Jiří Hřebíček
email: hrebicek@iba.muni.cz

Stefan Jensen
email: stefan.jensen@eea.europa.eu

Chris Steenmans
email: chris.steenmans@eea.europa.eu

The framework for Environmental Software Systems of the European Environment Agency

Keywords: EEA, environmental data, information system, environmental services, environmental monitoring, Eionet, SEIS, Copernicus, INSPIRE

The European Environment Agency (EEA) is the authoritative European environment node and hub, and a key initiator within networks of knowledge co-creation, sharing and use in European Union (EU). It ensures the quality, availability and accessibility of environmental data and information needed to support strategic area: informing policy implementation and assessing systemic challenges. It actively communicates data, information and knowledge to policymakers, the public, research communities and others (nongovernmental organizations, businesses) as well as to regional and international processes including those of the United Nation and its specialised agencies and promotes information governance as a driver of public empowerment and behavioural change. In the past few years the EEA's environmental information systems as well as environmental modelling with the support of environmental software systems have been supporting decision making processes within the EU Systems have undergone rapid development and grew up to support the knowledgebase of European Commission and EU member countries. Specifically, new infrastructure to support supply services (collection of data); networking (knowledge management); workflows (planning, automation, quality management); development of final products and public services (reports, web sites, public data and information services) were put in place. EEA strengthens the infrastructure for environmental data and information sharing both at the EEA and in the European Environment Information and Observation Network with Member Countries, taking into account the Shared Environmental Information System (SEIS) and the Infrastructure for Spatial Information in Europe (INSPIRE) developments. The paper presents the framework for the of development of EEA environmental software systems and information services accepted in Multiannual Working Plan of the EEA for 2014 -2018 and its implication for ICT.

Introduction

Environmental software systems in the European Union (EU) should support the current Environment Action Programme to 2020 (7th EAP) entitled Living well, within the limits of our planet entered into force in January 2014. This programme is based on a 2050 vision of EU centred on ecological limits, a circular economy and society's resilience. To move towards this vision, the 7th EAP sets out nine priority objectives, comprising three thematic objectives, four enabling objectives, one urban objective, and one global objective [START_REF]Decision No 1386/2013/EU of the European Parliament and of the Council of 20 November 2013 on a General Union Environment Action Programme to 2020 "Living well, within the limits of our planet[END_REF].

The 7th EAP aims at achieving existing objectives and targets in a mid-term perspective to 2020/2030, with EU policies [START_REF]EU action on climate[END_REF][START_REF]The Roadmap to a Resource Efficient Europe[END_REF][START_REF]EU Biodiversity Strategy to 2020 -towards implementation[END_REF]: the Climate and Energy Package 2020 and associated roadmaps; the EU Strategy for Adaptation to Climate Change; Europe 2020 and the Resource Efficiency Roadmap; the Biodiversity Strategy to 2020; and specific legislation for water, waste, air etc. In addition, the 7th EAP promotes new ways of thinking and innovation in order to realise the EU 2050 vision beyond existing policy targets. This gives a large framework of application Information and Communication Technologies (ICT) and updating and development of environmental software systems.

The overall aim of the 7th EAP is to step up the contribution of environment policy to the transition towards sustainability, understood as a resource-efficient, low-carbon economy in which natural capital is protected and enhanced, and the health and wellbeing of citizens is safeguarded. The 7th EAP is also the basis for EU involvement in global agendas such as Rio+20, the United Nations Framework Convention on Climate Change [START_REF]United Nations Framework Convention on Climate Change[END_REF], the Montreal Protocol on Substances that Deplete the Ozone Layer [START_REF]Montreal Protocol on Substances that Deplete the Ozone Layer[END_REF], and the Convention on Biological Diversity [START_REF]Convention on Biological Diversity[END_REF], as well being the basis for wider European activities, which are increasingly framed in a 2050 perspective [START_REF][END_REF].

The European Environment Agency (EEA) [9] is one of the most important agencies of the EU with ICT support to environment protection. It is located in Copenhagen, Denmark. Its main task is to provide sound, independent environmental information. The EEA is a major information source for those involved in developing, adopting, implementing and evaluating environmental policy, and also the general public. Currently, the EEA has 33 member countries. The Regulation [START_REF]401/2009 of the European Parliament and of the Council of 23 April 2009 on the European Environment Agency and the European Environment Information and Observation Network (Codified version)[END_REF] establishing the EEA was adopted by the EU in 1990. It came into force in late 1993and its work started in earnest in 1994. This Regulation also established the European Environment Information and Observation Network (Eionet) [11]. EEA's mandate is to help the EU and member countries make informed decisions about improving the environment, integrating environmental considerations into economic policies and moving towards sustainability and coordinate the Eionet.

The EEA, according to the above 7th EAP visions [START_REF]Decision No 1386/2013/EU of the European Parliament and of the Council of 20 November 2013 on a General Union Environment Action Programme to 2020 "Living well, within the limits of our planet[END_REF], aims to support sustainable development and to help achieve significant and measurable improvements in information about Europe's environment, through the provision of timely, targeted, relevant and reliable information to policy making agents and the public.

The aim of the paper is an introduction of the recent independent evaluation of the EEA and discussion how EEA operates in a complex, multi-level and multi-actor governance setting at EU (e.g. Copernicus, Eurostat, INSPIRE, SEIS), national, and global levels, which also includes research institutes (e.g. Joint Research Centre), businesses, and nongovernmental organizations (NGOs). We discuss the specific role of the EEA to build the capacity of for environmental software systems in member countries, using the Eionet as its unique partner to generate two-way flows of qualityassured environmental data and information.

The EEA's understanding of the nature of environmental challenges has evolved in recent decades, requiring corresponding changes to information flows and assessments with using ICT. The continuous flow of new information and updated scientific insights into environment and climate issues improves the knowledge base for environment and climate policies [START_REF]EU action on climate[END_REF][START_REF]The Roadmap to a Resource Efficient Europe[END_REF][START_REF]EU Biodiversity Strategy to 2020 -towards implementation[END_REF]. Fulfilling its role as an interface between science and policy, the EEA will work closely with the European Commission (EC), its DG Research and Innovation (RTD), DG Communications Networks, Content and Technology (CNECT), DG Environment (ENV), DG Health and Consumers (SANCO), Eurostat (ESTAT) and Joint Research Centre (JRC), and others in seeking to influence activities under the EU Framework Research Programmes (Horizon 2020 and earlier). The EEA also aims to exploit the insights that result from these programmes.

Furthermore, we introduce the key goals of environmental software systems and decision making processes of the EEA in the new Multiannual Work Programme 2014-2018 (MAWP) [START_REF]Expanding the knowledge base for policy implementation and long-term transitions[END_REF] and introduce important environmental software systems and ICT used in SEIS, Copernicus, INSPIRE activities.

2

Structure of the Multiannual Work Programme 2014-2018

To secure the knowledge and evidence base for a framework of the Priority objective 5 of the 7th EAP [START_REF]Decision No 1386/2013/EU of the European Parliament and of the Council of 20 November 2013 on a General Union Environment Action Programme to 2020 "Living well, within the limits of our planet[END_REF], the MAWP [START_REF]Expanding the knowledge base for policy implementation and long-term transitions[END_REF] is structured around four strategic areas:

• Strategic area 1 (SA1): Informing policy implementation. There is providing feedback and input to long-established and emerging policy frameworks, objectives, and targets through reporting on progress in recognised environmental themes, including links to those sectors that are the primary sources of environmental pressures, and through reporting on the state of and trends in natural environment systems (atmosphere, oceans, territories) using the DPSIR assessment framework (Driver Forces, Pressures, State, Impacts, Responses).

• Strategic area 2 (SA2): Assessing systemic challenges.

There is providing support to improving synergies and policy coherence across environmental, economic and social systems by applying established and experimental integrated assessment techniques and prospective analysis, with both a short-term and a long-term perspective. This work supports the long-term vision for 2050 set out in the 7th EAP. • Strategic area 3 (SA3): Knowledge co-creation, sharing and use. There is providing support to the work in the above areas by building and maintaining networks of people and information systems as the basis for sharing and co-creating content, whether that be data, indicators, or assessments, in a transparent manner with other actors at national, European and global levels. Communications, in the broadest sense of the word, will also play a major role in ensuring that information promotes a dialogue with a dialogue with stakeholders and reaches out to the society at large. Targeted information, communication and participation are important instruments for achieving significant and measurable improvement in Europe's environment, responding to emerging challenges and societal developments. • Strategic area 4 (SA4): EEA management. EEA management, administration, and operational services make up a fourth area of work. The guiding principles of this work area are strict adherence to all the principles, rules, and regulations that apply to the EEA, as well as continuous improvement of the efficiency and effectiveness of EEA management.

These strategic areas cover complete EEA/Eionet information processes. We can see at the bottom of the Fig. 1 the overall EEA/Eionet core processes from Monitoring (in the broadest sense) through Data, Information and Assessments to Knowledge. This value-adding chain is at the heart of the work of the EEA and Eionet.

The development of the architecture of the environmental software systems of EEA has evolved in line with the principles of Shared Environmental Information System (SEIS) [START_REF]Shared Environmental Information System[END_REF] and will contribute to the full implementation of the Infrastructure for Spatial Information in Europe (INSPIRE) [START_REF]INSPIRE Directive[END_REF] by 2019. The activities will support the European Open Data Portal [START_REF]EU Open Data Portal[END_REF], the Open Data Strategy of the EU 2020 Digital Agenda [START_REF]Digital Agenda for Europe[END_REF], and ensure compliance with the Århus Convention [17]. Highlights include the provision of:

• thematic data and expertise to support the involvement of EEA member and cooperating countries in international conventions and related activities, as described in many areas under SA1 and SA2;

• expertise, data, and assessments to ensure European contributions to global and United Nation (UN) activities, including to UN Environment Program (Global Environmental Outlook [18], UNEP-Live [19]) and the post Rio+20 activities [START_REF]Rio+20[END_REF]; • expertise and capacity-building in networking and information partnerships. This is based on Eionet and SEIS approaches, particularly towards cooperating countries and the European Neighbourhood and towards countries and regions following EU priorities. Involvement in the Global Earth Observation System of Systems (GEO/GEOSS) [21] and Copernicus [START_REF]Copernicus[END_REF] are included here.

Enhanced involvement of the Eionet network to decision making processes of the EEA is sought across the strategic areas in the fields of data flows, indicators, policy effectiveness analysis, integrated assessments, communications, and the use of new analytical methods and technologies.

Established European Topic Centres1 (ETCs), key components of Eionet, play an important role in the chain from environmental data to assessments, supporting the development and maintenance of the knowledge base in all areas of work under SA1, and also parts of SA2.

Shared Environmental Information System

The project Shared Environmental Information System (SEIS) [START_REF]Shared Environmental Information System[END_REF], [START_REF]Shared Environmental Information System[END_REF] is a mixture of components: it is a vision that encourages the modernisation of data handling of environmental information systems; a set of principals which underpin the decisions making; and a set of concrete activities where developers are, for example, supporting the establishment of a European spatial data infrastructure [START_REF]INSPIRE Directive[END_REF] together with the JRC and the EC; analysing environmental indicators and distributed exchange between the EU countries and EEA; and also working on governance issues in Mediterranean and North African countries. SEIS is based on seven principles [START_REF]Shared Environmental Information System[END_REF]. Environmental information should be:

1. Managed as close as possible to its source.

2.

Collected once, and shared with others for many purposes.

3. Readily available to easily fulfil environmental reporting obligations. 4. Easily accessible to all users. 5. Accessible to enable comparisons at the appropriate geographical scale, and citizen participation. 6. Fully available to the general public, and at the national level in the relevant national language(s). 7. Supported through common, free open software standards.

Under SEIS, EEA has worked with numerous EU institutes (e.g. DG ENV, JRC, ESTAT, etc.) and initiatives (Copernicus, INSPIRE) in order to reach successful implementation of its principles. These include:

• Achieving effective streamlining of legislative environmental reporting requirements, through such actions as the thematic strategy on air pollution (CAFE) [START_REF]Clean Air for Europe programme (CAFE[END_REF]; The data input side of SEIS is predominantly based on environmental monitoring, which happens at the EU country level -the monitoring happens based on environmental legislation that individual countries must adhere to.

•
SEIS is not only about making information available for public access, it is also about making the data available for network communication via standard APIs (application programming interfaces) and open data formats [START_REF]EU Open Data Portal[END_REF]. Implementing and supporting SEIS-friendly ICT tools is one of the core activities of the EEA.

Basically, everything you see on our EEA's website [9], [11] is harvestable via external systems and linked data spiders, so the environmental data and information can be easily re-used, integrated and re-distributed by to a wider network of users. As a practical example, organisations are now able to easily exchange their catalogues of datasets creating more complete federated dataset catalogues, also known as Open Data Catalogues. The ICT makes it almost effortless for the EEA to contribute to the EU Open Data Portal [START_REF]EU Open Data Portal[END_REF].

Through EU legislation and based on its's mandate, the EEA is in a position to promote free access to environmental data. Resource wise, the EEA cannot afford to keep paying for data, and there is a policy framework in EU which supports this issue. There are three things in particular: one is the Aarhus Convention [17], which addresses countries outside EU and focuses on free access to environmental information, but also ensures citizens having the opportunity to, for example, go to court if certain organisations are not sharing data. Secondly, the Publish Sector Information Directive (PSI) [START_REF]PSI Directive[END_REF] pushes public access administrators to make data available. And thirdly, the EEA has an access to information directive at the EU level that underpins the Aarhus Convention.

Another component of SEIS is remote sensing -data are received from satellites (e.g. Copernicus [START_REF]Copernicus[END_REF], GEOSS [START_REF]GEOSS Portal[END_REF]). As another source over the past five years, citizen science in Europe has grown massively, and citizens have become actively involved in the collection of environmental data. In addition, EEA and Eionet are cooperating with the research community to gather research and statistics data.

Copernicus

The Europe's earth observation programme, known as Copernicus (previously Global Monitoring for Environment and Security -GMES) [START_REF]Copernicus[END_REF], is an EU -led initiative in partnership with the European Space Agency (ESA) [30].

The origins of the Copernicus programme can be traced back to a series of meetings involving the EC and European space industry representatives in Baveno, Italy, in 1998. The result was the Baveno Manifesto, which called for a "long-term commitment to the development of space-based environmental monitoring services" in Europe [START_REF] Brachet | From initial ideas to a European plan: GMES as an exemplar of European space strategy[END_REF].

Currently in the early stages of implementation, Copernicus will produce data to inform EU, national and local level environmental policymaking and to support environmental monitoring, policy evaluation, modelling, forecasting and reporting. It is intended that Copernicus will make key contributions to EU flagship initiatives, including Resource-Efficient Europe [START_REF]The Roadmap to a Resource Efficient Europe[END_REF], which focuses on securing Europe's needs in terms of natural resources, such as food, soil, water, biomass, ecosystems, fuels and raw materials [START_REF]Science for Environment Policy. Future Brief: Earth Observation's Potential for the EU Environment. DG Environment[END_REF].

Copernicus can be considered as a building block together with other EU initiatives, such as SEIS and INSPIRE. As a member of the GEO, the EC is also collaborating with 88 participating governments to build a worldwide GEOSS [START_REF]GEOSS Portal[END_REF]. The aim is to inform environmental decision-making in a world facing increasing environmental pressures and to realise societal benefits such as improved management of energy resources and sustainable agriculture.

Earth Observation of the Land Services of Copernicus

By definition, the land service addresses a broad range of environmental data including soil, forests, ecosystems, biodiversity, water and waste. The land monitoring component of Copernicus became operational under the GMES Initial Operation (GIO) Land services, building on precursor activities, in particular, the Geoland2 [33] 7th Framework Programme (FP7) project.

Based on user consultation, a stepwise approach was defined starting with common "multi-purpose" information at Global, Pan-European and local level. The Pan-European and local land services are coordinated by the EEA in cooperation with DG ENV. A global extension of the land service is coordinated by the JRC and will provide basic terrestrial parameters relating to bio-geophysical factors, radiation and water, at global scale in near real-time. These will be relevant for crop monitoring, carbon budget, biodiversity and climate change monitoring at a worldwide level.

4.2

Copernicus and INSPIRE

Many Copernicus projects contributed in different ways to the development of INSPIRE. For instance, experts from the Copernicus Marine Service (MyOcean [START_REF] Myocean | [END_REF] project) contributed to the relevant INSPIRE data specifications. The discussions with Atmospheric Service (MACC [START_REF] Macc | [END_REF] project) and the downstream service PASODOBLE [36] were mainly related to metadata. The interaction with Land and Emergency Services and INSPIRE took place indirectly by the introduction and promotion of INSPIRE components within these services through the GIGAS [START_REF]GIGAS[END_REF] FP7 project.

The role of INSPIRE in Copernicus' in situ data component addressed by the GMES In-Situ Coordination (GISC [START_REF][END_REF]) project led by EEA. Its aim was to act between data providers and to develop an initial framework for in situ data that also takes into account how demand will change over time.

Copernicus Services Challenges

Prior to the Baveno Manifesto, it was recognised that real value would not emerge from satellite observations alone; space-based monitoring would need to be combined with data collected on the ground (in situ data). In the same way that meteorologists combine satellite and in situ observations to make predictions about the weather, Copernicus integrates satellite and in situ observations to yield useful analysis of a wide range of environmental data. Public and private sector partners are involved in data processing and the conversion of raw data from space into useful maps and applications-focused information, provided as Copernicus "services". Access to data is a real challenge for Copernicus as it relies on a number of scientific networks funded by research projects, entailing risks for long-term sustainability. Another challenge is the consistency of in situ data, which are often collected at national and local level, closely linked to the work on the INSPIRE Directive and SEIS initiative, as well as with the development of sectoral knowledge bases (for example, the water related system WISE [START_REF]Water Information System for Europe[END_REF]; BISE on biodiversity [27], and others on soils [33]).

INSPIRE

The INSPIRE Directive [START_REF]INSPIRE Directive[END_REF] laid down the general rules establishing the spatial data infrastructure (SDI) in the EU countries in support of EU environmental policies and policies or activities that may have an impact on the environment since 2007. This was very important for development of environmental software systems of the EEA and the EU countries. INSPIRE should be based on the infrastructures for spatial information that are created by the EU countries. These infrastructures should ensure that [START_REF]INSPIRE Directive[END_REF]:

• spatial data are stored, made available and maintained at the most appropriate level;

• it is possible to combine spatial data from different sources across the EU in a consistent way and share them between several users and applications; • it is possible for spatial data collected at one level of public authority to be shared between other public authorities; • spatial data are made available under conditions that do not unduly restrict their extensive use; • it is easy to discover available spatial data, to evaluate their suitability for the purpose and to know the conditions applicable to their use.

In particular, the spatial data sets and services provided by EU countries to EU institutions and bodies in order to fulfil their reporting obligations under EU legislation relating to the environment shall not be subject to any charging.

Article 17 of the INSPIRE Directive [START_REF]INSPIRE Directive[END_REF] defines the spatial data sharing requirements in more detail. It requires EU countries to adopt measures for the sharing of spatial data sets and services that enable its public authorities to gain access to these spatial data sets and services, and to exchange and use those spatial data sets and services for the purposes of public tasks that may have an impact on the environment. The measures should preclude any restrictions likely to create practical obstacles to the sharing that might occur at the point of use. Hence, procedures regarding, for example, property rights, licensing and charging must be fully compatible with the general aim of facilitating the sharing of spatial data sets and services between public authorities.

The state of implementation of INSPIRE [START_REF]Mid-term evaluation report on INSPIRE implementation[END_REF] shows that INSPIRE is being implemented across the EU with some delay, and non-uniformity, but so far in line with expected costs and benefits. INSPIRE is starting to achieve its objectives, which according to 92 % of respondents in the 2014 public consultation are as pertinent as ever. Moreover, as indicated in [START_REF][END_REF], INSPIRE is increasingly recognised as a foundation framework for integrating on a spatial basis and making more effective and efficient a range of policies affecting the environment. The strong connection established between the flagship Copernicus programme and INSPIRE can be a very significant element in the implementation of the INSPIRE directive in coming years.

INSPIRE Enabled Interoperability of Spatial Data Sets and Services

The measures defined by INSPIRE to achieve the interoperability of spatial data sets and services are without a doubt the core of INSPIRE, and one that sets it apart from other similar SDIs in the world. The most of these measures have yet to be implemented. Evidence from the public consultation also indicates that this part of INSPIRE is technically complex, which is perceived by about 20 % of respondents as an obstacle to implementation and use [START_REF]Mid-term evaluation report on INSPIRE implementation[END_REF].

There is little doubt that the measures put in place by INSPIRE are complex, but no alternative could be identified in order to achieve the interoperability objective. Whilst the actions related to interoperability are appropriate, further modifications might be taken into consideration in order to enable further benefits.

The INSPIRE roadmap for implementation spans until 2020 and it is therefore natural that there are still gaps in implementation, in particular for obligations for which deadlines have not yet passed. From the public consultation and the direct observations it is also evident that some aspects of the INSPIRE Directive -notably the coordination at national and cross-border levels, and the removal of obstacles to data sharing at the point of use -would increase the EU added value if better addressed.

Conclusion

The adoption of the EEA and Eionet model of implementing environmental software systems and environmental modelling and information systems as well as SEIS and INSPIRE principles at regional/Pan-European and international/global level can ensure coherence at all levels and also helps streamlining efforts at national level. Consequently, and decision making processes of the EEA shall take this into account, the fast developments in environmental software systems and related ICT, their links and synergies will need to be strengthened and further explored with initiatives such as the EU Digital Agenda [START_REF]Digital Agenda for Europe[END_REF], the European Earth Observation Programme (Copernicus) [START_REF]Copernicus[END_REF], [START_REF] Brachet | From initial ideas to a European plan: GMES as an exemplar of European space strategy[END_REF][START_REF]Science for Environment Policy. Future Brief: Earth Observation's Potential for the EU Environment. DG Environment[END_REF], the Global Earth Observation System of Systems (GEO/GEOSS) [START_REF]GEOSS Portal[END_REF], UNEP live [19], and other key initiatives related to data and information sharing [START_REF]Mid-term evaluation report on INSPIRE implementation[END_REF].

The knowledge of the EEA of the development of environmental software systems is increasingly co-created, shared and used in the Eionet network (flexibility in terms of membership, roles assumed, goal orientation, type of knowledge created, shared or used, etc are important factors here). A flexible and strategic vision on the EEA role as initiator, node, hub or switch is important to continue knowledge co-creation, sharing and use between EEA, Eionet and beyond.

Strengthening the integration of EEA and Eionet activities, including capacity building, remain central to the MAWP 2014-2018. An integral part of this is a deepening of Eionet via an enhanced collaboration and integration between EU countries and EEA activities following the principles of SEIS, INSPIRE and Copernicus.

Key objectives of EEA environmental software systems therefore are:

• Ensure the quality, availability and accessibility (based on SEIS and INSPIRE principles) of data and information needed to support SA 1 and SA 2. • Communicate actively data, information and knowledge to policymakers, the public, the academic world, to regional and international processes including those of the UN and its specialized agencies. • Promote information governance as a driver of public empowerment and behavioural change. • Provide support to the work in the above areas by building and maintaining networks of people and -where needed -environmental software systems as the basis for sharing and co-creating content, • Communications will also play a major role in making sure that information is targeted and ensures a dialogue with stakeholders and the society at large. Targeted information, communication and participation remain important instruments.

EEA will further enhance and focus its outreach capacities responding to emerging challenges and societal developments. Societal trends such as the ways to access information, networking, and co-creation of knowledge are influencing the way the EEA is asked to work and communicate.

Fig. 1 .

 1 Fig. 1. Strategic areas SA1, SA2, SA 3 and EEA/Eionet core processes (Source [12])

  Directive 2007/2/EC -Developing an infrastructure for spatial information in Europe (INSPIRE)[START_REF]INSPIRE Directive[END_REF];• Fostering a contemporary approach to the production, exchange and use of environmental data and information through, for example, the Water Information System for Europe (WISE)[START_REF]Water Information System for Europe[END_REF], European Marine Observation and Data Network (EMODnet) [26], BISE on biodiversity [27]; • Directive 2003/4/EC -Public access to environmental information (the Aarhus Convention) [17]; • The Copernicus initiative [22]; • European Environment Agency (EEA) services [9, 10]; • Group on Earth Observation (GEO) [21], which aims at building a Global Earth Observation System of Systems (GEOSS) [28]; • Other the EC funded activities for the effective distribution of open environmental software systems for environmental management.

European Topic Centres are currently eight consortia of institutions across EEA member countries dealing with a specific environmental topic and contracted by the EEA to perform specific activities as defined in the EEA Strategy and the MAWP and the Annual Management Plan.


Gerald Schimak
email: gerald.schimak@ait.ac.at

Denis Havlik
email: denis.havlik@ait.ac.at

Jasmin Pielorz
email: jasmin.pielorz@ait.ac.at

Crowdsourcing in Crisis and Disaster Management -Challenges and Considerations

Keywords: crowdsourcing, crowdtasking, data analysis, big data, social media, crises and disaster management, volunteers, volunteered geographic information 1

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Crowdsourcing is rapidly gaining recognition as important source of information in crisis situations. The potential of crowdsourcing in crisis management is undeniably enormous: past experiences, e.g. in Haiti and Libya [START_REF] Meier | Crisis Mapping in Action: How Open Source Software and Global Volunteer Networks Are Changing the World, One Map at a Time[END_REF], show that information obtained through crowdsourcing is often more detailed and just as accurate as the information gathered through hardware sensors and through official channels. Successfully managed crowdsourcing can result in nearly instantaneous situation awareness (crisis mapping). However, crowdsourcing requires adequate tools, capable moderators and professional crisis managers that are willing and able to accept the crowdsourcing as an additional tool with its own advantages and limitations. A very good overview of the challenges related to crowdsourcing in crisis situations has been given in [START_REF] Bott | The Role of Crowdsourcing for Better Governance in Fragile State Contexts; International Bank for Reconstruction and Development / The World Bank[END_REF] and a very critical analysis of the four use cases which are often mentioned as best practices is given in [START_REF] Sutherlin | A voice in the crowd: Broader implications for crowdsourcing translation during crisis[END_REF].

After accepting crowdsourcing as a powerful crisis management tool, the next logical step for crisis managers is to attempt tasking the individual crowd members and smaller groups with well-defined activities. Properly organized crowdtasking can bring multiple benefits over spontaneous crowd self-organization. For the start, the available volunteer resources can be mobilized more rapidly and deployed more effi-ciently to provide information where it is missing or to quickly confirm and improve unreliable or imprecise reports. Information received through crowdtasking is therefore inherently much more reliable and also easier to interpret than information that has been gathered by data mining from existing social networks. In addition, citizens could be individually instructed to help themselves and their neighbors, taking into account real needs, as well as individual aptitudes and capabilities.

However, it is important to understand that the "crowd" is inherently nonhierarchical, has means to self-organize and will do so with or without crisis managers. This is a challenge for the crisis managers, since their professional experience indicates that crisis management requires a hierarchic organisation and that selforganization of inexperienced civilians can be outright dangerous in crisis situations. On the other hand, the attempt to overmanage the crowd could severely limit the number of ad-hoc volunteers willing to participate in the crowdtasked activities.

On the subject of "using the crowd as a valuable crisis management resource" we therefore distinguish between: (1) information and activities of individuals and selforganized groups, which are supported by general-purpose platforms such as Facebook or Waze1 and (2) information and activities of loosely organized volunteer groups, whose activities are, to a certain extent, coordinated by professional crisis managers and supported by a dedicated mobile crowdtasking platform.

In section 2, we present some general remarks about crowdsourcing, thereafter we present several examples of crowdsourcing and crowdtasking at work, discuss key characteristics and challenges of crowdsourcing in crisis management and finally outline the generic methodology and functional architecture for future dedicated crisis management crowdtasking tools.

2

Crowdsourcing in Crisis Management

What is Crowdsourcing?

According to [START_REF] Robert | Crowdsourcing Techniques for Affective Computing[END_REF], the term crowdsourcing was first coined by Jeff Howe, who used it in an article published in June 2006 for the Wired magazine. Following Howe's original conception of the term, we define crowdsourcing as a "method for recruiting and organizing ad-hoc labor, using an open-call for participation" [START_REF] Howe | The rise of crowdsourcing[END_REF]. Crucial to this definition is the notion of an extremely fluid workforce, one that is devoid of managerial and contractual directives inherent to other, more traditional labor models, such as "outsourcing." The crowd has the freedom to do what it pleases, when it pleases, and it is often up to designers to find clever ways to recruit and retain this kind of ad-hoc workforce.

Unlike the crowd itself, the entity requesting crowdsourced work is rarely fluid or loosely defined. Indeed, the request for work usually comes from a distinct organization or individual (known as the "requester", in crowdsourcing parlance). This distinguishes crowdsourcing from other, more decentralized labor structures, such as commons-based peer production, as well as from the "smart mobs" which use the social networking for self-organization [START_REF] Rheingold | Smart Mobs: The Next Social Revolution[END_REF]. While crowdsourcing can take many forms, three dominant types of platforms have emerged in the past decade: (1) games with a purpose, (2) micro-task markets and (3) open innovation contests.

Social Data Mining and Crowdsourcing in Crisis Management

In the domain of crisis management, the term crowdsourcing usually refers to the gathering of information from the crowd in all phases of a crisis and results in socalled crowdsourced crisis mapping. The request for work in this type of crowdtasking usually remains at the level of general calls for action. In fact, the border towards social data mining is fuzzy because in this case data from general purpose social media (e.g. geo-tweets) is often used instead, or in addition to, the data received through dedicated platform such as Ushahidi 2 .

From an organizational point of view, social data mining is the simplest form of crowdsourcing. Information relevant to a crisis will automatically appear in the social media, even if nobody explicitly asks for it. However, the general purpose social networking platforms such as Facebook or Google+ lack some features which are important for the crisis managers.

To begin with, much of the information is shared only with friends and family and therefore cannot be used by crisis managers without raising privacy issues. If, however, people share their information with the public, automatic filtering and interpreting the available information (e.g. from twitter or from a fictive "friends of Red Cross" Facebook group) is challenging. When reporting about a crisis event, people can use any language, slang and choose a hashtag or topic they find relevant, making it very difficult to understand the actual content of a post. In addition, most posts or tweets currently have no geographical location tagged to them, so that a crisis manager cannot be sure about the exact location of an incident.

In order to circumvent these difficulties, crisis managers can organize an ad-hoc platform for gathering of information from the population. This has been successfully done in the past [START_REF] Meier | Crisis Mapping in Action: How Open Source Software and Global Volunteer Networks Are Changing the World, One Map at a Time[END_REF], and crisis mapper3 volunteers excel in using such information for ad-hoc mapping of the crisis development.

Crowdtasking and Micro-task Markets

In order to distinguish between this already established practice and more fine-grained crowdsourcing of both, information and more physical tasks, (e.g. "fill sacks with sand") in crisis situations, we use the word "crowdtasking" for the process, and "adhoc volunteers" for the participants.

Crowdtasking can be seen as a special form of a micro-task market. Both forms of crowdsourcing have in common that (1) organizers can request concrete and welldefined actions within a limited temporal and spatial scope; (2) participants are ex-plicitly asked to perform specific micro-tasks; and (3) participants are not obliged, or even expected, to work beyond the concrete tasks they choose to complete.

However, the crowdtasking puts much stronger emphasis on space and time. Adhoc volunteers are asked to visit particular location(s), report requested state parameters (e.g. "is this house damaged?") using a mobile device and possibly perform some limited physical actions (e.g. "help the inhabitants to prepare for evacuation"). Due to the inherent dynamics of the crisis, the tasks are often urgent and the probability of endangering the ad-hoc volunteers is not negligible.

In order to optimize the tasking process and minimize the risk for volunteers, tasking organizations need to know the volunteers' capabilities and whereabouts. Therefore the volunteers need to enroll prior to action. In the case of Team Austria, volunteers can register themselves online at any time and indicate their skills and address. However, a verification of the data by Red Cross personnel is required to reach the status of a trusted volunteer. In this way, triggering and tasking can be done very efficiently and with a minimal risk for volunteers.

Use Cases, Existing Systems or Applications

In this chapter we present several examples of crowdsourcing and crowdtasking for crisis management. The examples are ordered by complexity of the tasking process considered, starting with a passive approach of gathering relevant information from social media platforms and ending with a prototypic implementation of a dedicated crowdtasking methodology and platform, which has been developed specifically for crisis management.

Social Sensors for Security Assessments and Proactive Emergency Management

Social media statistics such as 20 million tweets related to hurricane "Sandy" or disaster-related photos shared at a rate of 10/sec on Instagram provide tangible evidence that security forces and civil protection agencies could greatly benefit from the effective blending of social media information into their processes. Due to its versatility, social media could serve in the crisis management domain as both a valuable data source for first responders and an effective communication tool to reach the public. However, despite the widespread use of social media in various domains, e.g. in marketing or finance, there is still no structured and effective way to leverage several social media in crisis management applications.

In order to tackle this challenge, the EU FP7 project SUPER (Social sensors for security assessments and proactive emergencies management) 4 aims at developing a holistic and privacy-friendly approach to exploit social media for emergencies and security incidents. In a joint effort of experts from the security and social media domain, the project addresses all phases of crisis management, i.e. before, during and after a crisis occurred and will integrate social media within existing security and management systems for tactical operations.

The exploitation of social media for emergencies requires efforts far beyond the adaptation, customization and effective blending of existing social media processing algorithms in the emergencies and security domains. This is because of the sensitive nature of security incidents and emergencies, their real-time nature, the fact that they are (more) susceptible to social networks compromise and manipulation, the need to combine multiple social media platforms and processing algorithms, and the dynamics of security and emergency management applications.

So far, the following key topics for research were identified by the project, providing a useful overview of the tools/methods lacking in crowdsourcing for the crisis management domain:

• Using behavioral theories in order to understand and model citizens' social mediabehavior before, during and after emergencies. • Automatic identification and verification of emergency-relevant information from social media in real-time. • Fast extensible search across multiple social media streams.

• Community analysis across social media sources to infer the sentiments, bias and motivations of reporters and the public during emergencies. • Behavioral analysis and sentiment identification.

• Topic Based Community Tracking.

• Leveraging Virtual Spaces for evaluating public opinion.

• Plug n' play integration and fusion of social sensors.

Resulting methods will be integrated into the SUPER concept as illustrated in Fig. 1, which provides an overview of how information stemming from social media will be processed and exploited. This can either happen through social sensors (i.e. algorithmic components that process social media and social networking feeds) or based on games, debates, role-playing and other applications that can solicit citizens' feedback in virtual spaces.

Apart from individual social sensors, SUPER will also offer possibilities for combining and reasoning over the outcomes of multiple processing algorithms, with a view to achieving credible results associated with the analysis of citizens' behavior, but also to become able to detect events in nearly real-time. The outcomes of the analysis of social media (based on both social sensors and virtual spaces applications) will be used to drive a number of security and emergency management applications, which will include COP generation, management of real-time operations, policy simulations and ahead planning of strategies associated with security and emergencies incidents.

SUPER will be validated in the scope of different and complementary scenarios, one dealing with emergency management during disasters (i.e. earthquakes) and another dealing with police services.

Environmental Crowdsourcing Techniques for Improved Water Quality in Rural India

A three-continent research consortium 6 is evaluating an environmental crowdsourcing technique that relies on inexpensive tests kits (53-cents) that turn purple in the presence of bacterial contamination.

With more than 10 million service points, India's rural drinking water system provides a real monitoring challenge for public health officials. Studied in eight villages during this summer (2014) and scheduled for more widespread evaluation in 2015, the technique could empower residents to check their own water quality and address the problems they find more efficiently. It would be impossible to pay somebody to go out and take all these samples, bring them back to a central lab and process them under controlled conditions.

The set-up is as following: volunteers are given the test kits, which include a test tube containing a material that changes color in the presence of E. coli, a bacterium 5 http://backend.mymeedia.com/data/super-fp7/c5627e14-ddf0-4b7b-935d-72d30164e5b4.pdf 6 The research has been sponsored by the U.S.

Real-Time Operations

Policy Simulations Decision Support often present in contaminated water. In their homes, the volunteers fill the tubes with water, either from a tap or from a home water storage container. They allow the water to incubate in the test tube overnight, and then use a mobile phone to text a series of numbers indicating, whether the test material remained yellow -corresponding to safe water -or turned purple indicating contamination. A programmed smartphone receives and analyzes the texts, automatically calculating overall water quality by village. The program accounts for errors in reporting by looking for trends in the overall results. We think this may be a scalable model for large-scale environmental monitoring that could be applied in other countries as well.

Beyond improving water quality, the testing program will empower rural citizens of India to take responsibility for their drinking water.

Using Crowdsourcing to Fight Climate Change Effects on Agriculture

Climate change is an important issue today affecting society in various ways. One domain that has a particular interest in understanding long-term changes of weather patterns is the agriculture sector.

Each year, farmers have to purchase and plant seeds that will survive appropriate weather conditions and eventually yield a healthy harvest. An unexpected cold spell or heavy rains can seriously damage crops, financially ruining a farmer or even leading to famines.

In order to combat this problem in developing nations, some scientists start making use of crowdsourcing techniques. As part of the Seeds for Needs initiative, for example, the CGIAR Research Program on Climate Change, Agriculture and Food Security 7 (CCAFS) has been looking at how to turn farmers into citizen observers.

According to [START_REF] Van Etten | Crowdsourcing Crop Improvement in Sub-Saharan Africa: A Proposal for a Scalable and Inclusive Approach to Food Security[END_REF] CCAFS's Jacob van Etten, the Seeds for Needs initiative can be understood as a series of projects addressing the issue to give farmers more access to crop varieties and landraces to help them adapt to climate change. As part of that initiative, the CCAFS is currently running a program in Vaishali8 , a district in India's north-eastern Bihar state, to test the robustness of different wheat varieties in this region.

Variety is key here: each kind of crop has unique traits, and some particular crop types may be more suitable than others to a region. Wheat, for example, is highly sensitive to heat during flowering, writes van Etten, so planting varieties of wheat that flower earlier and later than usual can ensure that at least part of the harvest is safe.

Here is where the crowdsourcing aspect of Seeds for Needs comes in. To find out which crop varieties perform best, CCAFS is turning farmers into citizen scientists by asking them to evaluate the harvest. Van Etten describes the project thus:

"Each farmer grows a combination of three varieties drawn from a broader set of ten. The farmer then ranks them according to different characteristics such as early vigour, yield, and grain quality. The idea is to make things as easy as possible for the farmers, and then we, the researchers, use some nifty statistics methods to combine the rankings and share the results with the farmers. With this information, farmers can then identify the best varieties for their conditions and preferences. Farmers become citizen crop scientists, actively contributing to science with their time, effort and expertise. In India, 800 farmers are now testing wheat varieties as citizen scientists."

Van Etten laid out his plan for a project like this in [START_REF] Van Etten | Crowdsourcing Crop Improvement in Sub-Saharan Africa: A Proposal for a Scalable and Inclusive Approach to Food Security[END_REF]. While [START_REF] Van Etten | Crowdsourcing Crop Improvement in Sub-Saharan Africa: A Proposal for a Scalable and Inclusive Approach to Food Security[END_REF] focused on another region, the underlying process is the same: distribute seeds to a large number of farmers, have them report the harvest results, analyze the data, and share the information among their communities. Adding gamification elements would increase the motivation to participate, the author writes. Farmers in areas with low internet penetration could report their observations through mobile phones.

The goal is to make the crop improvement process cheaper and faster, van Etten claims. The early results in Vaishali have been positive enough for CCAFS to plan two similar programs in East Africa9 and Central America. Information above was posted by Anton Root10 .

Resilience Enhancement by Crowdsourcing and -Tasking

Crisis and disaster management relies to a large extent on a community of volunteers with a strong commitment and almost professional experience in handling crisis situations. However, due to the increasing mobility of people, the enduring and formal membership in voluntary crisis management has lost its appeal and organizations such as the Red Cross are looking for new ways of loosely binding volunteers to their organizations.

The Austrian research project "Resilience Enhancement by Advanced Communication for Team Austria"11 (RE-ACTA, [START_REF] Sebald | The RE-ACTA Approach -Resilience Enhancement by Crowdsourcing and Crowdtasking[END_REF]) addresses this challenge by defining and testing new processes and workflows for volunteer coordination and developing a platform that supports these workflows in close collaboration with the Austrian Red Cross as end user. The prototype will allow pre-registered volunteers from the "Team Austria" to use their mobile devices for accepting tasks and communicating their results. At the same time, crisis managers will be able to use the platform as a crowdtasking tool for selecting a specific group of volunteers according to their profile and geo-location.

The concept of crowdtasking will be developed even further in the recently started project "Driving Innovation in Crisis Management for European Resilience"12 (DRIVER 13 ). This huge pan-European project intends to establish a set of experimental facilities for testing the crisis management methods and technologies in several EU member states. Within DRIVER, the concepts of crowdsourcing and crowdtasking will be tested in various scenarios and with different volunteer profiles, with the aim to establish the best practices for increasing the societal resilience through increased involvement of the citizens in the crisis management process. On a technical level, the crowdtasking platform is based on Ubicity platform for datadriven applications. The Ubicity platform is a result of complete rewrite of the experimental Mobile Data Acquisition Platform (MDAF, [START_REF] Havlik | Geospatial Service Interfaces and Encodings for Mobile Applications in EnviroInfo[END_REF]) backend with the intention to turn the research prototype in a robust product which can be used in crisis management. Ubicity is capable of storing and analyzing large amounts of unstructured and semi-structured geographically and temporally referenced data from multiple domains in near real-time 14 .

In order to ensure privacy of volunteer profiles, the storage of observations is separated from the volunteer data. For the production of situation reports and the monitoring, e.g. of social media, this platform has a generic interface that can be easily connected to different analysis or visualization tools. In order to support a continuous process of communication between crisis managers and volunteers, a web portal provides up-to-date information and allows volunteers to register at any time, as well as to download the mobile application. With the mobile application, volunteers can use their own smartphones to accept or reject tasks that the crisis manager sent out. Fig. 2 illustrates the functional building blocks of the whole platform as they are implemented in the RE-ACTA approach for crowdtasking management [START_REF] Sebald | The RE-ACTA Approach -Resilience Enhancement by Crowdsourcing and Crowdtasking[END_REF].

Fig. 2. Building Blocks of the RE-ACTA platform

The advantage of such a system is the increased awareness of the situation development in real-time based on spontaneous input from a large number of persons. Additionally, spontaneous volunteers can easily be included into the communication process by e.g. registering on a web portal. Spontaneous volunteers are automatically assigned lower level of trust by the crowdtasking system until their profiles are verified by the Red Cross personnel. As a result, they are assigned to easier and less critical tasks and the observations they provide are trusted less than the observations provided by pre-registered volunteers.

Key Challenges of Crowdsourcing and Crowdtasking in Crisis Management

Information Extraction and Uncertainty

Information gathered from citizens is a-priory less reliable than the one gathered from hardware sensors. In spite of the good experiences with use of this information so far, there is no guarantee that the citizens will not post misleading information in the future. The reason for false observations could be lack of knowledge, mischief or even the wish to have some fun [START_REF] Havlik | Robust and trusted crowd-sourcing and crowd-tasking in the Future Internet in Proceedings Environmental Software Systems -Fostering Information Sharing[END_REF]. Social data mining brings additional issue of machine-interpreting the messages written by and for humans. As illustrated in section 3.1, this is a difficult and error-prone task. Under the assumption that the misleading information is posted by a minority of users, the quality of information can be improved using statistical methods. These issues are far less pronounced in a crowdtasking use case, for following reasons:

• Since we have a good knowledge of the users' profile, it's easier to estimate the uncertainty of the received observations. • Observations are well-defined and therefore easier to process by a machine.

• In case of doubt, additional users can be easily asked to confirm an observation.

Self-organization

During a crisis event, the available resources should be rapidly deployed to the most appropriate tasks. In reality, the globally optimal resource allocation is impossible because the Command Centre has neither a perfect situational awareness nor the capacity to compute the right decision strategy in real-time. The usual strategy for reaching the near-optimal solution in these circumstances is to rely on a combination of written preparedness plans, (incomplete) situation maps and "gut feeling" of the experienced crisis managers. This is incompatible with the self-organization mechanisms of smart mobs and similar social-networking enabled ad-hoc organizations, where decisions are made without prior plans and hierarchical structured organization [START_REF] Deri | Rafalowski Ch. Accidental spillage from a container at large city port (Israel)[END_REF]. Self-organization is implicitly accepted as a way to go whenever the social data mining is used in the context of crisis management (as mentioned in section 3.1). This laisse-faire attitude drastically changes in the moment crisis managers consider the idea of steering and tasking the crowd. Our experience with the development of the crowdtasking system for the Austrian Red Cross (related to section 3.4) shows that the crisis managers consider the self-organization a liability and strive to implement a strict task assignment process which avoids the risk of self-organization. In particular, the ad-hoc volunteers should not be given a possibility to directly discuss the situation with other volunteers, invent own tasks or delegate tasks to other volunteers. This has initially led to a design of the system where tasks can be performed exactly once after accepting the assignment.

Apart from the obvious "wouldn't it be better to let them self-organize using your platform than using a platform you have no access to?" objection, we have recently discovered that some level of self-decision must be allowed by design in order to make the crowdtasking more resilient to mobile network disruptions. Since we can't rely on uninterrupted internet connection during the crisis, the ad-hoc volunteers must be pre-assigned with a basic set of tasks appropriate for the crisis situation already in the early warning phase and trusted to perform some of the pre-defined tasks as they fit if the network is interrupted. The next logical step in development would be to enable decentralized phone to phone networking and update the local situation view with the reports from nearby volunteers. In our opinion, the crisis managers will eventually have to accept and even nourish some level of self-organization in crowdtasking platform(s) under their control. Finding a right balance between "total control" and "full self-organization" is a difficult task and the end result is likely to be a platform which allows relatively high level of self-organization while still allowing the crisis managers to request tasks in a top-down manner as well as to interfere with the counterproductive self-organization efforts.

Incentives for Participation

When considering new crowdsourcing systems, perhaps the most important design question pertains to incentive structures. Since crowdsourcing systems are typically open to anyone, and do not rely on contractual relationships, people are not bound to participate. Instead, they must feel somehow compelled to participate, and so the factors that make a given system compelling might also those that make it succeed or fail. In the case of crisis management, the motivation is likely to be high during the crisis and low otherwise, so the main question we need to answer is: how to incite the participation in preparatory phase? There are many ways to attract crowdworkers, but researchers such as Malone [START_REF] Havlik | Future Internet enablers for VGI applications in EnviroInfo[END_REF] describes three main motivation factors which can be influenced by crowdsourcing platform owners as "money, love and glory" paradigm.

Money: The dominant incentive mechanism on micro-task markets is money. Different markets have different norms for remuneration rates, but higher pay generally gets more workers to do more work more quickly [START_REF] Malone | Harnessing Crowds: Mapping the Genome of Collective Intelligence[END_REF]. In some cases, money can be the only motivator that will work. When the work is tedious and unpleasant (such as transcribing pages and pages of hand-written documents), crowdworkers are unlikely to participate unless fair monetary compensation is guaranteed. Money is not neces-sarily the best way to ensure quality, however. Researchers studying MTurk (Amazon's Mechanical Turk service) have found that higher rates of pay do not necessarily lead to higher quality work as mentioned in [START_REF] Malone | Harnessing Crowds: Mapping the Genome of Collective Intelligence[END_REF] and [START_REF] Mason | Financial incentives and the "performance of crowds[END_REF].

Love: Of course, money is not always required to touch people's heart. If workers simply love the task itself, they will contribute their time for free. Some people love tasks that challenge them and encourage them to help and support, especially in emergency situation or when a disaster occurs.

Glory: Finally, the possibilities to boost own social status by becoming a "hero of the crowdtasking platform" is both a strong incentive and one that is relatively easily realized. Glory incentive relies on measuring the users' performance, e.g. in terms of "points" for performed tasks and giving the visibility to "best" users, e.g. by adding a "beginner"/"experienced"/"hero" attribute to their usernames, distinctively visualizing the different user categories on a map or listing the "heroes of the day" on a special memorial page.

How can we apply these motivation factors to crisis management? A-priory, the volunteers are most likely to be motivated primarily by the factors which we can't influence, such as "moral obligation" or the wish to make the world a better place.

Relying on money as a driving force for ad-hoc volunteers is counterproductive and could be even dangerous, since most of the work in crisis management is already performed by unpaid volunteers and the available budget is tight. However, the recompensation for charges incurred as a result of tasking (e.g. fuel costs) should be considered, if possible. Monetary compensations could play a more prominent role in preparatory phase, when tasks tend to be less exciting and the societal benefits more difficult to understand.

Helping others is a highly emotional activity. Simple ways to boost users' motivation at the "love" level are: pointing out the societal usefulness of the tasks, thanking the users for performed tasks and by visualizing the added value of the ad-hoc volunteers' work during and after the crisis. In a broader context, the "fun factor" can be seen as part of the love incentive. Presenting the tasking as a game (e.g. scavenger hunt tape of game) is therefore a good way to boost volunteers' motivation.

Finally, appealing to the "glory" factor is a good way to additionally strengthen the users' inner motivation for helping others by boosting the social status of the active volunteers. In addition, the "hero of today" score-tables can be used both to advertise the most helpful volunteers and to boost the love/fun factor by adding a touch of competition to the tasking platform.

Even more "glory" can be given to most useful ad-hoc outside of the crowdsourcing platform, e.g. by mentioning the importance of ad-hoc volunteers at post-crisis press event or inviting the most active volunteers to annual Christmas gala dinner. At this level a care must be taken not to over-compensate the ad-hoc volunteers with respect to the crisis management professionals and the institutional volunteers who do not participate in crowdtasking activities.

Ethical Considerations

Crowdsourcing is still an evolving field and many of the ethical implications it raises have yet to be resolved. Since internet has no borders, crowdsourcing could easily evolve into a digital sweatshop with the majority of the work done by "professional crowdworkers" from low income countries such as India [START_REF] Rogstadius | An Assessment of Intrinsic and Extrinsic Motivation in Crowdsourcing Markets[END_REF]. In addition, crowdworkers are often given tedious, stupefying tasks that contribute little towards gaining new, marketable job skills. More effort should therefore be undertaken to help crowdworkers develop new, meaningful skills that can be applied to other work domains, as shown in the example "Environmental crowdsourcing techniques for improved water quality in rural India" above.

Unlike remuneration, which is unlikely to play an important role in crisis management, in crowdtasking applications the issue of liability could seriously impede the ability of crisis managers to work with the crowd. Since organizers and volunteers both play an active role in the system, it is unclear to which level they can be made responsible for the accuracy or validity of the crowdtasked information. This becomes important in case of damage or injuries caused by crowdworkers or when decisions affect the safety of crowd members. In order to minimize this type of risks, organizers should assure that tasks are always accompanied with adequate instructions and warnings. In addition, organizers should try to educate the crowdworkers to comply with relevant safety standards and to certify this knowledge, e.g. through micro-learning.

The next issue concerns privacy and misuse of data stored in user profiles or misuse of user tracking information. As a rule, the user profile data should be protected by design and the sensitive data (e.g. health-related) should never be shared with third parties, nor even be accessible to operators. Tracking of the users' positions is a special case. In our opinion, the tracking functionality should only be used with explicit users' consent, and this consent should only be requested for a well-defined area and time duration. As described in [START_REF] Havlik | Robust and trusted crowd-sourcing and crowd-tasking in the Future Internet in Proceedings Environmental Software Systems -Fostering Information Sharing[END_REF], the targeted geofencing could even be implemented without the need to track the users.

On a positive side, the issue of falsified profiles (e.g. minors pretending to be 18+) is unlikely to become a serious problem as long as the tasking organizations stick to the policy of validating the users profile in a face to face (offline) session. Finally, parceling of the work into tiny bits is a good way to hide the motives behind task requests from the crowdworkers. A malicious organizer could thus easily trick crowdworkers to participate in unethical activities, such as identifying and tracking of dissidents in a large crowd of people.

Conclusions

In this paper, we have presented several crowdsourcing use cases, which are relevant for crisis management. The use cases range from merely using whatever information becomes available on the web to actively managing and tasking the crowd. Examples from large-scale crisis incidents and ongoing research projects show that crowdsourcing techniques have a lot to offer in the field of crisis management. In crises situations any environmental, economic and health sector can be affected. However, in order to obtain real-time, accurate and trustworthy information of ongoing crisis incidents and to establish crowdsourcing as additional tool for crisis manager, significant technological and methodological advances are necessary. Due to the massive user community of general purpose social networking platforms such as Facebook or Twitter, we see on a short time-scale two important developments with high impact in the CDM domain: (1) improved algorithms that accurately detect relevant information out of tons of data to a specific topic/theme (social media monitoring) and ( 2) methods for understanding emotions in the context of short messages or in posts.. On a long-term perspective, crowdtasking has the highest potential for improving the overall societal resilience. However, this is also the form of crowd use which is most difficult to implement and most sensitive to the issues mentioned in the previous section and most difficult to handle at the organizational level.

In principle, designing such a system is relatively simple, but in our opinion crowdsourcing tools are like English: it is easy to develop one, but quite difficult to do it in such a way that it optimally supports crisis managers, while at the same time attracting large numbers of users. In summary, the ideal crowdsourcing application should be designed to support crisis managers in managing issues related to user motivation, to mitigate privacy and ethical issues, as well as to support a right balance between top-down and self-organization of volunteers per design.

Fig. 1 .

 1 Fig. 1. SUPER Framework overview 5

Media and Social Networks Role Play Games SUPER Virtual Spaces Applications Debates Security Policies Knowledge Base Policy Modelling for Security and Emergencies (Nearly) Real-Time Event Detection Behavioural Analysis Integrated Search and Reasoning

  Institute of International Education. Partner agencies include the National Environmental Engineering Research Institute (NEERI) in India, and the London School of Hygiene and Tropical Medicine in the United Kingdom. Contacts: John Toon (jtoon@gatech.edu) Research News, Georgia Institute of Technology, 177 North Avenue, Atlanta, Georgia 30332-0181, USA.

	Social Sentiment Analysis Topic Tracking ...

Modular «Social» Sensors Plug n'Play Psychological Models and Behavioural Models for Security & Emergencies

	SUPER
	Event	Event
	Detection	Detection
	Algorithm	Algorithm
	#1	#N

...

Security and Emergencies Management Framework Planning COP Generation Trustworthiness ...

	Rumour
	Managem
	ent

https://www.waze.com/events

http://www.ushahidi.com/

http://crisismappers.net/

http://super-fp7.eu/

http://ccafs.cgiar.org/

http://ccafs.cgiar.org/atlas-ccafs-sites#.VIgyTjGG98E

http://www.bioversityinternational.org/e-library/publications/detail/seeds-for-needs-in-eastafrica/

[START_REF] Havlik | Robust and trusted crowd-sourcing and crowd-tasking in the Future Internet in Proceedings Environmental Software Systems -Fostering Information Sharing[END_REF] http://www.crowdsourcing.org/profile/anton-root/

5824[START_REF] Deri | Rafalowski Ch. Accidental spillage from a container at large city port (Israel)[END_REF] http://www.kiras.at/gefoerderte-projekte/detail/projekt/re-acta/; or http://www.

reacta.at 12 http://driver

-project.eu/ 13 http://driver-project.eu/sites/default/files/driver/files/contentfiles/articles/DRIVER%20Newsletter_1-2014.pdf

http://www.kiras.at/

Acknowledgments. The research leading to this paper has been performed in the scope of DRIVER and RE-ACTA projects. The DRIVER FP7 project has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 607798. RE-ACTA was funded within the framework of the Austrian Security Research Program KIRAS 15 by the Federal Ministry for Transport, Innovation and Technology.

Paper authors would also like to acknowledge the work of following developers: Clemens Geyer, Herman Huber and Peter Kutschera who developed the initial version of the MDAF software; Jan van Oort, who started the Ubicity development by rewriting the MDAF backend from scratch; Christoph Ruggenthaler, who is currently the main Ubicity backend developer; and Maria Egly, who is responsible for the mobile application as part of the Ubicity development.


Jonathan Yu

Nicholas Car

Adam Leadbetter

Bruce A Simons

Simon J D Cox
email: simon.cox@csiro.aualead@bodc.ac.uk

Towards Linked Data Conventions for Delivery of Environmental Data Using netCDF

Keywords: netCDF, linked data, data discovery, environmental data

netCDF is a well-known and widely used format to exchange arrayoriented scientific data such as grids and time-series. We describe a new convention for encoding netCDF based on Linked Data principles called netCDF-LD. netCDF-LD allows metadata elements, given as string values in current netCDF files, to be given as Linked Data objects. netCDF-LD allows precise semantics to be used for elements and expands the type options beyond lists of controlled terms. Using Uniform Resource Identifiers (URIs) for elements allows them to refer to other Linked Data resources for their type and descriptions. This enables improved data discovery through a generic mechanism for element type identification and adds element type expandability to new Linked Data resources as they become available. By following patterns already established for extending existing formats, netCDF-LD applications can take advantage of existing software for processing Linked Data and supporting more effective data discovery and integration across systems.

Introduction

United States National Science Foundation. The Open Geospatial Consortium (OGC) has adopted netCDF and formalized it as an implementation standard to support encoding of geospatial information to communicate and store multi-dimensional data 2 . netCDF datasets are typically published through service interfaces using Thematic Real-time Environmental Distributed Data Services (THREDDS), which allows users to find and access the data, and use them without necessarily downloading the entire file. The THREDDS Data Server (TDS) is a web service to access catalogues, metadata and data, and allows sub-sampling of the data using OpenDAP, OGC Web Coverage Service (WCS), OGC Web Map Service (WMS), and netCDF subset service interfaces. The netCDF format is not proprietary and THREDDS and TDS have been made available online at the Unidata website and via their GitHub repository (https://github.com/Unidata/thredds). As such, this technology stack has been widely used to publish large datasets and provide the ability to efficiently query subsets of the data. netCDF files are designed to be 'self-contained', through headers that describe the structure and content of the dataset. Standardization is based on a community 'convention' for a set of vocabulary names, with the Climate and Forecasting (CF) conventions [START_REF] Eaton | NetCDF Climate and Forecast (CF) Metadata Conventions[END_REF] most widely known. However, these names are only text values, and often conflate various concerns. These pose a key challenge in providing automatic semantic mediation between datasets to compare equivalent parameters by its conceptual meaning. A number of controlled vocabularies and ontologies currently enable the definition, publication and access of these semantics on the web [START_REF] Cox | A harmonized vocabulary for water quality. 11th International Conference on Hydroinformatics (HIC)[END_REF][START_REF] Cox | SISSVoc: A Linked Data API for access to SKOS vocabularies[END_REF][START_REF] Caracciolo | The agrovoc linked dataset[END_REF][START_REF] Leadbetter | The NERC Vocabulary Server: Version 2[END_REF][START_REF] Summers | LCSH, SKOS and Linked Data[END_REF], however, there is no facility in the netCDF headers to link to and reference externally defined terms and semantics. Although, the CF community and other communities publish standard names, the publishing of these names can be a lengthy process involving review of new proposed names by the respective committee, and some names may be duplicated and have different meaning or be incomplete [START_REF] Peckham | The CSDMS Standard Names: Cross-Domain Naming Conventions for Describing Process Models, Data Sets and Their Associated Variables[END_REF]. Figure 1 gives an example of the current practice where applications request data using multiple names that may refer to the same concept or set of vocabulary terms.

The World Wide Web Consortium (W3C) has developed principles for publishing data on the Web. Known as "Linked Data" [START_REF] Berners-Lee | Linked Data -Design Issues[END_REF], the primary feature is the use of HTTP Uniform Resource Identifiers (URIs) to link between any information resources (data or metadata), including non-hypertext formats, so that more information can be discovered. The Linked Data principles have stimulated the design of a lightweight extension to JSON [START_REF] Crockford | The application/json Media Type for JavaScript Object Notation[END_REF] which is a commonly-used data format in browser applications. JSON-LD (for "Linked Data") annotates JSON data with URIs, allowing JSON data to be interpreted as Linked Data with minimal additions to the original structure. The JSON-LD pattern has now been proposed for annotation of tabular data formatted as Comma-Separated-Values or similar in the W3C activity 'CSV on the Web' [START_REF] Herman | CSV on the Web Working Group Charter[END_REF].

Thus, extending netCDF by borrowing and adopting the *-LD pattern from JSON-LD and 'CSV on the Web' is a natural extension in order to link and reference existing controlled vocabularies and terms via the Semantic Web. In this paper, we describe initial work in defining netCDF-LD conventions, which allow netCDF to be interpreted as Linked Data with non-intrusive annotations. The netCDF-LD conventions allow the use of URIs to define the identity of a netCDF file, attribute names and attribute values. The aim is to enhance existing netCDF datasets with metadata that links to standardized vocabulary terms and to express precise semantics about the data so that it can be better discovered, integrated and used. By following patterns already established for extending existing formats, netCDF-LD applications will be able to take advantage of existing software for processing Linked Data; existing controlled vocabularies describing environmental domains published as Linked Data; and allow data, such as environmental data, to be more easily discovered, interpreted and integrated into applications, such as environmental models (see Fig. 2 for an example). Linked Data is a methodology for publishing data and metadata in a structured format so that links may be created and exploited between objects. The key enabling components are URIs, HTTP, the Resource Description Framework (RDF) and the SPARQL Protocol and RDF Query Language (SPARQL) [START_REF] Berners-Lee | Linked Data -Design Issues[END_REF]. RDF is the component which provides a data model for describing subject and their relationships to other objects via predicates thus forming 'triples'. RDF data is encoded in a number of serializations including RDF/XML, N-Triples, Turtle, and RDF/JSON. RDF data can be loaded into repositories called 'triple stores' and queried using the SPARQL language. Tooling has also been developed to exploit Linked Data as RDF such as Linked Data browsers and mashups, semantic search engines, and data extraction tools. To date, there are around 700 million triples across about 1,800 vocabularies discoverable in publicly available linked data services [START_REF] Lodstats | [END_REF].

JSON-LD

JSON-LD is a W3C recommendation and is a format that adds URIs to JSON data, allowing JSON data to be interpreted as Linked Data by supporting links from JSON data to other data [START_REF] Sporny | JSON-LD 1.0 -A JSON-based Serialization for Linked Data[END_REF]. JSON-LD provides an interchange language for libraries utilizing JSON, such as client applications, web services, and NoSQL or unstructured databases (e.g. CouchDB and MongoDB).

{ "@id": "http://foo.bar/linked_netCDF_example", "@type": [ "http://www.w3.org/ns/prov#Entity", "http://www.w3.org/ns/dcat#Dataset" ], "http://data.ba.gov.au/def/ba#dataOwner": [ {"@id": "http://data.ba.gov.au/id/person/car587"} ], "http://purl.org/dc/elements/1.1/created": [ {"@type": "http://www.w3.org/2001/XMLSchema#dateTime", "@value": "2014-10-15T23:23:25+11:00"} ], "http://purl.org/dc/elements/1.1/title": [ {"@value": "Victorian Biodiversity Atlas"} ] } Fig. 3. A JSON-LD object with ID ("@id") of http://data.ba.gov.au/dataset/f4107720 and type ("@type") http://www.w3.org/ns/prov#Entity & http://www.w3.org/ns/dcat#Dataset, each defined at their URIs. Other properties ("created", "title", "dataOwner") defined in well-known vocabularies such as Dublin Core or in a project-specific vocabulary.

JSON-LD introduces:

• URI identifiers for JSON objects • contexts or namespaces to disambiguate keywords in JSON documents • a linking mechanism • a way to associate datatypes with values such as dates and times • a way to express one or more graphs in a single document Tooling has been developed for reading JSON-LD data and creating an RDF graph, as well as serializing RDF data into JSON-LD, for example the rdfLib-jsonld plugin for Python, JSONLD-JAVA, and PHP-JSON-LD. JSON-LD contributes to the design of other microformats, such as the extension of GeoJSON with JSON-LD, called GeoJSON-LD.

The precedent of JSON-LD is being followed in the W3C 'CSV on the Web' [10] project, another effort to enrich a well-known format with more rigorous definitions and semantics. 'CSV on the Web' directly reuses key features of JSON-LD to allow terms to be assigned URIs and values bound to datatypes.

netCDF-LD

Given this uptake and prior work, we propose to apply the same approach for netCDF, to be called netCDF-LD. In this section we outline the conventions for netCDF-LD, based on the general approach from JSON-LD, and present examples of netCDF-LD encodings and the interpreted Linked Data content. The intention is that interpreted Linked Data content encoded as RDF could be used to support richer queries for data aggregation and query.

netCDF Conventions

netCDF-LD uses the "context" array concept from JSON-LD and maps it onto global variables within the NetCDF file. This allows variable names within the netCDF file to be used as shorthand for the URI of an external (Linked Data) resource, which should provide details of the variable definition. A reserved global variable, "contextid", assigns a URI to the netCDF file itself. The suffixes "_a" and "_ref" are reserved for the assignment of an RDF class and a URI to any variable level attribute within a netCDF file. The RDF datatype may be inferred from the declaration of the datatype in the netCDF file. The "_lang" variable level suffix is reserved for occasions when a human readable language is required to be assigned to an attribute or data value.

In the following sections, the reserved global attribute names and variable level suffixes are defined, and example encodings of a generic netCDF file and a Climate and Forecast (CF) metadata conventions compliant netCDF file are shown.

The "context-" Global Attributes.

The JSON-LD "@context" array is modelled in netCDF-LD as a series of global attributes with the "context-" prefix. netCDF does not allow the use of the "@" symbol as the opening character of an attribute name, and the "_" character is reserved for use by system attributes only.

Boilerplate code of three (with an optional fourth) "context-x" global attributes is recommended, and as many more as necessary may be added (see Fig. 4). Optionally, the rdf:datatype property may be declared as shown below in Fig. 5. Finally, a generic vocabulary from which all attribute URIs in the netCDF-LD file are used may be assigned as shown below in Fig. 6. Any given attribute name to be used on variables may be assigned a URI as shown below in Fig. 7.

:context-id = "http://foo.bar/baz"; :context-a = "http://www.w3.org/1999/02 /22-rdf-syntax-ns#type"; :context-ref = "http://www.w3.org/1999/02 /22-rdf-syntax-ns#resource";

Assigning URIs to Variable Level Attributes

A netCDF variable may be defined by a URI in netCDF-LD by adding a "ref" attribute with a value equal to the URI. variable:ref= "http://vocab.nerc.ac.uk /collection/P07/current/CFSN0600/";

The values of variable level attributes may be defined by URIs thus: variable:unit = "Meters"; variable:unit_ref = " http://qudt.org/vocab /unit#Meter";

Example Encodings and Resulting RDF Graphs

The following example encodes the netCDF file example from http://www.unidata.ucar.edu/software/netcdf/docs/CDL.html using the netCDF-LD conventions. In the example, the dimensions and variables blocks near the start of the file define the respective dimensions and variables contained in the metadata headers.

The next block provides the global attributes to describe boilerplate content for the contexts of the attributes as described in the previous section. LD attributes are specified next for the listed variables, e.g. lat, lon, time, and z, each having the appropriate URI references for its value. For example, 'lat:ref' allows the value to be bound to the URI 'http://vocab.nerc.ac.uk/collection/P07/current/CFSN0600/'. This is then used as the subject for the statements bound to the variable 'lat', so that the other attributes are related objects, e.g. its units, its type, and the data values. Figure 9 shows the RDF encoding from the netCDF-LD example where the URI values and data values are mapped. /op#ScaledQuantityKind"; :context-dcPartOf = "http://purl.org/dc/terms/isPartOf"; :context-a = "http://www.w3.org/1999/02/22-rdf-syntax-ns#type"; :Conventions = "LD-1.0"; lat:ref = "http://vocab.nerc.ac.uk/collection/P07/current /CFSN0600/"; lat:units = "degrees_north"; lat:units_ref = "http://qudt.org/vocab/unit#DegreeAngle"; lat:a = "http://environment.data.gov.au/def/op#ScaledQuantityKind"; lat:dcPartOf = "http://foo.bar/linked_netCDF_example"; lat:datalink = "http://www.w3.org/1999/02/22-rdf-syntax-ns#value"; lon:ref = "http://vocab.nerc.ac.uk/collection/P07/current /CFSN0554/"; lon:units = "degrees_east"; lon:units_ref = "http://qudt.org/vocab/unit#DegreeAngle"; lon:a = "http://environment.data.gov.au/def/op#ScaledQuantityKind"; lon:dcPartOf = "http://foo.bar/linked_netCDF_example"; lon:datalink = "http://www.w3.org/1999/02/22-rdf-syntax-ns#value"; time:units = "seconds"; time:units_ref = "http://qudt.org/vocab/unit#SecondTime"; time:a = "http://environment.data.gov.au/def/op#ScaledQuantityKind"; time:dcPartOf = "http://foo.bar/linked_netCDF_example"; z:units = "meters"; z:units_ref = "http://qudt.org/vocab/unit#Meter"; z:a = "http://environment.data.gov.au/def/op#quantityKind"; z:dcPartOf = "http://foo.bar/linked_netCDF_example"; z:valid_range = 0., 5000.; The use of Linked Data within netCDF headers makes the headers less readable by humans but far more powerful for machine interpretation. Tools able to understand and process Linked Data can "follow their nose" in order to collect more information about resources as needed. When encountering a header or other term not understood directly, such as a new variable type, a Linked Data tool will be able to dereference its type URI and obtain further metadata about that variable. With current string-based controlled terms, if a variable is not understood it is unusable. The issue of reduced human readability can, we believe, be bypassed with client tooling. Commonly used netCDF tools such as Python's ncdump program can easily be extended to render data from a netCDF-LD file as readable to humans as the headers from a regular netCDF file.

The inverse may not be true: it may not be possible to convert existing netCDF files into Linked Data files with as rich semantics as a netCDF-LD file would offer with new tooling. Such tools would need to contain all of the metadata about stringbased terms used in current netCDF files locally in order to reference it when needed -there is no equivalent to the "follow-your-nose" procedure for string values. This would be very hard to implement as instances of such a tool would need to be kept up-to-date with all possible netCDF terms metadata in order to be relevant. This precludes tools such as netCDF Markup Language (NcML) generators. 3netCDF-LD adapts related work on decorating netCDF variable names with URIs. Metadata linkages in netCDF were presented by [START_REF] Palmer | WaterML 2.0 -Timeseries -NetCDF Discussion Paper[END_REF]. In netCDF-U [START_REF] Bigagli | NetCDF Uncertainty Conventions[END_REF], the :ref and :rel suffixes are used to link netCDF variable declarations with URIs, but only relate specifically to concepts of uncertainty. In [START_REF] Yu | Enhancing water quality data service discovery and access using standard vocabularies[END_REF], a similar pattern is used to append variable declarations to link to ontology classes in the Observable Property ontology and concepts defined in the Water Quality vocabulary. However, these are limited to scaledQuantityKind_id, unit_id, substanceOrTaxon_id, procedure_id, and medium_id. netCDF-LD adapts these ideas for a generic mechanism for variable annotation and aligns it with current Linked Data best practice from JSON-LD. The approach used in netCDF-LD allows the whole netCDF metadata header to be translated into RDF as presented in this paper, compared with the other approaches which only map partial aspects of the netCDF metadata header.

Translating netCDF headers into RDF allows tooling to be developed to support data discovery and dataset aggregation. Figure 10 shows how the RDF descriptions from the netCDF-LD metadata headers can be harvested into a data brokering component. The data brokering component essentially would provide automated semantic mediation between netCDF datasets. This would allow applications or end users to browse or search over the available datasets based on the identifiers of the variables. An example query could be search for all datasets with 'chlorophyll concentration'.

Conclusion and Future Work

In this paper, we have proposed initial work towards a netCDF-LD specification which applies principles from the *-LD pattern to netCDF data. The intention of netCDF-LD is to allow it to be interpreted as Linked Data with non-intrusive annotations to standard netCDF encodings. netCDF-LD conventions were presented which aim to be consistent with Linked Data design principles, allowing the use of URIs to define the identity of a netCDF file, its attribute names and the attribute values. These conventions allow existing netCDF datasets to be enhanced with structured metadata conventions for linking to standardized vocabulary terms. This allows binding to precise semantic descriptions and vocabularies which enables netCDF data exposed via THREDDS to be better discovered, integrated and used. Also by following patterns already established for extending existing formats, netCDF-LD applications can take advantage of existing semantic web tooling for handling querying and reasoning over RDF triples translated from netCDF-LD data. How large volumes of data is to be encoded in netCDF-LD files has not been explored. The direct serialization of array data as JSON arrays, as given in Fig. 6, is unlikely to be attractive to netCDF with large data volumes (potentially much greater than 2GB for netCDF4) given its verbosity compared with binary data arrays. Future work will see us test various methods for data encoding while still retaining the same netCDF-LD approach to header metadata encoding. Semantic data formats such as WaterML present a number of options that may be used.

Fig. 1 .

 1 Fig. 1. Current practice of applications consuming netCDF data via TDS services

Fig. 2 .

 2 Fig. 2. Proposed data integration example consuming netCDF-LD

Fig. 4 .Fig. 5 .Fig. 6 .Fig. 7 .

 4567 Fig. 4. Boilerplate code for netCDF-LD using Common Data form Language (CDL) syntax.CDL provides a human readable text respresentation of netCDF data.

Fig. 8 .

 8 Fig. 8. Example netCDF-LD encoding

Fig. 9 .

 9 Fig. 9. RDF description based on example netCDF-LD metadata

Fig. 10 .

 10 Fig. 10. Using netCDF-LD to support data discovery and dataset aggregation via a data broker

Scientific data is increasingly being made available from environmental agencies, universities, research organisations and government departments via web services. There is, however, a need for better integration and discoverability across datasets from the various services. The Network Common Data Form (netCDF) is a suite of software libraries and a data format for producing array-oriented scientific data, which is commonly used to exchange environmental data organized as images, grids and time-series[START_REF] Eaton | NetCDF Climate and Forecast (CF) Metadata Conventions[END_REF] . netCDF has been developed and maintained by Unidata, which is part of the University Corporation for Atmospheric Research (UCAR) funded by the 1 http://www.unidata.ucar.edu/software/netcdf adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011

http://www.opengeospatial.org/standards/netcdf

See http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/ncml/ for a description of the NcML and links to generators.