This is the ninth volume of the Open Data journal for Agricultural Research
Agricultural research uses and produces many relevant data sets in studying agricultural systems across the globe, through its efforts in investigating conditions of global food (in)security at different spatial scales (from regional to national to continental. These data sets have a value to the specific research as these are analysed and investigated, leading to results and conclusions, that are published in peer-reviewed scientific journals or presented at scientific conferences. These data have a longer term value as a resource for the future than the specific research in which they are collected. Other researchers or experts can use these data in new analysis, meta-analysis, or different applications of modelling or statistical tools, leading to new insights for the future. The Open Data Journal for Agriculture Research (ODjAR) acts as a central hub for storing, curating and publishing the data sets as a resource for the future where publications and their authors get appropriate credit through citations and digital object identifiers for future reference.
Many different data sets exist, that are of value and deserve accreditation: experimental data, surveys, model inputs, model outputs, derived indicators and statistics, data assimilation and mark-ups, maps, measured data points. Unlike journal articles describing the main new insights and the most important lessons learned, these data sets are often lost when the funding period ends or the research is published, leading to a situation where these are difficult to reuse for other purposes, or difficult to re-use in reproducing the results described. With the advance of Open Access, Linked Open Data and Open data portals of governments, there is increasing awareness of the value of sharing data with others for further investigation, increased innovation, creation of jobs and better services. Also, governments and science funders are increasing their pressure for science to open up its data, as it is paid with tax-payer financial resources, and should thus have a public benefit.
Support and in-kind contributions towards realizing ODjAR is acknowledged from Wageningen University and Research Centre Library