisting of attribute–value pairs. It is used primarily to transmit
data between a server and web application, as an alternative to XML. JSON has been
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popularized by web services developed utilizing REST principles. There is a new breed of
databases such as MongoDB and Couchbase that store data natively in JSON format,
leveraging the pros of semi-structured data architecture.
2.2.3 Unstructured data
Unstructured data (or unstructured information) refers to information that either does not
have a pre-defined data model or is not organized in a pre-defined manner. This results in
irregularities and ambiguities that make it difficult to understand using traditional programs
as compared to data stored in “field” form in databases or annotated (semantically tagged)
in documents. Unstructured data can't be so readily classified and fit into a neat box: photos
and graphic images, videos, streaming instrument data, webpages, PDF files, PowerPoint
presentations, emails, blog entries, wikis and word processing documents.
In 1998, Merrill Lynch cited a rule of thumb that somewhere around 80-90% of all potentially
usable business information may originate in unstructured form. This rule of thumb is not
based on primary or any quantitative research, but nonetheless is accepted by some. IDC and
EMC project that data will grow to 40 zettabytes by 2020, resulting in a 50-fold growth from
the beginning of 2010. Computer World states that unstructured information might account
for more than 70%–80% of all data in organizations.
Software that creates machine-processable structure can utilize the linguistic, auditory, and
visual structure that exist in all forms of human communication. Algorithms can infer this
inherent structure from text, for instance, by examining word morphology, sentence syntax,
and other small- and large-scale patterns. Unstructured information can then be enriched and
tagged to address ambiguities and relevancy-based techniques then used to facilitate search
and discovery. Examples of "unstructured data" may include books, journals, documents,
metadata, health records, audio, video, analog data, images, files, and unstructured text such
as the body of an e-mail message, Web page, or word-processor document. While the main
content being conveyed does not have a defined structure, it generally comes packaged in
objects (e.g. in files or documents, …) that themselves have structure and are thus a mix of
structured and unstructured data, but collectively this is still referred to as "unstructured
data".
2.2.4 New generation big data
The new generation big data is in particular focusing on semi-structured and unstructured
data, often in combination with structured data.
In the BDVA reference model for big data technologies a distinction is done between 6
different big data types.
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2.2.4.1 Sensor data
Within the Databio pilots, several key parameters will be monitored through sensorial
platforms and sensor data will be collected along the way to support the project activities.
Two types of sensor data have been already identified and namely, a) IoT data from in-situ
sensors and telemetric stations, b) imagery data from unmanned aerial sensing platforms
(drones), c) imagery from hand-held or mounted optical sensors.
2.2.4.1.1 Internet of Things data
The IoT data are a major subgroup of sensor data involved in multiple pilot activities in the
Databio project. IoT data are sent via TCP/UDP protocol in various formats (e.g. txt with time
series data, json strings) and can be further divided into the following categories:
• Agro-climatic/Field telemetry stations which contribute with raw data (numerical
values) related to several parameters. As different pilots focus on different application
scenarios, the following table summarizes several IoT-based monitoring approaches
to be followed.
Table 2: Sensor data tools, resolution and spatial density
Pilot Mission, instrument Data resolution and spatial
density
A1.1,
B1.2,
C1.1,
C2.2
NP’s GAIAtrons, which are telemetry IoT stations
with modular/expandable design will be used to
monitor ambient temperature, humidity, solar
radiation, leaf wetness, rainfall volume, wind
speed and direction, barometric pressure
(GAIAtron atmo), soil temperature and humidity
(multi-depth) (GAIAtron soil)
Time step for data collection
every 10 minutes. One station
per microclimate zone (300ha -
1100 ha for atmo, 300ha -
3300ha for soil)
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A1.2,
B1.3
Field bound sensors will be used to monitor air
temperature, air moisture, solar radiation, leaf
wetness, rainfall, wind speed and direction, soil
moisture, soil temperature, soil EC/salinity, PAR,
and barometric pressure. These sensors consist
in technology platform of retriever and pups
wireless sensor network and SpecConnect, a
cloud based crop data management solution.
Time step for data collection is
customizable from 1 to 60
minutes; Field sensors will be
used to monitor 5 tandemly
located sites at a density: a) Air
temperature, air moisture,
rainfall, wind data and solar
radiation: one bloc of sensors
per 5 ha
b) Leaf wetness: two sensors
per ha
c) Soil moisture, soil
temperature and soil
EC/salinity: one combined
sensor per ha
A2.1 Environmental indoor: air temperature, air
relative humidity, solar radiation, crop leaf
temperature (remotely and in contact),
soil/substrate water content. Environmental
outdoor: wind speed and direction, evaporation,
rain, UVA, UVB
To be determined
B1.1 Agro-climatic IoT stations monitoring
temperature, relative and absolute humidity,
wind parameters
To be determined
• Control data in the parcels/fields measuring sprinklers, drippers, metering devices,
valves, alarm settings, heating, pumping state, pressure switches, etc.
• Contact sensing data that determine problems with great precision, speeding up the
use of techniques which help to solve problems
• Vessel and buoy-based stations which contribute with raw data (numerical values),
typically hydro acoustic and machinery data
2.2.4.1.2 Drone data
A specific subset of sensor data generated and processed within DataBio project is images
produced by cameras on-board drones or RPAS (Remotely Piloted Aircraft Systems). In
particular, some DataBio pilots will use optical (RGB), thermal or multispectral images and 3D
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point-clouds acquired from RPAS. The information generated by drone-airborne cameras is
usually Image Data (JPEG or JPEG2000). A general description of the workflow is provided
below.
Data acquired by the RGB sensor
The RGB sensor acquires individual pictures in .JPG format, together with their ‘geotag’ files,
which are downloaded from the RPAS and processed into:
• .LAS files: 3D point clouds (x, y, z), which are then processed to produce Digital Models
(Terrain- DTM, Surface-DSM, Elevation-DEM, Vegetation-DVM)
• .TIF files: which are then processed into an orthorectified mosaic. In order to obtain
smaller files, mosaics are usually exported to compressed .ECW format.
Data acquired by the thermal sensor
The Thermal sensor acquires a video file which is downloaded from the RPAS and:
• split into frames in .TIF format (pixels contain Digital Numbers: 0-255)
• 1 of every 10 frames is selected (with an overlap of about 80%, so as not to process an
excessive amount of information)
Data acquired by the multispectral sensor
The multispectral sensor acquires individual pictures from the 6 spectral channels in .RAW
format, which are downloaded from the RPAS and processed into:
• .TIF files (16 bits), which are then processed to produce a 6-bands .TIF mosaic (pixels
contain Digital Numbers: 0-255)
2.2.4.1.3 Data from hand-held or mounted optical sensors
Images from hand-held or mounted cameras will be collected using truck-held or hand held
full Range / high resolution UV-VIS-NIR-SWIR Spectroradiometer.
2.2.4.2 Machine-generated data
Machine-generated data in the DataBio project are data produced by ships, boats and
machinery used in agriculture and in forestry (such as tractors). These data will serve for
further analysis and optimisation of processes in the bio-economy sector.
For illustration purposes, examples of data collected by tractors in agriculture are described.
Tractors are equipped by the following units:
• Control units for data control, data collection and analyses including dashboards,
transmission control unit, hydrostatic or hydrodynamic system control unit, engine
control unit.
• Global Positioning System (GPS) units or Global System for Mobile Communications
(GSM) units for tractor tracking.
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• Unit for displaying characteristics of field/soil characteristics including area, quality,
boundaries and yields.
These units generate the following data:
• Identification of tractor + identification of driver by code or by RFID module.
• Identification of the current operation status.
• Time identification by the date and the current time.
• Precise tractor location tracking (daily route, starts, stops, speed).
• Tractor hours - monitoring working hours in time and place.
• Information from tachometer [Σ km] and [Σ working hrs and min].
• Identification of the current maintenance status.
• Tractor diagnostic: failure modes or failure codes
• Information about the date of the last calibration of each tractor systems +
information about setting, information about SW version, last update, etc.
• The amount of fuel in the fuel tank [L].
• Online information about sudden loss of fuel in the fuel tank.
• Fuel consumption per trip / per time period / per kilometer (monitoring of fuel
consumption in various dependencies e.g. motor load).
• Total fuel consumption per day [L/day].
• Engine speed [run/min].
• Possibility to online setup engine speed in range [run/min from - to], signaling when
limits are exceeding.
• Current position of accelerator pedal [% from scale 0-100 %].
• Charging level of the main battery [V].
• Current temperature of the cooling weather [C ͦ or F ͦ ].
• Current temperature of the motor oil [C ͦ or F ͦ ].
• Current temperature of after treatment [C ͦ or F ͦ ].
• Current temperature of the transmission oil [C ͦ or F ͦ ].
• Diagnosis gear shift [grades backward and forward].
• Current engine load [% from scale 0-100 %]
2.2.4.3 Geospatial data
The DataBio pilots will collect earth observation (EO) data from a number of sources which
will be refined during the project. Currently, it is confirmed that the following EO data will be
collected and used as input data:
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Table 3: Geospatial data tools, format and origin
Mission,
instrument
Format Origin
Sentinel-1, C-SAR SLC, GRD Copernicus Open Access Hub
(https://scihub.copernicus.eu/)
Sentinel-2, MSI L1C Copernicus Open Access Hub
(https://scihub.copernicus.eu/)
Information about the expected sizes will be added, when the information becomes available.
In addition to EO data, DataBio will utilise other geospatial data from EU, national, local,
private and open repositories including Land Parcel Identification System data, cadastral data,
Open Land Use map (http://sdi4apps.eu/open_land_use/), Urban Atlas and Corine Land
Cover, Proba-V data (www.vito-eodata.be).
The meteo-data will be collected mainly from EO systems based and will be collected from
European data sources such as COPERNICUS products, EUMETSAT H-SAF products, but also
other EO data sources such as VIIRS and MODIS and ASTER will be considered. As
complementary data sources, the weather forecast models output (ECMWF) and the regional
weather services output usually based on ground weather stations can be considered
according to the specific target areas of the pilots."
2.2.4.4 Genomics data
Within the DataBio Pilot 1.1.2 different data will be collected and produced. Three categories
of data have been already identified for the Pilot and namely, a) in-situ sensors (including
image capture) and farm data, b) genomic data from plant breeding efforts in Green Houses
produced using Next Generation Sequencers (NGS), c) biochemical data of tomato fruits
produced by chromatographs (LC/MS/MS, GS/MS, HPLC).
In-situ sensors/Environmental outdoor: Wind speed and direction, Evaporation, Rain, Light
intensity, UVA, UVB.
In-situ sensors/Environmental indoor: Air temperature, Air relative humidity, Crop leaf
temperature (remotely and in contact), Soil/substrate water content, crop type, etc.).
Farm Data:
• In-Situ measurements: Soil nutritional status.
• Farm logs (work calendar, technical practices at farm level, irrigation information,).
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• Farm profile (Static farm information, such as size
Table 4: Genomic, biochemical and metabolomic data tools, description and acquisition
Pilot A1.1.2 Mission, Instrument Data description and acquisition
Genomic
data
To characterize the genetic
diversity of local tomato
varieties used for breeding. To
use the genetic- genomic
information to guide the
breeding efforts (as a selection
tool for higher performance)
and develop a model to predict
the final breeding result in
order to achieve rapidly and
with less financial burden
varieties of higher performance.
Data will be produced using two
Illumina NGS Macchines.
Data produced from Illumina machines
stored in compressed text files (fastq).
Data will be produced from plant
biological samples (leaf and fruit).
Collection will be done in 2 different
plant stages (plantlets and mature
plants). Genomic data will be produced
using standard and customized
protocols at CERTH. Genomic data,
although plait text in format, are big-
volume data and pose challenges in
their storage, handling and processing.
Preliminary analysis will be performed
using the local HPC computational
facility.
Biochemical,
metabolomic
data
To characterize the biochemical
profile of fruits from tomato
varieties used for breeding.
Data will be produced from
different chromatographs and
mass spectrometers
Data will be mainly proprietary binary
based archives converted to XML or
other open formats. Data will be
acquired from biological samples of
tomato fruits.
While genomic data are stored in raw format as files, environmental data, which are
generated using a network of sensors, will be stored in a database along with the time
information and will be processed as time series data.
2.3 Historical data
In the context of doing machine learning and predictive and prescriptive analytics it is
important to be able to use historical data for training and validation purposes. Machine
learning algorithms will use existing historical data as training data both for supervised and
unsupervised learning. Information about datasets and the time periods concerned with
historical datasets to be used for DataBio can be found in Appendix A. Historical data can also
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serve as training complex event processing applications. In this case, historical data is injected
as “happening in real-time” therefore serving as testing the complex event driven application
in hand before running it in real-environment.
2.4 Expected data size and velocity
The big data “V” characteristics of Volume and Velocity is being described for each of the
identified data sets in the DataBio projects - typically with measurements of total historical
volumes and new/additional data per time unit. The DataBio-specific Data Volumes and
velocities (or injection rates) can be found in Appendix A.
2.5 Data beneficiaries
In this section, this document analyses the key data beneficiaries who will benefit from the
use of big data in several fields as analytics, data sets, business value, sales or marketing. This
section will consider both tangibles and intangibles concepts.
In examining the value of big data, it is necessary to evaluate who is affected by them and
their usage. In some cases, the individual whose data is processed directly receives a benefit.
Nevertheless, regarding Data Driven Bio-Economy, the benefit to the individual can be
considered as indirect. In other cases, the relevant individual receives no benefit attributable,
with big data value reaped by business, government, or society at large.
Concerning General Community, the collection and use of an individual’s data benefits not
only that individual, but also members of a proximate class, such as users of a similar product
or residents of a geographical area. In the case of organizations, Big Data analysis often
benefits those organizations that collect and harness the data. Data-driven profits may be
viewed as enhancing allocative efficiency by facilitating the free economy. The emergence,
expansion, and widespread use of innovative products and services at decreasing marginal
costs have revolutionized global economies and societal structures, facilitating access to
technology and knowledge and fomenting social change. With more data, businesses can
optimize distribution methods, efficiently allocate credit, and robustly combat fraud,
benefitting consumers as a whole.
On the other hand, big data analysis can provide a direct benefit to those individuals whose
information is being used. However, DataBio project is not directly involved on those specific
cases (see chapter6 about ethical issues).
Regarding general benefits, big data is creating enormous value for the global economy,
driving innovation, productivity, efficiency, and growth. Data has become the driving force
behind almost every interaction between individuals, businesses, and governments. The uses
of big data can be transformative and are sometimes difficult to anticipate at the time of initial
collection.
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This section does not provide a comprehensive taxonomy of big data benefits. It would be
pretentious to do so, ranking the relative importance of weighty social goals. Rather it posits
that such benefits must be accounted for by rigorous analysis considering the priorities of a
nation, society, or economy. Only then, can benefits be assessed within an economic
framework.
Besides those general concepts on Big Data Beneficiaries, it is possible to analyse the impact
of DataBio project results regarding the final users of the different technologies, tools and
services to be developed. Using this approach, and taking into account that more detailed
information is available at Deliverables D1.1, D2.1 and D3.1 regarding Agricultural, Forestry
and Fishery pilots definition, the main beneficiaries of big data are described in the following
sections.
2.5.1 Agricultural Sector
One of the proposed agricultural pilots is about the use of tractor units able to online send
information regarding current operations to the driver or farmer. The prototypes will be
equipped with units for tracking and tracing (GPS - Global Positioning System or GSM - Global
System for Mobile Communications) and the unit for displaying characteristics of soil units.
The proposed solution will meet Farmers requests on cost reduction and improved
productivity in order to increase their economic benefits following, also, sustainable
agriculture practices.
In other case, Smart farming services provided as irrigation through flexible mechanisms and
UIs (web, mobile, tablet compatible) will promote the adoption of technological tools (IoT,
data analytics) and collaboration with certified professionals to optimize farm productivity.
Therefore, Farming Cooperatives will obtain, again, cost reduction and improved productivity
migrating from standard to sustainable smart-agriculture practices. As a summary, main
beneficiaries of DataBio will be Farming cooperatives, farmers and land owners.
2.5.2 Forestry Sector
Data sharing and a collaborative environment enable improved tools for sustainable forest
management decisions and operations. Forest management services make data accessible for
forest owners, and other end users, and integrate this data for e-contracting, online purchase
and sales of timber and biomass. Higher data volumes and better data accessibility increase
the probability that the data will be updated and maintained.
DataBio WP2 will develop and pilot standardized procedures for collecting and transferring
Big Data based on DataBio WP4 platform from silvicultural activities executed in the forest.
As a summary, the Big Data beneficiaries related to WP2 – Forestry Pilots activities will be:
• Forest owners (private, public, timberland investors)
• Forest authority experts
• Forest companies
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• Contractors and service providers
2.5.3 Fishery Sector
Regarding WP3 – Fisheries Pilot, in Pilot A2: Small pelagic fisheries immediate operational
choices, the main users and beneficiaries of this pilot will be the ship owners and masters on
board small pelagic vessels. Modern pelagic vessels are equipped with increasingly complex
machinery systems for propulsion, manoeuvring and power generation. Due to that, the
vessel is always in an operational state, but the configuration of the vessel systems imposes
constraints on operation. The captain is tasked with safe operation of the vessel, while the
efficiency of the vessel systems may be increased if the captain is informed about the actual
operational state, potential for improvement and expected results of available actions.
The goal of the pilot B2: Oceanic tuna fisheries planning is to create tools that aid in trip
planning by presenting historical catch data as well as attempting to forecast where the fish
might be in the near future. The forecast model will be constructed from historical data of
catches with the data available by the skippers at that moment (oceanographical data, buoys
data etc). In that case, the main beneficiary of DataBio development will be tuna fisheries
companies. Therefore, as a summary, DataBio WP3 beneficiaries will be the broad range of
fisheries stakeholders from companies, captains and vessels owners.
2.5.4 Technical Staff
Adoption rates aside, the potential benefits of utilising big data and related technologies are
significant both in scale and scope and include, for example: better/more targeted marketing
activities, improved business decision making, cost reduction and generation of operational
efficiencies, enhanced planning and strategic decision making and increased business agility,
fraud detection, waste reduction and customer retention to name but a few. Obviously, the
ability of firms to realize business benefits will be dependent on company characteristics such
as size, data dependency and nature of business activity.
A core concern voiced by many of those participating in big data focused studies is the ability
of employers to find and attract the talent needed for both a) the successful implementation
of big data solutions and b) the subsequent realisation of associated business benefits.
Although ‘Data Scientist’ may currently be the most requested profile in big data, the
recruitment of Data Scientists (in volume terms at least) appears relatively low down the wish
list of recruiters. Instead, the openings most commonly arising in the big data field (as is the
case for IT recruitment) are development positions.
2.5.5 ICT sector
2.5.5.1 Developers
The generic title of developer is normally employed together with a detailed description of
the specific technical related skills required for the post and it is this description that defines
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the specific type of development activity undertaken. The technical skills most often cited by
recruiters in adverts for big data Developers are: NoSQL (MongoDB in particular), Java, SQL,
JavaScript, MySQL, Linux, Oracle, Hadoop (especially Cassandra), HTML and Spring.
2.5.5.2 Architects
More specifically, however, applicants for these positions are required to hold skills in a range
of technical disciplines including: Oracle (in particular, BI EE), Java, SQL, Hadoop and SQL
Server, whilst the main generic areas of technical Knowledge and competence required were:
Data Modelling, ETL, and Enterprise Architecture, Open Source and Analytics.
2.5.5.3 Analysts
Particular process/methodological skills required from applicants for analyst positions were
primarily in respect of: Data Modelling, ETL, Analytics and Data.
2.5.5.4 Administrators
In general, the technical skills most often requested by employers from big data
Administrators at that time were: Linux, MySQL and Puppet, Hadoop and Oracle, whilst the
process and methodological competences most often requested were in the areas of
Configuration Management, Disaster Recovery, Clustering and ETL.
2.5.5.5 Project Managers
The specific types of Project Manager most often required by big data recruiters are Oracle
Project Managers, Technical Project Managers and Business Intelligence Project Managers.
Aside from Oracle (and in particular BI EE, EBS and EBS R12), which was specified in over two-
thirds of all adverts for big data related Project Management posts, other technical skills often
needed by applicants for this type of position were: Netezza, Business Objects and Hyperion.
Process and methodological skills commonly required included ETL and Agile Software
Development together with a range of more ‘business focused’ skills, i.e. PRINCE2 and
Stakeholder Management.
2.5.5.6 Data Designers
The most commonly requested technical skills associated with these posts to have been
Oracle (particularly BIEE) and SQL followed by Netezza, SQL Server, MySQL and UNIX.
Common process and methodological skills needed were: ETL, Data Modelling, Analytics, CSS,
Unit Testing, Data Integration and Data Mining, whilst more general knowledge requirements
related to the need for experience and understanding of Business Intelligence, Data
Warehouse, Big Data, Migration and Middleware.
2.5.5.7 Data Scientists
The core technical skills needed to secure a position as a Data Scientist are found to be:
Hadoop, Java, NoSQL and C++. As was the case for other big data positions, adverts for Data
Scientists often made reference to a need for various process and methodological skills and
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competences. Interestingly however, in this case, such references were found to be much
more commonplace and (perhaps as would be expected) most often focused upon data
and/or statistical themes, i.e. Statistics, Analytics and Mathematics.
2.5.6 Research and education
Researchers, scientists and academics are one of the largest groups for data reuse. DataBio
data published as open data will be used for further research and for educational purposes
(e.g. thesis).
2.5.7 Policy making bodies
The DataBio data and results will serve as a basis for decision making bodies, especially for
policy evaluation and feedback on policy implementation. This includes mainly the European
Commission, national and regional public authorities.
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FAIR Data
The FAIR principle ensures that data can be discovered through catalogs or search engines, is
accessible through open interfaces, is compliant to standards to interoperable processing of
that data, and therefore can be easily being reused.
3.1 Data findability
3.1.1 Data discoverability and metadata provision
Metadata is, as its name implies, data about data. It describes the properties of a dataset.
Metadata can cover various types of information. Descriptive metadata includes elements
such as the title, abstract, author and keywords, and is mostly used to discover and identify a
dataset. Another type is administrative metadata with elements such as the license,
intellectual property rights, when and how the dataset was created, who has access to it, etc.
The datasets on the DataBio Infrastructure are either added locally, by a user, harvested from
existing data portals, or fetched from operational systems or IoT ecosystems. In DataBio, the
definition of a set of metadata elements is necessary in order to allow identification of the
vast amount information resources managed for which metadata is created, its classification
and identification of its geographic location and temporal reference, quality and validity,
conformity with implementing rules on the interoperability of spatial data sets and services,
constraints related to access and use, and organization responsible for the resource.
In addition, metadata elements related to the metadata record itself are also necessary to
monitor that the metadata created are kept up to date, and for identifying the organization
responsible for the creation and maintenance of the metadata. Such minimum set of
metadata elements is also necessary to comply with Directive 2007/2/EC and does not
preclude the possibility for organizations to document the information resources more
extensively with additional elements derived from international standards or working
practices in their community of interest.
Metadata referred to datasets and dataset series (particularly relevant for DataBio will be the
EO products derived from satellite imagery) should adhere to the profile originating from the
INSPIRE Metadata regulation with added theme-specific metadata elements for the
agriculture, forestry and fishery domains if necessary. This approach will ensure that
metadata created for the datasets, dataset series and services will be compliant with the
INSPIRE requirements as well international standards ISO EN 19115 (Geographic Information
– Metadata; with special emphasis in ISO 19115-2:2009 Geographic information -- Metadata
-- Part 2: Extensions for imagery and gridded data), ISO EN 19119 (Geographic Information –
Services), ISO EN 19139 (Geographic Information – Metadata – Metadata XML Schema) and
ISO EN ISO 19156 (Earth Observation Metadata profile of Observations & Measurements).
Besides, INSPIRE conformant metadata may be expressed also through the DCAT Application
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Profile1, which defines a minimum set of metadata elements to ensure cross-domain and
cross-border interoperability between metadata schemas used in European data portals. If
adopted by DataBio, such a mapping could support the inclusion of INSPIRE metadata in the
Pan-European Open Data Portal for wider discovery across sectors beyond the geospatial
domain.
A Distribution represents a way in which the data is made available. DCAT is a rather small
vocabulary, but deliberately leaves many details open. It welcomes “application profiles”:
more specific specifications built on top of DCAT resp GeoDCAT – AP as geospatial extension.
For sensors we will focused on SensorML. SensorML can be used to describe a wide range of
sensors, including both dynamic and stationary platforms and both in-situ and remote
sensors. Other possibility is Semantic Sensor Net Ontology which describes sensors and
observations, and related concepts. It does not describe domain concepts, time, locations,
etc. these are intended to be included from other ontologies via OWL imports. This ontology
is developed by the W3C Semantic Sensor Networks Incubator Group (SSN-XG).
In DataBio, there is a need for metadata harmonization of the spatial and non-spatial datasets
and services. GeoDCAT-AP was an obvious choice due to the strong focus on geographic
datasets. The main advantage is that it enables users to query all datasets in a uniform way.
GeoDCAT-AP is still very new, and the implementation of the new standard within EUXDAT
can provide feedback to OGC, W3C & JRC from both technical and end user point of view.
Several software components are available in the DataBio architecture that have varying
support for GeoDCAT-AP, being Micka2, CKAN3 and GeoNetwork4. For the DataBio purposes
we will need also integrate Semantic Sensor Net Ontology and SensorML.
For enabling compatibility with COPERNICUS, INSPIRE and GEOSS, the DataBio project will
make three extensions: i) Module for extended harvesting INSPIRE metadata to DCAT, based
on XSLT and easy configuration; ii)Module for user friendly visualisation of INSPIRE metadata
in CKAN; and iii)Module to output metadata in GeoDCAT-AP resp SensorDCAT. We plan use
Micka and CKAN systems. MICKA is a complex system for metadata management used for
building Spatial Data Infrastructure (SDI) and geo portal solutions. It contains tools for editing
and the management of spatial data and services metadata, and other sources (documents,
websites, etc.). CKAN supports DCAT to import or export its datasets. CKAN enables
harvesting data from OGC:CSW catalogues, but not all mandatory INSPIRE metadata elements
are supported. Unfortunately, the DCAT output does not fulfil all INSPIRE requirements, nor
is GeoDCAT-AP fully supported.
1 https://joinup.ec.europa.eu/asset/dcat_application_profile/description
2 http://micka.bnhelp.cz/
3 https://ckan.org/
4 http://geonetwork-opensource.org/
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An ongoing programme of spatial data infrastructure projects, undertaken with academic and
commercial partners, enables DataBio to contribute to the creation of standard data
specifications and policies. This ensures their databases remain of high quality, compatible
and can interact with one another to deliver data which provides practical and tangible
benefits for European society. The network’s mission is to provide and disseminate statistical
information which has to be objective, independent and of high quality. Federal statistics are
available to everybody: politicians, authorities, businesses and citizens.
3.1.2 Data identification, naming mechanisms and search keyword approaches
For data identification, naming and search keywords we will use INSPIRE data registry. The
INSPIRE infrastructure involves a number of items, which require clear descriptions and the
possibility to be referenced through unique identifiers. Examples for such items include
INSPIRE themes, code lists, application schemas or discovery services. Registers provide a
means to assign identifiers to items and their labels, definitions and descriptions (in different
languages). The INSPIRE Registry is a service giving access to INSPIRE semantic assets (e.g.
application schemas, meta/data codelists, themes), and assigning to each of them a persistent
URI. As such, this service can be considered also as a metadata directory/catalogue for
INSPIRE, as well as a registry for the INSPIRE "terminology". Starting from June 2013, when
the INSPIRE Registry was first published, a number of version have been released,
implementing new features based on the community's feedback. Now, recently, a new
version of the INSPIRE Registry has been published, which, among other features, makes
available its content also in RDF/XML:
http://inspire.ec.europa.eu/registry/5
The INSPIRE registry provides a central access point to a number of centrally managed INSPIRE
registers6. INSPIRE registry include:
● INSPIRE application schema register
● INSPIRE code list register
● INSPIRE enumeration register
● INSPIRE feature concept dictionary
● INSPIRE glossary
● INSPIRE layer register
● INSPIRE media-types register
● INSPIRE metadata code list register
● INSPIRE reference document register
● INSPIRE theme register
5 https://www.rd-alliance.org/group/metadata-ig/post/inspire-registry-rdf-representation-now-
supported.html
6 http://inspire.ec.europa.eu/registry/
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Most relevant for naming in metadata is INSPIRE metadata code list register, which contains
the code lists and their values, as defined in the INSPIRE implementing rules on metadata.7
3.1.3 Data lineage
Data lineage refers to the sources of information, such as entities and processes, involved in
producing or delivering an artifact. Data lineage records the derivation history of a data
product. The history could include the algorithms used, the process steps taken, the
computing environment run, data sources input to the processes, the organization/person
responsible for the product, etc. Provenance provides important information to data users
for them to determine the usability and reliability of the product. In the science domain, the
data provenance is especially important since scientists need to use the information to
determine the scientific validity of a data product and to decide if such a product can be used
as the basis for further scientific analysis. The provenance of information is crucial to making
determinations about whether information is trusted, how to integrate diverse information
sources, and how to give credit to originators when reusing information [REF-02]. In an open
and inclusive environment such as the Web, users find information that is often contradictory
or questionable. Reasoners in the Semantic Web will need explicit representations of
provenance information in order to make trust judgments about the information they use.
With the arrival of massive amounts of Semantic Web data (eg, via the Linked Open Data
community) information about the origin of that data, ie, provenance, becomes an important
factor in developing new Semantic Web applications. Therefore, a crucial enabler of the
Semantic Web deployment is the explicit representation of provenance information that is
accessible to machines, not just to humans. Data provenance as the information about how
data was derived. Both are critical to the ability to interpret a particular data item.
Provenance is often conflated with metadata and trust. Metadata is used to represent
properties of objects. Many of those properties have to do with provenance, so the two are
often equated. Trust is derived from provenance information, and typically is a subjective
judgment that depends on context and use [REF-03].
W3C PROV Family of Documents defines a model, corresponding serializations and other
supporting definitions to enable the interoperable interchange of provenance information in
heterogeneous environments such as the Web [REF-04]. Current standards include [REF-05]:
PROV-DM: The PROV Data Model [REF-06] - PROV-DM is a core data model for provenance
for building representations of the entities, people and processes involved in producing a
piece of data or thing in the world. PROV-DM is domain-agnostic, but with well-defined
extensibility points allowing further domain-specific and application-specific extensions to be
defined. It is accompanied by PROV-ASN, a technology-independent abstract syntax notation,
which allows serializations of PROV-DM instances to be created for human consumption,
7 http://inspire.ec.europa.eu/metadata-codelist
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which facilitates its mapping to concrete syntax, and which is used as the basis for a formal
semantics.
PROV-O: The PROV Ontology [REF-07] - This specification defines the PROV Ontology as the
normative representation of the PROV Data Model using the Web Ontology Language
(OWL2). This document is part of a set of specifications being created to address the issue of
provenance interchange in Web applications.
Constraints of the PROV Data Model [REF-08] - PROV-DM, the PROV data model, is a data
model for provenance that describes the entities, people and activities involved in producing
a piece of data or thing. PROV-DM is structured in six components, dealing with: (1) entities
and activities, and the time at which they were created, used, or ended; (2) agents bearing
responsibility for entities that were generated and activities that happened; (3) derivations of
entities from entities; (4) properties to link entities that refer to a same thing; (5) collections
forming a logical structure for its members; (6) a simple annotation mechanism.
PROV-N: The Provenance Notation [REF-09] - PROV-DM, the PROV data model, is a data
model for provenance that describes the entities, people and activities involved in producing
a piece of data or thing. PROV-DM is structured in six components, dealing with: (1) entities
and activities, and the time at which they were created, used, or ended; (2) agents bearing
responsibility for entities that were generated and activities that happened; (3) derivations of
entities from entities; (4) properties to link entities that refer to the same thing; (5) collections
forming a logical structure for its members; (6) a simple annotation mechanism.
Figure 2 [REF-10] is a generic data lifecycle in the context of a data processing environment
where data are first discovered by the user with the help of metadata and provenance
catalogues.
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Figure 2: The processing data lifecycle
During the data processing phase, data replica information may be entered in replica
catalogues (which contain metadata about the data location), data may be transferred
between storage and execution sites, and software components may be staged to the
execution sites as well. While data are being processed, provenance information can be
automatically captured and then stored in a provenance store. The resulting derived data
products (both intermediate and final) can also be stored in an archive, with metadata about
them stored in a metadata catalogue and location information stored in a replica catalogue.
Data Provenance is also addressed in W3C DCAT Metadata model [REF-11].
dcat:CatalogRecord describes a dataset entry in the catalog. It is used to capture provenance
information about dataset entries in a catalog. This class is optional and not all catalogs will
use it. It exists for catalogs where a distinction is made between metadata about a dataset
and metadata about the dataset's entry in the catalog. For example, the publication date
property of the dataset reflects the date when the information was originally made available
by the publishing agency, while the publication date of the catalog record is the date when
the dataset was added to the catalog. In cases where both dates differ, or where only the
latter is known, the publication date should only be specified for the catalog record. W3C
PROV Ontology [prov-o] allows describing further provenance information such as the details
of the process and the agent involved in a particular change to a dataset. Detailed
specification of data provenance is also additional requirements for DCAT – AP specification
effort [REF-12].
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3.2 Data accessibility
Through DataBio experiments with a large number of tools and technologies identified in WP4
and WP5, a common data access pattern shall be developed. Ideally, this pattern is based on
internationally adopted standards, such as OGC WFS for feature data, OGC WCS for coverage
data, OGC WMS for maps, or OGC SOS for sensor data.
3.2.1 Open data and closed data
Everyone from citizens to civil servants, researchers and entrepreneurs can benefit from open
data. In this respect, the aim is to make effective use of Open Data. This data is already
available in public domains and is not within the control of the DataBio project.
All data rests on a scale between closed and open because there are variances in how
information is shared between the two points in the continuum. Closed data might be shared
with specific individuals within a corporate setting. Open data may require attribution to the
contributing source, but still be completely available to the end user.
Generally, open data differs from closed data in three key ways8:
1. Open data is accessible, usually via a data warehouse on the internet.
2. It is available in a readable format.
3. It’s licensed as open source, which allows anyone to use the data or share it for non-
commercial or commercial gain.
Closed data restricts access to the information in several potential ways:
1. It is only available to certain individuals within an organization.
2. The data is patented or proprietary.
3. The data is semi-restricted to certain groups.
4. Data that is open to the public through a licensure fee or other prerequisite.
5. Data that is difficult to access, such as paper records that haven’t been digitized.
The perfect example of closed data could be information that requires a security clearance;
health-related information collected by a hospital or insurance carrier; or, on a smaller scale,
your own personal tax returns.
There are also other datasets used for the pilots, like e.g. cartography, 3D or land use data
but those are stored in databases which are not available through the Open Data portals.
Once the use case specification and requirements have been completed these data may also
be needed for the processing and visualisation within the DataBio applications. However, this
data – in its raw format – may not be made available to external stakeholders for further use
due to licensing and/or privacy issues. Therefore, at this stage, the data management plan
will not cover these datasets.
8 www.opendatasoft.com
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3.2.2 Data access mechanisms, software and tools
Data access is the process of entering a database to store or retrieve data. Data Access Tools
are end user oriented tools that allow users to build structured query language (SQL) queries
by pointing and clicking on the list of table and fields in the data warehouse.
Thorough computing history, there have been different methods and languages already that
were used for data access and these varied depending on the type of data warehouse. The
data warehouse contains a rich repository of data pertaining to organizational business rules,
policies, events and histories and these warehouses store data in different and incompatible
formats so several data access tools have been developed to overcome problems of data
incompatibilities.
Recent advancement in information technology has brought about new and innovative
software applications that have more standardized languages, format, and methods to serve
as interface among different data formats. Some of these more popular standards include
SQL, OBDC, ADO.NET, JDBC, XML, XPath, XQuery and Web Services.
3.2.3 Big data warehouse architectures and database management systems
Depending on the project needs, there are different possibilities to store data:
3.2.3.1 Relational Database
This is a digital database whose organization is based on the relational model of data. The
various software systems used to maintain relational databases are known as a relational
database management system (RDBMS). Virtually all relational database systems use SQL
(Structured Query Language) as the language for querying and maintaining the database. A
relational database has the important advantage of being easy to extend. After the original
database creation, a new data category can be added without requiring that all existing
applications be modified.
This model organizes data into one or more tables (or "relations") of columns and rows, with
a unique key identifying each row. Rows are also called records or tuples. Generally, each
table/relation represents one "entity type" (such as customer or product). The rows represent
instances of that type of entity and the columns representing values attributed to that
instance.
The definition of a relational database results in a table of metadata or formal descriptions of
the tables, columns, domains, and constraints.
When creating a relational database, the domain of possible values can be defined in a data
column and further constraints that may apply to that data value can be described. For
example, a domain of possible customers could allow up to ten possible customer names but
be constrained in one table to allowing only three of these customer names to be specifiable.
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An example of a relational database management system is the Microsoft SQL Server,
developed by Microsoft. As a database server, it is a software product with the primary
function of storing and retrieving data as requested by other software applications—which
may run either on the same computer or on another computer across a network (including
the Internet). Microsoft makes SQL Server available in multiple editions, with different feature
sets and targeting different users.
PostgreSQL – for specific domains: PostgreSQL, often simply Postgres, is an object-relational
database management system (ORDBMS) with an emphasis on extensibility and standards
compliance. As a database server, its primary functions are to store data securely and return
that data in response to requests from other software applications. It can handle workloads
ranging from small single-machine applications to large Internet-facing applications (or for
data warehousing) with many concurrent users; on macOS Server, PostgreSQL is the default
database. It is also available for Microsoft Windows and Linux.
PostgreSQL is developed by the PostgreSQL Global Development Group, a diverse group of
many companies and individual contributors. It is free and open-source, released under the
terms of the PostgreSQL License, a permissive software license. Furthermore, it is ACID-
compliant and transactional. PostgreSQL has updatable views and materialized views,
triggers, foreign keys; supports functions and stored procedures, and other expandability.
3.2.3.2 Big Data storage solutions
A NoSQL (originally referring to "non-SQL", "non-relational" or "not only SQL") database
provides a mechanism for storage and retrieval of data which is modeled in means other than
the tabular relations used in relational databases. Such databases have existed since the late
1960s, but did not obtain the "NoSQL" moniker until a surge of popularity in the early twenty-
first century, triggered by the needs of Web 2.0 companies such as Facebook, Google, and
Amazon.com. NoSQL databases are increasingly used in big data and real-time web
applications. NoSQL systems are also sometimes called "Not only SQL" to emphasize that they
may support SQL-like query languages.
Motivations for this approach include: simplicity of design, simpler "horizontal" scaling to
clusters of machines (which is a problem for relational databases), and finer control over
availability. The data structures used by NoSQL databases (e.g. key-value, wide column, graph,
or document) are different from those used by default in relational databases, making some
operations faster in NoSQL. The particular suitability of a given NoSQL database depends on
the problem it must solve. Sometimes the data structures used by NoSQL databases are also
viewed as "more flexible" than relational database tables.
MongoDB: MongoDB (from humongous) is a free and open-source cross-platform document-
oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-
like documents with schemas. MongoDB is developed by MongoDB Inc. and is free and open-
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source, published under a combination of the GNU Affero General Public License and the
Apache License.
MongoDB supports field, range queries, regular expression searches. Queries can return
specific fields of documents and also include user-defined JavaScript functions. Queries can
also be configured to return a random sample of results of a given size. MongoDB can be used
as a file system with load balancing and data replication features over multiple machines for
storing files. This function, called Grid File System, is included with MongoDB drivers.
MongoDB exposes functions for file manipulation and content to developers. GridFS is used
in plugins for NGINX and lighttpd. GridFS divides a file into parts, or chunks, and stores each
of those chunks as a separate document.
MongoDB based (but not restricted to) is GeoRocket, developed by Fraunhofer IGD. It
provides high-performance data storage and is schema agnostic and format preserving. For
more information please refer to D4.1 which describes the components applied in the DataBio
project.
3.3 Data interoperability
Data can be made available in many different formats implementing different information
models. The heterogeneity of these models reduces the level of interoperability that can be
achieved. In principle, the combination of a standardized data access interface, a standardized
transport protocol, and a standardized data model ensure seamless integration of data across
platforms, tools, domains, or communities.
When the amount of data grows, mechanisms have to be explored to ensure interoperability
while handling large volumes of data. Currently, the amount of data can still be handled using
OGC models and data exchange services. We will need to review this element during the
course of the project. For now, data interoperability is envisioned to be ensured through
compliance with internationally adopted standards.
Eventually, interoperability requires different phenotypes when being applied in various
“disciplinary” settings. The following figure illustrates that concept (source: Wyborn 2017).
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Figure 3: The “disciplinary data integration platform: where do you ssit? (source: Wyborn)
The intra-disciplinary type remains within a single discipline. The level of standardization
needs to cover the discipline needs, but little attention is usually paid to cross-discipline
standards. The multi-disciplinary situation has many people from different domains working
together, but eventually they all remain within their silos and data exchange is limited to the
bare minimum.
The cross-disciplinary setting is what we are experiencing at the beginning of DataBio. All
disciplines are interfacing and reformatting their data to make it fit. The model works as long
as data exchange is minor, but does not scale, as it requires bilateral agreements between
various parties. The interdisciplinary approach is targeted in DataBio. The goal here is to
adhere to a minimum set of standards. Ideally, the specific characteristics are standardized
between all partners upfront. This model adds minimum overhead to all parties, as a single
mapping needs to be implemented per party (or, even better, the new model is used natively
from now on). The transdisciplinary approach starts with data already provided as linked data
with links across the various disciplines, well-defined vocabularies, and a set of mapping rules
to ensure usability of data generated in arbitrary disciplines.
3.3.1 Interoperability mechanisms
Key to interoperable data exchange are standardized interfaces. Currently, the amount of
data processing and exchange tools is extremely large. We expect a consolidation of the
number of tools during the first 15 months of the project. We will revise the requirements set
by the various pilots and the data sets made available regularly to ensure that proper
recommendations can be given at any time.
3.3.2 Inter-discipline interoperability and ontologies
A key element to interoperability within and across disciplines are shared semantics, but the
Semantic Web is still in its infancy and it is not clear to which extent it will become widely
accepted within data intensive communities in the near future. It requires graph-structures
for data and/or metadata, well defined vocabularies and ontologies, and lacks both the