br Experimental design materials and methods The data is

Experimental design, materials and methods The data is prepared using Autodesk123D catch services [2] based on approximately 40 pictures of each object from various angles. We place the objects on a textured background consisting of a collection of newspapers. The acquired 3D mesh models require post-processing in order to result in a clean and fully specified model. We use the software Meshlab [3] to remove parts of the mesh that belongs to the surroundings. As a last step we manually ensure that no holes exist in the objects using the software Blender [4].
Acknowledgment Florian T. Pokorny and Danica Kragic acknowledge support from the Knut and Alice Wallenberg Foundation. Danica Kragic was also supported by the EU Grant FLEXBOT (FP7-ERC-279933) and RoboHow.Cog (FP7-ICT-288533) and the Swedish Research Council. Yasemin Bekiroglu was supported by EU Grants RoboHow.Cog (FP7-ICT-288533) and EU H2020 SARAFun, 644938.
Data, experimental design, materials and methods
Processed data in supplementary files All profiles are provided in RES2DINV default output format (INV), in a PNG format with or without a fixed common color scale, in XYZ-R ascii files and in VTK/VTU 3D sections. The common color scale profiles for the \"normal\" INNO-406 (L2 norm) are also presented as 3D models in KMZ format using the technique of Van Noten [5].
Acknowledgements The 2008–2010 geophysical survey of the Hockai Fault Zone was possible thanks to the help of ROB colleagues and of many students from ULB. Special thanks to Corentin Caudron and Nathalie Maricq for that amazing August on the field. Thanks to Dimitri Kusters, Baudouin Bukasa and Raphaël De Plaen. Special thanks goes to the team of the Mont Rigi Scientific Station (Université de Liège): Louis Leclercq and Michel Matthys (and Mergez). Thomas Lecocq was bursar FRIA (2006–2010) under contract number FC76908.
The attached file () contains text, dummy and metric variables. It consists of a number of variables (showed in column) such as on-farm structural features (land size, crops, livestock units, grain and straw yields, etc.), socio-economic (straw price, willingness to accept, off-farm job, income, membership of farm union, etc.) and personal (farmer׳s age, family members, educational level, etc.). Experimental design, materials and methods The data source for this analysis was a survey of farmers in 24 municipalities in Foggia Province of Apulia Region, south-eastern Italy. Based on the list of farmers in the official state census [1], a stratified sample was designed on the basis of median values of farmland used to grow cereals within each municipality. The municipalities were selected throughout Foggia Province at a distance of up to 70km from an energy plant site, which is being built in the municipality of Sant’Agata di Puglia. To assess the straw price (EURha−1 equiv. per annum) on energy market, we used the contingent valuation (CV) methodology. Data survey from 203 farmers has been used to construct supply curves for the current straw market and for the hypothetical straw market for energy uses, while a two-step Heckman model [2] has been applied in the econometric regression.
Acknowledgements This data survey is partly carried out within the STAR*AgroEnergy project funded by European Commission, Directorate-General for Research & Innovation, SP4 – Capacities, Coordination and Support Action, Seventh Framework Programme (FP7), Report 2011-1. Grant agreement No. 286269.
Data The data contains certainty equivalent discount rates and discount factors for Australia, estimated based on the prominent Cox-Ingersoll-Ross model [1] of stochastic interest rates (Table 1). The provided rates can be used for discounting costs and benefits of investment projects with a lifetime of up to 200 years.
Experimental design, materials and methods The data provides certainty equivalent discount rates for Australia for different time horizons and various choices of initial risk-free interest rates at the time when the project is invested (t=0). It can be used for discounting cash-flows, costs and benefits of investment projects with a duration between zero and 200 years. Therefore, the data is particularly useful for the evaluation of projects with a very long lifetime that are often characterized by a relatively high degree of uncertainty about interest and discount rates.