Cassier Maurice. Due to new forms of collective invention, research networks or consortia, new rules are required to manage this knowledge, allowing for both a mutual sharing of knowledge and at the same time a certain degree of individual protection, or the right for university researchers to publish material, while keeping a reservation on results asked for by industrial firms.
Researchers working on the Bridge lipase project came up with an ingenious way to diffuse data combining, at the same time, the possibilities to maintain the temporary reservation of rights for the data owner, to share the data within the collective sphere of the project and lastly to allow a relatively rapid publication of the data.
This set of rules, developed by the researchers without any help from a legal department, will most likely be followed on scientific networks. These agreement consortia are bringing to light new rules, if not new rights, as regards knowledge management. XXX1X-4, , Ce consortium mixte, universitaire et industriel, ne part pas de rien. Il existe des relations, parfois anciennes, entre les laboratoires publics et les laboratoires industriels dans le domaine de. Enfin, pour cette classe d'enzymes, les laboratoires industriels ne peuvent se passer des laboratoires publics 5.
In addition to these ten centres, the OFCE serves as both a research centre and economic forecasting organization. The CDSP UMS provides documented and scientifically validated socio-political data for research by archiving, disseminating, and contributing to international survey programs. It also supports training in data collection and analysis. The CERI UMR analyses foreign societies, international relations, and political, social, and economic phenomena across the world from a comparative and historical perspective.
The first includes political attitudes, behaviour and parties; the second involves political thought and the history of ideas. Its five major research programmes address fundamental issues such as higher education and research, healthcare, sustainable development, the evolution of firms, and the transformation of the state.
Research in the Department of Economics EA contributes to the development of methodology and economic analysis. Inside this boundary, within the control zone, the library can lay claim to those resources that have been selected as part of the Collection, and assert curation, or stewardship, of those resources to ensure their consistent availability over the long term. The boundary of the traditional library was easy to define. It was the building that contained and protected the selected physical resources over which the library asserted control and curation responsibility.
The transition from the physically contained library bricks and mortar to the networked digital library has fractured this formerly well-defined control zone. Consider social science research. Like academic libraries, these institutions established control zones permitting data quality and provenance to be preserved, and sometimes enhanced, while making them widely available to the social science community through cooperative inter-institutional arrangements, abroad as well as in the USA.
In each of these areas there is a growing interest in crowd-sourced citizen science , which engages numerous volunteers as participants in large-scale scientific endeavors. Our particular experience is with the eBird project, which originated at the Cornell Laboratory of Ornithology. For over a decade, this highly successful citizen science project has collected observations from volunteer participants worldwide.
By nature, citizen science must contend with the problem of highly variable observer expertise and experience. How can we trust data collected or aggregated by individuals who lack traditional scientific credentials such as academic degree, publication record, or institutional affiliation? The reasons for this breakdown are not difficult to discern. For the researcher, an enticing array of data is now available from non-traditional sources, such as social media platforms. Data mashups, often mixing traditional and non-traditional sources, are becoming increasingly common, sometimes with clear and substantial benefits.
As a result, the traditional criteria for assessing data integrity are being challenged. In the following section, we present two examples to think through the implications of new data.
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Just as libraries cannot return to the era of control over physical resources within bricks and mortar institutions, 23 it would be unrealistic for any science to deny the reality and potential benefits of a sociotechnical knowledge infrastructure that mixes the formal with the informal. At the same time, in many cases adding data from uncontrolled and potentially unreliable sources may jeopardize historically successful modes of knowledge production. Examples from weather forecasting and epidemiology will illustrate some of these risks.
At the same time, meteorologists eagerly anticipated them, and they ultimately proved of enormous value. Yet most forecasters never tried to acquire most of these data, and actually discarded much of what they did receive. The reason: pre-computer forecasting techniques simply could not use it within the short time hours available for creating a useful forecast. Even climatologists, who did not face the time pressure of forecasting, could not before computers make use of much of the available data directly.
Instead, they developed a system of distributed calculation. Weather stations were asked to pre-compute such figures as monthly average temperatures and report only those, rather than provide all the raw data to central collectors, for whom the calculating burden would have been overwhelming. The pioneers of this all-important technique faced a different problem.
Weather models divide the atmosphere into three-dimensional grids and compute transformations of mass, energy, and momentum among grid boxes on a short time-step every few minutes.
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Every grid point must be supplied with a value at each time step; they cannot simply be zeroed out. Yet most instrument observations of weather are taken every few hours not minutes , and very few weather stations or other instruments are located exactly at the grid points used by the models. So forecasters developed techniques for interpolating grid point values, in time and in space, from observations. In other words, they went from a pre-computer situation in which the large amounts of available data were never used, to a post-computer situation in which most data used were actually generated by calculations interpolation , rather than measured directly.
First, uneven global coverage is more of a problem than is insufficient data volume. Second, observations inevitably contain errors due to instrument bias, weather station siting, local topography, and dozens of other factors.
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Third, since the error characteristics of observations are not perfectly known, the best forecast centers now generate dozens of different data sets from the observations, in order to simulate the likely range of possibility of the true state of the atmosphere. Then they run forecasts on each of these data streams. In other words, simulation models albeit guided by real observations give you better data than do your instruments. Or, to say it even more provocatively, simulated data—appropriately constrained—are better than real data.
As it happened, though, interpreting the photographs proved much more difficult than most anticipated. Taken from a great distance, at strange angles, the photographs showed weather systems clearly but were hard to relate to existing standard measurements, such as temperature, pressure, and wind speed. Certainly they were not yet used as direct inputs to weather forecasts. These instruments, first flown around , generate very large amounts of data continuously unlike many other weather data sources, such as surface stations, which take readings on a periodic basis.
Because weather forecast models required values only at regular grid points, and because they already stretched the limits of computer power, in the s and s forecasters converted these continuous, volumetric satellite measurements into periodic point measurements—in effect treating the satellites as if they were radiosondes. This massive data reduction went on for two decades before computer power became sufficient to incorporate satellite data directly. They held out the promise of solving large numbers of clinical puzzles at one swoop.
Recall that pundits such as Anderson 30 imagine that pattern-finding algorithms will now quasi-automatically generate new truths from large datasets. In medicine of the s, however, more data led to more falsehoods. Researchers who were used to straining to detect any effect at all were suddenly able to easily detect small statistical associations of questionable clinical value, and they did so, and these were published.
Trained to worry habitually about false negatives that is, the danger of missing genuine, but small effects , researchers had little experience in worrying about false positives , which proliferated. New data destabilized old knowledge, leading to recent assessments that the bulk of all published findings in biomedicine are incorrect.
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Advances in computers and networks allowed researchers to use more data, but analyzing these data highlighted some of the flaws in Frequentist methods. Advances in computers and networks also gave researchers the computational power to make alternative, Bayesian models tractable. Some medical statisticians now advocate that the discovery of an apparently important new finding in a research study should be taken first and foremost as evidence of an error, as any associational study should properly be expected to find nothing of value.
In the Ioannidis call to action, in addition to more data, successful epidemiology demands Bayesian statistical methods, a new system of scientific publication that rewards replication and tracks null findings, and ultimately a revaluation of the role of data itself. In the context of epidemiology, new data led to a proliferation of contradictory findings. Although the datasets presumably had a known provenance, the discipline is now arguing that the researchers themselves are not trustworthy and that data need to be widely shared in order to allow all new claims to be extensively checked.
In the second section we saw that new data sources and practices can destabilize disciplines in unpredictable ways. Disciplines can be characterized as path dependencies, in the sense that they represent the continuing imprint of historical choices and accidents. As administrative units and long-lasting professional organizations, they shape not only the nature of research, but also the reward systems—especially promotion and tenure decisions—that drive scholarly careers.
Yet close examination immediately reveals that most disciplines encompass a wide variety of methodologies, epistemologies, publication practices, and other norms.