ABSTRACT We present preliminary findings from a three-year research project comprised of longitud... more ABSTRACT We present preliminary findings from a three-year research project comprised of longitudinal qualitative case studies of data practices in four large, distributed, highly multidisciplinary scientific collaborations. This project follows a 2 \(\times \) 2 research design: two of the collaborations are big science while two are little science, two have completed data collection activities while two are ramping up data collection. This paper is centered on one of these collaborations, a project bringing together scientists to study subseafloor microbial life. This collaboration is little science, characterized by small teams, using small amounts of data, to address specific questions. Our case study employs participant observation in a laboratory, interviews ( \(n=49\) to date) with scientists in the collaboration, and document analysis. We present a data workflow that is typical for many of the scientists working in the observed laboratory. In particular, we show that, although this workflow results in datasets apparently similar in form, nevertheless a large degree of heterogeneity exists across scientists in this laboratory in terms of the methods they employ to produce these datasets—even between scientists working on adjacent benches. To date, most studies of data in little science focus on heterogeneity in terms of the types of data produced: this paper adds another dimension of heterogeneity to existing knowledge about data in little science. This additional dimension makes more complex the task of management and curation of data for subsequent reuse. Furthermore, the nature of the factors that contribute to heterogeneity of methods suggest that this dimension of heterogeneity is a persistent and unavoidable feature of little science.
Online citizen science projects involve recruitment of volunteers to assist researchers with the ... more Online citizen science projects involve recruitment of volunteers to assist researchers with the creation, curation, and analysis of large datasets. Enhancing the quality of these data products is a fundamental concern for teams running citizen science projects. Decisions about a project's design and operations have a critical effect both on whether the project recruits and retains enough volunteers, and on the quality of volunteers' work. The processes by which the team running a project learn about their volunteers play a critical role in these decisions. Improving these processes will enhance decision-making, resulting in better quality datasets, and more successful outcomes for citizen science projects. This paper presents a qualitative case study, involving interviews and long-term observation, of how the team running Galaxy Zoo, a major citizen science project in astronomy, came to know their volunteers and how this knowledge shaped their decision-making processes. This paper presents three instances that played significant roles in shaping Galaxy Zoo team members' understandings of volunteers. Team members integrated heterogeneous sources of information to derive new insights into the volunteers. Project metrics and formal studies of volunteers combined with tacit understandings gained through on-and offline interactions with volunteers. This paper presents a number of recommendations for practice. These recommendations include strategies for improving how citizen science project team members learn about volunteers, and how teams can more effectively circulate among themselves what they learn. The International Journal of Digital Curation is an international journal committed to scholarly excellence and dedicated to the advancement of digital curation across a wide range of sectors. The IJDC is published by the University of Edinburgh on behalf of the Digital Curation Centre. ISSN: 1746-8256. URL: http://www.ijdc.net/ Copyright rests with the authors. This work is released under a Creative Commons Attribution Licence, version 2.0. For details please see https://creativecommons.org/licenses/by/4.0/
For decades, the big science and little science dichotomy has served as a starting point for many... more For decades, the big science and little science dichotomy has served as a starting point for many analyses of scientific research and data practices, including studies used to inform the construction of scientific knowledge infrastructures. We challenge this dichotomy by presenting findings from longitudinal, qualitative case studies of data life cycles in two scientific domains, each centered around a large, distributed scientific collaboration. One is astronomy and the Sloan Digital Sky Survey (SDSS). The other is the deep subseafloor biosphere and the Center for Dark Energy Biosphere Investigations (C-DEBI). We show that some critical stages of the data life cycle in each domain unfold in big science contexts while other critical stages occur in little science contexts. Furthermore, these big and little science contexts shape each other dynamically. This challenging of the big and little science dichotomy has implications for the building of scientific knowledge infrastructures, including those supporting data management.
International Journal of Digital Curation, May 29, 2014
Citizen Cyberscience Projects (CCPs) that recruit members of the public as volunteers to process ... more Citizen Cyberscience Projects (CCPs) that recruit members of the public as volunteers to process and produce large datasets promise a great deal of benefits to scientists and science. However, if this promise is to be realised, and citizen science-produced datasets are to be widely-used by scientists, it is essential that these datasets win the trust of the scientific community. This task of securing credibility involves, in part, applying standard scientific procedures to clean-up datasets formed by volunteer contributions. However, the management of volunteers’ behaviour in terms of how they contribute also plays a significant role in improving both the quality of individual contributions and the overall robustness of the resultant datasets. This can assist CCPs in securing a reputation for producing trustworthy datasets.
Through a case study of Galaxy Zoo, a CCP set up to generate datasets based on volunteer classifications of galaxy morphologies, this paper explores how those involved in running the project manage volunteers. In particular, it focuses on how methods for crediting volunteer contributions motivate volunteers to provide higher quality contributions and to behave in a way that better corresponds to statistical assumptions made when combining volunteer contributions into datasets. These have made a significant contribution to the success of the project in securing trust in these datasets, which have been well-used by other scientists.
Implications for practice are then presented for CCPs, providing a list of considerations to guide choices regarding how to credit volunteer contributions to improve the quality and trustworthiness of citizen science-produced datasets.
Proceedings of the Joint Conference on Digital Libraries, 2014
The promise of technology-enabled, data-intensive scholarship is predicated upon access to knowle... more The promise of technology-enabled, data-intensive scholarship is predicated upon access to knowledge infrastructures that are not yet in place. Scientific data management requires expertise in the scientific domain and in organizing and retrieving complex research objects. The Knowledge Infrastructures project compares data management activities of four large, distributed, multidisciplinary scientific endeavors as they ramp their activities up or down; two are big science and two are small science. Research questions address digital library solutions, knowledge infrastructure concerns, issues specific to individual domains, and common problems across domains. Findings are based on interviews (n=113 to date), ethnography, and other analyses of these four cases, studied since 2002. Based on initial comparisons, we conclude that the roles of digital libraries in scientific data management often depend on the scale of data, the scientific goals, and the temporal scale of the research projects being supported. Digital libraries serve immediate data management purposes in some projects and long-term stewardship in others. In small science projects, data management tools are selected, designed, and used by the same individuals. In the multi-decade time scale of some big science research, data management technologies, policies, and practices are designed for anticipated future uses and users. The need for library, archival, and digital library expertise is apparent throughout all four of these cases. Managing research data is a knowledge infrastructure problem beyond the scope of individual researchers or projects. The real challenges lie in designing digital libraries to assist in the capture, management, interpretation, use, reuse, and stewardship of research data.
Proceedings of the 77th American Society for Information Science and Technology (ASIS&T) Annual Meeting 2014, Seattle, WA
What motivates the building of databases by scientific collaborations? In this paper, we argue th... more What motivates the building of databases by scientific collaborations? In this paper, we argue that not only are databases being built to support scientific work per se, but also with the intention of performing a variety of social functions. To explore this, we present findings from a longitudinal ethnographic case study of a large, multidisciplinary, distributed scientific project studying subseafloor microbial life. A critical element of this project’s Data Management Plan is the construction of a data portal. We found a range of factors motivating not only the very construction of this portal, but also the inclusion of particular features. In addition to scientific factors relating to improved curation and accessibility of diverse and scarce data, we argue that the building of the portal is also motivated by social factors. One such factor is the attempt to build a community of domain researchers to endure beyond the end of this project in 2020. Another motivation is the possibility of using the portal as a tool to demonstrate the productivity of the project’s scientific domain in negotiations about the allocation of scarce and valuable ocean drilling cruise resources amongst multiple, competing scientific domains. Considering the social factors, in addition to scientific factors, that motivate the construction of scientific databases enriches accounts of how these databases are built.
Philosophical Transactions of the Royal Society, 2010
Volunteer computing projects (VCPs) have been set up by groups of scientists to recruit members o... more Volunteer computing projects (VCPs) have been set up by groups of scientists to recruit members of the public who are asked to donate spare capacity on their personal computers to the processing of scientific data or computationally intensive models. VCPs serve two purposes: to acquire significant computing capacity and to educate the public about science. A particular challenge for these scientists is the retention of volunteers as there is a very high drop-out rate. This paper develops recommendations for scientists and software engineers setting up or running VCPs regarding which strategies to pursue in order to improve volunteer retention rates. These recommendations are based on a qualitative study of volunteers in a VCP (climateprediction.net). A typology of volunteers has been developed, and three particularly important classes of volunteers are presented in this paper: for each type of volunteer, the particular benefits they offer to a project are described, and their motivations for continued participation in a VCP are identified and linked to particular strategies. In this way, those setting up a VCP can identify which types of volunteers they should be particularly keen to retain, and can then find recommendations to increase the retention rates of their target volunteers.
Volunteer computing projects are computationally-intensive scientific research projects which see... more Volunteer computing projects are computationally-intensive scientific research projects which seek to meet their need for data processing capacity by asking members of the public to download and process project data on their personal computers. This paper presents findings from a study of the interactions in an online forum of participants in such a project, climateprediction.net. It focuses upon the beliefs of one group of participants, who have been identified as particularly critical to the success of volunteer computing projects, about the organizational policies of climateprediction.net and what are appropriate and inappropriate ways for other participants to behave in the forums.
77th American Society for Information Science and Technology (ASIS&T) Annual Meeting 2014
What motivates the building of databases by scientific collaborations? In this paper, we argue th... more What motivates the building of databases by scientific collaborations? In this paper, we argue that not only are databases being built to support scientific work per se, but also with the intention of performing a variety of social functions. To explore this, we present findings from a longitudinal ethnographic case study of a large, multidisciplinary, distributed scientific project studying subseafloor microbial life. A critical element of this project’s Data Management Plan is the construction of a data portal. We found a range of factors motivating not only the very construction of this portal, but also the inclusion of particular features. In addition to scientific factors relating to improved curation and accessibility of diverse and scarce data, we argue that the building of the portal is also motivated by social factors. One such factor is the attempt to build a community of domain researchers to endure beyond the end of this project in 2020. Another motivation is the possibility of using the portal as a tool to demonstrate the productivity of the project’s scientific domain in negotiations about the allocation of scarce and valuable ocean drilling cruise resources amongst multiple, competing scientific domains. Considering the social factors, in addition to scientific factors, that motivate the construction of scientific databases enriches accounts of how these databases are built.
British Sociological Association Annual Conference 2014
"Allied to the movement promoting Open Access publishing is the Open Data movement, which aims to... more "Allied to the movement promoting Open Access publishing is the Open Data movement, which aims to facilitate and
encourage the open sharing of research data amongst scientists across multiple disciplines and institutions. Studies of
scientists’ data practices link barriers to data-sharing with lack of appropriate infrastructure, cultural issues regarding
norms and reward structures, and lack of trust amongst researchers. However, there have been fewer studies of
actual instances of successful data-sharing. Furthermore, little attention has been paid to the implications of
successful data-sharing for the structures of collaborative scientific work.
This paper will present findings from a longitudinal ethnographic case study of a large, distributed, multidisciplinary
collaborative project studying subseafloor microbial life. This project aims to build a community involving researchers
from disparate backgrounds, and to develop infrastructure for the exchange of knowledge, methods, and data. This
case study is therefore an ideal opportunity for studying the co-emergence of scientific collaboration, infrastructure for
data-sharing, and data-sharing practices.
By carefully deconstructing a single observed instance of data-sharing, this paper shows that the sharing of data
between researchers in different institutions or disciplines is a rare and fragile accomplishment that involves the
interplay of multiple factors. These include high levels of trust between researchers, alignment of researchers’
interests, opportunism in exploiting possibilities afforded by infrastructures, and all this underpinned by serendipity.
Conversely, when data-sharing does occur, it promotes new scientific work across disciplinary and institutional
boundaries, reconfiguring the structure of the collaboration."
This paper presents a longitudinal ethnographic study of a large, multidisciplinary scientific pr... more This paper presents a longitudinal ethnographic study of a large, multidisciplinary scientific project studying subseafloor microbial life.
To study this life, scientists need rock and water samples taken from the seafloor. To collect these samples, scientists participate in ocean drilling cruises, such as those carried out as part of the International Ocean Discovery Program (IODP). The IODP and its forerunners have been operating since the 1960s. Initially, these cruises only involved physical scientists (such as geochemists). In recent years, microbiologists have become part of these cruises. This has involved reallocating space on cruise ships to the construction of a microbiology laboratory.
To better persuade physical scientists to concede space on these ships, the study of subseafloor microbe has been directed towards areas of interest to physical scientists, such as the effect of microbes on the physical environment. This has promoted the recruitment of physical scientists by microbiological laboratories. These laboratories are now increasingly multidisciplinary, resulting in a greater diversity of data products and recordkeeping practices.
Thus, it can be seen that spatial configurations in one location (the ships) co-shape the material artefacts produced in separate geographically-distinct location. This co-shaping is mediated by a variety of both human and non-human actors.
Citizen Cyberscience Projects (CCPs) that recruit members of the public as volunteers to process ... more Citizen Cyberscience Projects (CCPs) that recruit members of the public as volunteers to process and produce large datasets promise a great deal of benefits to scientists and science. However, if this promise is to be realised, and citizen science-produced datasets are to be widely-used by scientists, it is essential that these datasets win the trust of the scientific community. This task of securing credibility involves, in part, applying standard scientific procedures to clean-up datasets formed by volunteer contributions. However, the management of volunteers’ behaviour in terms of how they contribute also plays a significant role in improving both the quality of individual contributions and the overall robustness of the resultant datasets. This can assist CCPs in securing a reputation for producing trustworthy datasets.
Through a case study of Galaxy Zoo, a CCP set up to generate datasets based on volunteer classifications of galaxy morphologies, this paper explores how those involved in running the project manage volunteers. In particular, it focuses on how methods for crediting volunteer contributions motivate volunteers to provide higher quality contributions and to behave in a way that better corresponds to statistical assumptions made when combining volunteer contributions into datasets. These have made a significant contribution to the success of the project in securing trust in these datasets, which have been well-used by other scientists.
Implications for practice are then presented for CCPs, providing a list of considerations to guide choices regarding how to credit volunteer contributions to improve the quality and trustworthiness of citizen science-produced datasets.
Earth scientists increasingly work in distributed, multidisciplinary projects. To promote the sha... more Earth scientists increasingly work in distributed, multidisciplinary projects. To promote the sharing of data across such a project, it is vital to improve long-term preservation of data in formats accessible to scientists in multiple disciplines with diverse needs, tools and scientific practices. When developing data management plans and infrastructure, it is important to ask: - What data are generated? - Where are these data preserved and shared? - What are the processes by which these data become 'dark'? - What are the infrastructural and social factors that shape these processes? In response to these questions, we present findings from the first year of a case study of the Center for Dark Energy Biosphere Investigations (C-DEBI), an NSF Science and Technology Center studying microbial life in the deep subseafloor biosphere. Our case study is funded by the Sloan Foundation and the NSF. It involves observation in laboratories, interviews, attendance of scientific meetings, and document analysis. At the laboratory level, we observed scientists mainly working on individual projects, or in a team of two or three. There is infrequent sharing of laboratory-generated data across C-DEBI. Where it does happen, it often takes place following discovery of the data through informal networks or serendipitous encounters with the data's creator. Instead, most of the laboratory-generated data become dark data. These data are typically preserved on a scientist's personal computer in ways particular to the individual, frequently not in a form meaningful to others. Other scientists are often not even aware that these data exist. Furthermore, the scientist tends to take care to preserve these data only as long as they require them: data loss can occur over time. Some data - those which support findings in a paper - may be deposited in a disciplinary database. However, these data are the end result of extensive processing: earlier versions of datasets can be lost. Also, in some disciplines, scientists are not required to deposit these data. Hence the data deposited in these databases are only a fraction of the total laboratory-generated data. Thus, the vast majority of laboratory-generated data is not preserved long-term in a form discoverable or usable to other scientists. We found that the multidisciplinary, distributed nature of C-DEBI can exacerbate the factors that contribute to data staying dark. These include: - It can be hard for scientists to conceptualize how their data may be useful to scientists in other disciplines. This reduces scientists' willingness to perform the work of long-term data preservation; - Fewer opportunities for building of informal networks or serendipitous encounters for data discovery than if scientists were collocated; - Many scientists are part of C-DEBI for a short time only (1-3 years), getting involved through the awarding of C-DEBI grants and fellowships. After their involvement, they may no longer maintain contacts across disciplines within C-DEBI. This makes it harder for them to see how their data might be useful to others, and for others to discover these data through word-of-mouth; - Resistance to standardization of data practices. Multidisciplinary work involves bringing multiple perspectives to bear on research questions. Scientists are concerned that standardization could prematurely foreclose as-yet-unexplored scientific methods.
Collaborative projects, such as e-Science projects producing software and tools to support the wo... more Collaborative projects, such as e-Science projects producing software and tools to support the work of scientists, involve the management and coordination of work of a wide range of personnel, and also involve these personnel working together on joint activities. Effective communication practices are essential for the success of e-Science projects, for a number of reasons and in a number of different senses. For instance, there will be many one-way transfers of information, such as project management informing new personnel of the project’s vision and goals, the communication of work plans (including individuals’ roles, the model of software development employed by the project, timescales and deadlines) from those who have created them to those expected to execute them, and the presentation of work by an individual or team to their colleagues. Such transfers of information may in turn give rise to further communication. This transfer of information can give rise to further communication, including the giving of feedback, for instance whether a software developer feels that the tasks they have been assigned are realistically achievable, and requests for clarification. Communication may also take the form of negotiations or discussions. These could be regarding either day-to-day issues, for instance a team prioritising the tasks they need to complete and dividing work amongst team members, or higher-level issues such as seeking to define the project's overall vision, or constructing an overall plan of work.
The large-scale, multi-disciplinary, multi-institutional, geographically-distributed, and sometimes international nature of e-Science projects, however, frequently impedes the establishment of effective communication practices. One major reason is that their distributed nature necessitates the frequent use of Information Communication Technologies (ICTs, such as e-mail, telephone, and Instant Messaging). It has often been reported that ICTs often do not afford the same richness of communication as face-to-face communication (de Rooij et al. 2007), or can be beset by technological difficulties (Olson & Olson 2000). Because the impact of ICTs upon effective communication practices has thus been widely considered in the existing literature, this paper will instead consider the impact of a variety of other issues which impede communication flows.
Through qualitative case studies of e-Science projects, this paper aims to highlight some issues arising in these e-Science projects that may impede successful communication flows amongst project partners, and to provide recommendations for addressing these issues. These recommendations will often be based upon methods employed in the case studies when they have proved successful. It is hoped that they will prove very useful to Principal Investigators and Project Managers of existing and future e-Science projects in establishing communication practices that contribute to the success of their projects.
5th IEEE International Conference on e-Science, Dec 10, 2009
The acquisition by developers of e-science applications of a thorough understanding of the requir... more The acquisition by developers of e-science applications of a thorough understanding of the requirements of end-users has been recognized as playing a critical role in the usability of such applications. However, there is another dimension to such an understanding that also plays an important role, namely the extent to which these developers converge on a shared understanding of these requirements. This paper considers why such a shared understanding is important, and highlights possible obstacles to this that may arise in the context of e-science projects. A research project, consisting of qualitative case studies of two projects, is being undertaken, with the goal of producing recommendations for improving shared understanding of end-users' requirements amongst developers of e-science applications.
Volunteer computing projects (VCPs) have been set up by groups of scientists to recruit members o... more Volunteer computing projects (VCPs) have been set up by groups of scientists to recruit members of the public who are asked to donate spare capacity on their personal computers to the processing of scientific data or computationally intensive models. VCPs serve two purposes: to acquire significant computing capacity and to educate the public about science. A particular challenge for these scientists is the retention of volunteers as there is a very high drop-out rate. This paper develops recommendations for scientists and software engineers setting up or running VCPs regarding which strategies to pursue in order to improve volunteer retention rates. These recommendations are based on a qualitative study of volunteers in a VCP (climateprediction.net). A typology of volunteers has been developed, and three particularly important classes of volunteers are presented in this paper: for each type of volunteer, the particular benefits they offer to a project are described, and their motivations for continued participation in a VCP are identified and linked to particular strategies. In this way, those setting up a VCP can identify which types of volunteers they should be particularly keen to retain, and can then find recommendations to increase the retention rates of their target volunteers.
ABSTRACT We present preliminary findings from a three-year research project comprised of longitud... more ABSTRACT We present preliminary findings from a three-year research project comprised of longitudinal qualitative case studies of data practices in four large, distributed, highly multidisciplinary scientific collaborations. This project follows a 2 \(\times \) 2 research design: two of the collaborations are big science while two are little science, two have completed data collection activities while two are ramping up data collection. This paper is centered on one of these collaborations, a project bringing together scientists to study subseafloor microbial life. This collaboration is little science, characterized by small teams, using small amounts of data, to address specific questions. Our case study employs participant observation in a laboratory, interviews ( \(n=49\) to date) with scientists in the collaboration, and document analysis. We present a data workflow that is typical for many of the scientists working in the observed laboratory. In particular, we show that, although this workflow results in datasets apparently similar in form, nevertheless a large degree of heterogeneity exists across scientists in this laboratory in terms of the methods they employ to produce these datasets—even between scientists working on adjacent benches. To date, most studies of data in little science focus on heterogeneity in terms of the types of data produced: this paper adds another dimension of heterogeneity to existing knowledge about data in little science. This additional dimension makes more complex the task of management and curation of data for subsequent reuse. Furthermore, the nature of the factors that contribute to heterogeneity of methods suggest that this dimension of heterogeneity is a persistent and unavoidable feature of little science.
Online citizen science projects involve recruitment of volunteers to assist researchers with the ... more Online citizen science projects involve recruitment of volunteers to assist researchers with the creation, curation, and analysis of large datasets. Enhancing the quality of these data products is a fundamental concern for teams running citizen science projects. Decisions about a project's design and operations have a critical effect both on whether the project recruits and retains enough volunteers, and on the quality of volunteers' work. The processes by which the team running a project learn about their volunteers play a critical role in these decisions. Improving these processes will enhance decision-making, resulting in better quality datasets, and more successful outcomes for citizen science projects. This paper presents a qualitative case study, involving interviews and long-term observation, of how the team running Galaxy Zoo, a major citizen science project in astronomy, came to know their volunteers and how this knowledge shaped their decision-making processes. This paper presents three instances that played significant roles in shaping Galaxy Zoo team members' understandings of volunteers. Team members integrated heterogeneous sources of information to derive new insights into the volunteers. Project metrics and formal studies of volunteers combined with tacit understandings gained through on-and offline interactions with volunteers. This paper presents a number of recommendations for practice. These recommendations include strategies for improving how citizen science project team members learn about volunteers, and how teams can more effectively circulate among themselves what they learn. The International Journal of Digital Curation is an international journal committed to scholarly excellence and dedicated to the advancement of digital curation across a wide range of sectors. The IJDC is published by the University of Edinburgh on behalf of the Digital Curation Centre. ISSN: 1746-8256. URL: http://www.ijdc.net/ Copyright rests with the authors. This work is released under a Creative Commons Attribution Licence, version 2.0. For details please see https://creativecommons.org/licenses/by/4.0/
For decades, the big science and little science dichotomy has served as a starting point for many... more For decades, the big science and little science dichotomy has served as a starting point for many analyses of scientific research and data practices, including studies used to inform the construction of scientific knowledge infrastructures. We challenge this dichotomy by presenting findings from longitudinal, qualitative case studies of data life cycles in two scientific domains, each centered around a large, distributed scientific collaboration. One is astronomy and the Sloan Digital Sky Survey (SDSS). The other is the deep subseafloor biosphere and the Center for Dark Energy Biosphere Investigations (C-DEBI). We show that some critical stages of the data life cycle in each domain unfold in big science contexts while other critical stages occur in little science contexts. Furthermore, these big and little science contexts shape each other dynamically. This challenging of the big and little science dichotomy has implications for the building of scientific knowledge infrastructures, including those supporting data management.
International Journal of Digital Curation, May 29, 2014
Citizen Cyberscience Projects (CCPs) that recruit members of the public as volunteers to process ... more Citizen Cyberscience Projects (CCPs) that recruit members of the public as volunteers to process and produce large datasets promise a great deal of benefits to scientists and science. However, if this promise is to be realised, and citizen science-produced datasets are to be widely-used by scientists, it is essential that these datasets win the trust of the scientific community. This task of securing credibility involves, in part, applying standard scientific procedures to clean-up datasets formed by volunteer contributions. However, the management of volunteers’ behaviour in terms of how they contribute also plays a significant role in improving both the quality of individual contributions and the overall robustness of the resultant datasets. This can assist CCPs in securing a reputation for producing trustworthy datasets.
Through a case study of Galaxy Zoo, a CCP set up to generate datasets based on volunteer classifications of galaxy morphologies, this paper explores how those involved in running the project manage volunteers. In particular, it focuses on how methods for crediting volunteer contributions motivate volunteers to provide higher quality contributions and to behave in a way that better corresponds to statistical assumptions made when combining volunteer contributions into datasets. These have made a significant contribution to the success of the project in securing trust in these datasets, which have been well-used by other scientists.
Implications for practice are then presented for CCPs, providing a list of considerations to guide choices regarding how to credit volunteer contributions to improve the quality and trustworthiness of citizen science-produced datasets.
Proceedings of the Joint Conference on Digital Libraries, 2014
The promise of technology-enabled, data-intensive scholarship is predicated upon access to knowle... more The promise of technology-enabled, data-intensive scholarship is predicated upon access to knowledge infrastructures that are not yet in place. Scientific data management requires expertise in the scientific domain and in organizing and retrieving complex research objects. The Knowledge Infrastructures project compares data management activities of four large, distributed, multidisciplinary scientific endeavors as they ramp their activities up or down; two are big science and two are small science. Research questions address digital library solutions, knowledge infrastructure concerns, issues specific to individual domains, and common problems across domains. Findings are based on interviews (n=113 to date), ethnography, and other analyses of these four cases, studied since 2002. Based on initial comparisons, we conclude that the roles of digital libraries in scientific data management often depend on the scale of data, the scientific goals, and the temporal scale of the research projects being supported. Digital libraries serve immediate data management purposes in some projects and long-term stewardship in others. In small science projects, data management tools are selected, designed, and used by the same individuals. In the multi-decade time scale of some big science research, data management technologies, policies, and practices are designed for anticipated future uses and users. The need for library, archival, and digital library expertise is apparent throughout all four of these cases. Managing research data is a knowledge infrastructure problem beyond the scope of individual researchers or projects. The real challenges lie in designing digital libraries to assist in the capture, management, interpretation, use, reuse, and stewardship of research data.
Proceedings of the 77th American Society for Information Science and Technology (ASIS&T) Annual Meeting 2014, Seattle, WA
What motivates the building of databases by scientific collaborations? In this paper, we argue th... more What motivates the building of databases by scientific collaborations? In this paper, we argue that not only are databases being built to support scientific work per se, but also with the intention of performing a variety of social functions. To explore this, we present findings from a longitudinal ethnographic case study of a large, multidisciplinary, distributed scientific project studying subseafloor microbial life. A critical element of this project’s Data Management Plan is the construction of a data portal. We found a range of factors motivating not only the very construction of this portal, but also the inclusion of particular features. In addition to scientific factors relating to improved curation and accessibility of diverse and scarce data, we argue that the building of the portal is also motivated by social factors. One such factor is the attempt to build a community of domain researchers to endure beyond the end of this project in 2020. Another motivation is the possibility of using the portal as a tool to demonstrate the productivity of the project’s scientific domain in negotiations about the allocation of scarce and valuable ocean drilling cruise resources amongst multiple, competing scientific domains. Considering the social factors, in addition to scientific factors, that motivate the construction of scientific databases enriches accounts of how these databases are built.
Philosophical Transactions of the Royal Society, 2010
Volunteer computing projects (VCPs) have been set up by groups of scientists to recruit members o... more Volunteer computing projects (VCPs) have been set up by groups of scientists to recruit members of the public who are asked to donate spare capacity on their personal computers to the processing of scientific data or computationally intensive models. VCPs serve two purposes: to acquire significant computing capacity and to educate the public about science. A particular challenge for these scientists is the retention of volunteers as there is a very high drop-out rate. This paper develops recommendations for scientists and software engineers setting up or running VCPs regarding which strategies to pursue in order to improve volunteer retention rates. These recommendations are based on a qualitative study of volunteers in a VCP (climateprediction.net). A typology of volunteers has been developed, and three particularly important classes of volunteers are presented in this paper: for each type of volunteer, the particular benefits they offer to a project are described, and their motivations for continued participation in a VCP are identified and linked to particular strategies. In this way, those setting up a VCP can identify which types of volunteers they should be particularly keen to retain, and can then find recommendations to increase the retention rates of their target volunteers.
Volunteer computing projects are computationally-intensive scientific research projects which see... more Volunteer computing projects are computationally-intensive scientific research projects which seek to meet their need for data processing capacity by asking members of the public to download and process project data on their personal computers. This paper presents findings from a study of the interactions in an online forum of participants in such a project, climateprediction.net. It focuses upon the beliefs of one group of participants, who have been identified as particularly critical to the success of volunteer computing projects, about the organizational policies of climateprediction.net and what are appropriate and inappropriate ways for other participants to behave in the forums.
77th American Society for Information Science and Technology (ASIS&T) Annual Meeting 2014
What motivates the building of databases by scientific collaborations? In this paper, we argue th... more What motivates the building of databases by scientific collaborations? In this paper, we argue that not only are databases being built to support scientific work per se, but also with the intention of performing a variety of social functions. To explore this, we present findings from a longitudinal ethnographic case study of a large, multidisciplinary, distributed scientific project studying subseafloor microbial life. A critical element of this project’s Data Management Plan is the construction of a data portal. We found a range of factors motivating not only the very construction of this portal, but also the inclusion of particular features. In addition to scientific factors relating to improved curation and accessibility of diverse and scarce data, we argue that the building of the portal is also motivated by social factors. One such factor is the attempt to build a community of domain researchers to endure beyond the end of this project in 2020. Another motivation is the possibility of using the portal as a tool to demonstrate the productivity of the project’s scientific domain in negotiations about the allocation of scarce and valuable ocean drilling cruise resources amongst multiple, competing scientific domains. Considering the social factors, in addition to scientific factors, that motivate the construction of scientific databases enriches accounts of how these databases are built.
British Sociological Association Annual Conference 2014
"Allied to the movement promoting Open Access publishing is the Open Data movement, which aims to... more "Allied to the movement promoting Open Access publishing is the Open Data movement, which aims to facilitate and
encourage the open sharing of research data amongst scientists across multiple disciplines and institutions. Studies of
scientists’ data practices link barriers to data-sharing with lack of appropriate infrastructure, cultural issues regarding
norms and reward structures, and lack of trust amongst researchers. However, there have been fewer studies of
actual instances of successful data-sharing. Furthermore, little attention has been paid to the implications of
successful data-sharing for the structures of collaborative scientific work.
This paper will present findings from a longitudinal ethnographic case study of a large, distributed, multidisciplinary
collaborative project studying subseafloor microbial life. This project aims to build a community involving researchers
from disparate backgrounds, and to develop infrastructure for the exchange of knowledge, methods, and data. This
case study is therefore an ideal opportunity for studying the co-emergence of scientific collaboration, infrastructure for
data-sharing, and data-sharing practices.
By carefully deconstructing a single observed instance of data-sharing, this paper shows that the sharing of data
between researchers in different institutions or disciplines is a rare and fragile accomplishment that involves the
interplay of multiple factors. These include high levels of trust between researchers, alignment of researchers’
interests, opportunism in exploiting possibilities afforded by infrastructures, and all this underpinned by serendipity.
Conversely, when data-sharing does occur, it promotes new scientific work across disciplinary and institutional
boundaries, reconfiguring the structure of the collaboration."
This paper presents a longitudinal ethnographic study of a large, multidisciplinary scientific pr... more This paper presents a longitudinal ethnographic study of a large, multidisciplinary scientific project studying subseafloor microbial life.
To study this life, scientists need rock and water samples taken from the seafloor. To collect these samples, scientists participate in ocean drilling cruises, such as those carried out as part of the International Ocean Discovery Program (IODP). The IODP and its forerunners have been operating since the 1960s. Initially, these cruises only involved physical scientists (such as geochemists). In recent years, microbiologists have become part of these cruises. This has involved reallocating space on cruise ships to the construction of a microbiology laboratory.
To better persuade physical scientists to concede space on these ships, the study of subseafloor microbe has been directed towards areas of interest to physical scientists, such as the effect of microbes on the physical environment. This has promoted the recruitment of physical scientists by microbiological laboratories. These laboratories are now increasingly multidisciplinary, resulting in a greater diversity of data products and recordkeeping practices.
Thus, it can be seen that spatial configurations in one location (the ships) co-shape the material artefacts produced in separate geographically-distinct location. This co-shaping is mediated by a variety of both human and non-human actors.
Citizen Cyberscience Projects (CCPs) that recruit members of the public as volunteers to process ... more Citizen Cyberscience Projects (CCPs) that recruit members of the public as volunteers to process and produce large datasets promise a great deal of benefits to scientists and science. However, if this promise is to be realised, and citizen science-produced datasets are to be widely-used by scientists, it is essential that these datasets win the trust of the scientific community. This task of securing credibility involves, in part, applying standard scientific procedures to clean-up datasets formed by volunteer contributions. However, the management of volunteers’ behaviour in terms of how they contribute also plays a significant role in improving both the quality of individual contributions and the overall robustness of the resultant datasets. This can assist CCPs in securing a reputation for producing trustworthy datasets.
Through a case study of Galaxy Zoo, a CCP set up to generate datasets based on volunteer classifications of galaxy morphologies, this paper explores how those involved in running the project manage volunteers. In particular, it focuses on how methods for crediting volunteer contributions motivate volunteers to provide higher quality contributions and to behave in a way that better corresponds to statistical assumptions made when combining volunteer contributions into datasets. These have made a significant contribution to the success of the project in securing trust in these datasets, which have been well-used by other scientists.
Implications for practice are then presented for CCPs, providing a list of considerations to guide choices regarding how to credit volunteer contributions to improve the quality and trustworthiness of citizen science-produced datasets.
Earth scientists increasingly work in distributed, multidisciplinary projects. To promote the sha... more Earth scientists increasingly work in distributed, multidisciplinary projects. To promote the sharing of data across such a project, it is vital to improve long-term preservation of data in formats accessible to scientists in multiple disciplines with diverse needs, tools and scientific practices. When developing data management plans and infrastructure, it is important to ask: - What data are generated? - Where are these data preserved and shared? - What are the processes by which these data become 'dark'? - What are the infrastructural and social factors that shape these processes? In response to these questions, we present findings from the first year of a case study of the Center for Dark Energy Biosphere Investigations (C-DEBI), an NSF Science and Technology Center studying microbial life in the deep subseafloor biosphere. Our case study is funded by the Sloan Foundation and the NSF. It involves observation in laboratories, interviews, attendance of scientific meetings, and document analysis. At the laboratory level, we observed scientists mainly working on individual projects, or in a team of two or three. There is infrequent sharing of laboratory-generated data across C-DEBI. Where it does happen, it often takes place following discovery of the data through informal networks or serendipitous encounters with the data's creator. Instead, most of the laboratory-generated data become dark data. These data are typically preserved on a scientist's personal computer in ways particular to the individual, frequently not in a form meaningful to others. Other scientists are often not even aware that these data exist. Furthermore, the scientist tends to take care to preserve these data only as long as they require them: data loss can occur over time. Some data - those which support findings in a paper - may be deposited in a disciplinary database. However, these data are the end result of extensive processing: earlier versions of datasets can be lost. Also, in some disciplines, scientists are not required to deposit these data. Hence the data deposited in these databases are only a fraction of the total laboratory-generated data. Thus, the vast majority of laboratory-generated data is not preserved long-term in a form discoverable or usable to other scientists. We found that the multidisciplinary, distributed nature of C-DEBI can exacerbate the factors that contribute to data staying dark. These include: - It can be hard for scientists to conceptualize how their data may be useful to scientists in other disciplines. This reduces scientists' willingness to perform the work of long-term data preservation; - Fewer opportunities for building of informal networks or serendipitous encounters for data discovery than if scientists were collocated; - Many scientists are part of C-DEBI for a short time only (1-3 years), getting involved through the awarding of C-DEBI grants and fellowships. After their involvement, they may no longer maintain contacts across disciplines within C-DEBI. This makes it harder for them to see how their data might be useful to others, and for others to discover these data through word-of-mouth; - Resistance to standardization of data practices. Multidisciplinary work involves bringing multiple perspectives to bear on research questions. Scientists are concerned that standardization could prematurely foreclose as-yet-unexplored scientific methods.
Collaborative projects, such as e-Science projects producing software and tools to support the wo... more Collaborative projects, such as e-Science projects producing software and tools to support the work of scientists, involve the management and coordination of work of a wide range of personnel, and also involve these personnel working together on joint activities. Effective communication practices are essential for the success of e-Science projects, for a number of reasons and in a number of different senses. For instance, there will be many one-way transfers of information, such as project management informing new personnel of the project’s vision and goals, the communication of work plans (including individuals’ roles, the model of software development employed by the project, timescales and deadlines) from those who have created them to those expected to execute them, and the presentation of work by an individual or team to their colleagues. Such transfers of information may in turn give rise to further communication. This transfer of information can give rise to further communication, including the giving of feedback, for instance whether a software developer feels that the tasks they have been assigned are realistically achievable, and requests for clarification. Communication may also take the form of negotiations or discussions. These could be regarding either day-to-day issues, for instance a team prioritising the tasks they need to complete and dividing work amongst team members, or higher-level issues such as seeking to define the project's overall vision, or constructing an overall plan of work.
The large-scale, multi-disciplinary, multi-institutional, geographically-distributed, and sometimes international nature of e-Science projects, however, frequently impedes the establishment of effective communication practices. One major reason is that their distributed nature necessitates the frequent use of Information Communication Technologies (ICTs, such as e-mail, telephone, and Instant Messaging). It has often been reported that ICTs often do not afford the same richness of communication as face-to-face communication (de Rooij et al. 2007), or can be beset by technological difficulties (Olson & Olson 2000). Because the impact of ICTs upon effective communication practices has thus been widely considered in the existing literature, this paper will instead consider the impact of a variety of other issues which impede communication flows.
Through qualitative case studies of e-Science projects, this paper aims to highlight some issues arising in these e-Science projects that may impede successful communication flows amongst project partners, and to provide recommendations for addressing these issues. These recommendations will often be based upon methods employed in the case studies when they have proved successful. It is hoped that they will prove very useful to Principal Investigators and Project Managers of existing and future e-Science projects in establishing communication practices that contribute to the success of their projects.
5th IEEE International Conference on e-Science, Dec 10, 2009
The acquisition by developers of e-science applications of a thorough understanding of the requir... more The acquisition by developers of e-science applications of a thorough understanding of the requirements of end-users has been recognized as playing a critical role in the usability of such applications. However, there is another dimension to such an understanding that also plays an important role, namely the extent to which these developers converge on a shared understanding of these requirements. This paper considers why such a shared understanding is important, and highlights possible obstacles to this that may arise in the context of e-science projects. A research project, consisting of qualitative case studies of two projects, is being undertaken, with the goal of producing recommendations for improving shared understanding of end-users' requirements amongst developers of e-science applications.
Volunteer computing projects (VCPs) have been set up by groups of scientists to recruit members o... more Volunteer computing projects (VCPs) have been set up by groups of scientists to recruit members of the public who are asked to donate spare capacity on their personal computers to the processing of scientific data or computationally intensive models. VCPs serve two purposes: to acquire significant computing capacity and to educate the public about science. A particular challenge for these scientists is the retention of volunteers as there is a very high drop-out rate. This paper develops recommendations for scientists and software engineers setting up or running VCPs regarding which strategies to pursue in order to improve volunteer retention rates. These recommendations are based on a qualitative study of volunteers in a VCP (climateprediction.net). A typology of volunteers has been developed, and three particularly important classes of volunteers are presented in this paper: for each type of volunteer, the particular benefits they offer to a project are described, and their motivations for continued participation in a VCP are identified and linked to particular strategies. In this way, those setting up a VCP can identify which types of volunteers they should be particularly keen to retain, and can then find recommendations to increase the retention rates of their target volunteers.
Volunteer computing projects are computationally-intensive scientific research projects which see... more Volunteer computing projects are computationally-intensive scientific research projects which seek to meet their need for data processing capacity by asking members of the public to download and process project data on their personal computers. This paper presents findings from a study of the interactions in an online forum of participants in such a project, climateprediction.net. It focuses upon the beliefs of one group of participants, who have been identified as particularly critical to the success of volunteer computing projects, about the organizational policies of climateprediction.net and what are appropriate and inappropriate ways for other participants to behave in the forums.
Data in deep-sea biosphere research often live a double life. While the original data generated o... more Data in deep-sea biosphere research often live a double life. While the original data generated on IODP expeditions are highly structured, professionally curated, and widely shared, the downstream data practices of deep-sea biosphere laboratories are far more localized and ad hoc. These divergent data practices make it difficult to track the provenance of datasets from the cruise ships to the laboratory or to integrate IODP data with laboratory data. An in-depth study of the divergent data practices in deep-sea biosphere research allows us to: - Better understand the social and technical forces that shape data stewardship throughout the data lifecycle; - Develop policy, infrastructure, and best practices to improve data stewardship in small labs; - Track provenance of datasets from IODP cruises to labs and publications; - Create linkages between laboratory findings, cruise data, and IODP samples. In this paper, we present findings from the first year of a case study of the Center for Dark Energy Biosphere Investigations (C-DEBI), an NSF Science and Technology Center that studies life beneath the seafloor. Our methods include observation in laboratories, interviews, document analysis, and participation in scientific meetings. Our research uncovers the data stewardship norms of geologists, biologists, chemists, and hydrologists conducting multi-disciplinary research. Our research team found that data stewardship on cruises is a clearly defined task performed by an IODP curator, while downstream it is a distributed task that develops in response to local need and to the extent necessary for the immediate research team. IODP data are expensive to collect and challenging to obtain, often costing $50,000/day and requiring researchers to work twelve hours a day onboard the ships. To maximize this research investment, a highly trained IODP data curator controls data stewardship on the cruise and applies best practices such as standardized formats, proper labeling, and centralized storage. In the laboratory, a scientist is his or her own curator. In contrast to the IODP research parties, laboratory research teams analyze diverse datasets, share them internally, implement ad hoc data management practices, optimize methods for their specific research questions, and release data on request through personal transactions. We discovered that while these workflows help small research teams retain flexibility and local control - crucial in exploratory deep-sea biosphere research - they also hinder data interoperability, discoverability, and consistency of methods from one research team to the next. Additional consequences of this contrast between IODP and lab practices are that it is difficult to track the provenance of data and to create linkages between laboratory findings, cruise data, and archived IODP samples. The ability to track provenance would add value to datasets and provide a clearer picture of the decisions made throughout the data lifecycle. Better linkages between the original data, laboratory data, and samples would allow secondary researchers to locate IODP data that may be useful to their research after laboratory findings are published. Our case study is funded by the Sloan Foundation and NSF.
Uploads
Through a case study of Galaxy Zoo, a CCP set up to generate datasets based on volunteer classifications of galaxy morphologies, this paper explores how those involved in running the project manage volunteers. In particular, it focuses on how methods for crediting volunteer contributions motivate volunteers to provide higher quality contributions and to behave in a way that better corresponds to statistical assumptions made when combining volunteer contributions into datasets. These have made a significant contribution to the success of the project in securing trust in these datasets, which have been well-used by other scientists.
Implications for practice are then presented for CCPs, providing a list of considerations to guide choices regarding how to credit volunteer contributions to improve the quality and trustworthiness of citizen science-produced datasets.
encourage the open sharing of research data amongst scientists across multiple disciplines and institutions. Studies of
scientists’ data practices link barriers to data-sharing with lack of appropriate infrastructure, cultural issues regarding
norms and reward structures, and lack of trust amongst researchers. However, there have been fewer studies of
actual instances of successful data-sharing. Furthermore, little attention has been paid to the implications of
successful data-sharing for the structures of collaborative scientific work.
This paper will present findings from a longitudinal ethnographic case study of a large, distributed, multidisciplinary
collaborative project studying subseafloor microbial life. This project aims to build a community involving researchers
from disparate backgrounds, and to develop infrastructure for the exchange of knowledge, methods, and data. This
case study is therefore an ideal opportunity for studying the co-emergence of scientific collaboration, infrastructure for
data-sharing, and data-sharing practices.
By carefully deconstructing a single observed instance of data-sharing, this paper shows that the sharing of data
between researchers in different institutions or disciplines is a rare and fragile accomplishment that involves the
interplay of multiple factors. These include high levels of trust between researchers, alignment of researchers’
interests, opportunism in exploiting possibilities afforded by infrastructures, and all this underpinned by serendipity.
Conversely, when data-sharing does occur, it promotes new scientific work across disciplinary and institutional
boundaries, reconfiguring the structure of the collaboration."
To study this life, scientists need rock and water samples taken from the seafloor. To collect these samples, scientists participate in ocean drilling cruises, such as those carried out as part of the International Ocean Discovery Program (IODP). The IODP and its forerunners have been operating since the 1960s. Initially, these cruises only involved physical scientists (such as geochemists). In recent years, microbiologists have become part of these cruises. This has involved reallocating space on cruise ships to the construction of a microbiology laboratory.
To better persuade physical scientists to concede space on these ships, the study of subseafloor microbe has been directed towards areas of interest to physical scientists, such as the effect of microbes on the physical environment. This has promoted the recruitment of physical scientists by microbiological laboratories. These laboratories are now increasingly multidisciplinary, resulting in a greater diversity of data products and recordkeeping practices.
Thus, it can be seen that spatial configurations in one location (the ships) co-shape the material artefacts produced in separate geographically-distinct location. This co-shaping is mediated by a variety of both human and non-human actors.
Through a case study of Galaxy Zoo, a CCP set up to generate datasets based on volunteer classifications of galaxy morphologies, this paper explores how those involved in running the project manage volunteers. In particular, it focuses on how methods for crediting volunteer contributions motivate volunteers to provide higher quality contributions and to behave in a way that better corresponds to statistical assumptions made when combining volunteer contributions into datasets. These have made a significant contribution to the success of the project in securing trust in these datasets, which have been well-used by other scientists.
Implications for practice are then presented for CCPs, providing a list of considerations to guide choices regarding how to credit volunteer contributions to improve the quality and trustworthiness of citizen science-produced datasets.
The large-scale, multi-disciplinary, multi-institutional, geographically-distributed, and sometimes international nature of e-Science projects, however, frequently impedes the establishment of effective communication practices. One major reason is that their distributed nature necessitates the frequent use of Information Communication Technologies (ICTs, such as e-mail, telephone, and Instant Messaging). It has often been reported that ICTs often do not afford the same richness of communication as face-to-face communication (de Rooij et al. 2007), or can be beset by technological difficulties (Olson & Olson 2000). Because the impact of ICTs upon effective communication practices has thus been widely considered in the existing literature, this paper will instead consider the impact of a variety of other issues which impede communication flows.
Through qualitative case studies of e-Science projects, this paper aims to highlight some issues arising in these e-Science projects that may impede successful communication flows amongst project partners, and to provide recommendations for addressing these issues. These recommendations will often be based upon methods employed in the case studies when they have proved successful. It is hoped that they will prove very useful to Principal Investigators and Project Managers of existing and future e-Science projects in establishing communication practices that contribute to the success of their projects.
Through a case study of Galaxy Zoo, a CCP set up to generate datasets based on volunteer classifications of galaxy morphologies, this paper explores how those involved in running the project manage volunteers. In particular, it focuses on how methods for crediting volunteer contributions motivate volunteers to provide higher quality contributions and to behave in a way that better corresponds to statistical assumptions made when combining volunteer contributions into datasets. These have made a significant contribution to the success of the project in securing trust in these datasets, which have been well-used by other scientists.
Implications for practice are then presented for CCPs, providing a list of considerations to guide choices regarding how to credit volunteer contributions to improve the quality and trustworthiness of citizen science-produced datasets.
encourage the open sharing of research data amongst scientists across multiple disciplines and institutions. Studies of
scientists’ data practices link barriers to data-sharing with lack of appropriate infrastructure, cultural issues regarding
norms and reward structures, and lack of trust amongst researchers. However, there have been fewer studies of
actual instances of successful data-sharing. Furthermore, little attention has been paid to the implications of
successful data-sharing for the structures of collaborative scientific work.
This paper will present findings from a longitudinal ethnographic case study of a large, distributed, multidisciplinary
collaborative project studying subseafloor microbial life. This project aims to build a community involving researchers
from disparate backgrounds, and to develop infrastructure for the exchange of knowledge, methods, and data. This
case study is therefore an ideal opportunity for studying the co-emergence of scientific collaboration, infrastructure for
data-sharing, and data-sharing practices.
By carefully deconstructing a single observed instance of data-sharing, this paper shows that the sharing of data
between researchers in different institutions or disciplines is a rare and fragile accomplishment that involves the
interplay of multiple factors. These include high levels of trust between researchers, alignment of researchers’
interests, opportunism in exploiting possibilities afforded by infrastructures, and all this underpinned by serendipity.
Conversely, when data-sharing does occur, it promotes new scientific work across disciplinary and institutional
boundaries, reconfiguring the structure of the collaboration."
To study this life, scientists need rock and water samples taken from the seafloor. To collect these samples, scientists participate in ocean drilling cruises, such as those carried out as part of the International Ocean Discovery Program (IODP). The IODP and its forerunners have been operating since the 1960s. Initially, these cruises only involved physical scientists (such as geochemists). In recent years, microbiologists have become part of these cruises. This has involved reallocating space on cruise ships to the construction of a microbiology laboratory.
To better persuade physical scientists to concede space on these ships, the study of subseafloor microbe has been directed towards areas of interest to physical scientists, such as the effect of microbes on the physical environment. This has promoted the recruitment of physical scientists by microbiological laboratories. These laboratories are now increasingly multidisciplinary, resulting in a greater diversity of data products and recordkeeping practices.
Thus, it can be seen that spatial configurations in one location (the ships) co-shape the material artefacts produced in separate geographically-distinct location. This co-shaping is mediated by a variety of both human and non-human actors.
Through a case study of Galaxy Zoo, a CCP set up to generate datasets based on volunteer classifications of galaxy morphologies, this paper explores how those involved in running the project manage volunteers. In particular, it focuses on how methods for crediting volunteer contributions motivate volunteers to provide higher quality contributions and to behave in a way that better corresponds to statistical assumptions made when combining volunteer contributions into datasets. These have made a significant contribution to the success of the project in securing trust in these datasets, which have been well-used by other scientists.
Implications for practice are then presented for CCPs, providing a list of considerations to guide choices regarding how to credit volunteer contributions to improve the quality and trustworthiness of citizen science-produced datasets.
The large-scale, multi-disciplinary, multi-institutional, geographically-distributed, and sometimes international nature of e-Science projects, however, frequently impedes the establishment of effective communication practices. One major reason is that their distributed nature necessitates the frequent use of Information Communication Technologies (ICTs, such as e-mail, telephone, and Instant Messaging). It has often been reported that ICTs often do not afford the same richness of communication as face-to-face communication (de Rooij et al. 2007), or can be beset by technological difficulties (Olson & Olson 2000). Because the impact of ICTs upon effective communication practices has thus been widely considered in the existing literature, this paper will instead consider the impact of a variety of other issues which impede communication flows.
Through qualitative case studies of e-Science projects, this paper aims to highlight some issues arising in these e-Science projects that may impede successful communication flows amongst project partners, and to provide recommendations for addressing these issues. These recommendations will often be based upon methods employed in the case studies when they have proved successful. It is hoped that they will prove very useful to Principal Investigators and Project Managers of existing and future e-Science projects in establishing communication practices that contribute to the success of their projects.