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Citizen Science 2.0: Data Management Principles to Harness the Power of the Crowd
Abstract
Citizen science refers to voluntary participation by the general public in scientific endeavors. Although citizen science
has a long tradition, the rise of online communities and user-generated web content has the potential to greatly expand its
scope and contributions. Citizens spread across a large area will collect more information than an individual researcher can.
Because citizen scientists tend to make observations about areas they know well, data are likely to be very detailed. Although
the potential for engaging citizen scientists is extensive, there are challenges as well. In this paper we consider one such
challenge – creating an environment in which non-experts in a scientific domain can provide appropriate and accurate data
regarding their observations. We describe the problem in the context of a research project that includes the development of
a website to collect citizen-generated data on the distribution of plants and animals in a geographic region. We propose an
approach that can improve the quantity and quality of data collected in such projects by organizing data using instance-based
data structures. Potential implications of this approach are discussed and plans for future research to validate the design
are described.
... Improving data quality based on the data model is not a new idea [6,32,46,59]. This research aims to improve data and information quality by integrating quality characteristics into the citizen science platform's design, mainly focusing on the data model and user interface. ...
... Some researchers have investigated how citizen science platforms' data quality can be increased by training citizens [62], using reputation models [63], and using attribute filtering methods for data input [59]. These are excellent choices for increasing the quality of data and information, but they require more from citizens than making changes to the platform would. ...
... Some people trust data from citizen science platforms less than other sources because citizens are considered to be non-professionals who provide inaccurate data [6,22,64,65]. However, this is not necessarily true, and even if it is, there are methods to increase the quality of data on the platform [17,32,59,66]. ...
The quality of the user-generated content of citizen science platforms has been discussed widely among researchers. Content is categorized into data and information: data is content stored in a database of a citizen science platform, while information is context-dependent content generated by users. Understanding data and information quality characteristics and utilizing them during design improves citizen science platforms’ overall quality. This research investigates the integration of data and information quality characteristics into a citizen science platform for collecting information from the general public with no scientific training in the area where content is collected. The primary goal is to provide a framework for selecting and integrating data and information quality characteristics into the design for improving the content quality on platforms. The design and implementation of a citizen science platform that collects walking path conditions are presented, and the resulting implication is evaluated. The results show that the platform’s content quality can be improved by introducing quality characteristics during the design stage of the citizen science platform.
... Primary biodiversity data records the presence or absence of a certain taxon (of plant or animal etc.) in a particular place and time; this data has many applications: evolutionary research questions, ecological management issues (climate change, invasive species), epidemiology or natural disaster management (Soberón & Peterson, 2004;Lukyanenko, Parsons & Wiersma, 2011). ...
... Verification by professional experts is contrary to the spirit of citizen science, according to Lukyanenko, Parsons & Wiersma (2011). ...
... However this solution is only applicable to projects which are modelled after a social network principle. Lukyanenko, Parsons & Wiersma (2011) propose that volunteers should not provide a direct classification of the taxa observed, but describe them. This method purports to be more open to non-experts as well as less prone to classification errors. ...
Technological developments open up new opportunities for collaboration between biodiversity researchers and the general public. Three exemplary case studies were reviewed from literature: digitizing museum specimens, text-mining archived expedition journals and handling environmental monitoring data. Data management principles were applied to refine the ensuing requirements. Specific requirements were found to exist in three areas: collecting data, sharing data and improving data quality. Implications for data governance and quality control are discussed.
... Primary biodiversity data records the presence or absence of a certain taxon (of plant or animal etc.) in a particular place and time; this data has many applications: evolutionary research questions, ecological management issues (climate change, invasive species), epidemiology or natural disaster management (Soberón & Peterson, 2004;Lukyanenko, Parsons & Wiersma, 2011). (Irwin, 1995;cited in Catlin-Groves, 2012) reflects technological developments enabling new modes of public engagement on a larger scale than was possible previously (Rubio Iglesias, 2014). ...
... Verification by professional experts is contrary to the spirit of citizen science, according to Lukyanenko, Parsons & Wiersma (2011). ...
... Attribute-based data collection Lukyanenko, Parsons & Wiersma (2011) propose that volunteers should not provide a direct classification of the taxa observed, but describe them. This method purports to be more open to non-experts as well as less prone to classification errors. ...
Technological developments open up new opportunities for collaboration between biodiversity researchers and the general public. Three exemplary use cases were examined: digitizing museum specimens, text-mining archived expedition journals and handling environmental monitoring data. Data management principles were applied to refine and map the ensuing requirements to specific deliverables: data policy, standards and procedures; workflows, integration architectures and data products; data quality awareness and improvement methods. Implications for data governance and quality control are discussed.
... As we will see further, the number of studies and activities that engage citizenry as 'scientists' has been on the rise (Cohn 2008;Gouveia & Fonseca 2008; Catlin--Groves 2012; Morzy 2014) due to information technologies, engaging citizens in larger numbers. Despite the long tradition of citizen science, the rise of online communities and their contributions have the potential to greatly expand its scope and contributions (Lukyanenko et al. 2011). As Newman et al. (2012) suggested, the future of citizen science lies with emerging technologies such as smartphones and other mobile, web--enabled equipment. ...
... Citizen science has been recognised as a way to include stakeholders and the public in general in planning and management activities of local ecosystems (Pollock & Whitelaw 2005;Lynam et al. 2007), influencing policy making, raise new questions and co--create a new scientific culture (SOCIENTIZE 2014). Moreover, citizen science can help with understanding of processes that cannot rely on traditional field research because they are broader in scale or occurring on poorly accessible places, or simply because there needs to be critical mass for meaningful data collection (Dickinson et al. 2010;Lukyanenko et al. 2011), e.g. through crowdsourcing for community intelligence in health (Hesse et al. 2011). ...
... Franzoni & Sauermann (2013) noted e.g. that projects' outcomes are increasingly disclosed through blogs and projects' websites; therefore the research that relies on citizen science practice is not always submitted to scientific peer review channels. But because citizen scientists tend to make observations about areas they know well, data are likely to be very detailed (Lukyanenko et al. 2011). ...
... As we will see further, the number of studies and activities that engage citizenry as 'scientists' has been on the rise (Cohn 2008;Gouveia & Fonseca 2008; Catlin--Groves 2012; Morzy 2014) due to information technologies, engaging citizens in larger numbers. Despite the long tradition of citizen science, the rise of online communities and their contributions have the potential to greatly expand its scope and contributions (Lukyanenko et al. 2011). As Newman et al. (2012) suggested, the future of citizen science lies with emerging technologies such as smartphones and other mobile, web--enabled equipment. ...
... Citizen science has been recognised as a way to include stakeholders and the public in general in planning and management activities of local ecosystems (Pollock & Whitelaw 2005;Lynam et al. 2007), influencing policy making, raise new questions and co--create a new scientific culture (SOCIENTIZE 2014). Moreover, citizen science can help with understanding of processes that cannot rely on traditional field research because they are broader in scale or occurring on poorly accessible places, or simply because there needs to be critical mass for meaningful data collection (Dickinson et al. 2010;Lukyanenko et al. 2011), e.g. through crowdsourcing for community intelligence in health (Hesse et al. 2011). ...
... Franzoni & Sauermann (2013) noted e.g. that projects' outcomes are increasingly disclosed through blogs and projects' websites; therefore the research that relies on citizen science practice is not always submitted to scientific peer review channels. But because citizen scientists tend to make observations about areas they know well, data are likely to be very detailed (Lukyanenko et al. 2011). ...
From citizen science to do it yourself science
An annotated account of an on-going movement
This report offers a selection of projects that account for an emerging movement that is not what often is described as citizen science but what we designate here by do it yourself science. The report accounts for private or community based initiatives that use scientific methods combined with other forms of enquiry to engage with techno-scientific issues and societal challenges. The first section of the report focuses on what is usually described as citizen science where in most cases projects are led by institutions, such as universities or other research institutions, which organise, call or promote different forms of citizen involvement in their endeavours. The second part of the report looks into developments in what is designated as do it yourself science. It outlines developments in this deeper form of engagement of citizenry with techno-‐ science, where the DIY scientist appears as someone who tinkers, hacks, fixes, recreates and assembles objects and systems in creative and unexpected directions, usually using open-source tools and adhering to open paradigms to share knowledge and outputs with others. We also observe that although these movements link well with other changes of the scientific endeavour, such as open science, the ‘do it yourself’ movement takes us to another dimension of engagement, of greater agency and transformative power of research and innovation. We conclude that Irwin’s imagination of a citizen science is gradually emerging, at the moment materialised in the on-going DIY science movement and others alike. The European Commission should seize such momentum as well
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Corporate author(s): European Commission, Joint Research Centre Private author(s): Susana Nascimento, Ângela Guimarães Pereira, Alessia Ghezzi Themes: Research policy and organisation Target audience: Scientific Key words: applied sciences, research and development, innovation, information technology, technology, scientific cooperation, scientific research, scientific education, popularising science, social impact, research report
... Citizen science is increasingly looked upon to reduce information acquisition costs and facilitate discoveries [28]. The promise of citizen science increasingly motivates the design science community to address many sociotechnological issues arising in this domain [29][30][31][32]. ...
... Based on the identified threats to IQ and user engagement, the project sponsors decided to redesign the project following an instance-based approach to citizen science proposed in [29,40,41]. Under the instance-based data model [42], users are not forced to classify instances using predefined classes (such as biological species), which relaxes the constraint for non-experts to understand and conform to a chosen taxonomy. ...
... The attributes can be queried post hoc to infer classes of interest (e.g., species). According to the design theory in [29,40,41], adopting an attribute and instancebased approach leads to increase in accuracy and user participation (see Fig. 2 for a graphical representation of the theory). Considering the objectives, it became unclear which of the many specific design decisions would be suitable. ...
Despite increased acceptance of design science research, concerns about rigor and relevance permeate the research community. One way to increase rigor is by codifying design knowledge into design theories. While this idea is gaining popularity, it is unclear how to approach design theorizing in a scientifically rigorous, yet practically relevant, way. In this paper, we address one particularly murky issue in design science research: reconciling theoretical abstractness with practicality. Since many design theories are moderately abstract, a gap exists between theoretical propositions and concrete issues faced in practice. We present a case study of real information system (IS) development where these issues become evident. Based on the identified issues we provide four theory-driven recommendations including specification of transformational rules, developing or imagining a real IS artifact, specification of boundary conditions and over-specification of the theoretical core. The consequences of these recommendations for design science theorizing are discussed.
... Participants in citizen science projects are referred to as 'citizens' as a way to indicate that they participate in a personal capacity, rather than hired by an institution. They have also been called 'volunteers' (to reflect their informal involvement), 'sensors' , to highlight a focus in data collection (Goodchild, 2007), 'contributors' , when following scientific protocols (Wiggins & Crowston, 2011), 'a crowd' , when they perform crowdsourcing tasks (Lukyanenko, Parsons, & Wiersma, 2011) or 'collaborators' , when they were involved in design of the scientific experiment (Reed, Raddick, & Lardner, 2013). We will use the terms 'citizen' to describe any participant from the general public, whether they are collecting and submitting data, or they are consumers of information and observers in the research process. ...
... The possibility of public involvement in scientific projects has many direct and indirect benefits to society. At the societal level, it contributes to education and empowerment and can also often give participants something in return: people may find it interesting to be involved in a scientific process, they might consider it their civic duty to contribute to a better understanding of the world around them or they may see participation as an opportunity to take part in something which fits and advances their values and world view (Lukyanenko et al., 2011). At the project level, it has been shown that projects that involve participants have a greater focus on societal innovation and social change (Hochgerner, 2013), making citizens active agents of change. ...
For landscape research to function in a democratic landscape governance, it must achieve two things. One, it must integrate stakeholder perspectives at multiple steps of the research process, and two, it must effectively communicate its knowledge and insights. Citizen science can be described as the involvement of the public in the scientific process, through a range of different approaches. We ask what such approaches can bring the landscape research and its stakeholders closer together. We survey the field of citizen science and present a number of typologies of approaches. Next, we introduce three applications of citizen science in the landscape context and examine them under the lens of the typologies. We find that each case employs citizen science to include stakeholders in different ways, but each of them limited to just one stage of the research process. Finally, we suggest ways forward for landscape research to achieve an integrative relationship between researchers and stakeholders.
... As a result, some requirements may originate from system owners or sponsors, but the actual information comes from distributed heterogeneous users. Many such users lack domain expertise (e.g., bird taxonomy or product knowledge) and have views or conceptualizations of the subject matter that are incongruent with the views of project sponsors and other users (Erickson et al. 2012;Lukyanenko et al. 2011). Unable to reach every potential contributor, analysts cannot construct an accurate and complete representation of modeled domains. ...
... Indeed, once the abovementioned citizen science project was implemented, the analysis of contributions suggested users sometimes made guesses when classifying species and, potentially, avoided contributing when unable to satisfy the constraints placed by the underlying abstraction-based conceptual model. These conclusions about poor information quality concurred with concerns in scientific community about low quality of citizen contributions(Flanagin and Metzger 2008;Lukyanenko et al. 2011;Rowland 2012;Snäll et al. 2011;Wiersma 2010). ...
Traditionally, the research and practice of conceptual modeling assumed all relevant information about a domain could be discovered through users-analyst communication. The increasing reliance of organizations on externally produced information, such as online user-generated content, challenges this and other long-held assumptions underlying conceptual modeling. The information systems used in these environments are open information systems, in which at least some users permanently reside outside the original context of systems analysis, design, and implementation. In this setting, it is often impossible to predict how potential users may conceptualize a domain and employing traditional conceptual modeling approaches is highly constraining. This paper reviews core assumptions of conceptual modeling research and evaluates their applicability to open information systems. We illustrate key issues in the context of online citizen science, which relies on open information systems to collect contributions of ordinary people for scientific research. Specific conceptual modeling challenges are identified. To address these challenges, we use theoretical foundations in philosophy and psychology to develop conceptual modeling principles to guide open information systems development. We conclude by outlining implications for practice and suggest directions for future conceptual modeling research.
... It is clearly difficult to a priori anticipate what kind of information non-experts can provide, and creating unnecessary constraints can undermine potential gains from such initiatives. Moreover, amateur observers are often unable to record information consistently and in compliance with the requirements of a given scientific domain, leading to a what appears to be a tradeoff between levels of participation and data quality (Parsons,Lukyanenko and Wiersma, 2011). Yet, a close examination of the tradeoff shows that data quality may be affected by the way data is collected and stored (Lukyanenko, Parsons and Wiersma, 2011). ...
... Application design typically follows database development and closely reflects objects defined in a database. Ultimately it is the database schema that impacts the information collection activities (Lukyanenko et al., 2011).Wand and Wang (1996)define data quality deficiency as " an inconformity between the view of the real-world system that can be inferred from a representing information system and the view that can be obtained by directly observing the real-world system " (p. 89). ...
With the proliferation of unstructured data sources and the growing role of crowdsourcing, new data quality challenges are emerging. Traditional approaches that investigated quality in the context of structured relational databases viewed users as data consumers and quality as a product of an information system. Yet, as users increasingly become information producers, a reconceptualization of data quality is needed. This paper contributes by exploring data quality challenges arising in the era of user-supplied information and defines data quality as a function of conceptual modeling choices. The proposed approach can better inform the practice of crowdsourcing and can enable participants to contribute higher quality information with fewer constraints.
... Organizations are increasingly looking to harness UGC to better understand customers, develop new products, and improve quality of services (e.g., healthcare or municipal) (Barwise and Meehan 2010; Culnan et al. 2010; Whitla 2009). Scientists and monitoring agencies sponsor online UGC systems -citizen science information systems -that allow ordinary users to provide observations of local wildlife, report on weather conditions, track earthquakes and wildfires, or map their neighborhoods (Flanagin and Metzger 2008; Haklay 2010; Hand 2010; Lukyanenko et al. 2011). Despite the growing reliance on UGC, a pervasive concern is the quality of data produced by ordinary people. ...
... Online users are typically volunteers, resulting in a user base with diverse motivations and variable domain knowledge (Arazy et al. 2011; Coleman et al. 2009). When dealing with casual contributors external to the organization, traditional approaches to information quality (IQ) management break down (Lukyanenko and Parsons 2011;). Traditionally, information production processes are assumed to be designed to support the needs of data consumers – typically employees or others associated with the sponsoring organizations that require information for decision-making and other tasks (Lee and Strong 2003; Redman 1996). ...
The rise and increased ubiquity of online interactive technologies such as social media or crowdsourcing (Barbier et al. 2012; de Boer et al. 2012; Doan et al. 2011; Whitla 2009) creates a fertile environment for field experimentation, affording researchers the opportunity to develop, test and deploy innovative design solutions in a live setting. In this research, we use a real crowdsourcing project as an experimental setting to evaluate innovative approaches to conceptual modeling and improve quality of user-generated content (UGC). Organizations are increasingly looking to harness UGC to better understand customers, develop new products, and improve quality of services (e.g., healthcare or municipal) (Barwise and Meehan 2010; Culnan et al. 2010; Whitla 2009). Scientists and monitoring agencies sponsor online UGC systems -citizen science information systems -that allow ordinary users to provide observations of local wildlife, report on weather conditions, track earthquakes and wildfires, or map their neighborhoods (Flanagin and Metzger 2008; Haklay 2010; Hand 2010; Lukyanenko et al. 2011). Despite the growing reliance on UGC, a pervasive concern is the quality of data produced by ordinary people. Online users are typically volunteers, resulting in a user base with diverse motivations and variable domain knowledge (Arazy et al. 2011; Coleman et al. 2009). When
... Recently, citizen science issues have been receiving increased attention from the IS community [e.g. 2,32,38,60]. ...
... Application design typically follows database development and closely reflects objects defined in a database. Ultimately it is the database schema that impacts the information collection activities [32]. ...
With the proliferation of unstructured data sources and the growing role of crowdsourcing, new data quality challenges are emerging. Traditional approaches that investigated quality in the context of structured relational databases viewed users as data consumers and quality as a product of an information system. Yet, as users increasingly become information producers, a reconceptualization of data quality is needed. This paper contributes by exploring data quality challenges arising in the era of user-supplied information and defines data quality as a function of conceptual modeling choices. The proposed approach can better inform the practice of crowdsourcing and can enable participants to contribute higher quality information with fewer constraints.
... Furthermore, these ''Big Data'' approaches often involve the danger of collapsing the complexity of entire human personalities into assumptions constructed from simple data (e.g. website clicks) and usually miss the unique domain-specific knowledge users have [14]. Thus, it has been suggested that information providers should structure their input in a contextualized way when sharing their data, utilizing semantic web technologies [15], such as linked data and ontologies [16], [17], and evaluate the quality of information shared by other providers [18]. ...
Cryptoeconomic incentives in the form of blockchain-based tokens are seen as an enabler of the sharing economy that could shift society towards greater sustainability. Nevertheless, knowledge of the impact of these tokens on human sharing behavior is still limited and this poses a challenge to the design of effective cryptoeconomic incentives. This study applies the theory of self-determination to investigate the impact of such tokens on human behavior in an information-sharing scenario. By utilizing an experimental methodology in the form of a randomized control trial with a
$2 \times 2$
factorial design involving 132 participants, the effects of two token incentives on human information-sharing behavior are analyzed. Individuals obtain these tokens in exchange for their shared information. Based on the collected tokens, individuals receive a monetary payment and build reputation. Besides investigating the effect of these incentives on the quantity of shared information, the study includes quality characteristics of the information, such as
accuracy
and
contextualization
. The focus on quantity while excluding quality has been identified as a limitation in previous work. In addition to confirming previously known effects such as a crowding-out of intrinsic motivation by incentives, which also exists for blockchain-based tokens, the findings of this paper point to a hitherto unreported
interaction effect
between multiple tokens when applied simultaneously. The findings are critically discussed and put into the context of recent work and ethical considerations. The theory-based-empirical study is of interest to those investigating the effect of cryptoeconomic tokens or digital currencies on human behavior and supports the community in the design of effective personalized incentives for sharing economies.
... Furthermore, these ''Big Data'' approaches often involve the danger of collapsing the complexity of entire human personalities into assumptions constructed from simple data (e.g. website clicks) and usually miss the unique domain-specific knowledge users have [14]. Thus, it has been suggested that information providers should structure their input in a contextualized way when sharing their data, utilizing semantic web technologies [15], such as linked data and ontologies [16], [17], and evaluate the quality of information shared by other providers [18]. ...
Cryptoeconomic incentives in the form of blockchain-based tokens are seen as an enabler of the sharing economy which could shift society towards greater sustainability. Nevertheless, knowledge about the impact of those tokens on human sharing behavior is still limited, which challenges the design of effective cryptoeconomic incentives. This study applies the theory of self-determination to investigate the impact of those tokens on human behavior in an information sharing scenario. By utilising an experimental methodology in the form of a randomized control trial with a 2x2 factorial design involving 132 participants, the effects of two token incentives on human information sharing behavior are analysed. Individuals obtain these tokens in exchange for their shared information. Based on the collected tokens, individuals receive a monetary payment and build reputation. Besides investigating the effect of these incentives on the quantity of shared information, the study includes quality characteristics of information, such as accuracy and contextualisation. The focus on quantity while excluding quality has been identified as a limitation in previous work. Besides confirming previously known effects such as a crowding out of intrinsic motivation by incentives which also exists for blockchain-based tokens, the findings of this work show a until now unreported interaction effect between multiple tokens when applied simultaneously. The findings are critically discussed and put into context of recent work and ethical considerations. The theory-based, empirical study is of interest to those investigating the effect of cryptoeconomic tokens or digital currencies on human behavior and supports the community to design effective personalized incentives for sharing economies.
... To further our understanding of their impact and to better comprehend environmental chemicals in general, additional information detailing their use type and associated chemical classes is urgently needed. In this context, substance databases (DBs) play an essential role as information sources for research, governmental institutions, regulation, citizens' science, and companies alike ( [3]; see also Appendix A Database Compendium References). To date, an unintelligibly broad range of DBs is publicly available (see Appendix A Database Compendium References). ...
With an ever-increasing production and registration of chemical substances, obtaining reliable and up to date information on their use types (UT) and chemical class (CC) is of crucial importance. We evaluated the current status of open access chemical substance databases (DBs) regarding UT and CC information using the “Meta-analysis of the Global Impact of Chemicals” (MAGIC) graph as a benchmark. A decision tree-based selection process was used to choose the most suitable out of 96 databases. To compare the DB content for 100 weighted, randomly selected chemical substances, an extensive quantitative and qualitative analysis was performed. It was found that four DBs yielded more qualitative and quantitative UT and CC results than the current MAGIC graph: The European Bioinformatics Institute DB, ChemSpider, the English Wikipedia page, and the National Center for Biotechnology Information (NCBI). The NCBI, along with its subsidiary DBs PubChem and Medical Subject Headings (MeSH), showed the best performance according to the defined criteria. To analyse large datasets, harmonisation of the available information might be beneficial, as the available DBs mostly aggregate information without harmonising them.
... The case study of Roy et al. (2012) found that the environmental/biodiversity fields are more in demand. Lukyanenko, Parsons, and Wiersma (2011) studied citizen-generated data on the distribution of plants and animals to improve the quantity and quality of data with data management principles. The study pointed out that it is necessary to pay attention to data management to ensure the data quality when ordinary people use the information system more to collect data. ...
There has been considerable growth in citizen science in academic contributions—researches by the paradigms of different disciplines and by the activities of citizens when undertaking data collecting, data processing, and data analyzing for disseminating results. These researches have proved the importance of data management practices—urgent to carry out the data life cycle. This study aims to analyze the scientific data contribution of citizen science under the data life cycle approach. It investigates 1,020 citizen science projects within the DataONE life cycle framework, which includes data management plan, data collection, data quality assurance, data documentation, data discovery, data integration, data preservation, and data analysis. As the major finding, the result of this study shows that the data management plan is developed with the leading of universities, which are the host of the majority of citizen science projects. The processes of data collection, data quality assurance, data documentation, data preservation, and data analysis are well organized with the systematic tool in the Information and Communications Technology (ICT) age; meanwhile the citizen science projects are cumulative. Data discovery has mostly linked with SciStarter (citizen science community site) and Facebook (social media). In data integration, it is found that most of the projects integrate with global observation. Finally, the study provides the process and procedure of citizen science data management in an effort to contribute the scientific data and the design of data life cycle to academic and governmental works.
... Despite community initiatives lying in the target domain of ONSNs, as of yet, these platforms do not offer any specific tool support for their ideation, organization or implementation beyond generic communication capabilities . In a local and community context, crowdsourcing has presented itself as a suitable approach for mobilizing a local group of individuals, for example in case of participative urban design (Mueller et al., 2018), urban planning (Seltzer and Mahmoudi, 2012) or citizen science (Lukyanenko et al., 2011). Defined as the outsourcing of a task previously performed by a designated agent to a large group of individuals via an open call (Howe, 2006), crowdsourcing has been demonstrated to be able to produce innovative ideas and to enable collaborative problem-solving (Hammon and Hippner, 2012). ...
The social connectedness of a community, characterized by aspects such as social support, social trust and civic engagement, plays an important role in determining the well-being of its inhabitants. Neighborhood activism and volunteering through community initiatives can improve this social connected-ness. Online neighborhood social networks (ONSNs) afford users functionality for social interaction, information sharing as well as peer-support and aim to improve community connectedness with platforms such as Nextdoor exhibiting rapid growth in recent years. However, as of yet, ONSNs do not provide specific tool support for implementing community initiatives beyond generic communication capabilities. We propose crowdsourcing as a suitable approach for mobilizing neighbors to ideate, participate in and collaboratively implement community initiatives on ONSNs. Using a design science research approach, we develop design goals and design principles for crowd-sourced community initiatives based on literature and empirical data from two case neighborhoods. We instantiate these design principles into a proof-of-concept artifact in the context of an existing ONSN. Based on our evaluation, we derive implications for establishing crowd-sourced community initiatives on ONSNs. We contribute to research on crowdsourcing and ONSNs with nascent design knowledge which guides researchers and practitioners in designing crowd-based artifacts in the context of local communities.
... Online citizen-science projects, such as volunteer transcription platforms, have several known strengths and weaknesses (Lukyanenko et al. 2011, Newman et al. 2012. A tremendous strength of online projects is the ability to reach an Internet-scale audience. ...
The digitization of biocollections is a critical task with direct implications for the global community who use the data for research and education. Recent innovations to involve citizen scientists in digitization increase awareness of the value of biodiversity specimens; advance science, technology, engineering, and math literacy; and build sustainability for digitization. In support of these activities, we launched the first global citizen-science event focused on the digitization of biodiversity specimens: Worldwide Engagement for Digitizing Biocollections (WeDigBio). During the inaugural 2015 event, 21 sites hosted events where citizen scientists transcribed specimen labels via online platforms (DigiVol, Les Herbonautes, Notes from Nature, the Smithsonian Institution's Transcription Center, and Symbiota). Many citizen scientists also contributed off-site. In total, thousands of citizen scientists around the world completed over 50,000 transcription tasks. Here, we present the process of organizing
... Many opportunities are emerging, so conceptual modelers can expand their scope. To empower conceptual modelers for data analytics, they might become citizen data scientist (e.g., [20]), engage in a management role as a Chief Data Officer (to identify business opportunities and datafication) or as a Big Data Architect (to select platforms, design architectures, and technologies). ...
The era of big data has resulted in the development and applications of technologies and
methods aimed at effectively using massive amounts of data to support decision-making and
knowledge discovery activities. In this paper, the five Vs of big data, volume, velocity, variety,
veracity, and value, are reviewed, as well as new technologies, including NoSQL databases that
have emerged to accommodate the needs of big data initiatives. The role of conceptual modeling
for big data is then analyzed and suggestions made for effective conceptual modeling efforts with
respect to big data.
... Many opportunities are emerging, so conceptual modelers can expand their scope. To empower conceptual modelers for data analytics, they might become citizen data scientist (e.g., [20]), engage in a management role as a Chief Data Officer (to identify business opportunities and datafication) or as a Big Data Architect (to select platforms, design architectures, and technologies). ...
The era of big data has resulted in the development and applications of technologies and methods aimed at effectively using massive amounts of data to support decision-making and knowledge discovery activities. In this paper, the five Vs of big data, volume, velocity, variety, veracity, and value, are reviewed, as well as new technologies, including NoSQL databases that have emerged to accommodate the needs of big data initiatives. The role of conceptual modeling for big data is then analyzed and suggestions made for effective conceptual modeling efforts with respect to big data.
... The error rates that are observed across these studies raise important questions concerning the accuracy of species identification using field guides. Such identifications are used for supporting a wide range of actions, such as the monitoring of endangered species [33][34][35] and the drafting of appropriate management plans [36][37][38] . An understanding of error rates needs to be factored into such important conservation activities. ...
Accurate species identification is fundamental when recording ecological data. However, the ability to correctly identify organisms visually is rarely questioned. We investigated how experts and non-experts compared in the identification of bumblebees, a group of insects of considerable conservation concern. Experts and non-experts were asked whether two concurrent bumblebee images depicted the same or two different species. Overall accuracy was below 60% and comparable for experts and non-experts. However, experts were more consistent in their answers when the same images were repeated, and more cautious in committing to a definitive answer. Our findings demonstrate the difficulty of correctly identifying bumblebees using images from field guides. Such error rates need to be accounted for when interpreting species data, whether or not they have been collected by experts. We suggest that investigation of how experts and non-experts make observations should be incorporated into study design, and could be used to improve training in species identification.
... The institutions and the laws or the policies and measures constitute constraints (the legitimized content the connects beings and entities is the very content that constricts them) that affect the way these entities relate to one another. (Lukyanenko, R., et al., 2011). It is evident, after the research conducted that the aforementioned do not constitute independent research parameters, instead they are influenced by one another and perpetually interdependent. ...
This book negotiates the complex relationship between the identity of modern multicultural city and its development as well as its social cohesion, on the horizon of globalization and crisis. Specifically, it expounds on an interdisciplinary approach regarding the actual phenomenon of multiculturalism in contemporary cities. The relationships created within this context between the different cultural groups and their impact on the city itself are described. It relates the concepts of identity and diversity and their effects on the socioeconomic development of the city. In addition, it also relates to social cohesion existing in urban areas, plus the dynamics of relationships developed within a complex context. All within the context of political science, sociology and management science, while combining the scientific theory of cultural landscape, cultural theory, systemic analysis and sustainable development in an original theory is created, the "intercultural identity of the city". What is more, while intercultural identity is the keystone of this hypothesis, it proposes the creation of a specific model planning of sustainable development and social cohesion in the modern and post-modern cities of the future. This is ongoing research which aims to deepen the understanding and interconnection between networks and cities' identities, but also strives to develop the methodology acquired from the feedback and information that will emerge from the implementation of this model in real life. In brief, this book, introduces the term human intercultural city for the first time. It expounds on an original scientific theory, coining the term "intercultural city identity" and "Human Intercultural Cities (H.I.C.)". This is what makes it a pioneer monograph as well as a necessary notion for the survival of modern cities so much for now as for the future. The negative impact of globalization and the subsequent economic crisis in metropolitan centers will be inevitable. Furthermore, this monograph may be used as a handy tool for academics, researchers, educators, policymakers, marketers and students as well as for the local communities of various cities in order to plan effectively and implement techniques that will lead humanity to a real exit from the crisis, hence helping to build a humanistic future for their cities.
... The developers then map those observations, arranged with other more authoritative data. In the same class of applications, the electronic atlas of the flora of British Columbia, E-Flora BC, is compiled from a variety of (authoritative) databases with relevant flora information, and supplemented with mapped photo records from citizen scientists (Klinkenberg 2014); NLNature, or Newfoundland and Labrador Nature, encourages participants to observe wildlife and then post details and pictures of the plants, animals, and other interesting features (e.g., rocks, landmarks) they sighted to the online atlas (Lukyanenko et al. 2011); Weather Underground integrates measurements from over 34,000 personal weather stations to provide local weather forecasts; with its "Did You Feel It?" application, the United States Geological Survey (USGS) taps into information from people experiencing earthquakes. Organized by ZIP codes, the information collected includes a description of people's observations during the earthquake, the extent of the damage, and a questionnaire that aims to crowd-source the relative intensity of the event; and finally, Waze has been called a "social traffic app" that automatically collects travel times from users' smartphones and encourages users to manually submit information on other road conditions. ...
“Volunteered geographic information” (VGI) is the term most widely used to describe a variety of user contributions on the participatory Geoweb. These contributions range from coordinate locations and geometries to categorical observations, attribute tags, numeric measurements, and content ratings, as well as complex narratives, photos, and videos. Although researchers are creating and studying Geoweb applications, different types of VGI, and the related phenomena of neogeography, citizen science, and crowd-sourcing, systematic characterizations of user-contributed local knowledge are scarce. In this paper, we propose criteria to distinguish types of user-generated data and contents, and relate these to types of Geoweb applications. The proposed classification provides a conceptual framework to examine the participatory Geoweb, facilitate the processing of user contributions, and identify possible gaps in the data/content types currently used. This approach could help improve the effectiveness of current Geoweb applications, and increase the uptake of the valuable geographic information they generate.
... Throughout the paper, we use a specific example of an OIE – namely, citizen science – to illustrate concepts. In particular, we consider a data-oriented citizen science project in which contributors provide sightings of natural history phenomena (Lukyanenko et al., 2011). CIEs avoid data diversity issues and provide controls to assure data quality. ...
Traditionally, information systems were developed within organizations for use by known audiences for known purposes. Advances in information technology have changed this landscape dramatically. The reach of information systems frequently extends beyond organizational boundaries for use by unknown audiences and for purposes not originally anticipated. Individuals and informal communities can generate and use information in ways previously restricted to formal organizations. We term applications with these characteristics open information environments (OIEs). OIEs are marked by diversity of information available, flexibility in accommodating new sources, users and uses, and information management with minimal controls on structure, content, and access. This creates opportunities to generate new information and use it in unexpected ways. However, OIEs also come with challenges in managing the semantic diversity, flexibility of use, and information quality issues arising from the range of users and lack of controls. In this paper, we propose a set of principles for managing OIEs effectively. We outline a research program to examine the potential of OIEs, the challenges they present, and how to design OIEs to realize the benefits while mitigating the challenges. We highlight our ongoing research in this area, and conclude with a call for more research on this important phenomenon.
... As we discussed earlier, the assumption that users can be identified, reached and engaged in consensus building is becoming inadequate in a growing number of cases. Aside from the difficulty of identifying and reaching all potential users in distributed and dynamic settings, many potential users may lack domain expertise (e.g., consumer products knowledge) and have unique views or conceptualizations that are unstable and incongruent with those of project sponsors and other users (Erickson et al. 2012; Lukyanenko et al. 2011). However, since a consensus is no longer feasible, the resulting system may be critically defective. ...
Traditionally, the research and practice of conceptual modeling assumed relevant information about a domain is determined in advance to be used as input to design. The increasing ubiquity of systems – characterized by heterogeneous and transient users, customizable features, and open or extensible data standards – challenges a number of long-held propositions about conceptual modeling. We raise the question whether conceptual modeling as commonly understood is an impediment to systems development and should be phased out. We discuss the motivation for rethinking approaches to conceptual modeling, consider traditional approaches to conceptual modeling and provide empirical evidence of the limitations of traditional conceptual modeling. We then propose three directions for future conceptual modeling research.
... Concerns about quality pervade discussions of user-generated content and its applications (Hochachka et al., 2012;Flanagin and Metzger, 2008;Mackechnie et al., 2011;Rowland, 2012;Wiggins and Crowston, 2011). Casual users often lack sufficient expertise, have little stake in the success of open information systems, may resist training, and cannot be held accountable for the low quality of contributed data (Coleman et al., 2009;Lukyanenko et al., 2011). Perceived or actual low quality of user-generated content 3 Following Wang (1998) and Redman (1996), we use the terms information and data interchangeably. ...
Online user-generated content has the potential to become a valuable social and economic resource. In many domains – including business, science, health and politics/governance – content produced by ordinary people is seen as a way to expand the scope of information
... Parsons et al. (2011) suggest relaxing the requirement to classify observed instances and, instead, permitting recording contributions in terms of instances and attributes. 3 Instance-based data collection does not require contributors to classify instances and permits storing any attribute associated with the observed instance, thus removing constraints on information capture ( Lukyanenko et al. 2011). Contributors can supply attributes and classes based on their domain knowledge. ...
User-generated content (UGC) is becoming a valuable organizational resource, as it is seen in many cases as a way to make more information available for analysis. To make effective use of UGC, it is necessary to understand information quality (IQ) in this setting. Traditional IQ research focuses on corporate data and views users as data consumers. However, as users with varying levels of expertise contribute information in an open setting, current conceptualizations of IQ break down. In particular, the practice of modeling information requirements in terms of fixed classes, such as an Entity-Relationship diagram or relational database tables, unnecessarily restricts the IQ of user-generated data sets. This paper defines crowd information quality (crowd IQ), empirically examines implications of class-based modeling approaches for crowd IQ, and offers a path for improving crowd IQ using instance-and-attribute based modeling. To evaluate the impact of modeling decisions on IQ, we conducted three experiments. Results demonstrate that information accuracy depends on the classes used to model domains, with participants providing more accurate information when classifying phenomena at a more general level. In addition, we found greater overall accuracy when participants could provide freeform data compared to a condition in which they selected from constrained choices. We further demonstrate that, relative to attribute-based data collection, information loss occurs when class-based models are used. Our findings have significant implications for information quality, information modeling, and UGC research and practice.
Official data are not sufficient for monitoring the United Nations Sustainable Development Goals (SDGs): they do not reach remote locations or marginalized populations and can be manipulated by governments. Citizen science data (CSD), defined as data that citizens voluntarily gather by employing a wide range of technologies and methodologies, could help to tackle these problems and ultimately improve SDG monitoring. However, the link between CSD and the SDGs is still understudied. This article aims to develop an empirical understanding of the CSD-SDG link by focusing on the perspective of projects which employ CSD. Specifically, the article presents primary and secondary qualitative data collected on 30 of these projects and an explorative comparative case study analysis. It finds that projects which use CSD recognize that the SDGs can provide a valuable framework and legitimacy, as well as attract funding, visibility, and partnerships. But, at the same time, the article reveals that these projects also encounter several barriers with respect to the SDGs: a widespread lack of knowledge of the goals, combined with frustration and political resistance towards the UN, may deter these projects from contributing their data to the SDG monitoring apparatus.
Supplementary information:
The online version contains supplementary material available at 10.1007/s11625-021-01001-1.
This chapter summarized state-of-the-art data sources and sourcing methods of agro-geoinformatics. The data mainly comes from four sources: satellite, airborne, and in-situ sensors, and human reports. Overall, the satellite datasets have the best spatial and temporal coverages. The airborne and in-situ datasets are mostly project-specific or site-specific. Human reports provide brief descriptions using concise terms and numbers to answer basic questions. The data from various sources are often overlapped spatially, temporally, spectrally, and/or thematically and can be combined to obtain comprehensive understanding of the crop fields. Data sourcing also has three major options: conventional, cloud-based, and crowdsourcing. Conventional sourcing depends on human surveyors, is often labor-intensive, and has very tedious administrative processes. Cloud based approach simplifies the collection and distribution of big amount of collected data. The cutting-edge crowdsourcing approach largely lowers the cost of data gathering and retrieval. The future development is towards Internet-based, mobile friendly, big data, low-cost, robustness, and high-performance data distribution.
Crowdsourcing promises to expand organizational knowledge and “sensor” networks dramatically, making it possible to engage ordinary people in large-scale data collection, often at much lower cost than that of traditional approaches to gathering data. A major challenge in crowdsourcing is ensuring that the data that crowds provide is of sufficient quality to be usable in organizational decision-making and analysis. We refer to this challenge as the Problem of Crowd Information Quality (Crowd IQ). We need to increase quality while giving contributors the flexibility to contribute data based on their individual perceptions. The design science research project produced several artifacts, including a citizen science information system (NLNature), design principles (guidelines) for the development of crowdsourcing projects, and an instance-based crowdsourcing design theory. We also made several methodological contributions related to the process of design science research and behavioral research in information systems. Over the course of the project, we addressed several challenges in designing crowdsourcing systems, formulating design principles, and conducting rigorous design science research. Specifically, we showed that: design choices can have a sizable impact in the real world; it can be unclear how to implement design principles; and design features that are unrelated to design principles can confound efforts to evaluate artifacts. During the project, we also experienced challenges for which no adequate solution was found, reaffirming that design is an iterative process.
This chapter discusses the design and deployment of a citizen science application to inventory iconic medicinal non-timber forest products in the wild, such as black cohosh, ramps, and Bloodroot. The application is called PlantShoe (a pun on ‘Gumshoe’) and is used on mobile devices to collect data in the field about forest medicinal plants and their growing conditions. The users’ data is fed into a database, which they can manage, study, and share. Plantshoe data is a part of a larger regional community and consortium which is collecting information about the ecology and distribution of medicinal forest plants. Such analyses can help forest farmers and wild stewards in their processes of site selection and management of these valuable botanicals. We describe our usability engineering in the development of the PlantShoe application and enumerate key design tradeoffs we encountered. Thus, the design decisions and results of PlantShoe provide rich material for the design of future technology on the trail.
Dominant forms of contemporary big-data based digital citizen science do not question the institutional divide between qualified experts and lay-persons. In our paper, we turn to the historical case of a large-scale amateur project on biogeographical birdwatching in the late nineteenth and early twentieth century to show that networked amateur research (that produces a large set of data) can operate in a more autonomous mode. This mode depends on certain cultural values, the constitution of specific knowledge objects, and the design of self-governed infrastructures. We conclude by arguing that the contemporary quest for autonomous citizen science is part of a broader discourse on the autonomy of scientific research in general. Just as the actors in our historical case positioned themselves against the elitism of gentlemen scientists, avant-garde groups of the twenty first century like biohackers and civic tech enthusiasts position themselves against the system of professional science—while “digital citizen science” remains to oscillate between claims for autonomy and realities of heteronomy, constantly reaffirming the classic lay-expert divide.
The emergence of crowdsourcing as an important mode of information production has attracted increasing research attention. In this article, the authors review crowdsourcing research in the data management field. Most research in this domain can be termed tasked-based, focusing on micro-tasks that exploit scale and redundancy in crowds. The authors' review points to another important type of crowdsourcing – which they term observational – that can expand the scope of extant crowdsourcing data management research. Observational crowdsourcing consists of projects that harness human sensory ability to support long-term data acquisition. The authors consider the challenges in this domain, review approaches to data management for crowdsourcing, and suggest directions for future research that bridges the gaps between the two research streams.
The emergence of crowdsourcing as an important mode of information production has attracted increasing research attention. In this article, the authors review crowdsourcing research in the data management field. Most research in this domain can be termed tasked-based, focusing on micro-tasks that exploit scale and redundancy in crowds. The authors' review points to another important type of crowdsourcing – which they term observational – that can expand the scope of extant crowdsourcing data management research. Observational crowdsourcing consists of projects that harness human sensory ability to support long-term data acquisition. The authors consider the challenges in this domain, review approaches to data management for crowdsourcing, and suggest directions for future research that bridges the gaps between the two research streams.
Interest in citizen science and the number of related projects have increased considerably during the last decade. Citizen science revolves around gathering data and using it. This means, that data storing is a vital part of any citizen science project and can affect the success or failure. Many researches focus on the citizen side, while the data side is often left out. This study aims to fill the gap by trying to find the current data storing practices in the field of citizen science. A systematic literature review was conducted and multiple similarities in data storing and management techniques were identified between different citizen science projects. Results show that most projects used a traditional relational database to store data, a separate web interface to add, use, modify, and access the data, and data validation was left to users by having them vote on existing data. Data models always considered the data provider (citizen) but left out the end user in their design. In the future, the results will be compared to ongoing citizen science project and see if it is possible to improve the efficiency and overall quality of citizen science databases.
The emergence of crowdsourcing as an important mode of information production has attracted increasing research attention. In this article, the authors review crowdsourcing research in the data management field. Most research in this domain can be termed tasked-based, focusing on micro-tasks that exploit scale and redundancy in crowds. The authors' review points to another important type of crowdsourcing-which they term observational-that can expand the scope of extant crowdsourcing data management research. Observational crowdsourcing consists of projects that harness human sensory ability to support long-term data acquisition. The authors consider the challenges in this domain, review approaches to data management for crowdsourcing, and suggest directions for future research that bridges the gaps between the two research streams.
This paper addresses how technology-mediated mass collaboration offers a dramatically innovative alternative for producing IS research. We refer to this emerging genre as the crowdsourced research genre and develop a framework to structure discourse on how it may affect the production of IS research. After systematically traversing the alternative genre’s landscape using the framework, we propose a research agenda of the most substantial and imminent issues for the successful development of the genre, including contributor incentives, scholarly contribution assessment, anonymity, governance, intellectual property ownership, and value propositions. In addressing this research agenda, we reflect on what might be learned from other areas in which crowdsourcing has been established with success.
The emergence of Web 2.0, open source software tools, and geosocial networks, along with associated mobile devices and available government data, is widely considered to have altered the nature and processes of place-based digital participation. Considerable theorizing has been dedicated to the geographic version of Web 2.0, the geospatial Web (Geoweb). To assess the theories, we draw on four years of empirical work across Canada that considers the nature of public participation on the Geoweb. We are driven by the question of how easy or difficult it is to ?do? Geoweb-enabled participation, particularly participation as envisioned by researchers such as Arnstein and planning practitioners. We consider how the Geoweb could transform methods by which citizens and nonprofit organizations communicate with the state on environmental issues that affect their lives. We conduct a meta-analysis of twelve research cases and derive new findings that reach across the cases on how the Geoweb obliges us to redefine and unitize participation. This redefinition reifies existing digital inequalities, blurs distinctions between experts and nonexperts, heterogenizes the state as an actor in the participation process, reassigns participation activities in a participation hierarchy, and distances participation from channels of influence.
With the proliferation of crowdsourcing, improving the quality of user-generated data has become a major managerial concern. While traditional data quality research focused on structured data and corporate use, data created via the Internet is often discretionary and heterogeneous. Modern users are also information creators, which sometimes involves performing rudimentary data modeling activities. As the result, it is becoming increasingly difficult, and sometimes impossible, to anticipate all kinds of information that users might want to record in the information system, and the way they wish to record it. Although mediated by interfaces, programming logic and data structures, modern users emerge as data quality shapers.
The potential of the Geoweb to harness public spatial knowledge is increasingly recognized. Using these technologies, the public can volunteer their locational experiences. In the case of the 2003 Okanagan Mountain Park Fire, these memories need to be captured or they will soon be forgotten. Collaborating with the Kelowna Fire Museum, this paper describes the creation of an online participatory map that documents public experiences of the fire. Through a map-interface, participants contribute their own multimedia information and comment on the contributions of others. This community-based research examines an individual’s willingness to volunteer their knowledge. Results examine participant engagement in terms of passive or active map use, perspectives of participants-as-experts, and broader themes of how the Geoweb can educate and preserve experiences about this event. Results demonstrate that while the mapping tool encourages users to interact with information about the fire, there are challenges in adding their own experiences.
Recently, there has been intense interest in crowdsourcing - wherein an organization calls upon the
general public to carry out specific tasks in support of organizational objectives (see Doan et al., 2011).
Applications of crowdsourcing are growing and include corporate product development, marketing
research, public policy, and scientific research. Specially-built crowdsourcing platforms, such as Amazon
Mechanical Turk or CrowdFlower, provide pools of 'crowdworkers' for hire.
Among other uses1, crowdsourcing promises to dramatically expand organizational “sensor” networks,
making it possible to collect large amounts of data from diverse audiences. As organizations increasingly
employ crowdsourcing to collect information to support internal decision making, questions about the
quality of information created by members of the crowd become critical (Sheppard et al., 2014,
forthcoming; Antelio et al., 2012; Kremen et al., 2011; Arazy et al., 2011; Alabri and Hunter, 2010). Several
approaches to information quality in crowdsourcing have been proposed (Lukyanenko et al., 2011;
Wiggins et al., 2011). These include collaborative or peer review, leveraging redundancy in the crowds,
and user training. Collaboration and peer review, for example, is the basis for iSpot (www.ispot.org.uk), a
project that relies on social networking for collaborative identification of species of plants and animals
(Silvertown, 2010). Crowd data can also be reviewed by experts (Sheppard et al., 2014, forthcoming;
Hochachka et al., 2012). Whenever possible, organizations leverage redundancy in the crowds (e.g., by
asking multiple observers to independently report on the same phenomena) (Franklin et al., 2011; Liu et
al., 2012). Training is a common approach, especially when there are established standards to which
contributions should adhere (Dickinson et al., 2010; Foster-Smith and Evans, 2003).
This paper investigates the impact of conceptual modeling on the information completeness dimension of information quality in the context of user-generated content. We propose a theoretical relationship between conceptual modeling approaches and information completeness and hypothesize that traditional class-based conceptual modeling negatively affects information completeness. We conducted a field experiment in the context of citizen science in biology. The empirical evidence demonstrates that users assigned to an instantiation that is based on class-based conceptual modeling provide fewer observations than users assigned to an instance-based condition. Users in the instance-based condition also provided a greater number of new classes of organisms. The findings support the proposed hypotheses and establish that conceptual modeling is an important factor in evaluating and increasing information completeness in user-generated content.
Classifying phenomena is a central aspect of cognition. Similarly, specifying classes of interest is a central aspect of information systems analysis and design. We extend principles originally developed to guide classification in information systems to the general problem of organizing scientific knowledge. Two fundamental cognitive principles underlie the choice of classes. First, classes should encapsulate inferences about the properties of their instances. Second, collections of classes should provide economy of storage and processing. This leads to a view of classes as carriers of domain knowledge in the form of inferences about situations, rather than containers for information. In this paper, we show how this view, originally developed in the IT context, can be extended to other disciplines, notably the natural sciences. We explain how the principles of inference and economy can guide the choice of individual classes and collections of classes. Moreover, we present a generalized classification-based information processing system (CIPS) model. We propose that scientific theories can be represented by class structures as defined in our model and demonstrate how this can be done by applying CIPS to analyze an example from the philosophy of science literature dealing with nuclear physics. The example demonstrates two advantages of the CIPS approach: first, it can provide a simpler, more scalable, and more informative account of the phenomena than a competing approach (dynamic frames); second, the resolution of inconsistencies between theory and observation can be framed in terms of changes to classification structures, and the principles can even guide such changes.
The "real world" nature of field-based citizen science involves unique data management challenges that distinguish it from projects that involve only Internet-mediated activities. In particular, many data contribution and review practices are often accomplished "offline' via paper or general-purpose software like Excel. This can lead to integration challenges when attempting to implement project-specific ICT with full revision and provenance tracking. In this work, we explore some of the current challenges and opportunities in implementing ICT for managing volunteer monitoring data. Our two main contributions are: a general outline of the workflow tasks common to field-based data collection, and a novel data model for preserving provenance metadata that allows for ongoing data exchange between disparate technical systems and participant skill levels. We conclude with applications for other domains, such as hydrologic forecasting and crisis informatics, as well as directions for future research.
In most of the world's coastal fisheries, bycatch of marine birds is rarely monitored, and thus the impact on populations is poorly known. We used marine bird strandings to assess the impact of entanglement in Pacific Northwest coastal net salmon fisheries. We compared the magnitude and species composition of fisheries-associated strandings (FAS) to baseline data collected at beaches monitored by citizen-science programs in Washington State and British Columbia, and to seabirds salvaged from gillnets during observer programs. Carcass encounter rates were 16.4 carcasses/km [95% confidence interval (CI): 11.2 to 21.7] for FAS and 1.00 carcasses/km (95% CI: 0.87 to 1.14) for baseline data. Declines in fisheries effort were associated with decreasing FAS, although declines in at-sea seabird abundance may also be at play. Common Murres Uria aalge comprised most of the carcasses in both the FAS (86%) and bycatch studies (71%). Although the total count of murre FAS represented a small fraction (1.3%-6.6%) of baseline mortality accumulated for the Salish Sea over the same period, murre FAS added 0.2%-2.9% to annual mortality rates. Considering the effects of other natural and anthropogenic mortality agents on murres in the region, this species might benefit from further protection. Given the complexity of salmon fisheries management and the ubiquitous distribution of seabirds in the Salish Sea, we recommend the comprehensive adoption of gillnet gear modification to reduce seabird bycatch, a solution that may prove to be beneficial for the vitality of seabird populations and of the fishing industry.
New technologies are rapidly changing the way we collect, archive, analyze, and share scientific data. For example, over the next several years it is estimated that more than one billion autonomous sensors will be deployed over large spatial and temporal scales, and will gather vast quantities of data. Networks of human observers play a major role in gathering scientific data, and whether in astronomy, meteorology, or observations of nature, they continue to contribute significantly. In this paper we present an innovative use of the Internet and information technologies that better enhances the opportunity for citizens to contribute their observations to science and the conservation of bird populations. eBird is building a web-enabled community of bird watchers who collect, manage, and store their observations in a globally accessible unified database. Through its development as a tool that addresses the needs of the birding community, eBird sustains and grows participation. Birders, scientists, and conservationists are using eBird data worldwide to better understand avian biological patterns and the environmental and anthropogenic factors that influence them. Developing and shaping this network over time, eBird has created a near real-time avian data resource producing millions of observations per year.
Categorizations which humans make of the concrete world are not arbitrary but highly determined. In taxonomies of concrete objects, there is one level of abstraction at which the most basic category cuts are made. Basic categories are those which carry the most information, possess the highest category cue validity, and are, thus, the most differentiated from one another. The four experiments of Part I define basic objects by demonstrating that in taxonomies of common concrete nouns in English based on class inclusion, basic objects are the most inclusive categories whose members: (a) possess significant numbers of attributes in common, (b) have motor programs which are similar to one another, (c) have similar shapes, and (d) can be identified from averaged shapes of members of the class. The eight experiments of Part II explore implications of the structure of categories. Basic objects are shown to be the most inclusive categories for which a concrete image of the category as a whole can be formed, to be the first categorizations made during perception of the environment, to be the earliest categories sorted and earliest named by children, and to be the categories most codable, most coded, and most necessary in language.
Citizen science, the involvement of volunteers in research, has increased the scale of ecological field studies with continent-wide, centralized monitoring efforts and, more rarely, tapping of volunteers to conduct large, coordinated, field experiments. The unique benefit for the field of ecology lies in understanding processes occurring at broad geographic scales and on private lands, which are impossible to sample extensively with traditional field research models. Citizen science produces large, longitudinal data sets, whose potential for error and bias is poorly understood. Because it does not usually aim to uncover mechanisms underlying ecological patterns, citizen science is best viewed as complementary to more localized, hypothesis-driven research. In the process of addressing the impacts of current, global “experiments” altering habitat and climate, large-scale citizen science has led to new, quantitative approaches to emerging questions about the distribution and abundance of organisms across space and time.
Advances in positioning, Web mapping, cellular communications and wiki technologies have outpaced the original visions of GSDI programs around the world. By tapping the distributed knowledge, personal time and energy of volunteer contributors, GI voluntarism is beginning to relocate and redistribute selected GI productive activities from mapping agencies to networks of non-state volunteer actors. Participants in the production process are both users and producers, or 'produsers' to use a recent neologism. Indeed, GI voluntarism ultimately has the potential to redistribute the rights to define and judge the value of the produced geographic information and of the new production system in general. The concept and its implementation presents a rich collection of both opportunities and risks now being considered by leaders of public and private mapping organizations world-wide. In this paper, the authors describe and classify both the types of people who volunteer geospatial information and the nature of their contributions. Combining empirical research dealing with the Open Source software and Wikipedia communities with input from selected national mapping agencies and private companies, the authors propose a taxonomy of voluntary geospatial information contributors. Differentiating between three different contexts in which these volunteer contributors operate – market-driven, social networking, and civic/governmental – the authors describe key opportunities, constraints and factors to consider in each case when determining whether and how to assess information provided by such sources.
Information intensive industries, such as health care, rely extensively on the ability to store, process, analyze, and use data. Although other information intensive industries have adopted information technology aggressively and reaped the benefits that result from usage, the health care industry has been notoriously slow to implement information systems, with some researchers suggesting that health care is 10-15 years behind other industries. Recognizing the critical importance of decision quality in the health care sector, together with the need to improve the speed and efficiency of operations, many have called for the transformation of the health care industry through widespread adoption and usage of information technology (IT). In this chapter, we define and discuss health information technology (HIT) and the extensive opportunities for IS research in this field. In particular, we direct our attention to the electronic personal health record (PHR) and investigate the justification for adoption of a class of software that we label a discretionary application. Finally, we report findings from an empirical investigation of PHR usage and show that specific demographic and health conditions drive value for PHRs and ultimately usage intentions.
In this paper we present a trust and reputation model to classify and filter collaboratively contributed geographic information.
We hypothesize that users contribute information in a collaborative system akin to Web 2.0 collaborative applications. We
build on previous work where trust is proposed as a proxy for information quality and propose a spatial trust model to filter
and extract high quality information about urban growth behaviors contributed by users. The motivating scenario involves residents
of recently urbanized areas taking into account their interactions with their surroundings. The main contribution of this
paper is a formal trust and reputation model that takes into account the spatial context of users and their contributions.
The proliferation of information sources as a result of networked computers and other interconnected devices has prompted
significant changes in the amount, availability, and nature of geographic information. Among the more significant changes
is the increasing amount of readily available volunteered geographic information. Although volunteered information has fundamentally enhanced geographic data, it has also prompted
concerns with regard to its quality, reliability, and overall value. This essay situates these concerns as issues of information
and source credibility by (a) examining the information environment fostering collective information contribution, (b) exploring the environment
of information abundance, examining credibility and related notions within this environment, and leveraging extant research
findings to understand user-generated geographic information, (c) articulating strategies to discern the credibility of volunteered
geographic information (VGI), including relevant tools useful in this endeavor, and (d) outlining specific research questions
germane to VGI and credibility.
This article addresses how information systems architecture can be used to support organizations in the 1990s-organizations that face the dual challenge of "speed and flexibility" and "low cost and efficiency." At the heart of this challenge is the basic notion that information systems have been anything but flexible in the past and that, for many firms, information systems are more disablers of flexibility than enablers. The article discusses two architectural solutions to this problem: "the high road and the low road," and the benefits and pitfalls of teach. We conclude that neither solution will succeed on its own and that firms need to combine elements of both to meet the challenges of the 1990s. This article is based on some of the things we have learned through research, case writing, and consulting wile working with a variety of organizations over the past three years. These experience shave illustrated the importance of and the struggle with IS architecture for today's global competitors. The content is intended to help guide, provoke, stimulate, and entertain others who believe that the integration of information technology with organizational strategy and structure os of paramount concern to senior managers.
A separate and distinct interaction with both the actual e-vendor and with its IT Web site interface is at the heart of online shopping. Previous research has established, accordingly, that online purchase intentions are the product of both consumer assessments of the IT itself-specifically its perceived usefulness and ease-of-use (TAM)-and trust in the e-vendor. But these perspectives have been examined independently by IS researchers. Integrating these two perspectives and examining the factors that build online trust in an environment that lacks the typical human interaction that often leads to trust in other circumstances advances our understanding of these constructs and their linkages to behavior. Our research on experienced repeat online shoppers shows that consumer trust is as important to online commerce as the widely accepted TAM use-antecedents, perceived usefulness and perceived ease of use. Together these variable sets explain a considerable proportion of variance in intended behavior. The study also provides evidence that online trust is built through (1) a belief that the vendor has nothing to gain by cheating, (2) a belief that there are safety mechanisms built into the Web site, and (3) by having a typical interface, (4) one that is, moreover, easy to use.
This study examines the role of information quality in the success of initial phase interorganizational data exchanges. We propose perceived information quality as a factor of perceived risk and trusting beliefs, which will directly affect intention to use the exchange. The study also proposes that two important system design factors, control transparency and outcome feedback, will incrementally influence perceived information quality. An empirical test of the model demonstrated that perceived information quality predicts trusting beliefs and perceived risk, which mediate the effects of perceived information quality on intention to use the exchange. Thus, perceived information quality constitutes an important indirect factor influencing exchange adoption. Furthermore, control transparency had a significant influence on perceived information quality, while outcome feedback had no significant incremental effect over that of control transparency. The study contributes to the literature by demonstrating the important role of perceived information quality in interorganizational systems adoption and by showing that information cues available to a user during an initial exchange session can help build trusting beliefs and mitigate perceived exchange risk. For managers of interorganizational exchanges, the study implies that building into the system appropriate control transparency mechanisms can increase the likelihood of exchange success.
Non-scientists are now participating in research in ways that were previously impossible, thanks to more web-based projects to
collect and analyse data. Here we suggest a way to encourage broader participation while increasing the quality of data.
Participation may be passive, as when someone donates their computer's 'downtime' to projects such as SETI@home, or active,
as when someone uses eBird to log birds they have spotted. Unfortunately, the prevailing data-collection and storage practices
for active projects inhibit participation by non-experts.
The Internet, Web 2.0 and Social Networking technologies are enabling citizens to actively participate in citizen science projects by contributing data to scientific programs. However, the limited expertise of contributors can lead to poor quality or misleading data being submitted. Subsequently, the scientific community often perceive citizen science data as not worthy of being used in serious scientific research. In this paper, we describe a technological framework that combines data quality improvements and trust metrics to enhance the reliability of citizen science data. We describe how trust models provide a simple and effective mechanism for measuring the reliability of community-generated data. We also describe filtering services that remove untrustworthy data, and enable confident re-use of the data. The resulting services are evaluated in the context of the Coral Watch project which uses volunteers to collect data on coral reef bleaching.
Fundamental misunderstandings about classification can lead scientists down unproductive or dangerous paths, argue Jeffrey Parsons and Yair Wand.
Many studies have shown that subjects are faster at categorizing objects into "basic" concepts than into more general superordinate concepts. However, all of these studies have used a categorization task in which single, isolated objects are identified. There is good reason to believe that superordinate concepts are typically used to refer to collections of objects rather than to individual objects. For example, people more often use the term furniture to refer to a number of pieces of furniture rather than to name a single piece. This suggest that superordinate concepts include information about multiple objects and their common relations, particularly the typical scenes in which such objects appear. Four experiments examined this possibility by investigating whether the basic concept advantage will decrease or reverse itself when subjects are asked to categorize an object as part of a scene. The results showed that the basic-superordinate difference did decrease when subjects categorized objects in scenes. Furthermore, when an object was placed in an inappropriate scene, there was more interference for superordinate identifications. The results suggest qualitative differences in the representations of superordinate and basic concepts.
Database design commonly assumes, explicitly or implicitly, that instances must belong to classes. This can be termed the assumption of inherent classification. We argue that the extent and complexity of problems in schema integration, schema evolution, and interoperability are, to a large extent, consequences of inherent classification. Furthermore, we make the case that the assumption of inherent classification violates philosophical and cognitive guidelines on classification and is, therefore, inappropriate in view of the role of data modeling in representing knowledge about application domains. As an alternative, we propose a layered appro...
In this Introduction' we shall sketch the business of ontology, or metaphysics, and shall locate it on the map of learning. This has to be done because there are many ways of construing the word 'ontology' and because of the bad reputation metaphysics has suffered until recently - a well deserved one in most cases. 1. ONTOLOGICAL PROBLEMS Ontological (or metaphysical) views are answers to ontological ques tions. And ontological (or metaphysical) questions are questions with an extremely wide scope, such as 'Is the world material or ideal - or perhaps neutral?" 'Is there radical novelty, and if so how does it come about?', 'Is there objective chance or just an appearance of such due to human ignorance?', 'How is the mental related to the physical?', 'Is a community anything but the set of its members?', and 'Are there laws of history?'. Just as religion was born from helplessness, ideology from conflict, and technology from the need to master the environment, so metaphysics - just like theoretical science - was probably begotten by the awe and bewilderment at the boundless variety and apparent chaos of the phenomenal world, i. e. the sum total of human experience. Like the scientist, the metaphysician looked and looks for unity in diversity, for pattern in disorder, for structure in the amorphous heap of phenomena - and in some cases even for some sense, direction or finality in reality as a whole.
The amateur birding community has a long and proud tradition of contributing to bird surveys and bird atlases. Coordinated activities such as Breeding Bird Atlases and the Christmas Bird Count are examples "of citizen" science projects. With the advent of technology, Web 2.0 sites such as eBird have been developed to facilitate online sharing of data and thus increase the potential for real-time monitoring. However, as recently articulated in an editorial in this journal and elsewhere, monitoring is best served when based on a priori hypotheses. Harnessing citizen scientists to collect data following a hypotheticodeductive approach carries challenges. Moreover, the use of citizen science in scientific and monitoring studies has raised issues of data accuracy and quality. These issues are compounded when data collection moves into the Web 2.0 world. An examination of the literature from social geography on the concept of "citizen" sensors and volunteered geographic information (VGI) yields thoughtful reflections on the challenges of data quality/data accuracy when applying information from citizen sensors to research and management questions. VGI has been harnessed in a number of contexts, including for environmental and ecological monitoring activities. Here, I argue that conceptualizing a monitoring project as an experiment following the scientific method can further contribute to the use of VGI. I show how principles of experimental design can be applied to monitoring projects to better control for data quality of VGI. This includes suggestions for how citizen sensors can be harnessed to address issues of experimental controls and how to design monitoring projects to increase randomization and replication of sampled data, hence increasing scientific reliability and statistical power.
Conceptual modelling in information systems development is the creation of an enterprise model for the purpose of designing the information system. It is an important aspect of systems analysis. The value of a conceptual modelling language (CML) lies in its ability to capture the relevant knowledge about a domain. To determine which constructs should be included in a CML it would be beneficial to use some theoretical guidelines. However, this is usually not done. The purpose of this paper is to promote the idea that theories related to human knowledge can be used as foundations for conceptual modelling in systems development. We suggest the use of ontology, concept theory, and speech act theory. These approaches were chosen because: (1) they deal with important and different aspects relevant to conceptual modelling and (2) they have already been used in the context of systems analysis. For each approach we discuss: the rationale for its use, its principles, its application to conceptual modelling, and its limitations. We also demonstrate the concepts of the three approaches by analysing an example. The analysis also serves to show how each approach deals with different aspects of modelling.
In the context of personalization technologies, such as Web-based product-brokering recommendation agents (RAs) in electronic commerce, existing technology acceptance theories need to be expanded to take into account not only the cognitive beliefs leading to adoption behavior, but also the affect elicited by the personalized nature of the technology. This study takes a trust-centered, cognitive and emotional balanced perspective to study RA adoption. Grounded on the theory of reasoned action, the IT adoption literature, and the trust literature, this study theoretically articulates and empirically examines the effects of perceived personalization and familiarity on cognitive trust and emotional trust in an RA, and the impact of cognitive trust and emotional trust on the intention to adopt the RA either as a decision aid or as a delegated agent. An experiment was conducted using two commercial RAs. PLS analysis results provide empirical support for the proposed theoretical perspective. Perceived personalization significantly increases customers' intention to adopt by increasing cognitive trust and emotional trust. Emotional trust plays an important role beyond cognitive trust in determining customers' intention to adopt. Emotional trust fully mediates the impact of cognitive trust on the intention to adopt the RA as a delegated agent, while it only partially mediates the impact of cognitive trust on the intention to adopt the RA as a decision aid. Familiarity increases the intention to adopt through cognitive trust and emotional trust.
The instance-based data model has been proposed as a way of conceptualizing data that alleviates many problems associated with existing class-based data models, such as relational and object-oriented approaches. In this paper, we review the instance-based approach and propose alternative data structures to support its implementation. We analyze the complexity of operations under three of these structures and identify the suitability of different structures under different query and update regimes. We also briefly discuss two implementations of this model using two data structures, and outline the results of an empirical test of the performance of each system under a variety of query and update operations.
The Financial Accounting Standards Board (FASB) describes its public interest function as “…developing standards that result in accounting for similar transactions and circumstances in a like manner and different transactions and circumstances…in a different manner (Facts about FASB).” This statement implies that rule-makers possess an expertise that makes analogizing transactions or circumstances to other transactions or circumstances unproblematic. In this paper we utilize two instances of standard-setting, SFAS 123R and SFAS 143, to demonstrate from FASB's analogic reasoning in these cases that similarity and dissimilarity are not so easily ascertained. A judgment about similarity invariably involves ignoring some perspectives of similarity that would lead to substantially different conclusions about the appropriate accounting. We also illustrate via the two examples the inherent value judgments that underlie the conclusions reached by FASB and how these value judgments raise questions about the ethics of the current standard-setting process.
Developers use class hierarchies to model "real world" objects and as metaphors to organize their programs. We (the authors) believe that developers intuitively understand the variety of everyday objects: this cup, that water, those pencils, or in computer terms, this number, that window, those records. We also believe that developers approve of only one concept of class hierarchy, one out of many possible concepts.Many hidden assumptions lurk behind object-oriented inheritance. Traditional object-oriented concepts of hierarchy are obsessively top-down and ignore many obvious bottom-up relationships. Authors who write about object-oriented programming usually define class hierarchies in traditional or archaic terms, referring to the theory of Realism and ancient Greek philosophy. But, just because somebody sees a similarity between concepts in Smalltalk or C++ and concepts described by Plato and other philosophers, does not mean that the perception is appropriate. While the correspondences between classes and concepts from philosophy are interesting, they are not the only possible or useful correspondences. In Women, Fire, and Dangerous Things on page 6, Lakoff writes, "In fact, until very recently, the classical theory of categories was not even thought of as a theory. It was taught in most disciplines not as an empirical hypothesis but as an unquestionable, definitional truth."We want to change that assumption and expand the possibilities of what class hierarchies can be. Specifically, we introduce a cognitive science perspective of objects and the basic level of a class hierarchy. We believe that developers use software classes in a middle-out manner, just like people use linguistic categories. We believe that this middle-out interpretation explains some of the limits to modeling with traditional object-oriented inheritance. We use the basic level to distinguish between the type relationships within a class hierarchy: above the basic level, developers use runtime type relationships, and below the basic, level developers use compile-time type relationships. We also show that developers do program above the basic level.This paper is organized as follows. In the first section, we discuss class and hierarchy from a cognitive science point of view. In the second section, we define the basic level and describe a middle-out concept of hierarchy. In the third section, we explain the developer's version of basic level through the example of "feeding the animals" and show how it relates to type checking. We also describe some of the techniques that developers use to program above the basic level. And, in the fourth section, we argue that many traditional concepts of inheritance fail above the basic level. We argue that the subset and prototype concepts of hierarchy do not work in general, and we argue that Liskov's Substitution Principle and Meyer's Open-Closed Principle only work below the basic level.
We trained students (grades 3–10) through classroom presentations to survey an Oregon white oak (Quercus garryana) stand in Washington, USA, and compared their data to those obtained from professionals. In May and July 2002, 607 students and 8 professionals surveyed 59 and 22 50-m transects, respectively. We enumerated oaks and ponderosa pines (Pinus ponderosa), measured diameter at breast height, and rated the crown shape of oaks. Oak diameter at breast height measurements and tree counts were consistent between students and professionals (α = 0.05), but subjective crown assessments and live or dead status differed. Students tended to overreport relatively rare pines and larger oaks relative to professionals. This project provided resource managers with data describing oak diameter at breast height and distribution while educating students about the ecology of local wildlife habitat.
In recent months there has been an explosion of interest in using the Web to create, assemble, and disseminate geographic
information provided voluntarily by individuals. Sites such as Wikimapia and OpenStreetMap are empowering citizens to create
a global patchwork of geographic information, while Google Earth and other virtual globes are encouraging volunteers to develop
interesting applications using their own data. I review this phenomenon, and examine associated issues: what drives people
to do this, how accurate are the results, will they threaten individual privacy, and how can they augment more conventional
sources? I compare this new phenomenon to more traditional citizen science and the role of the amateur in geographic observation.
Volunteers are potentially a huge scientific resource but there is scepticism among some scientists about the reliability of data collected by inexperienced people. An assessment was therefore made of the ability of a group of 13 volunteers, recruited by the Earthwatch Institute, to collect valid data in a project that aimed to map the distribution and abundance of common littoral organisms on shores of the Isle of Cumbrae, Scotland. The volunteers ranged considerably in age, educational background, knowledge and experience. They were capable of performing straight-forward tasks, such as learning to identify species, recording their occurrence on specific parts of the shore and making length measurements of samples of some gastropods. They made some recording errors during the fieldwork but similar errors were also made by experienced scientists and it is recommended therefore that all ecological studies should include quality control of data whether or not they involve volunteers. The assessment of abundance was problematic. Volunteers’ assessments for some species were inconsistent and there is evidence that individuals interpreted the scale in different ways. It is suggested that these problems stemmed from: (1) a lack of field experience in the volunteers; (2) inadequate guidelines on the use of the abundance scale; and (3) insufficient training before field surveys commenced. However, projects themselves may benefit in unexpected ways from the input of volunteers. They contributed taxonomic and computing skills to the current project. Members of the group also offered new insights by developing hypotheses relating to populations of gastropods during their fieldwork. These were tested and could have formed the basis of longer-term research programmes. There were also educational benefits for the volunteers who were involved in the project. These included increased knowledge of marine issues and clearer understanding of the ways in which scientific studies are undertaken.
Recently, studies of B2C e-commerce have used intention theory to understand the role of trust of Internet transactions but most have investigated only a component of e-commerce (e.g., initial adoption or continuance) and neglected the role of good relations with the consumer in ensuring a successful sustained relationship. Therefore, a model that went beyond intention and included key relational concepts (satisfaction, value, loyalty, etc.) was developed. Trust and its components are a major part of this model, which was based on strong theoretical foundations. Fifteen hypotheses were formulated. Data on the constructs were collected from 420 respondents and analyzed using elliptical re-weighted least squares as the estimation method to test model validity and the hypotheses. An additional relationship between satisfaction and customer loyalty was investigated. Implications for researchers and practitioners are discussed.
Although images are visual information sources with little or no text associated with them, users still tend to use text to describe images and formulate queries. This is because digital libraries and search engines provide mostly text query options and rely on text annotations for representation and retrieval of the semantic content of images. While the main focus of image research is on indexing and retrieval of individual images, the general topic of image browsing and indexing, and retrieval of groups of images has not been adequately investigated. Comparisons of descriptions of individual images as well as labels of groups of images supplied by users using cognitive models are scarce. This work fills this gap. Using the basic level theory as a framework, a comparison of the descriptions of individual images and labels assigned to groups of images by 180 participants in three studies found a marked difference in their level of abstraction. Results confirm assertions by previous researchers in LIS and other fields that groups of images are labeled using more superordinate level terms while individual image descriptions are mainly at the basic level. Implications for design of image browsing interfaces, taxonomies, thesauri, and similar tools are discussed.
A citizen scientist is a volunteer who collects and/or processes data as part of a scientific enquiry. Projects that involve citizen scientists are burgeoning, particularly in ecology and the environmental sciences, although the roots of citizen science go back to the very beginnings of modern science itself.
What is the sequence of processing steps involved in visual object recognition? We varied the exposure duration of natural images and measured subjects' performance on three different tasks, each designed to tap a different candidate component process of object recognition. For each exposure duration, accuracy was lower and reaction time longer on a within-category identification task (e.g., distinguishing pigeons from other birds) than on a perceptual categorization task (e.g., birds vs. cars). However, strikingly, at each exposure duration, subjects performed just as quickly and accurately on the categorization task as they did on a task requiring only object detection: By the time subjects knew an image contained an object at all, they already knew its category. These findings place powerful constraints on theories of object recognition.
Two experiments compared performance in an object detection task, in which participants categorized photographs as objects and nonobject textures, and an object categorization task, in which photographs were categorized into basic-level categories. The basic-level categorization task was either easy (e.g., dogs vs. buses) or difficult (e.g., dogs vs. cats). Participants performed similarly in the detection and the easy-categorization tasks, but response times to the difficult-categorization task were slower. This latter finding is difficult to reconcile with the conclusions of Grill-Spector and Kanwisher (2005)5.
Grill-Spector , G. and
Kanwisher , N. 2005. Visual recognition: As soon as you know it is there, you know what it is. Psychological Science, 16: 152–160. [CrossRef], [PubMed], [Web of Science ®]View all references who reported equivalent performance on detection and basic-level categorization tasks and took this as evidence that figure–ground segregation and basic-level categorization are mediated by the same mechanism.
An ontological model of an information system that provides precise definitions of fundamental concepts like system, subsystem, and coupling is proposed. This model is used to analyze some static and dynamic properties of an information system and to examine the question of what constitutes a good decomposition of an information system. Some of the major types of information system formalisms that bear on the authors' goals and their respective strengths and weaknesses relative to the model are briefly reviewed. Also articulated are some of the fundamental notions that underlie the model. Those basic notions are then used to examine the nature and some dynamics of system decomposition. The model's predictive power is discussed
Categorizing objects in isolation and in scenes - What a superordinate is good for rPublished under License Creative Commons Attribution Non-commercial Citizen science 2.0: data management principles to harness the power of the crowd
- G L Murphy
- E J Wisniewski
- Parsons
- Wiersma
Murphy, G.L., Wisniewski, E.J.: Categorizing objects in isolation and in scenes - What a superordinate is good for. Journal of Experimental Psychology: Learning 15 572-586 (1989) rPublished under License Creative Commons Attribution Non-commercial. Citation: Lukyanenko R, J Parsons, YF Wiersma. 2011. Citizen science 2.0: data management principles to harness the power of the crowd. Lecture Notes in Computer Science 6629:465-473.
The Reliability of Citizen Science: A Case Study of Oregon White Oak Stand Surveys) rPublished under License Creative Commons Attribution Non-commercial. Citation: Lukyanenko R Citizen science 2.0: data management principles to harness the power of the crowd
- W E G Aaron
- M T Tudor
- W M V Haegen
- Parsons
- Wiersma
Aaron, W.E.G., Tudor, M.T., Haegen, W.M.V.: The Reliability of Citizen Science: A Case Study of Oregon White Oak Stand Surveys. Wildlife Society Bulletin 34 1425-1429 (2006) rPublished under License Creative Commons Attribution Non-commercial. Citation: Lukyanenko R, J Parsons, YF Wiersma. 2011. Citizen science 2.0: data management principles to harness the power of the crowd. Lecture Notes in Computer Science 6629:465-473.