Introduction by Dr Lim Teck Ghee
A revolutionary form of scientific enquiry process may be taking shape right now. This new form associated with data-intensive open science from a wide range of sources has many implications that are only now beginning to be studied.
As the author of the report on it below points out, the impact of data-intensive open science on research practice and research outcomes is potentially substantive and far-reaching. There are implications for everyone involved in scientific research from funding organizations to research and information communities and higher education institutions.
One of the most intriguing implications is that certain key areas of scientific research may not require large funds and elaborate and expensive infrastructures to arrive at results.
This implication is especially important to countries such as Malaysia where the conventional research infrastructure is not well established and the countries lag behind in their traditional research foundations.
In the post below we reproduce a summary of the report. It may be of special interest to young Malaysians that are asking questions on the way they will need to pursue scientific enquiry and engage in scientific research in the future.
One caveat: a key requirement for engagement in open science is mastery of the English language.
Note: I am indebted to my colleague Prof. James Graham for drawing my attention to this report.
From the executive summary of 'Open science at web-scale: Optimising participation and predictive potential' by Dr Liz Lyon, UKONL
This Report has attempted to draw together and synthesise evidence and opinion associated
with data-intensive open science from a wide range of sources. The original specification for the work was highly selective in its choice of areas to study, and this Report addresses only three of these areas in any depth:
- open science including open notebook science : making methodologies, data and results available on the Internet, through transparent working practices
- citizen science including volunteer computing : where volunteers who may not have
scientific training, perform or manage research-related tasks such as observation,
measurement or computation - predictive science : data-driven science which enables the forecasting, anticipation or prediction of specific outcomes.
Synthetic science (research which combines science and engineering methods to design and build novel biological entities), and Immersive science (used to describe research involving virtual and simulated worlds), are referenced, but require more detailed examination. Fuller definitions of the terms and areas examined in this study have been provided in Section 3. In addition, the Report addresses data informatics and the supporting role of libraries for these particular aspects of open science.
The work was undertaken through a mix of desk research, including analysis from the peer reviewed literature, presentations, selective blogs, wiki content, social network discussion, and by consultation with a small group of leading thinkers and researchers. The Report was also informed by presentations and talks given by the author during 2009.
The Report is positioned as a Consultative document, which it is hoped will stimulate andcontribute to community discussion in the UK, but also fuel the open science debate on the global stage. Whilst many questions have been asked here, they will require fuller articulation and investigation in other fora. The economic implications will require detailed analysis and the societal benefits should be reviewed and evaluated. The consultative questions are clearly indicated in boxes in the text and are reproduced in full in the Executive Summary.
| Consultation Challenge 1: Scale, Complexity and Predictive Potential Data-intensive science powered by contemporary computational hardware, software and research techniques, enables scientists to perform experiments and calculations at different orders of magnitude of scale and volume: research that was completed in a year can now be repeated in a weekend. Sustained growth in data modelling, complex simulations and visualisations, facilitate interpretation and analysis by humans and machines, leading to the development of predictive science scenarios in a wider range of disciplines. Examples of data intensive science at these extremes of scale, which enable forecasting and predictive assertions, have been described. Assessments of the accuracy and robustness of predictions are linked to uncertainty quantification, the accuracy of the underlying model, and the integrity of the data. Key questions address community awareness and understanding of the potential implications and impact of (open) data-intensive science at new extremes of scale and complexity, and the service requirements for associated data curation and preservation. |
| Consultation Challenge 2: Continuum of Openness Open science has been presented in this Report as a continuum, which is helpful in positioning the range of behaviours and practices observed in different disciplines and contexts. The twin aspects of openness (access and participation), have been separated to facilitate scoping the full potential of the open science vision and a listing of the perceived values and benefits of open science is given. Available evidence suggests that transparent data sharing and data re-use are far from commonplace and some of the reasons for this are examined. Peer production approaches to data curation are in their infancy but offer considerable promise as scaleable models which could be migrated to other disciplines. The more radical open notebook science methodologies are currently on the “fringe??? and it is not clear whether uptake and adoption will grow in other disciplines and contexts. The challenge of “openness??? across its range of interpretations, demands that we address the awareness and understanding of fundamental open science concepts, supplemented by probing exploration of practitioner experience. |
| Consultation Challenge 3: Citizen Science 21st Century team science has been empowered by the proliferation of social Web tools enabling globally distributed groups to work together, but we can also envisage team science embracing interested amateurs and citizens, as well as research professionals. Some established and compelling exemplars of citizen science are given, but it is noted that this model may be more suited to certain domains and types of research. However, the growth of mobile phone use in citizen journalism, for public census work and participative surveys and the the development of sensor-rich mobile devices, suggest that there is great potential for more participatory methodologies to benefit scientific research, though some significant privacy and legislative issues remain unanswered. The influence of computer gaming approaches to motivate participants in volunteer computing initiatives is described, and the development of citizen science Web services, system architectures and the design of appropriate interfaces, is briefly explored. We need to learn much more about how the public interact with these services to maximise the value and benefit from such investment. The basic questions probing citizen science, raise significant philosophical and pragmatic issues for professional scientists, research funding bodies, higher education institutions and the wider community. |
| Consultation Challenge 4: Credentials, Incentives and Rewards The potential impact of these changing practices on established business models for science and scholarly communications is raised: new notions of reputation and trust are developing which challenge established norms. There is brief discussion of the current journal publishing model with associated citation metrics for UK research assessment, which does not reward data sharing, social Web contributions or peer production approaches to data curation. Some novel proposals which seek to include such parameters in research assessment metrics are presented. The implications on research funder policies, future science investment planning and scholarly communication business models are not fully understood, but it is clear that the lack of incentives for data sharing and participatory methodologies, are a barrier to the wider adoption of the open science agenda. The consultative questions explore incentivising data sharing and re-use, and strategies for enabling more open participation, in the context of open science and scholarly communications. |
| Consultation Challenge 5: Institutional Readiness and Response The open science agenda, with the data-intensive science at extremes of scale described in this Report, has significant implications for higher education institutions at policy, planning and operational levels. This Report raises some preliminary points and an Open Science Institutional Readiness Checklist is given as a brief aide memoire for institutions. It is hoped that by asking basic questions which explore institutional awareness, policy, planning and research practice, the community will begin to explore these substantive issues in more depth. |
| Consultation Challenge 6: Data Informatics Capacity and Capability Particular attention has been paid to the provision of data informatics capacity and capability and the role of the Library in this context. The Report asserts that Libraries are well-placed to support research data management but that new skills and roles will need to be embraced by the professional LIS community. Modifications to LIS courses will be required and there are similar training implications for new-entrant researchers and postgraduates, to equip them with the skills and methodologies required for data-intensive science. The UK Digital Curation Centre is a key resource, although the increasing demands on this relatively modest service are challenging. The consultative questions explore the embedding of skills required for open data-intensive science, the role of the Library and Information Services and implications for postgraduate training and LIS curriculum development. What is the research community view on the current provision of data informatics skills for postgraduates and research staff? If current curricula and training are not meeting needs, how can the position be improved? Should basic data informatics training be a core element of courses? Who should provide this training? What are the costs? |
Finally, it is intended that recommendations for further work will arise from the subsequent community and stakeholder discussion.
Full report @ http://www.jisc.ac.uk/publications/documents/opensciencerpt.aspx#downloads
