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Data supporting The eMLR(C*) method to determine decadal changes in the global ocean storage of anthropogenic CO2
(2018)The determination of the decadal change in anthropogenic CO2 in the global ocean from repeat hydrographic surveys represents a formidable challenge, which we address here by introducing a seamless new method. This method builds on the extended multiple linear regression (eMLR) approach [Friis et al., 2005] to identify the anthropogenic CO2 signal, but in order to improve the robustness of this method, we fit C* [Gruber and Sarmiento, 2002] ...Dataset -
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Dataset on German farmers risk preference, risk perception and risk management strategies
(2017)The extent to which people are willing to take on risk, i.e. their risk preferences as well as subjective risk perception plays a major role in explaining their behavior. This is of particular relevance in agricultural production, which is inherently risky. The data presented here was collected amongst a total of 64 German farmers in 2015. It includes results of three different risk preference elicitation methods (multiple price list, ...Dataset -
Spatio-temporal data on territories of the Holy Roman Empire
(2021)This data set captures dynamic spatial and non-spatial aspects of territories of the Holy Roman Empire (HRE) in 16th century Europe. Each line in the data files corresponds to a territory and each column to an attribute. Spatial attributes are the geo-coordinates of the perimeter of the territories which allow us to map the territories on a map, compute neighbourhood relations and surface areas. Non-spatial attributes include the foundation ...Dataset -
Static spatial data on territories of the Holy Roman Empire
(2021)This data set captures static spatial and non-spatial aspects of territories of the Holy Roman Empire (HRE) in 16th century Europe. Each line in the data files corresponds to a territory and each column to an attribute. Spatial attributes are the geo-coordinates of the perimeter of the territories. This allows us to map territories on a map, compute neighbourhood relations and surface areas. Non-spatial attributes include the foundation ...Dataset -
Process patents
(2019)The dataset contains the outcome of a large classification exercise. It contains patent filings at the European Patent Office and the United States Patent and Trademark Office and its corresponding "process shares" which is calculated with different methods. The process share indicates to which degree a patent is a process patent rather than a product patent. The shares have been calculated based on the classification of patent claims ...Dataset