e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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ROOT - A C++ framework for petabyte data storage, statistical analysis and visualization


ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community. designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the 17ree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, inClUding linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular. the RooFit package allows the user to perform complex data modeling and fitting while the RooStats library provides abstractions and implementations for advanced statistical tools. Multivariate classification methods based on machine learning techniques are available via the TMVA package. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets. using onthe-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks - e.g. data mining in HEP - by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way. Program summary Program title: ROOT Catalogue identifier: AEFA_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEFA-v1- 0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: LGPL No. of lines in distributed program, including test data, etc.: 3 044 581 No. of bytes in distributed program, including test data, etc.: 36 325 133 Distribution format., tar.gz Programming language: C++ Computer: Intel 1386, Intel x86-64, Motorola PPC, Sun Sparc, HP PA-RISC Operating system: GNU/Linux, Windows XP/Vista, Mac OS X, FreeBSD, OpenBSD, Solaris, HP-LIX, AIX Has the code been vectorized or parallelized?: Yes RAM: > 55 Mbytes Classification: 4, 9, 11.9, 14 Nature of problem: Storage, analysis and visualization of scientific data Solution method: Object store, wide range of analysis algorithms and visualization methods Additional comments: For an up-to-date author list see: http://root.cern.ch/drupal/content/rootdevelopment-team and http://root.cern.ch/drupa]/content/former-root-developers Running time: Depending on the data size and complexity of analysis algorithms References: [1] http://root.cern.ch. (C) 2009 Elsevier B.V. All rights reserved.

  • CH
  • US
  • CERN_Org_Europ_Rech_Nucl (CH)
  • US_DOE_US_Dept_Energy (US)
  • New_York_Univ (US)
Data keywords
  • machine learning
  • research data
  • data model
Agriculture keywords
  • farm
Data topic
  • big data
  • information systems
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
  • US_DOE_US_Dept_Energy (US)
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e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.