Arie Purwanto
email: a.purwanto@tudelft.nl

Anneke M G Zuiderwijk

Marijn Janssen
email: a.janssen@tudelft.nl

Citizens' Motivations for Engaging in Open Data Hackathons

Keywords: Open Data, Open Government Data, Agriculture, Citizen Engagement, Hackathon

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Introduction

Open Government Data (OGD) provides opportunities for innovation [START_REF] Jetzek | Data-Driven Innovation through Open Government Data[END_REF] and for improving the daily life of citizens [2]. One particular example of a sector in which OGD is a promising source of innovation is agriculture. This sector mainly concerns the quality and sustainability of farms and their environment, as well as efficient and smart farming [START_REF] Wolfert | Big Data in Smart Farming -A review[END_REF]. The use of public agricultural data potentially benefits stakeholders involved in the farming sector. For instance, farmers can improve the precision of farming processes and management by using water quality data, agribusinesses can offer smart farming products based on weather data to help farmers make decisions about when to plant a particular vegetable, and government organizations can be more accurate at giving subsidies to farmers based on fertilizer purchase data. Citizens can engage with the mineral indicator data combined with public participation for keeping their eyes on the environmental and health impacts of farming practices as well. Using open agriculture data, citizens can contribute to solving societal challenges in feeding the growing world, environmental sustainability, food safety, and health [START_REF] Poppe | A European Perspective on the Economics of Big Data[END_REF].

The creation of the above-mentioned benefits requires the engagement of citizens in the use of open agriculture data. Engaging with open agriculture data, however, embraces challenging tasks in understanding and processing voluminous data captured from various sources such as sensors installed in tractors and soils, satellite imagery, soil and water indicators, and statistics [START_REF] Wolfert | Big Data in Smart Farming -A review[END_REF][START_REF] Poppe | A European Perspective on the Economics of Big Data[END_REF][START_REF] Janssen | Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology[END_REF]. In addition, knowledge of farming processes, as important parts of the food chain which affect sustainability and consequences on food safety issues and contribute to health, is needed [START_REF] Poppe | A European Perspective on the Economics of Big Data[END_REF]. Therefore, one needs to collaborate with others who possess diverse skills and knowledge required to create value out of agriculture data. Collaboration among citizens in groups to develop new OGD-based products and services is typically facilitated and stimulated in open data hackathons promoted and supported by governments [START_REF] Susha | Data collaboratives as "bazaars"? A review of coordination problems and mechanisms to match demand for data with supply[END_REF].

An open data hackathon is an offline, face-to-face competition sponsored by government agencies in a centralized location that brings together citizens with different backgrounds (e.g., programmers, designers, students) to intensively collaborate in small teams for a short amount of time (e.g., 12 hours, 24 hours, 2 days) to create artifacts (e.g., ideas, mockups, design, prototypes, applications) using OGD [START_REF] Concilio | Empowering Citizens with Open Data by Urban Hackathons[END_REF][START_REF] Hartmann | Opportunities and Challenges for Civic Engagement: A Global Investigation of Innovation Competitions[END_REF]. Typically, at the end of the competition, each team presents the final idea in front of juries, and a winning team usually earns a prize (e.g., money, investment, support). In a hackathon, organizers and sponsors provide nearly all resources and support needed by the teams to work efficiently [START_REF] Concilio | Empowering Citizens with Open Data by Urban Hackathons[END_REF][START_REF] Briscoe | Digital Innovation: The Hackathon Phenomenon[END_REF], including catering services, sleeping bags/area, comfortable facilities (gaming device, sports hall), internet connection, electricity (cables), and stationaries. The provision of technical support from open data providers or event organizers is also common for hackathons.

Although research on the socio-technical conditions of OGD utilization, both enabling and disabling factors, has been widely established [START_REF] Safarov | Utilization of open government data: A systematic literature review of types, conditions, effects and users[END_REF], yet only a handful of studies investigate the drivers of citizen engagement in open data hackathons [START_REF] Gama | Crowdsourced Software Development in Civic Apps -Motivations of Civic Hackathons Participants[END_REF]. Previous research showed that citizens' motivations to participate in hackathons are heterogeneous [START_REF] Juell-Skielse | Is the Public Motivated to Engage in Open Data[END_REF]. For instance, in a Swedish hackathon on public transportation, the motivation is primarily associated with fun and enjoyment [START_REF] Juell-Skielse | Is the Public Motivated to Engage in Open Data[END_REF], while in a Brazilian city hackathon, contributing to solutions of social problems and networking are the main drivers [START_REF] Gama | Crowdsourced Software Development in Civic Apps -Motivations of Civic Hackathons Participants[END_REF]. These studies show the need to differentiate between different types of open data hackathons and instead of black boxing citizen engagement with OGD, the context should be taken into account [START_REF] Zuiderwijk | Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology[END_REF]. This study contributes to existing research by providing insights into citizens' motivations to engage in open data hackathons in the sector of agriculture.

The main research question we aim to answer in this paper is: "Why do citizens engage in open data hackathons in the agriculture sector?" This study is among the first to provide insights on OGD engagement in the agricultural sector. It contributes to research concerning the mapping of citizens' motivations to engage in open data hackathons. The results of the study may help policymakers to formulate a strategy for sustaining open data engagement which takes multidimensional approaches into account.

2

Research Methodology

Case Study Design

Case studies can be used to investigate a real-world situation over which researchers have little or no control [START_REF] Yin | Case Study Research: Design and Methods[END_REF]. Case study research is the preferred research approach for this study since we aim to answer why citizens engage in open data hackathons which is an ill-understood topic in the OGD utilization context. A multiple-case study design was selected because its evidence is often considered more convincing and, therefore, the overall study is accounted for being more robust compared to a singlecase study [START_REF] Herriott | Multisite Qualitative Policy Research: Optimizing Description and Generalizability[END_REF]. The agricultural sector was selected because of its enormous potential to solve problems related to malnutrition, food security, sustainability, and other societal problems [START_REF] Poppe | A European Perspective on the Economics of Big Data[END_REF]. We examined the motivations of 161 citizens for participating in a selection of 11 Dutch hackathons in the agricultural sector held from 2016 to 2018. These 11 cases were selected for the following reasons 1) the authors have access to participant data, 2) the first author of this paper participated in two of the hackathons, namely FarmHack (FH) 6 and FH12 and obtained in-depth insight, and 3) the cases are diverse with regard to the types of outcomes competed in the hackathons (i.e., idea, design, application/prototype, visualization) and the focus of the challenges (i.e., problem-driven, data-driven, or both).

The hackathons were organized by FarmHack.NL, a Dutch company which focuses on developing an ecosystem of coders, hackers, developers, planners, designers, domain experts, civil servants and farmers that enables innovation in the agricultural sector using data and technology. Typically, each hackathon offers different themes as described in Table 1. To participate in the hackathons, as long as seats are available, a citizen is only required to register through FarmHack.NL's website and to complete a registration form. The hackathons were for free and the participants were provided with catering services (i.e., coffee breaks, a breakfast, two lunches, and two dinners), sleeping area, internet connection, electricity (cables), wireless network, stationaries and even a guided tour to sites or museums related to the theme. Each hackathon typically lasted for one and a half days.

Each hackathon was organized as follows. First, on the first day's morning, all participants gathered and received an explanation. Each challenge raised in the hackathon was presented by a team leader who was typically an employee of a sponsoring organization. Then, each team leader discussed the challenge in detail in a small group where interested participants joined. This activity was run twice and participants were free to change group. Next, participants chose and joined a team working for a specific challenge. Thereafter, 'hacking' started in these groups, guided by a framework developed by FarmHack.NL which contained questions that should be answered to achieve the desired solution of the challenge. The framework concerned both the technical aspects, such as data and technology involved and social aspects of the solutions. On the second day of the hackathon, teams had to present their solutions to the challenges, followed by a question and answer session, and ended by the announcement of winners and prizes they won. The prizes were varying across hackathons, ranging from 500 to 20,000 euros.

Data Collection and Analysis

Multiple sources of evidence were collected from October 2017 to February 2019 at several points in time, as described in Table 2. The collected data include the Farm-Hack.NL webpages, notes taken from unstructured interviews with six participants and observations in two hackathons, and multiple qualitative surveys from participant registration data. Each type of collected data was analyzed for different purposes. Data from the FarmHack.NL webpages and the observational and unstructured interviews notes were used to describe the characteristics of each hackathon and the variance among the hackathons, while the qualitative survey data were analyzed in three stages. First, the participants' backgrounds and roles were grouped and the reasons for participating were coded based on the framework developed based on our literature review (See Section 3). We also included new codes emerged in the data. Then the codes were mapped into a classification of motivations for participating in the hackathons. Finally, the new map of roles and factors were analyzed using simple descriptive statistics (i.e., cross tabulation). The analysis was conducted by the first author and reviewed by the second and third authors. The authors also made the qualitative survey and analysis dataset available online as open research data on 4TU Center for Research Data repository at https://doi.org/10.4121/uuid:879be853-ba9d-463d-a2db-51a076e9ce6e.

A Framework for Analyzing Citizens' Motivations to Engage in Open Data Hackathons

In this section, we develop a framework for the analysis of our cases. We do so by searching, collecting and selecting open data literature which investigates factors that influence citizens to engage with OGD or to engage in hackathons using Scopus database. We apply the combination of the following keywords open data or open government data and use, engagement, or hackathon. We include six publications which are deemed relevant for this study (see Table 3). Based on the selected papers, we observed that many factors influence citizens to engage with OGD or to engage in an open data hackathon: intrinsic motivations such as fun and enjoyment and intellectual challenge [START_REF] Juell-Skielse | Is the Public Motivated to Engage in Open Data[END_REF]; extrinsic motivations concerning performance expectancy [START_REF] Zuiderwijk | Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology[END_REF] or relative advantage [START_REF] Weerakkody | Open data and its usability: an empirical view from the Citizen's perspective[END_REF], learning and developing skills, and networking [START_REF] Gama | Crowdsourced Software Development in Civic Apps -Motivations of Civic Hackathons Participants[END_REF][START_REF] Juell-Skielse | Is the Public Motivated to Engage in Open Data[END_REF]; effort expectancy [START_REF] Zuiderwijk | Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology[END_REF] related to ease of use [START_REF] Wirtz | Citizen and Open Government: An Empirical Analysis of Antecedents of Open Government Data[END_REF]; social influence [START_REF] Zuiderwijk | Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology[END_REF][START_REF] Purwanto | Citizen engagement in an open election data initiative: A case study of Indonesian's "Kawal Pemilu[END_REF] including contributing to societal benefits [START_REF] Gama | Crowdsourced Software Development in Civic Apps -Motivations of Civic Hackathons Participants[END_REF][START_REF] Purwanto | Citizen engagement in an open election data initiative: A case study of Indonesian's "Kawal Pemilu[END_REF]; and data quality [START_REF] Purwanto | Citizen engagement in an open election data initiative: A case study of Indonesian's "Kawal Pemilu[END_REF].

We synthesize the empirical findings and propose a framework of citizens' motivations to engage in open data hackathons. Intrinsic and extrinsic motivations of a participant are viewed based on the source of rewards (internal or external) for engaging in the hackathon [START_REF] Lakhani | Why Hackers Do What They Do: Understanding Motivation and Effort in Free/Open Source Software Projects[END_REF]. A developer/programmer will enjoy building a prototype/application that solves a problem competed in a hackathon. Even if the problem requires a higher level of challenge compared to the developer's current capabilities/skills, he or she will strive to solve it, because he or she feels that his or her status and reputation is at stake.

Performance expectancy and relative advantage is related to the degree to which an individual perceives that engaging in open data hackathons will help him or her attain gains in job performance [START_REF] Venkatesh | User Acceptance of Information Technology: Toward a Unified View[END_REF] or will be advantageous to him or her [START_REF] Rogers | Diffusion of Innovations[END_REF]. The developer can also be motivated by delayed benefits that may be received after participating in a hackathon: learning new skills from teammates or expanding the network with prospective employers or investors. Weerakkody, Irani, Kapoor, Sivarajah and Dwivedi [START_REF] Weerakkody | Open data and its usability: an empirical view from the Citizen's perspective[END_REF] Learning and developing skills Juell-Skielse, Hjalmarsson, Johannesson and Rudmark [START_REF] Juell-Skielse | Is the Public Motivated to Engage in Open Data[END_REF], Gama [START_REF] Gama | Crowdsourced Software Development in Civic Apps -Motivations of Civic Hackathons Participants[END_REF] Networking Effort expectancy "the degree of ease associated with the use of the system" [20, p. 450] Ease of use Wirtz, Weyerer and Rösch [START_REF] Wirtz | Citizen and Open Government: An Empirical Analysis of Antecedents of Open Government Data[END_REF], Zuiderwijk, Janssen and Dwivedi [START_REF] Zuiderwijk | Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology[END_REF] Social influence "the degree to which an individual perceives that important others believe he or she should use the new system." [20, p. 451] Influence from a social relationship Purwanto, Zuiderwijk and Janssen [START_REF] Purwanto | Citizen engagement in an open election data initiative: A case study of Indonesian's "Kawal Pemilu[END_REF], Zuiderwijk, Janssen and Dwivedi [START_REF] Zuiderwijk | Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology[END_REF] Contribute to societal benefits Gama [START_REF] Gama | Crowdsourced Software Development in Civic Apps -Motivations of Civic Hackathons Participants[END_REF], Purwanto, Zuiderwijk and Janssen [START_REF] Purwanto | Citizen engagement in an open election data initiative: A case study of Indonesian's "Kawal Pemilu[END_REF] Data quality "data that are fit for use by data consumers" [23, p.6]

Accuracy

Purwanto, Zuiderwijk and Janssen [START_REF] Purwanto | Citizen engagement in an open election data initiative: A case study of Indonesian's "Kawal Pemilu[END_REF] A participant's effort expectancy is related to the degree of ease associated with the use of open data and technology for solving a hackathon's challenge. It also concerns the participant's perceived capabilities/skills required for creating solutions which reciprocally affects the perceived ease of use. The more complex the challenge, the bigger the potential of the participant for being felt bored or anxious which in turn degrades his or her motivation [START_REF] Csikszentmihalyi | Beyond Boredom and Anxiety: The Experience of Play in Work and Games[END_REF].

Participants could be influenced by their social relationships to engage in a hackathon. Supervisors might urge their employees to participate in a hackathon. Social influence can also take form as norms and behaviors established in a hackathon team to accomplish shared goals. As a result, participants will be driven to contribute to the benefits of the team or society by solving a hackathon challenge. Data quality has been associated with technical conditions for OGD utilization [START_REF] Safarov | Utilization of open government data: A systematic literature review of types, conditions, effects and users[END_REF]. Hypothetically, the higher the quality of data, the more it will be used [START_REF] Zuiderwijk | Socio-technical impediments of open data[END_REF].

Results

The qualitative survey data received from the hackathon organizer consists of the potential roles of participants and the motivations they participated in the hackathons.

The organizer asked participants to select one or more roles (i.e., business developer, marketer, data analyst, developer, concept thinker, data owner, or others). If the participant's role is not provided in the list, "other" can be selected. We created ten groups of roles which include the original categories and four new groups: Manager, Problem Owner, Student, and Unknown. Manager concerns participants from managerial positions such as CTO (Chief of Technology Officer) and project managers. Problem owner represents citizens who are practitioners having expertise in the hackathon theme, for example, landscape architect and agriculture advisor. When no role was entered by the participant, we used the label Unknown. If a participant has been assigned more than one role, we group him or her into a role by considering the substance of the reasons for participation. The role group which we assigned might be different than one of the roles that the participant stated. For example, we found a participant who declared that he is a data analyst and concept thinker, but we grouped him as a student because he was a junior in a university. Another example concerns a participant saying that he or she is a business developer, data analyst, developer, and concept thinker, whom we grouped into developer because he or she wanted to use technical expertise in GIS and R programming in the hackathon. Thus, several roles were reassigned. We evaluated the motivations for participation against the framework described in the previous section. We found that the factors that influence citizens to participate in the hackathon were heterogeneous and a citizen may be motivated by many factors. From the 161 records, we extracted 201 codes representing the reasons. These codes were grouped into the constructs proposed in the framework based on their similarity of meanings. For example, we interpreted a participant's expectancy of the manure market transparency as an aspiration to contribute to societal benefits. Motivations that did not fit in our initial framework, for example, "the conventional farming is not a sustainable system" were grouped into 'Other'. We cross-tabulated the frequency of constructs and arranged them in Table 4. Most participants mention their participation in the hackathons as part of their work (n=50) such as looking for a job or business opportunity, representing a company, or selling ideas or a product. Personal benefits such as 'winning a prize' and 'pizza' were the least mentioned by participants (n=2).

Unsurprisingly, the results showed that constructs related to effort expectancy and data quality were not mentioned by participants regardless of their roles since the data and their quality are unknown to participants and so are the efforts required for utilizing them. Although data was mentioned frequently in the motivations, most participants conceive that it has the potential to improve the agricultural sector, but never refer to its quality.

Intrinsic motivations

Fun and enjoyment are prominent in the developer and problem owner groups. This result indicates that the developers and problem owners enjoyed participating in hackathons specifically because of the topic itself. The developers seemed to enjoy applying technical aspects such as programming. Among these developers, three participants indicated that they enjoy hacking activities by saying "software hacking in the agro sector is my thing," and "I like working in agricultural robotics and IoT."

Interestingly, two developers have engaged in a hackathon before and they wanted to continue the hacking experience. The problem owners were interested in the topic of the hackathons because it is something that they have to deal with every day. One of them said the topic was an "interesting subject, in line with my daily practice." Participants who were driven by intellectual challenge mainly felt challenged to apply and exchange their skills, ideas, knowledge, or expertise.

Extrinsic motivations

Work is the most influential motivation and stands out in the group of citizens in the roles of business developer, marketer, and student. Participants considered their engagement in the hackathon as part of their jobs. For example, three participants participated to collect data that can enrich their research or thesis. Thirteen participants were motivated to look for a new opportunity either for their companies or careers.

Interestingly, it appears that participants who have a non-technical background (e.g., problem owner) would like to learn more about how data and applications can help them. While those from a technical background (e.g., data analyst, developer) mainly wanted to upgrade their skills or knowledge or learn new techniques or methods.

Only five out of 161 participants said to be motivated by networking. One of them did not specify what kind of network he or she wanted to create, while others wanted to expand to a specific network. A fisherman's technician wanted to create a network with other technicians who can help design a particular trawler. A researcher sought for a network of developers and data analysts for a case study. A data analyst looked for other people who are enthusiastic about agriculture, technology, and data. And, a user interface designer wanted to get in touch with companies to show them what his or her company can do.

Only two participants mentioned personal benefits: a data analyst who wanted a pizza and a developer motivated to win a prize.

Social influence

At least four types of social entity were mentioned to be influential to the participants: supervisor (i.e., a participant's team leader), colleague (e.g., data scientist, farmer), company (e.g., FarmHack.NL), and family (e.g., uncle, partner). Usually, a participant influenced by a social relationship would also have other reasons to participate at the same time. For example, a participant who was urged by his or her supervisor to participate, inarguably, means that he or she performs a job in the hackathon. Different reasons to contribute to societal benefits were observed. One participant wanted a change: more transparency in the manure market, while others wanted to contribute to practical improvement and innovation in the agricultural sector or encourage the involvement of the community. Some participants wanted to contribute to the teams working out for a solution to the hackathon challenges. A data analyst said that he or she wanted to work in a team "to solve a challenging problem." A developer stated that "with my experience in IT, drone technology and precision farming, I think I can make a nice contribution to this challenge." Nine participants were motivated to work in an interdisciplinary team composed of citizens from different back-ground and discipline. By teamworking, participants can learn from each other, exchange or even create new ideas, as well as try to solve a challenge together.

Previous experience

Previous hackathon experience was a factor not found in the literature review, but our qualitative survey data showed that it was an important motivation for some participants. Four participants said that they had participated in hackathons before the FarmHacks and wanted to continue participating.

Discussion

The results show that most citizens (n=50) appear extrinsically motivated by workrelated performance to engage in the open agricultural data hackathons. In contrast, citizens with the developer and problem owner roles (n=21) are intrinsically motivated by the fun and enjoyment that open data hackathons bring. This finding is in line with previous research investigating the motivations of participants of open transportation data hackathons in Sweden [START_REF] Juell-Skielse | Is the Public Motivated to Engage in Open Data[END_REF], and open city data hackathons in Australia and New Zealand [START_REF] Gama | Crowdsourced Software Development in Civic Apps -Motivations of Civic Hackathons Participants[END_REF].

From the results, we can observe that, on the one hand, most citizens seemed to be driven by only one motive (130 out of 161 participants). On the other hand, some were influenced by multiple motives (31 participants). Citizens who have only one motivation appeared highly motivated because they focus on only one goal in a hackathon, while citizens with multiple motives might want to continually engage in the hackathons until their multiple goals are achieved. Indeed, individuals, such as hackers engaging in free/open source software projects, can be influenced by different, and sometimes contradictory, motivations [START_REF] Lakhani | Why Hackers Do What They Do: Understanding Motivation and Effort in Free/Open Source Software Projects[END_REF].

Focusing on the multiple motives-driven citizens, we suggest that a pattern of hierarchical relationship exists between motivations, especially social influence and work. A citizen who was asked by his or her supervisor (socially influenced) to engage in a hackathon for delivering support for participants is one of the examples of hierarchical motivation. This implies that participating in a hackathon as part of employment is sometimes determined by social influence as suggested by Zuiderwijk, Janssen and Dwivedi [START_REF] Zuiderwijk | Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology[END_REF].

Work, observed as the main motivation, indicates that most participants prioritize their personal gains. Work also indicates that many companies will likely send their employees to participate in hackathons to look for an opportunity to expand their businesses. Within this frame, we can assume that profit-oriented themes are the most preferred in the context of open agricultural data use. This further indicates that companies are valuing the economic impacts of using open data. Hence, stimulating agriculture companies to become involved in open agriculture data engagement is an important agenda for open data policymakers and hackathon organizers.

Conclusion

This paper aims to explore the motivations of citizens who engage in open agriculture data hackathons. Based on a case study of 11 open agriculture data hackathons held between 2016 and 2018 in the Netherlands, we found that participants of these hackathons have different roles, including business developer, concept thinker, data analyst, data owner, developer, manager, marketer, problem owner, and student. This paper sheds light upon the mapping of factors (i.e., intrinsic motivations, extrinsic motivations, effort expectancy, social influence, and data quality) that drive citizen engagement in open data hackathons based on their roles. In the cases we studied, most of the surveyed citizens were driven by extrinsic motivation, i.e., perform-ing work. They considered their engagement as part of their work performance. However, among the examined roles, most developers and problem owners appear to be influenced by intrinsic motivation related to the fun and enjoyment of being engaged in the hackathons. Among the analyzed factors, effort expectancy and data quality seem to be uninfluential since no participants mention reasons associated with these factors. This is predictable because the quality of the open agricultural data and the efforts required to utilize the data are unknown to participants.

The above-mentioned conclusions indicate that a pattern of relationship exists between motivations and roles and thus, this study advances the discussion to identify different roles which were not investigated in previous research on the motivations of citizens in hackathons. Our results show that it is important for open data policymakers and hackathon organizers to consider different approaches based on citizens' roles when organizing open data hackathons. Hence, a different strategy should be used to involve, for example, citizens sent by companies compared to developers and problem owners who join the hackathon because they like to discuss the topics of the hackathons.

The limitation of this study concerns the intermediating factors such as personal background (e.g., age, gender) or other situational conditions (e.g., how far the hackathon location is from a participant's house) which might play a role in citizens' motivation but were not taken into account. We suggest that future research explores the relationship between intermediating factors and citizens' motivations.

This paper contributes to the literature by providing insights in the motivations of citizens engaging in open agriculture data hackathons in comparison with hackathons in other sectors, and by mapping citizens' roles to their motivations for engaging in such hackathons.

Table 1 .

 1 The overview of eleven cases of Dutch agricultural hackathons organized by Farm-Hack.NL.

	Code Themes	Outcomes	Focus	Year Respondents
	FH1	Data visualization for pie farmer	Visualization Data-driven	2016
	FH2	Drones, satellites and crop protection Application	Problem-driven	2016
	FH3	From farmer to city	Application	Problem-driven	2016
	FH4	Network technology and sustainable livestock farming	Design, application	Problem-driven	2016
	FH5	AgriVision Hackathon	Application	Data-driven	2017
	FH6	Manure Hack	Application	Data-driven	2017
	FH7	Smart Dairy Farming	Application	Data-driven	2017

Table 2 .

 2 Data collection strategy.

	Data Source	Data Type
	Documents	22 hackathon webpages
	Survey	11 qualitative surveys (n=161)
	Interviews	Notes from six unstructured interviews
	Participant-observations Notes from FH6 and FH12 observations

Table 3 .

 3 A framework of citizens' motivations to engage in open data hackathons.

	Factors	Definition	Constructs	Source
	Intrinsic motivation	"doing something because it is inherently interesting or enjoyable" [22, p. 859]	Fun and enjoyment Intellectual challenge	Juell-Skielse, Hjalmarsson, Johannesson and Rudmark [12]
				Wirtz, Weyerer and Rösch
			Performance	[17], Zuiderwijk, Janssen
	Extrinsic motivation	come" [22, p. 859] it leads to a separate out-"doing something because	advantage expectancy/relative	and Dwivedi [13],

Table 4 .

 4 The mapping of citizen roles and motivations to engage in open data hackathons.1 1 The table has been made available on 4TU Center for Research Data repository at https://doi.org/10.4121/uuid:29296049-0222-4df3-9ba7-acdd4949d1b9


Olivier Gérard
email: olivier.gerard@u-picardie.fr

Laure Brisoux Devendeville
email: laure.devendeville@u-picardie.fr

Corinne Lucet
email: corinne.lucet@u-picardie.fr

Planning problem in Healthcare domain

Keywords: Planning, healthcare, optimization, 0-1 linear programming

Introduction

The complexity of planning in healthcare domain is an issue that is increasingly being highlighted by hospitals. This kind of problem belongs to the Resource Constrained Project Scheduling Problem (RCPSP) family that is NP-Hard (M.R. Garey and D.S. Johnson 1979) (P. [START_REF] Baptiste | Constraint-Based Scheduling and Planning[END_REF]. The RCPSP problem consists in finding the best assignment of resources and start times to a set of appointments. Scheduling problems have been the subject of many studies for decades in various fields (R.N. Anthony 1965) (J. [START_REF] Blazewicz | Handbook on Scheduling[END_REF], and they are of increasing interest in healthcare domain (B. Shnits et al. 2019). Through better patient care and better management of staff time schedules, health facilities want to reduce their costs while improving patient care. Nowadays, schedules are mostly designed by hand, a difficult and time-consuming task that can be challenged by various kind of unexpected events. The structure of the considered problems differs according to the institutions, their size and the number of resources taken into account. The institutions' needs are differents, and the criteria for evaluating a schedule may also change from one institution to another or from one department to another within the same institution. In this paper we present an efficient 0-1 linear programming model for various concrete scenarios in healthcare.

The rest of this article is structured as follows. In section 2, we describe and formalize our scheduling problem. In section 3 we present some instances and the corresponding results obtained by the CPLEX solver. In section 4, we conclude with some remarks and perspectives.

0-1 Linear programming model

The horizon H is decomposed into timeslots. We have a finite set of resources R. Each resource r ∈ R is characterised by a set of properties Π r that determines which roles a resource will be able to hold in an appointment. To each resource r ∈ R is also associated a set of timeslot t such that Available r t = 1 if resource r is available at timeslot t. For example, an orthopedic surgeon who is available the first hour over H =< t 1 , t 2 , t 3 , t 4 > will be represented as ressource r with properties orthopedic surgeon and orthopaedist and with the set of available timeslots < 1, 1, 0, 0 >. He will be able to perform surgical operations and medical consultations. A is a set of appointments, such that each appointment a ∈ A is characterized by its duration duration a , a feasibility interval ES a and LS a , qtreq π a the amount of resources with property π required by a. Essential a and Emergency a are two coefficients used to respectivly quantify the importance and the urgency of appointment a. They both occur as penalties in the objective function. Each appointment a is also defined by a set of resources R a having one of the properties required by a and Π a the set of properties required by a. P reAssigned a is a set of couples (resource, property) pre-assigned to a.

We define decision variables x and y, with x r,π a = 1 if resource r with the property π is assigned to appointment a and y t a = 1 if appointment a starts at the timeslot t. We now present the hard constraints mentioned above.

A resource may have multiple properties, and thus be able to perform multiple roles. However, resource r can only be allocated to appointment a with exactly one of its properties π if a is scheduled:

∀ a ∈ A, ∀ r ∈ R a , π∈Πr x r,π a ≤ 1 (1)
If resource r does not have property π, it cannot be allocated to appointment a with this property π:

∀ r ∈ R, π∈Π\Πr a x r,π a = 0 (2)
If resource r does not have any of the properties π required by appointment a, it cannot be allocated to a:

∀ a ∈ A, r∈R\Ra π∈Π x r,π a = 0 (3)
Each appointment a must be planned into feasibilty interval determined by ES a and LS a :

∀a ∈ A, t∈H y t a × ES a ≤ t∈H y t a × t ≤ LS a (4)
If appointment a is planned, it is necessary to allocate the required quantity of resource with property π:

∀ a ∈ A, ∀ π ∈ Π a , r∈Ra x r,π a = qtreq π a × t∈[ESa;LSa] y t a (5)
Each resource r, allocated to appointment a, must be available for the complete duration of a:

∀ a ∈ A, ∀ r ∈ R a , ∀ t ∈ [ES a ; LS a ], duration a × π∈Πa x r,π a -y t a × t+durationa-1 t =t dispo r t ≤ (1 -y t a ) × H (6)
An appointment a is at most scheduled once:

∀ a, ∈ A t∈[ESa;LSa] y t a ≤ 1 (7)
Resources are not allocated if the appointment a is not scheduled:

∀ a ∈ A, |R| × |Π| × t∈[ESa;LSa] y t a ≥ r∈Ra π∈Πa x r,π a (8)
Resource r cannot be allocated to different appointments on same timeslot t:

∀ a ∈ A, ∀ b ∈ A, ∀ r ∈ R a ∩ R b , ∀ t ∈ [max(duration a ; duration b ); |H|], π∈Πr x r,π a + t-durationa t =t y t a + π∈Πr x r,π b + t-duration b t =t y t b ≤ 3 (9)
In most cases, an appointment is associated to a specific resource. The following constraint ensures that the resources r with their property π in P reAssigned a are allocated to appointment a: ∀a ∈ A, ∀(r, π) ∈ P reAssigned a , x r,π a = 1 (10)

The quality of a solution is evaluated by an objective function f that computes the sum of the unplanned appointments a ∈ A, weighted by the importance factor Essential a and the sum of the differences between the start date of an appointment a and ES a , weighted by the emergency factor Emergency a . The objective is to find a valid solution while minimizing objective function defined in equation 11.

f = a∈A (1 - t∈[ESa;LSa] y t a ) × Essential a + a∈A t∈[ESa;LSa] y t a × t -ES a LS a -ES a × Emergency a (11)

Experimentations and results

We generated instances from four different scenarios with the help of various planners from different health care facilities in France who face daily concrete problems.

The first scenario SurgDep concerns a surgical department and involves 16 patients, 2 orthopedic surgeons, 2 gastric surgeons and 4 operating rooms. Each patient needs one operation (with a duration from 3 to 7 timeslots), either an orthopedic or a gastric one, for a total of 16 appointments. The horizon is represented by 23 timeslots (i.e. 1 day). Every patient is available over the horizon, surgeons and rooms are available from timeslots 2 to 21.

The second scenario RehabCenter concerns a follow-up care and rehabilitation facility and involves 24 patients, 12 doctors and 12 specialists (6 of each speciality). Each patient requires 2 medical consultations and 2 sessions of each speciality for a total of 96 appointments. The duration of all appointments is 4 timeslots. The horizon is represented by 20 timeslots (i.e. 1 day). All resources have a lunch break of 4 timeslots and are available during 16 timeslots.

The next scenario Admission represents the admission of 8 patients into a hospital and involves 5 differents specialists. Each patient has a care program and needs 9 appointments (one appointment with a duration of 2 timeslots, others with a duration of 1 timeslot), for a total of 72 appointments. The horizon is represented by 120 timeslots (i.e. 5 days). All resources have a lunch break of 4 timeslots and are available during 18 timeslots each day.

The last scenario CardioRehab concerns a cardiovascular rehabilitation facility and involves 16 patients and 10 specialists (2 of each speciality). Each patient follows a care program and needs 8 appointments (with a duration of 2 timeslots), for a total of 128 appointments. The horizon is represented by 104 timeslots (i.e. 5 days). All resources have a lunch break of 4 timeslots and are available during 16 timeslots each day. Medical staff has in addition 2 × 8 timeslots off.

From each of these four scenarios, we generated three instances, starting with no important nor urgent appointments and then increasing the number of essential (Ess) and urgent appointments (Em). We implemented the model under CPLEX and we run tests on Intel i5-8350U processor. We limit the computation time to two hours, and we report the results in Table 1. Objective value column corresponds to the objective function in equation 11. If CPLEX is unable to find an optimal solution within the time limit, we report the upper and lower bound. Time column is the running time in seconds that CPLEX needs to work out the optimal solution. For all scenarios other than the CardioRehab scenario, CPLEX finds an optimal solution before the computation time limit. We notice that the increased number of important and urgent appointments implies an increase in computing time, exept for the scenario SurgDep. The calculation time also increases with the number of appointments involved in the scenario.

Conclusion & perspectives

In this paper we have described and formalized concrete scheduling prolems. We proposed a 0-1 linear programming model able to solve various scenarios in healthcare. We implemented this model under CPLEX and generated some instances in order to test it. The optimality has been reached for most instances and this model has proven to be effective on various scheduling issues in the medical domain. This will be a reliable benchmark to compare different approaches addressing these problems. We plan to develop a genetic algorithm to solve larger instances.

Table 1 .

 1 CPLEX results on scenario benchmarks

	Instance name	Time	Objective value
	SurgDep Ess0Em0	797	0
	SurgDep Ess1Em1	2737	5.8160
	SurgDep Ess2Em2	2085	19.4235
	CardioRehab Ess0Em0 >7200	0-4
	CardioRehab Ess1Em1 >7200	12.7059-16.7059
	CardioRehab Ess2Em2 >7200	18.8015-23.0589
	Admission Ess0Em0	246	0
	Admission Ess1Em1	659	4.5098
	Admission Ess2Em2	671	9.1734
	RehabCenter Ess0Em0 1117	0
	RehabCenter Ess1Em1 3015	36
	RehabCenter Ess2Em2 6956	72

This project is supported by LORH project (CIFRE N o 2018/0425 between Evolucare and MIS Laboratory)


Carlos M García

Mariano I Cantero

Yarko Niño

Marcelo H García

Bahareh Doroudian
email: bahareh.doroudian@epfl.ch

David Hurther

Ulrich Lemmin

Discussion of "Turbulence Measurements with Acoustic Doppler Velocimeters" by

The paper by Garcia et al. deals with the problem of correctly measuring turbulence parameters with acoustic Doppler velocimeters ͑ADV; trade names ADV for Sontek and NDV for Nortek͒. The authors focus on the effects of sampling frequency and Doppler noise on turbulence parameters. To avoid loss of turbulence information, they suggest that data should be sampled above a determined frequency. In addition, noise should be removed by estimating the noise contribution. Their approach is based on a model-derived procedure. First, it would have been of interest to compare the modeled spectra with those obtained from their measurements to validate their model and instrument assumptions for the flow cases discussed. Second, the deviation from the -5/3 slope in the measured spectra due to filtering and/or noise effects has not been highlighted.

We investigated the authors' conclusions using a Vectrino ͑Nortek͒ ADV. Different from their instruments, a Vectrino has four receivers symmetrically spaced around the central emitter. The applied sampling frequencies, the relative position, and the size of the measuring volume, however, were identical to the NDV. Using four receivers allows measuring the vertical velocity component simultaneously in the two planes. This configuration enables the direct estimation of noise effects so that suitable correction procedures such as the one proposed by Hurther and Lemmin ͑2001; hereinafter called HLP͒ can be applied. The HLP takes advantage of the redundancy of the vertical velocity obtained in the two instrument planes.

It should be noted that Doppler noise is composed of several contributions that can be estimated ͑Garbini et [START_REF] Garbini | Measurement of fluid turbulence based on pulsed ultrasound techniques. Part 1: Analysis[END_REF]Lhermitte andLemmin 1990, 1994;[START_REF] Hurther | A constant beam width transducer for 3D acoustic Doppler profile measurements in open channel flows[END_REF][START_REF] Voulgaris | Evaluation of the acoustic Doppler velocimeter ͑ADV͒ for turbulence measurements[END_REF]Trowbridge 1998͒ or eliminated ͑Garbini et al. 1982;Hurther andLemmin 1998, 2001;[START_REF] Blanckaert | Means of noise reduction in acoustic turbulence measurements[END_REF] For three receiver instruments, such as those used by the authors, the procedure proposed by Voulgaris and Trowbridge ͑1998͒ can be applied who emphasize that overestimates have to be expected. A sufficiently high sampling frequency is required for successful measurements.

Our measurements were carried out in an open channel at the LHE-EPFL. The channel is 0.60 m wide and 17 m long. The bottom was covered with a 0.1-m-thick gravel layer ͑size range 3-8 mm; d 50 = 5.5 mm͒. In this experiment, water depth and the measuring volume of the instrument were respectively 0.14 m and 0.05 m above the bed. The convective velocity is 0.6 ms -1 . Data were recorded about 12 m from the channel entrance where turbulence is well developed for at least 3 min with sampling frequencies of 100, 75, 50, 25, and 10 Hz. The instrument was mounted downward looking with one receiver plane oriented along the flow and the second one in the transversal direction. Two experiments were carried out. In the first one, mean values for correlation and SNR were about 84 and 24 dB. In this experiment we used hydrogen bubbles as "seeding material" ͑Blanckaert and Lemmin 2006͒. For all sampling frequencies, the data appeared "clean" with only a few spikes. In the second experiment, mean values for correlation and SNR were 81 and 22 dB without any seeding procedure. Although these quality parameters were high, frequent spikes were observed, in particular in the longitudinal plane.

Following Nezu and Nakagawa ͑1993͒, u, v and w denote the velocity fluctuation and uЈ, vЈ, and wЈ denote the RMS values ͑turbulence intensities͒. For each of the sampling frequencies, the turbulence intensities and spectra of each velocity were calculated. The noise spectra were obtained using the HLP by calculating the cross spectrum between the two vertical components. The noise spectrum of the longitudinal component u was determined as outlined in HLP and subtracted from the original spectrum of u. To fit a curve to the noise corrected spectrum ͑NCS͒, we kept the NCS at the low frequency end and curve fitted the NCS points starting where the -5 / 3 slope is established in the spectrum and ending at the Nyquist frequency. An estimate of the variance can be obtained by integrating the surface under the spectral curve. This was done for all three spectra resulting in u orig Ј for the original spectrum, u cor Ј for the NCS, and u fit Ј for the fitted one.

Fig. 1 shows a typical result for the data sampled at 100 Hz. As can be seen, both of the vertical velocities, ͑w 1 ͒ the longitudinal plane and particularly ͑w 2 ͒ in the transversal plane closely follow the -5 / 3 slope over an extended region. Both noise spectra are nearly flat indicating white noise. Fig. 2 shows spectra of the longitudinal components ͑original and NCS͒ as well as a fitted spectrum. Although we can see that the slope of the NCS in the midfrequency range is close to -5 / 3, at the high frequency end, the noise is not completely removed by the HLP method, resulting in significant scatter.

The aforementioned procedure was executed for all velocity components and sampling frequencies. The results for the longitudinal component are summarized in Table 1. It can be seen that u orig Ј decreases by a factor of almost two between the highest and the lowest sampling frequency. However, it decreases slower between 100 and 50 Hz than between 50 and 10 Hz. The values for the NCS are by a factor of two lower than those of the original spectrum for 100 Hz and remain constant until 50 Hz. They then drop by about 10% for 10 Hz. This difference between the original data and the NCS confirms the observation by Lohrmann et al.

͑1994͒ that uncorrected data are biased to higher values. The values obtained from the fitted spectra show a difference of about 10% between the highest and the lowest frequency. In between, the variation is random.

The magnitude of the values from the different spectra indicates the importance of proper noise removal. Comparing u orig Ј and u cor Ј , the decreasing tendency in u orig Ј for decreasing sampling frequency can essentially be attributed to noise effects and not to filtering related to the sampling frequency. If filtering had been the dominant cause of the unresolved velocity scales in the higher spectral range, the slope of the spectra in this region would have been greater than the -5 / 3 value. This is not always the case for different sampling frequencies.

In this study, we documented the importance of proper noise removal. Considering the sampling frequency criteria suggested by Nezu and Nakagawa ͑1993͒, turbulence should not be sampled below 75 Hz in our case. However u cor Ј remains constant down to 50 Hz. In other words, our observation demonstrates that the Vectrino ADV is a robust instrument because it still produces reliable results well below that threshold value. Thus, when proper sampling criteria are respected, filtering due to change in sampling frequency has no effect on the results.

The curve fitting of the noise corrected spectrum was done to determine whether the uncorrected noise and aliasing may affect the estimates. For our results the difference is about 5%. Considering that curve fitting is not an ideal procedure and that other undetermined uncertainties in the measuring procedure remain, this value appears acceptable and indicates that those deviations do not significantly affect the results.

We have applied this procedure to the second data set in which a fairly large number of spikes occurred. For the present analysis, we did not eliminate the spikes from the data set. These spikes may be due to random noise or aliasing. Although aliasing can be dealt with by using procedures modified from those suggested by Franca and Lemmin ͑2006͒, random noise is difficult to eliminate from the u, v, and w velocity data. Furthermore, it has to be remembered that due to the system configuration, spikes in one velocity component may also affect the other components. Thus, spike removal procedures such those as suggested by Goring and Nikora ͑2002͒ have to be applied with caution. The Vectrino ADV allows for recording beam velocities instead of u, v, and w velocities. This recording has a great advantage in that spikes can be removed individually from each beam time series before constructing the u, v, and w velocities. This allows for a much more objective approach than previous ones ͑Goring and Nikora 2002͒.

The results in Table 1 indicate that the overall trends observed in the first data set are reproduced in the second one. However, the level of all values is roughly double that of the first data set. This shows that noise removal by the HLP cannot eliminate the effects of spikes and that spikes have a much more detrimental effect on the quality of the results than the sampling aspect previously mentioned. On the other hand, it appears from our results that sampling at low frequency would be the better strategy in this case. Taking the first data set as reference, the noise corrected and fitted results at low sampling frequencies in the spiked data are closest to those observed in the first data set at frequencies above the threshold level.

In a final test we applied the HLP to a data set taken in the same channel in a flow of 0.09 m water depth and a convective velocity of 0.32 ms -1 , which is about 50% of the convective velocity in the experiments above. Again, we used hydrogen bubble seeding. Results in Table 1 show that the original spectra vary randomly. Thus there is no filtering effect related to the sampling frequency. This is even more obvious in the NCS, which remains constant down to 25 Hz. The fitted data which depend on the indication of a -5 / 3 trend in the spectrum show poor results for the 10 Hz case. Overall these data indicate once more that apart from spike removal, noise removal is the most important process for increasing the reliability of the data.

Our analysis has shown that the recommendations and conclusions by the authors cannot be considered as a universal guide when making turbulence measurements with ADVs. Our investigation has demonstrated that four-receiver ADV instruments, such as the Vectrino, open up new ways to treat data that lead to greatly improved results in turbulent flows. This suggests that using modern ADV instrumentation, turbulence studies can be carried out along the following procedure: • Ensure that the flow has sufficient scattering targets. Wherever seeding is needed, hydrogen bubble seeding ͑Blanckaert and Lemmin 2006͒ has proven to give excellent results in large channel installations where injection of small particles is technically and economically not feasible. • Record data as beam velocities at sampling frequencies near and preferably above the threshold level as indicated by Nezu and Nakagawa ͑1993͒. This allows for subsequent spike removal by de-aliasing procedures ͑Franca and Lemmin 2006͒ or data splicing such as spline procedures over adjacent points. • Transform beam velocities into u, v, and w velocities and apply noise removal procedures such as HLP ͑Hurther and Lemmin 2001͒. The noise removal procedure can be further extended as suggested by Blanckaert and Lemmin ͑2006͒.

Fig. 1 .

 1 Fig. 1. Turbulence spectra of the u, v, and w velocity components sampled at 100 Hz; also shown are the noise spectra for the two vertical velocities

Table 1 .

 1 Estimates of the Three Turbulence Intensities ͑in 10 -2 ms -1 ͒ for the Longitudinal Velocity Sampled at Different Frequencies

	f		h = 0.14 m ͑exp 1͒			h = 0.14 m ͑exp 2͒			h = 0.09 m
	͑Hz͒	u orig Ј	u cor Ј	u fit Ј	u orig Ј	u cor Ј	u fit Ј	u orig Ј	u cor Ј	u fit Ј
	100	8.83	4.89	4.69	15.16	8.66	6.16	4.69	3.74	3.74
	75	7.93	4.89	4.24	13.96	8.06	6.16	5.65	3.74	3.00
	50	7.81	4.89	4.69	11.31	6.92	5.47	5.19	3.74	3.00
	25	5.47	4.35	4.12	11.83	7.41	4.69	4.89	3.74	3.46
	10	4.89	4.24	4.00	7.21	5.65	5.00	5.65	4.12	2.82