Introduction Electronic computers and other information and communications technologies (ICTs) have been important to scientists in their work since their invention (by scientists) in the 1950s. Without powerful machines able to carry out many thousands of calculations in seconds it is unlikely that man would have ventured far into space let alone landed on the moon or devised spacecraft such as the Cassini Explorer and the Hubble Telescope that are currently mapping the Universe. We would not

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... ave been able to start decoding the human genome without the ability to share huge databases of genetic information nor design drugs to target specific interactions within the human body without sophisticated three-dimensional computer modelling systems. In fact it would be difficult to identify a scientific development in the past 30 years that did not rely on the use of computers for data processing and storage. The World Wide Web itself was developed by Tim Berners-Lee to facilitate sharing of information among scientists, in this case, particle physicists working at CERN in the 1980s. Following a similar meteoric rise, the use of ICT now plays a major role within science education. In their summary review of the importance of ICT to UK schools Ofsted (2009) commend a range of activities found in school science departments that includes the use of digital video cameras to record experiments and employing the video in subsequent presentations, and the use of data logged from environmental sensors within the school building to learn about heat loss and sustainability. They also highlight the use of handheld personal digital assistants (PDAs) to collect data and images by students working collaboratively in class and on field trips. In all, there appear to be five forms of ICT used within school science which are relevant to teaching and learning (Osborne and Hennessy, 2003) . These include:  tools for data capture, processing and interpretation including data logging systems, data analysis software (e.g. 'Insight'), databases and spreadsheets (eg 'Excel');  multimedia software for simulation of processes and carrying out 'virtual experiments' (e.g. 'Science Investigations 1', 'Chemistry Set', 'Multimedia Science School');  information systems such as CD-Rom encyclopaedias, the World Wide Web and school based learning platforms;  publishing and presentation tools (e.g. 'Word', 'PowerPoint');  digital recording equipmentstill and video cameras;  computer projection technology -interactive whiteboards or data projectors + screens. Of these, by far the most relevant to the everyday work of professional scientists working in industry, research and manufacture today are the tools for data capture, processing (including modelling) and interpretation. Scientists also rely on information systems such as electronic databases and journals to support them in their research in addition to presentation tools to publish their work. McFarlane and Sakellariou (2002) point out the iterative nature of this process; their model (Figure 9.1) shows how scientists' work with ICT can be used to structure students' experience of science at the school level. Figure 9.1. A model of the iterative process of science that can be used to structure experience of science at the school level with some examples of uses of ICT (from McFarlane, 2000). However, developments in ICT are characterized by the speed with which things change and recent advances in computing such as the development of social networking (web 2.0) tools, grid computing and PDAs have now further changed the way scientists can work. This chapter focuses on these relatively new developments in ICTs that have enabled scientists to change the way they collect, record, analyse and share information as part of their work. It is therefore organized into four sections that acknowledge the key roles played by data including its collection, storage and processing, and communication (of the information derived from the data) in teaching How Science Works with ICT. The first section of the chapter focuses on data collection and includes an acknowledgement of the central role electronic monitoring devices play in logging the data from experiments in locations as diverse as a centrally heated, climate controlled university laboratory and the permanently frozen, wind swept Antarctic wastes. Real time data logging has been central to scientists' understanding of many processes and now, new handheld devices mean that both scientists and students studying science, young and old, can collect and analyse data, on the spot, wherever they happen to be. It includes a vignette from a research project investigating how science teacher trainees explored the potential of PDAs for supporting science teaching and learning. One aspect then springs to mind: what are the scientists going to do with all the data now being collected and how can it be stored safely and responsibly? The latest mainframe computer installed at the University of Bristol, Blue Crystal 2, has 73 terabytes of storage. That's 73 million megabytes, enough to store the complete genome sequences of over 24,000 individuals or over 14 million copies of the complete works of Shakespeare. Thus the next section of the chapter addresses the storage and sharing of scientific data. Parallels are drawn for the science teacher attempting to manage good practice in the classroom environment. The third section of the chapter focuses on the role of ICT in processing large quantities of data. Many of today's scientists work on projects such as modelling protein folding, weather forecasting and brain imaging. Computer-based modelling is also common in schools where it has been found that encouraging students to build models enables them to develop an understanding of both modelling as a process in science investigation and of the scientific ideas that they are attempting to model (Brodie et al., 1994; Webb, 1993) . The most complex models such as those used for weather forecasting and climate change predictions are run on networks of powerful computers such as Blue Crystal 2 forming distributed, computer power sharing grids. In addition to producing complex visualisations that aid scientists in interpreting patterns in their data, the grids can also enable collaborative data analysis. There are a number of international projects where scientists, across the globe, are sharing their data with school students to engage them in conducting real science experiments. Scientists don't necessarily need powerful research or industry sponsored computing facilities, many are finding web 2.0 tools such as wikis and blogs enable them to engage in necessary debate with their peers over social, ethical and environmental impact of their findings. The final section of this chapter considers how the internet has changed the way scientists publish their work and enabled new avenues for professional discourse. This is being mirrored in secondary schools, where Ofsted (2009) report that students make the best use of ICT in communicating their ideas and presenting their work. This section includes a vignette of a research project investigating the use of online discussion for teaching about ethical issues in science. Lastly it addresses the need for science teachers to teach their students how to check online publications for reliability and validity. Data collection Collecting data through observation is central to scientists' work. They use a wide range of electronic sensors to detect and record physical and chemical changes in their investigations. Similarly, much use of ICT in school science lessons has centred on data logging where software running on a laptop or desktop PC is used to display recordings from simple sensors measuring temperature, light, pH levels etc. Whilst such experiments can seem complex to set up, for sensors need first to be plugged into an interface that must be connected to the computer; they are generally thought to provide valuable learning opportunities. Frost (2010) hosts details of over 40 data logging experiments taken from UK science classrooms on his Dataloggerama website. Using such tools for data capture and display frees students from laborious processes (Osborne and Hennessy, 2003) that include the need to regularly take readings and to plot the relevant points on a graph of their data. This freedom allows students working together to discuss the shape of a graph as it emerges in real time on the computer screen. Newton (1997) found such talk can help develop students' appreciation of the meaning of patterns in their data and their skills in communicating about it. Data logging sensors can also be connected to handheld computers or PDAs and taken outside the classroom to collect data in the real world environment. This allows students to collect authentic data and enables them to see 'on the spot' how their recordings relate to the processes being observed. From his experience of the Science Learning in Context project, Krajcik (2001) describes this learning in real world environments and enabled through the use of handheld devices as contextualized, active and constructed through interaction with others. The project itself involved students from schools in Michigan and Washington using handheld Palm Pilots with probeware for a range of data logging activities outside the classroom. In a typical example a class of grade seven students monitored the quality of water in a nearby stream with pH, temperature, conductivity and dissolved oxygen probes (sensors). Their teachers reported that the students showed enhanced understanding and motivation for learning in this way (Novak and Gleason, 2001) . In particular the