Contributors: Jing Gao
... 16S rRNA gene sequencing is performed to analyze the changes in composition of raw milk microbiota of mastitis dairy cows supplemented with different probiotics.
Model and output for Vadsaria et al, "Development of a sequential tool LMDZ-NEMO-med-V1 for global to regional past climate simulation over the Mediterranean basin: an early Holocene case study", GMD publication
Contributors: Vadsaria, Tristan, Li, Laurent, Ramstein, Gilles, Dutay, Jean-Claude
... These files are related to the publication: "Development of a sequential tool LMDZ-NEMO-med-V1 for global to regional past climate simulation over the Mediterranean basin: an early Holocene case study" (currently under review). Description of the files as in "README.txt": "vadsaria_et_al_LMDZ-NEMO-med_model.tar.gz" contains the LMDZ-nemo-med-v1 model (three components: atmospheric global model, atmospheric regional model and oceanic regional model). It includes starting files and codes to run the model. "output-for-vadsaria-et-al-gmd.tar.gz" contains the model output presented in the main article. It also provides python scripts to compute the overturning function, the freshwater budget and the stratification index as well.
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Contributors: Lopez Lopez, Ricardo, Perez Sanchez, Vicente, Ramon Soria, Pablo, Martin Alcantara, Antonio, Fernandez Feria, Arrue Ulles, Begoña, Ollero Baturone, Anibal
... This dataset contains data from different gliding flights with an ornithopter in low wind conditions. For each experiment, the inertial information is provided. Additionally, the flights have been recorded from three different points of view to track and triangulate its position. The videos are provided and the position of the camera has been determined using a Leica Total Station system with submillimeter accuracy. A sample of the 2D track of the ornithopter is provided for each video and experiment. The tracking along the three cameras are synchronized. ------Camera Pose structure------ Three cameras with four points: three to measure orientation and the last one the lens position. The last two points are the measured fall. Camera 1 -> top left, bottom left and top right. Camera 2 -> top left, top rigth and bottom right. Camera 3 -> top left, bottom left and top right. Then there are 14 rows. The pattern is: Point1, Point2, Point3 and Lens Position. ------IMU structure------ time, quaternion w, quaternion x, quaternion y, quaternion z, accelerometer x, accelerometer y, accelerometer z, Gyroscope x, Gyroscope y, Gyroscope z, magnetometer x ,magnetometer y ,magnetometer z units: time->ms, accelerometer->g, gyroscope->ยบ/s
Contributors: Colby Johanson
... A framework that allows for the development of custom online experiments and surveys.
Contributors: Sherratt, Tim
... The proceedings of Australia's Commonwealth Parliament are recorded in Hansard, which is available online through the Parliamentary Library's ParlInfo database. This repository includes Jupyter notebooks to harvest and explore XML formatted versions of Hansard.
Contributors: Massimo Lauria, Jan Elffers, Jakob Nordström, Marc Vinyals
... CNF generator in DIMACS format. It produces common families of CNFs.
... Features default string representation in @autodict is now more readable. Legacy representation is still available through a parameter. Fixed #29. pyfields can now be used as the source for the list of attributes, in @autohash, @autodict, and @autoclass. Fixes #28 new @autorepr decorator. Previously this feature was only available through @autodict, it can now be used without it. autorepr is supported in @autoclass, and if users set autodict=False by default it will be enabled. Fixed Fixed #30 and #31. Misc / bugfixes Major refactoring: more readable and maintainable code. Fixed @autodict behaviour when the list was vars(self) and used together with @autoprops: with some options the private names were appearing and with others the public property names were appearing. Now the public property names always appear if they exist. See documentation page for details.
Data produced for "Crop switching reduces agricultural losses from climate change in the United States by half"
Contributors: Rising, James, Naresh, Devineni
... This data archive includes all of the results from the models used to produce the paper "Crop switching reduces agricultural losses from climate change in the United States by half". It contains three main archives: 1. predictors.zp: The temperature and water stress indicators for each crop, along with county-level log-yields. Both the Bayesian and OLS models are fit to this data. Static covariates are available in us-bioclims-new.csv. 2. mcmc-outputs.zip: Each of the variables fit in the Bayesian model, for each MCMC draw from the posterior distribution. The contained directory includes for each crop versions with constant variance (-variance) and under cross-validation (-cv). The counties are ordered according to fips-usa.csv. 3. landuse.zip: Optimization model results. The results/ directory contains the optimization results for each set of assumptions presented in the appendix, applied to the average yield levels. The results-mc/ directory contains profit, yield, and optimization results for each draw of the MCMC independently.