Dag Endresen

Chief Engineer - Geo-Ecology Research Group
Image of Dag Endresen
Norwegian version of this page
Phone +47 22851654
Mobile phone +47 40612982
Visiting address Administration building NHM Tøyen Sars gate 1 0318 Oslo
Postal address Postboks 1172, Blindern 0318 Oslo

Researcher identifier

ORCID logo  https://orcid.org/0000-0002-2352-5497

Academic interests

I am the manager for the Norwegian participant node of the Global Biodiversity Information Facility (GBIF). The secretariat for GBIF Norway is hosted by the University of Oslo Natural History Museum. I provide support and teaching related to biodiversity data publication and analysis. My academic interests include biodiversity informatics, including data publishing standards, knowledge organization systems, geographic information management, and predictive modeling of biodiversity data in an ecological setting (including modeling of traits useful for plant breeding programs in agriculture).


Before joining GBIF Norway and the Natural History Museum in Oslo, I was the Knowledge Systems Engineer at the GBIF secretariat. I also have previous experience as the GBIF Node Manager for the Nordic Genetic Resources Center (NordGen) from 2004 to 2010 and as the vice-chair of the GBIF Nodes Committee between 2006 and 2010. At the Nordic Gene Bank (NGB) and Nordic Genetic Resources Center (NordGen, Alnarp, Sweden) I was the IT manager and head of the section for documentation of plant genetic resources. My Ph.D. research at Copenhagen University (KU LIFE, Denmark) was organized in close collaboration with Bioversity International (CGIAR, Rome, Italy), the International Center for Agricultural Research in Dry Areas (ICARDA, CGIAR, Aleppo, Syria), and NordGen. My research contributed to a new approach for “focused identification of germplasm strategy” (FIGS) using predictive computer modeling methods to identify useful genetic resources. The FIGS approach identifies and utilizes a predictive link between the environmental patterns and ecology of locations where traditional cultivars and landraces were developed during long-term farming and target trait properties of interest for plant breeding and crop research programs. By using such predictive models we can substantially reduce the number of cultivars crop scientists need to screen to identify novel genetic diversity required to maintain and improve food crops.

  • Ph.D. degree from Copenhagen University, 2011
  • Master of Technology (Civil Engineer) degree from NTNU, 1996



Tags: GBIF, data publishing, research data, open science, data science
Published Oct. 10, 2012 5:33 PM - Last modified Feb. 13, 2021 8:32 AM