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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Comparison of models using different censoring scenarios for days open in Spanish Holstein cows


Days open data from 113 569 lactation records in 774 Spanish Holstein herds were analysed using standard linear models under two different editing procedures, and with two alternative methodologies that account for censoring: a censored linear model (CLM) and a Weibull survival analysis (SA) model. The first editing procedure excluded from the linear model all censored records for days open (LMnc), and the second defined days open as days from calving to the last known insemination or culling date, treating censored records as complete (LM). Sire variance estimates for days open were 61, 70 and 139 for LMnc, LM and CLM, respectively, and 0.026 for SA on a logarithmic scale. Heritability estimates were 0.05, 0.06 and 0.08 with LMnc, LM and CLM, respectively. Rankings of sires varied between methodologies: sire evaluations from LMnc and LM had rank correlations with evaluations from SA equal to -0.65 and -0.82, respectively, and of 0.71 and 0.87 with evaluations from CLM. The rank correlation between evaluations from SA and CLM was -0.98, suggesting stronger agreement of sire rankings between models that take censoring into account.The SA model had a better predictive ability of daughter fertility at early stages of lactation than the other methods, as measured by chi-squared statistics for predicted pregnancy status at 75, 103, 140, or 200 days post partum in a split data set. The CLM also predicted daughter fertility more accurately than any of the two standard linear models.

  • ES
  • US
  • Univ_Politecn_Madrid_UPM (ES)
  • Univ_Wisconsin_Madison (US)
Data keywords
  • open data
Agriculture keywords
    Data topic
    • modeling
    Document type

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    Institutions 10 co-publis
    • Univ_Politecn_Madrid_UPM (ES)
    • Univ_Wisconsin_Madison (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.