This questionnaire was used during an experiment in co-learning between humans and AI-systems. In the experiment, a human participant collaborated with a robot in a simulated urban-search-and-rescue task. We designed sequences of interactions intended to facilitate co-learning between the two team members. We call these Learning Design Patterns (LDPs). Half of the participant-group (control) performed two runs of the task without intervention of LDPs, while the experimental group performed two LDPs successively after the first task run. The goal of the experiment was to empirically investigate the effects of the LDPs on learning task-critical knowledge, team situational awareness, and on team performance. The questionnaire consists of three parts: questions asked after each run (both groups), and questions asked after each LDP (experimental group only).
We set two elevational transects on contrasting bedrock types, i.e. slate and granite, in a subtropical forest. Ten and nine sampling sites were distributed in the slate and granite transects, respectively. We surveyed the plant community and calculated the above-ground biomass. Five replicate samples were collected in each site, which resulted in a total of 95 samples. We measured phosphorus concentration (P) in the mineral soil, soil microbial biomass, fine-root, litter, and bedrock. In the uploaded data, we present all data mentioned above.
Contributors:Sven Wernersson, Göran Carlström, Andreas Jakobsson, Mikael Akke
Data used for article.
- 'SSoptimization' = Matlab scripts for generating NUS sample schemes.
- 'sample_schemes' = sample schemes used for downsampling of accordion datasets
- 'Matlab' = Matlab scripts for accordion analysis and downsampling
- 'relaxation_data' = R1, R2, R1rho relaxation rates. Peaklist with (1H,15N) assignments.
- 'accordion_datasets' = raw accordion datasets, acquired on a Varian VNMRS 600MHz spectrometer.