How to Make the Dream Come True: The Astronomers' Data Manifesto

Astronomy is one of the most data-intensive of the sciences. Data technology is accelerating the quality and effectiveness of its research, and the rate of astronomical discovery is higher than ever. As a result, many view astronomy as being in a 'Golden Age', and projects such as the Virtual Observatory are amongst the most ambitious data projects in any field of science. But these powerful tools will be impotent unless the data on which they operate are of matching quality. Astronomy, like other fields of science, therefore needs to establish and agree on a set of guiding principles for the management of astronomical data. To focus this process, we are constructing a 'data manifesto', which proposes guidelines to maximise the rate and cost-effectiveness of scientific discovery.Comment: Submitted to Data Science Journal Presented at CODATA, Beijing, October 200

Geomagnetism : review 2009

The Geomagnetism team measures, records, models and interprets variations in the Earth’s natural magnetic fields, across the world and over time. Our data and expertise help to develop scientific understanding of the evolution of the solid Earth and its atmospheric, ocean and space environments. We also provide geomagnetic products and services to industry and academics and we use our knowledge to inform and educate the public, government and the private sector

The essential value of long-term experimental data for hydrology and water management

We would like to thank the European Research Council ERC for funding the VeWa project and most of Tetzlaff's time (project GA 335910 VeWa). No data were used in producing this manuscript.Peer reviewedPublisher PD

From Science to e-Science to Semantic e-Science: A Heliosphysics Case Study

The past few years have witnessed unparalleled efforts to make scientific data web accessible. The Semantic Web has proven invaluable in this effort; however, much of the literature is devoted to system design, ontology creation, and trials and tribulations of current technologies. In order to fully develop the nascent field of Semantic e-Science we must also evaluate systems in real-world settings. We describe a case study within the field of Heliophysics and provide a comparison of the evolutionary stages of data discovery, from manual to semantically enable. We describe the socio-technical implications of moving toward automated and intelligent data discovery. In doing so, we highlight how this process enhances what is currently being done manually in various scientific disciplines. Our case study illustrates that Semantic e-Science is more than just semantic search. The integration of search with web services, relational databases, and other cyberinfrastructure is a central tenet of our case study and one that we believe has applicability as a generalized research area within Semantic e-Science. This case study illustrates a specific example of the benefits, and limitations, of semantically replicating data discovery. We show examples of significant reductions in time and effort enable by Semantic e-Science; yet, we argue that a "complete" solution requires integrating semantic search with other research areas such as data provenance and web services

Instability and its relation to precipitation over the Eastern Iberian Peninsula

International audienceSynoptic situations producing rainfall at four rawinsonde observatories at eastern Spain are classified as stratiform or convective depending on dynamic and thermodynamic instability indices. Two daily radiosonde and daily-accumulated precipitation data from four observatories in Eastern Spain are used: Madrid-Barajas (MB), Murcia (MU), Palma de Mallorca (PA) and Zaragoza (ZA). We calculated two thermodynamic instability indices from radiosonde data: CAPE and LI. Likewise, from ERA40 reanalysis data we have calculated the Q vector divergence over the Iberian Peninsula and Balearic Islands, as a parameter describing dynamical instability. Synoptic situations producing rainfall were classified as convective or stratiform, satisfying a criterion based on the values of dynamic and thermodynamic indices at each observatory. It is observed that the number of days with stratiform precipitation related to the total number of precipitation days follows a consistent annual pattern

OGC SWE-based Data Acquisition System Development for EGIM on EMSODEV EU Project

The EMSODEV[1] (European Multidisciplinary Seafloor and water column Observatory DEVelopment) is an EU project whose general objective is to set up the full implementation and operation of the EMSO distributed Research Infrastructure (RI), through the development, testing and deployment of an EMSO Generic Instrument Module (EGIM). This research infrastructure will provide accurate records on marine environmental changes from distributed local nodes around Europe. These observations are critical to respond accurately to the social and scientific challenges such as climate change, changes in marine ecosystems, and marine hazards. In this paper we present the design and development of the EGIM data acquisition system. EGIM is able to operate on any EMSO node, mooring line, sea bed station, cabled or non-cabled and surface buoy. In fact a central function of EGIM within the EMSO infrastructure is to have a number of ocean locations where the same set of core variables are measured homogeneously: using the same hardware, same sensor references, same qualification methods, same calibration methods, same data format and access, and same maintenance procedures.Peer ReviewedPostprint (published version

Towards a Taxonomy of the Model-Ladenness of Data

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include data conversion, data correction, data interpolation, data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data