Figures, plotting scripts, and data for "A fast, low-cost, and stable memory algorithm for implementing multicomponent transport in direct numerical simulations"
Contributors: Fillo, Aaron J., Schlup, Jason, Beardsell, Guillaume, Blanquart, Guillaume, Niemeyer, Kyle E.
... This dataset contains the figures, as well as the necessary plotting scripts and data to reproduce them, for the article "A fast, low-cost, and stable memory algorithm for implementing multicomponent transport in direct numerical simulations" by Aaron J. Fillo, Jason Schlup, Guillaume Beardsell, Guillaume Blanquart, and Kyle E. Niemeyer (2019). In addition, the code used to generate the eigenvalues in Table 1 is included. The scripts were run in Matlab 2019a, though none of the versions used should be version-dependent. Furthermore, non-standard functions are included with dependencies hard-coded. We used export_fig (https://github.com/altmany/export_fig) to generate high-quality figures, and redistribute the version used here for reproducibility (export_fig was developed by Oliver J. Woodford and Yair M. Altman, and made available openly under the BSD 3-Clause License). The code included in this dataset is released under the BSD 3-Clause License (see LICENSE.txt for details), other than the source of export_fig, as described. The figures are shared under the Creative Commons Attribution 4.0 International License (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
Contributors: Giuseppe Giorgi
... This document purports to describe in detail the mathematical framework for a computationally efficient computation of nonlinear Froude-Krylov forces (NLFK) in 6 degrees of freedom (DoFs) for axisymmetric floating objects. Additionally, this document also acts as a reference manual to a set of Matlab scripts, forming a demonstration toolbox to show the capabilities of the NLFK approach and provide an easy, operative, and ready-to-use implementation of the method. The toolbox is openly available at DOI: 10.5281/zenodo.3517130. This document and the toolbox are licensed with a Creative-Commons-By-Attribution-Share-Alike (CC-BY-SA) license. Note that this is the first version of the toolbox, so any feedback and potential corrections are welcome. Moreover, the user is highly invited to contact the author for any doubt, ideas or suggestions, deeper investigation, higher-complexity problems, and eventually collaboration. Finally, note that this toolbox is the precursor of an open source software, coded in a lower-level coding language than Matlab, hence much faster, which will be virtually shared by mid 2021. This work has received funding from the European Research Council under the Horizon 2020 Programme (H2020-MSCA-IF-2018)/ grant agreement no 832140.
Contributors: C Ferreira, Jailton
... The transformation of coordinates and time from an inertial frame to another inertial frame is obtained without using rigid measuring-rods and clocks as primitive entities. The obtained transformation is applied to some cases.
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Contributors: Sigrist, Lukas, Gomez, Andres, Thiele, Lothar
... Dataset Information This dataset presents long-term term indoor solar harvesting traces and jointly monitored with the ambient conditions. The data is recorded at 6 indoor positions with diverse characteristics at our institute at ETH Zurich in Zurich, Switzerland. The data is collected with a measurement platform  consisting of a solar panel (AM-5412) connected to a bq25505 energy harvesting chip that stores the harvested energy in a virtual battery circuit. Two TSL45315 light sensors placed on opposite sides of the solar panel monitor the illuminance level and a BME280 sensor logs ambient conditions like temperature, humidity and air pressure. The dataset contains the measurement of the energy flow at the input and the output of the bq25505 harvesting circuit, as well as the illuminance, temperature, humidity and air pressure measurements of the ambient sensors. The following timestamped data columns are available in the raw measurement format, as well as preprocessed and filtered HDF5 datasets: V_in - Converter input/solar panel output voltage, in volt I_in - Converter input/solar panel output current, in ampere V_bat - Battery voltage (emulated through circuit), in volt I_bat - Net Battery current, in/out flowing current, in ampere Ev_left - Illuminance left of solar panel, in lux Ev_right - Illuminance left of solar panel, in lux P_amb - Ambient air pressure, in pascal RH_amb - Ambient relative humidity, unit-less between 0 and 1 T_amb - Ambient temperature, in centigrade Celsius The following publication presents and overview of the dataset and more details on the deployment used for data collection. A copy of the abstract is included in this dataset, see the file abstract.pdf. L. Sigrist, A. Gomez, and L. Thiele. "Dataset: Tracing Indoor Solar Harvesting." In Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19), 2019. Folder Structure and Files processed/ - This folder holds the imported, merged and filtered datasets of the power and sensor measurements. The datasets are stored in HDF5 format and split by measurement position posXX and and power and ambient sensor measurements. The files belonging to this folder are contained in archives named yyyy_mm_processed.tar, where yyyy and mm represent the year and month the data was published. A separate file lists the exact content of each archive (see below). raw/ - This folder holds the raw measurement files recorded with the RocketLogger [1, 2] and using the measurement platform available at . The files belonging to this folder are contained in archives named yyyy_mm_raw.tar, where yyyy and mmrepresent the year and month the data was published. A separate file lists the exact content of each archive (see below). LICENSE - License information for the dataset. README.md - The README file containing this information. abstract.pdf - A copy of the above mentioned abstract submitted to the DATA '19 Workshop, introducing this dataset and the deployment used to collect it. raw_import.ipynb [open in nbviewer] - Jupyter Python notebook to import, merge, and filter the raw dataset from the raw/ folder. This is the exact code used to generate the processed dataset and store it in the HDF5 format in the processed/folder. raw_preview.ipynb [open in nbviewer] - This Jupyter Python notebook imports the raw dataset directly and plots a preview of the full power trace for all measurement positions. processing_python.ipynb [open in nbviewer] - Jupyter Python notebook demonstrating the import and use of the processed dataset in Python. Calculates column-wise statistics, includes more detailed power plots and the simple energy predictor performance comparison included in the abstract. processing_r.ipynb [open in nbviewer] - Jupyter R notebook demonstrating the import and use of the processed dataset in R. Calculates column-wise statistics and extracts and plots the energy harvesting conversion efficiency included in the abstract. Furthermore, the harvested power is analyzed as a function of the ambient light level. Dataset File Lists Processed Dataset Files The list of the processed datasets included in the yyyy_mm_processed.tar archive is provided in yyyy_mm_processed.files.md. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums. Raw Dataset Files A list of the raw measurement files included in the yyyy_mm_raw.tar archive(s) is provided in yyyy_mm_raw.files.md. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums. Dataset Revisions v1.0 (2019-08-03) Initial release. Includes the data collected from 2017-07-27 to 2019-08-01. The dataset archive files related to this revision are 2019_08_raw.tar and 2019_08_processed.tar. For position pos06, the measurements from 2018-01-06 00:00:00 to 2018-01-10 00:00:00 are filtered (data inconsistency in file indoor1_p27.rld). v1.1 (2019-09-09) Revision of the processed dataset v1.0 and addition of the final dataset abstract. Updated processing scripts reduce the timestamp drift in the processed dataset, the archive 2019_08_processed.tar has been replaced. For position pos06, the measurements from 2018-01-06 16:00:00 to 2018-01-10 00:00:00 are filtered (indoor1_p27.rld data inconsistency). Dataset Authors, Copyright and License Authors: Lukas Sigrist, Andres Gomez, and Lothar Thiele Contact: Lukas Sigrist (firstname.lastname@example.org) Copyright: (c) 2017-2019, ETH Zurich, Computer Engineering Group License: Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) References  L. Sigrist, A. Gomez, R. Lim, S. Lippuner, M. Leubin, and L. Thiele. Measurement and validation of energy harvesting IoT devices. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.  ETH Zurich, Computer Engineering Group. RocketLogger Project Website, https://rocketlogger.ethz.ch/.  L. Sigrist. Solar Harvesting and Ambient Tracing Platform, 2019. https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing
Contributors: Ghahari, Shabnam
... DUDTeN Directed and UnDirected Temporal Network The DUDTeN software is created to estimate dynamic functional connectivity (DFC) and calculate undirected and directed temporal measures in a very simple way. Only, load your data, select the options, save the result, and see them. DUDTeN is a user-friendly software that users no need to write codes and only by selecting the options could investigate the Temporal Networks. So, this software helps to increase the speed of studies in the temporal network field. The "DUDTeN v1.0" currently only available for Windows OS. The DUDTeN executable file includes the version 9.3 (R2017b) of the MATLAB Runtime. So, when double-clicking the DUDTeN.exe to run it, the required version of MATLAB Runtime is also installed. Therefore, don't need to download and install any required files. In a few minutes, you can install and use DUDTeN. For the first time, double-click DUDTeN.exe, wait for a few seconds/minutes, the installer opens, follow the steps for installing, finally, installer installs DUDTeN. Then, double-click DUD10 icon to run and use it. In the Manual_DUDTeN.pdf, the steps of installation DUDTeN is presented. Also, how to use this software, the description of parts and options, and the examples are expressed.
... This package contains the anonymized dataset, R notebook results, and R code for processing the meaning preserving transformations and human subject study. See the README file for more details.
A contagion measure provably superior to the reproduction number: theory and a case study of the Yemen cholera epidemic, Datasets
Contributors: Tankayev, Timur, Tovey, Craig
... Data used for Yemen cholera epidemic simulation.
... This package contains anonymized Dataset, R notebook results, and R code for processing the meaning preserving transformations on the corpus study and the human subject study. See the README file for more details.
Contributors: Gaurav Baruah, Christopher F Clements, Arpat Ozgul
... Datasets for the paper appearing in Journal of Animal ecology : "Eco-evolutionary processes underlying early warning signals of population declines". Also GitHub repository link :https://github.com/GauravKBaruah/ECO-EVO-EWS-DATA