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2,878 results for “code and data”
Warragamba inflow data, predictors and code
<p>This repository contains data and code used for the analyses presented in Devanand et al. (2023) Can climate knowledge enable Warragamba Dam, Sydney, Australia to be used to manage flood risk?, 10.1088/1748-9326/ad0afb. The csv files contain historical inflow into the Warragamba dam in eastern Australia for the period 1970-2022, the large scale climate indices, and antecedent soil moisture. The climate predictors are obtained from https://psl.noaa.gov/gcos_wgsp/Timeseries/. The shape file 'warragamba_catchment.shp' is the catchment area that is used to estimate catchment average soil moisture from the AWRA-L datatset (https://dx.doi.org/10.25914/NDY0-J367). The python notebook 'probability_of_dam_inflows.ipynb' contains the code used to estimate inflow exceedance probabilities as a function of the predictors.</p>
Figure data and code used in Inconsistent definitions of GDP: Implications for estimates of decoupling
<p>Figure code in R and underlying data to reproduce all figures in the article "Inconsistent definitions of GDP: Implications for estimates of decoupling".</p>
Data and Code for Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances
<p>Data and code for analyses in Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances.</p> <p>See publication and ReadMe file for analysis description and further details. </p>
Programmable multi-photon quantum interference in a single spatial mode -- Data and code for simulations
<h2>Description of the data and file structure</h2> <p>This Dataset contains data files with experimental results for the manuscript "<strong>Programmable multi-photon quantum interference in a single spatial mode</strong>" (pre-print version at <a href="https://arxiv.org/abs/2305.11157">https://arxiv.org/abs/2305.11157</a>).</p> <p>The CSV files contain the measured output distributions of our time-bin interferometer, for the various experiments we run. In the first column is the number of counts detected and in the following columns the corresponding output modes. The counts were detected by post-processing the time-tags of the recorded single photon events (a detailed explanation can be found in the Supplementary Informations of the paper).The number of counts is reported for all possible combinations of output modes in order to reconstruct the entire output distribution of collisionless events.</p> <p>The text file contains the data points of the time-bin HOM histogram shown in the paper.</p> <p> </p> <h2>Code/Software</h2> <p>We also provide the Jupyter Notebook (LoopExperiment.ipynb) we used to simulate the experiments, developed by Dr. Tobias Guggemos.</p> <p>The Loop-based architecture is a photonic experiment, that allows scalable implementation of Boson Sampling and arbitrary unitaries on a photonic platform. It can be implemented as a single, sequenced or nested architecture.</p> <p>We use the python framework Perceval to simulate our experiments. We simulate the conversion of the time-bin encoded setup as path encoded photonic qubits.</p> <p>More details can be found in the Notebook.</p>
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Two-bubble simulation and gravitational wave spectrum codes and data
<p><span>Code and data used in the paper with title</span><a href="https://doi.org/10.1103/PhysRevD.104.075039"><span> <em>Vacuum bubble collisions: from microphysics to gravitational waves </em>by Oliver Gould, Satumaaria Sukuvaara, and David Weir</span></a><span> [</span><a href="https://arxiv.org/abs/2107.05657"><span>arXiv:2107.05657</span></a><span>]. </span></p> <p><span>The field simulation and gravitational wave spectrum calculation codes are based on Gravitational radiation from colliding vacuum bubbles by Arthur Kosowsky, Michael S. Turner and Richard Watkins [</span><a href="https://inspirehep.net/literature/324187"><span>Inspire</span></a><span>].</span></p> <p><span>Contains files:</span></p> <ul> <li> <p><span>two_bubbles_code-v1.0.1.zip is a snapshot of a</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_code/"><span> git repository</span></a><span>, corresponding to</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_code/-/tree/v1.0.1?ref_type=tags"><span> commit v1.0.1</span></a><span>. Contains the codes with which the majority of the data was produced.</span><span><br><br></span></p> </li> <li> <p><span>two_bubbles_data-v1.0.1.zip is a snapshot of a</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_data/"><span> git repository</span></a><span>, corresponding to</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_data/-/tree/v1.0.1?ref_type=tags"><span> commit v1.0.1</span></a><span>. It contains the majority of data used in the paper. Note however that the simulation pickle files are examples run on a coarser lattice due to Zenodo file size restrictions. Apart from few exceptions, the data in this file was produced by the codes in two_bubbles_code-v1.0.1.zip.</span><span><br><br></span></p> </li> </ul> <p><span>README.md files, specifying and explaining the contents and usage, are included within. The v1.0.1 of</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_code/-/blob/v1.0.1/README.md?ref_type=tags"><span> </span><span>code README.md</span></a><span> and the</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_data/-/blob/v1.0.1/README.md?ref_type=tags"><span> </span><span>data README.md</span></a><span> can be found from the repositories as well.</span></p> <p><span>The update v1.0.1 updates the README and fixes a small error in the calculation of the gravitational wave spectrum. We thank Toby Opferkuch for pointing this out. The error in the code does not affect the results in two_bubbles_data-v1.0.0.zip or the paper as they were produced with a slightly earlier version of the code, before the appearance of this error. The version two_bubbles_data-v1.0.1 updates the README, clarifying some points.</span></p>
Code and Data for "Anticoncentration and state design of random tensor networks"
<p>We investigate quantum random tensor network states where the bond dimensions scale polynomially with the system size, N. Specifically, we examine the delocalization properties of random Matrix Product States (RMPS) in the computational basis by deriving an exact analytical expression for the Inverse Participation Ratio (IPR) of any degree, applicable to both open and closed boundary conditions. For bond dimensions χ∼γN, we determine the leading order of the associated overlaps probability distribution and demonstrate its convergence to the Porter-Thomas distribution, characteristic of Haar-random states, as γ increases. Additionally, we provide numerical evidence for the frame potential, measuring the 2-distance from the Haar ensemble, which confirms the convergence of random MPS to Haar-like behavior for χ≫\sqrt{N}. We extend this analysis to two-dimensional systems using random Projected Entangled Pair States (PEPS), where we similarly observe the convergence of IPRs to their Haar values for χ≫\sqrt{N}. These findings demonstrate that random tensor networks with bond dimensions scaling polynomially in the system size are fully Haar-anticoncentrated and approximate unitary designs, regardless of the spatial dimension.</p>
Coding data to accompany "A quantitative approach to sociotopography in Austronesian languages"
<p>Dataset consists of csv files with sample languages identified by name and Glottocode. Coding for four sociolinguistic variables, as well as an overall "orientation type." Each file corresponds to a different method for coding languages employing multiple spatial orientation strategies, as described in the document coding.pdf.</p> <p><strong>Orientation type</strong></p> <ul> <li>land-sea = axis oriented orthogonal to the coast, based on opposition between landward (inland) and seaward (toward the coast), regardless of whether these terms reflect PAN *daya and *lahud </li> <li>land-sea* = land-sea systems in which the land-sea opposition is indistinguishable from geophysical elevation</li> <li>coastal = axis oriented parallel to the coast, often but not necessarily co-lexified with vertical `up' and `down'</li> <li>elevation = axis that distinguishes global or geophysical elevation with respect to deictic center </li> <li>riverine = axis oriented parallel to the river, typically with secondary axis orientated orthogonal to river</li> <li>cardinal = axis fixed according to conventions which do not vary with local geography (although they may be motivated by environmental factors such as wind and the sun)</li> </ul> <p><strong>Distribution</strong></p> <ul> <li>distributed</li> <li>island</li> <li>village</li> </ul> <p><strong>Economy</strong></p> <ul> <li>diversified</li> <li>agriculture</li> <li>subsistence</li> </ul> <p><strong>Geography</strong></p> <ul> <li>diversified</li> <li>inland</li> <li>coast</li> </ul> <p><strong>Terrain</strong></p> <ul> <li>mountainous</li> <li>non-mountainous</li> </ul>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
Data and code for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps"
<p>Data and code used for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps" by Rumpf et al., submitted December 2021 to Science</p> <p>See file ReadMe.txt for a description of the content and the original publication for further explanations.</p> <p>You are free to use these data and code for scientific purposes but are obliged to cite the above-mentioned publication.<br> For further questions, contact sabine.rumpf@unibas.ch</p>
Data for the paper 'Reducing networks of ethnographic codes co-occurrence in anthropology'
<p>Pseudonymized data supporting the paper "Reducing networks of ethnographic codes co-occurrence in anthropology", published in "Advances in Quantitative Ethnography. Fourth International Conference on Quantitative Ethnography (ICQE 2022), Copenhagen, Denmark, October 15–19, 2022, Proceedings", and edited by Amanda Barany and Crina Damsa. The paper is part of the POPREBEL project. The data were gathered in the spring and summer of 2021, as a part of a larger research project on populism in Central and Eastern Europe, to be completed by the end of 2022. They consist of 17 semi-structured interviews with Polish-speaking Internet users, who used social media to seek and share information about health against the backdrop of the COVID-19 pandemic. Research participants were asked about their opinion on the current state of affairs in their respective countries, and their political choices over the years and at present.</p> <p><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data).</p>
Data and code used in manuscript: Basal freeze-on generates complex ice-sheet stratigraphy
<p>Mapped plumes location obtained from ice-sheet radio echo sounding data of North Greenland (https://data.cresis.ku.edu/data/rds/ for 2010-2014_Greenland files) and map of calculated freeze-on index are found in 'FreezeOnIndex_MappedPlume_Data.nc'. Model code of the three models used to obtain the findings shown in the manuscript 'Basal freeze-on generates complex ice-sheet stratigraphy'. As well as code to calculate the freeze-on index.</p>
Data and code for figures: Intrinsic Kerr amplification for microwave electromechanics
<p>This directory contains the datasets and code for generating the figures in the research article "Intrinsic Kerr amplification for microwave electromechanics", <em>Appl. Phys. Lett.</em> 124, 243503 (2024).</p>
Pyenson, Huisken, Gupta, and Rehan (2024): Code & Data
<p>Code & data to accompany publication of Pyenson, Huisken, Gupta, and Rehan (2024).</p>
Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing (code, data and scripts to reproduce paper results)
<p>This repository contains the data, code, and scripts required to reproduce the results of the paper "Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing" by Daniele De Sensi, Salvatore Di Girolamo and Torsten Hoefler, presented at the 2019 International Conference for High Performance Computing, Networking, Storage, and Analysis. </p> <p>This repository does not contains the code of the library used to automatically tune the routing algorithm, which can be found at http://doi.org/10.5281/zenodo.3372785</p>
Data sets for "Magnetic helicity dissipation and production in an ideal MHD code"
<pre>The tar archive Helicity_in_IdealMHDCode.tar contains an index.html file with links to a directory with "Add-ons" to the FLASH code and the flash.par file. We also list the IDL directory with secondary data and plot routines for each figure used in the paper "Magnetic helicity dissipation and production in an ideal MHD code" by Axel Brandenburg (Nordita) and Evan Scannapiecoo (Arizona State University) with the URL https://arxiv.org/abs/1910.06074.</pre>
Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought
<p>This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
AMBON diversity & community composition, data & code
<p>Data and R code for analyzing diversity and community composition of eight assemblages in the Northeast Chukchi Sea in 2015 and 2017. For details of the analysis, results and interpretation, see:</p> <p><em>Mueter, F.J., Iken, K., Cooper, L.W., Grebmeier, J.M., Kuletz, K.J., Hopcroft, R.R., Danielson, S.L., Collins, R.E., Cushing, D. Changes in diversity and species composition across multiple assemblages in the northeast Chukchi Sea during two contrasting years are consistent with borealization. Oceanography (In Press).</em></p>
Data and code related to the paper: "Integrated stretchable pneumatic strain gauges for electronics-free soft robots"
<p>This folder contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Anastasia Koivikko, Vilma Lampinen, Mika Pihlajamäki, Kyriacos Yiannacou, Vipul Sharma & Veikko Sariola, "Integrated Stretchable Pneumatic Strain Gauges for Electronics-Free Soft Robots", Communications Engineering, 1, 14 (2022).</p> <p><a href="https://doi.org/10.1038/s44172-022-00015-6">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure. In most cases, the folder contains scripts named <strong>plot<...>.m</strong> that recreate the actual plots. Some folders also have a scripts <strong>analyze<...>.m</strong> to analyze the data; these need to be run before the actual plotting.</p> <p>For more details, please see the paper.</p>
Data and code related to the paper: "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation"
<p>This archive contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Kyriacos Yiannacou, Vipul Sharma and Veikko Sariola, "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation", <em>Langmuir</em> 2022, 38, 38, 11557–11564.</p> <p><a href="https://doi.org/10.1021/acs.langmuir.2c01061">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>The acoustofluidic controller software is the same as in our previous paper and is archived <a href="https://doi.org/10.5281/zenodo.4593021">here</a>.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure(s) and/or movie(s). Within each folder, the raw data files are under the folder `data/`. Once ran, the scripts produce another folder called `output/`, to which they place the created plots and movies. Most folder contain a script name `plot_*.m` that makes the figure(s) and `video_*.m` that generates the video(s). You will need `ffmpeg` installed to convert the serial images into a video.<br> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.