Journal:Technology transfer and true transformation: Implications for open data
| Full article title | Technology transfer and true transformation: Implications for open data |
|---|---|
| Journal | Data Science Journal |
| Author(s) | Bezuidenhout, Louise |
| Author affiliation(s) | University of Oxford |
| Primary contact | Email: louise dot bezuidenhout at insis dot ox dot ac dot uk |
| Year published | 2017 |
| Volume and issue | 16 |
| Page(s) | 26 |
| DOI | 10.5334/dsj-2017-026 |
| ISSN | 1683-1470 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://datascience.codata.org/articles/10.5334/dsj-2017-026/ |
| Download | https://datascience.codata.org/articles/10.5334/dsj-2017-026/galley/678/download/ (PDF) |
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Abstract
When considering the “openness” of data, it is unsurprising that most conversations focus on the online environment—how data is collated, moved, and recombined for multiple purposes. Nonetheless, it is important to recognize that the movements online are only part of the data lifecycle. Indeed, considering where and how data are created—namely, the research setting—are of key importance to open data initiatives. In particular, such insights offer key understandings of how and why scientists engage with in practices of openness, and how data transitions from personal control to public ownership.
This paper examines research settings in low/middle-income countries (LMIC) to better understand how resource limitations influence open data buy-in. Using empirical fieldwork in Kenyan and South African laboratories, it draws attention to some key issues currently overlooked in open data discussions. First, many of the hesitations raised by the scientists about sharing data were as much tied to the speed of their research as to any other factor. Thus, it would seem that the longer it takes for individual scientists to create data, the more hesitant they are about sharing it. Second, the pace of research is a multifaceted bind involving many different challenges relating to laboratory equipment and infrastructure. Indeed, it is unlikely that one single solution (such as equipment donation) will ameliorate these “binds of pace.” Third, these “binds of pace” were used by the scientists to construct “narratives of exclusion” through which they remove themselves from responsibility for data sharing.
Using an adapted model of technology first proposed by Elihu Gerson, the paper then offers key ways in which these critical “binds of pace” can be addressed in open data discourse. In particular, it calls for an expanded understanding of laboratory equipment and research speed to include all aspects of the research environment. It also advocates for better engagement with LMIC scientists regarding these challenges and the adoption of frugal/responsible design principles in future open data initiatives.
Keywords: technology, low/middle-income countries, data sharing, research, pace
Introduction
The issue of increasing the openness of data online is a global priority. Indeed, open data is increasingly featuring on agendas of both high- and low/middle-income country development plans.[1] Nevertheless, data sharing in low/middle-income countries (LMICs) is challenged by a number of widely-recognized issues. These include a lack of resources for sharing activities[2] as well as for research activities more generally. Strategically increasing research capacity in LMICs—and thus the ability of LMIC researchers to participate in the open data movement—is intrinsically tied (at least in part) to the need for increasing the availability of laboratory and ICT equipment.
Unpacking the links between laboratory equipment and open data
It is recognized that the lack of up-to-date laboratory equipment hampers not only the ability to conduct certain types of research, but has an overall impact on the pace and efficiency of research. How to best address this lack of physical research resources is becoming a topic for directed intervention, and a number of different organizations have been set up to address issues relating to equipment provision. These include databases of equipment[a], equipment donation schemes[b], or equipment collaborations, as well as increased equipment budgets in many funded grants.[c]
Despite the value of these initiatives, a coordinated and sustained approach to research equipment in LMICs remains elusive for two key reasons. First, a lack of empirical evidence detailing the contextual heterogeneity of LMIC research environments challenges targeted interventions. Second, the absence of LMIC scientists in more general discussions on scientific research practices makes it difficult to pinpoint key issues that may be prevalent within these research settings. Thus, capacity building initiatives are often challenged by the absence of a clear picture of what equipment are needed and best deployed in LMIC regions. It is therefore highly possible that other interventions are critically needed if this resource shortfall is to be effectively addressed.
The challenges of increasing research capacity through equipment-related interventions have far-reaching implications for LMIC research. In this special edition, and in related papers[3][4][5], we argue for a stronger connection between the discussions of open data and the research environment in which data are generated. The physical—as well as the social and regulatory aspects of research environments—influences how scientists are able to create, curate, and disseminate data, and thus the ability of scientists to contribute and re-use data online. Moreover—and often overlooked—the characteristics and challenges of personal research environments can influence the importance that scientists attach to the open data movement.[3][4][5]
Footnotes
- ↑ Such as the EPSRC’s database https://equipment.data.ac.uk/ (discussed later)
- ↑ Such as Seeding Labs (discussed later)
- ↑ For example, see http://www.esrc.ac.uk/funding/guidance-for-applicants/changes-to-equipment-funding/
References
- ↑ Schwegmann, C. (February 2013). "Open Data in Developing Countries" (PDF). EPSI Platform. https://www.europeandataportal.eu/sites/default/files/2013_open_data_in_developing_countries.pdf. Retrieved 02 May 2017.
- ↑ Bull, S. (October 2016). "Ensuring Global Equity in Open Research". Wellcome Trust. doi:10.6084/m9.figshare.4055181. https://figshare.com/articles/Review_Ensuring_global_equity_in_open_research/4055181. Retrieved 02 May 2017.
- ↑ 3.0 3.1 Bezuidenhout, L.; Kelly, A.H.; Leonelli, S.; Rappert, B. (2016). "‘$100 Is Not Much To You’: Open Science and neglected accessibilities for scientific research in Africa". Critical Public Health 27 (1): 39–49. doi:10.1080/09581596.2016.1252832.
- ↑ 4.0 4.1 Bezuidenhout, L.; Rappert, B. (2016). "What hinders data sharing in African science?". Fourth CODESRIA Conference on Electronic Publishing: 1–13. http://www.codesria.org/spip.php?article2564&lang=en.
- ↑ 5.0 5.1 Bezuidenhout, L.; Leonelli, S.; Kelly, A.H.; Rappert, B. (2017). "Beyond the digital divide: Towards a situated approach to open data". Science and Public Policy 44 (4): 464–75. doi:10.1093/scipol/scw036.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. The original article lists references alphabetically, but this version—by design—lists them in order of appearance. Footnotes have been changed from numbers to letters as citations are currently using numbers. "Bezuidenhout et al forthcoming" (from the original) has since been published, and this version includes the updated citation.
