Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
477
datasets available to search
ShareScore release 0.9.0
Dataset results
477 results for “input data”
Suggested Taxonomy: Tracking Technologies to Effectively Capture and Input Key Data on the Blockchain
<p>Within the paper titled "Transparency with Blockchain and Physical Tracking Technologies: Enabling Traceability in Raw Material Supply Chains" (Mater. Proc. 2021, 5(1), 1; <a href="https://doi.org/10.3390/materproc2021005001">https://doi.org/10.3390/materproc2021005001</a>), we consider the majority of tracking technologies to be part of the IoT ecosystem and suggest a taxonomy with their key features, benefits and use cases in the mining industry. Although technologies such as markers and QR/Barcodes are not necessarily electronic devices, they can integrate with other IoT objects and provide or qualify a digital identity. The common element connecting all these technologies is that they include functionalities that can capture and communicate granular, timely, relevant and accurate data, which can be automatically or manually entered into the blockchain. </p> <p>We have analysed the following most common physical tracking methods which will be described and exemplified in more detail below:</p> <ul> <li> <p>Video monitoring (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t001">Table 1</a>)</p> </li> <li> <p>Bar and QR codes (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t002">Table 2</a>)</p> </li> <li> <p>Markers and taggants (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t003">Table 3</a>)</p> </li> <li> <p>Cellular, near range and low power network tracking tools (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t004">Table 4</a>)</p> </li> <li> <p>Satellite network tracking tools (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t005">Table 5</a>)</p> </li> </ul>
Input data and analyzed data of "Topology of synaptic connectivity constrains neuronal stimulus representation (...)"
<p>This dataset contains the input data, as well as the analyzed data that our <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">preprint</a></p> <p><em><strong>Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies</strong></em></p> <p>to be found on <a href="https://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">bioRxiv</a> is based on. The input data (<em>input_data.zip</em>) contains everything that is needed to run the full <a href="https://github.com/BlueBrain/topological_sampling/">analysis pipeline</a> from start to the generation of the figures found in the manuscript. However, some of the analysis steps can be computationally heavy, so we also provide the output of these expensive steps, that can be simply used in conjunction with jupyter notebooks (<em>notebooks.zip)</em> to generate the figures.</p> <p><strong>Overview</strong></p> <p>An overview image can be found <a href="https://raw.githubusercontent.com/BlueBrain/topological_sampling/master/toposampling_pipeline_overview.png"><strong>here</strong></a></p> <p>Blue squares denote input / output files (that are part of this dataset). Grey circles denote steps of the analysis pipeline (that are implemented in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>). Red rectangles denote configuration files (that are part of this dataset and also in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>).</p> <p>This Dataset can also be browsed, downloaded and accessed as linked open data from the <a href="https://bbp.epfl.ch/nexus/web/studios/public/topological-sampling/studios/data:a7cc7e9f-53c5-4940-929c-95f4c4f57728?workspaceId=data:165e54c5-e8f6-4d85-ac94-53bc3dfe5cd4">BBP knowledge Graph based Data studios</a>.</p> <p><strong>Contained file types and their structure</strong></p> <p>Here, we provide four types of files. Configuration files specify analysis parameters and define the expected locations of the data files. Input files are the inputs into the analysis pipeline. Analyzed files are the outputs of said pipeline. Finally, we provide a number of jupyter notebooks that use the analyzed files to generate the manuscript figures. If you want to re-run the entire analysis pipeline, you need the code and configuration files from the <a href="https://github.com/BlueBrain/topological_sampling/">repository</a>, the input files and notebooks; the analyzed files will be generated as you run the pipeline. For information how to run this, refer to the <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a>. If you only want to generate the figures, you still need the code and configuration files from the repository, as it contains a package related to reading the result files; further, you need the analyzed files in addition to the input files. Of course, you can also run parts of the analysis pipeline and download the outputs for the rest.</p> <p>To run everything smoothly, the files have to be placed into the expected file structure. You can look up and configure the file structure in the configuration files. Below, we describe the default layout, which is very simple (<em>root</em> is where you placed the code from our <a href="https://github.com/BlueBrain/topological_sampling/">repository</a> and can be any location on your file system):</p> <ul> <li>Configuration files<em>: </em>Part of the <a href="https://github.com/BlueBrain/topological_sampling/">repository.</a> Placed into <em>root/working_dir/configs</em></li> <li>Input data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>input_data.zip</em> -- Input data. Contains details on the model used in the manuscript and the output (spike times) of the simulation described in the manuscript. Within the file: <ul> <li>For details, see <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a></li> </ul> </li> </ul> </li> <li>Analyzed data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>classifier_features_results.zip </em>-- Output of the "classifier" step. Results of stimulus classification on the data in <em>features.zip</em></li> <li><em>classifier_manifold_result</em>s.zip -- Output of the "classifier" step. Results of stimulus classification on the data in <em>extracted_components.zip</em></li> <li><em>community_database.zip</em> -- Output of "gen_topo_db". Various topological parameters related to the close neighborhood of neurons in the model</li> <li><em>extracted_components.zip </em>-- Output of "manifold_analysis". Results of factor analysis on the spike times in the <em>input_data</em></li> <li><em>features.zip</em> -- Output of "topological_featurization". A new dimensionality reduction method we introduce in the <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">manuscript</a></li> <li><em>split_spike_trains.zip -- </em>Output of "split_time_windows". The spike trains, split into time windows that are the responses to individual stimuli injected in the simulation</li> <li><em>structural_parameters.zip</em><em> -- </em>Output of "Structural tribe analysis". Values for the topological parameters in <em>community_database.zip</em> associated with the neuron samples specified in <em>tribes.zip</em></li> <li><em>structural_parameters_vol.zip</em> -- Output of "Structural tribe analysis". Same as above, but for volumetric neuron samples.</li> <li><em>triads.zip</em> -- Output of "Triad-counts". Over- and under-expression of triad motifs in the samples in <em>tribes.zip</em>.</li> <li><em>tribes.zip</em><em> -- </em>Output of "sample_tribes". Specific neuron samples that are then analyzed further.</li> </ul> </li> <li>Notebooks: Place into <em>root/notebooks</em> and unzip in place <ul> <li><em>notebooks.zip</em><em> -- </em>Jupyter notebooks. Run them to generate the figures in the manuscript.</li> </ul> </li> </ul> <p> </p> <p><strong>Updates:</strong></p> <p>v1.1.0 (2020/12/11): Added some additional control cases to the results for figure 7. These results will probably not be updated on bioRxiv, but go into the submission to a journal.</p> <p>v1.2.0 (2021/10/05): Updated the notebooks.zip with changes we made in response to reviewers' feedback.</p>
OnSSET processed GIS input data for 20 Sub Sahara Africa countries
<p>This repository contains the processed geospatial data files needed to run the Open-Source Spatial Electrification Tool (OnSSET), for 20 countries in Sub Sahara Africa. These data files were created by the KTH team for the Global Electrification Platform (GEP) model (https://electrifynow.energydata.info/). To access result files and geospatial population clusters, please go to https://energydata.info/dataset/?q=gep.</p>
Input data for performing a model evaluation of the sectional aerosol module SALSA embedded to PALM model system 6.0
<p>This dataset includes the input information applied to perform a model evaluation study of the PALM model system together with the sectional aerosol module SALSA. </p> <p>The content:</p> <ul> <li>PIDS_STATIC: building height and leaf area density data</li> <li>PIDS_AERO_<simulation time>_<number of aerosol size bins>: aerosol emission data as size bin specific surface emissions (level of detail 2) and aerosol background concentrations</li> <li>PIDS_CHEM_<simulation time>: emission data and background concentrations of gaseous compounds</li> </ul> <p>PIDS_STATIC contains static data and is therefore the same for all simulations.</p> <p>See the model documentation https://palm.muk.uni-hannover.de/trac/wiki/doc for further details.</p>
Input data to model multiple effects of large-scale deployment of grass in crop-rotations at European scale
<p>This is the input dataset to a Python script (<a href="https://github.com/oskeng/MF-bio-grass">https://github.com/oskeng/MF-bio-grass</a>) used to model the effects of widespread deployment of grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts.</p> <p>For more information about the dataset and the study, see the original article:</p> <p>Englund, O., Mola-Yudego, B., Börjesson, P., Cederberg, C., Dimitriou, I., Scarlat, N., Berndes, G. Large-scale deployment of grass in crop rotations as a multifunctional climate mitigation strategy. GCB Bioenergy</p>
Solanum pimpinellifolium input data collected from the TPA - used for GWAS
<p>The GWAS input data used for mapping the candidate genes in S. pimpinellifolium collection, exposed to salt stress in The Plant Accelerator experiment (TPA). The phenotypic data was collected in an experiment was performed by Mitchell Morton while being a PhD student in the group of Prof. Mark Tester at KAUST. The genotypic data was collected by Magdalena Julkowska, and used for sequencing. The SNPs were called by Elodie Ray, and subsequently curated by Magdalena Julkowska for GWAS analysis. The GWAS was conducted by Magdalena Julkowska. </p>
Input data - Wasteaware Cities Benchmark Indicators - WABI 2023 - Global data analytics
<p>This is the input dataset for the research publication "<em>Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning</em>". It features </p> <ul> <li>Metadata info used by R codes</li> <li>Full data set for the WABI, used by the R codes</li> <li>Data required for plotting the map in Figure 1</li> </ul> <p>The independent variables data set refers to specific indicators of the WABI methodology (<a href="https://www.sciencedirect.com/science/article/pii/S0956053X14004905">https://www.sciencedirect.com/science/article/pii/S0956053X14004905</a>) which generates solid waste management and resource recovery profiles for cities. It is applied here for 40 cities around the world. The data set contains also values for a series of explanatory variables, which are measures of the level of socioeconomic development at country level.</p> <p> </p> <p> </p> <p> </p>
Input data from: Mammalian forelimb evolution is driven by uneven proximal-to-distal morphological diversity
<p>Vertebrate limb morphology often reflects the environment due to variation in locomotor requirements. However, proximal and distal limb segments may evolve differently from one another, reflecting an anatomical gradient of functional specialization that has been suggested to be impacted by the timing of development. <span>Here we explore whether the temporal sequence of bone condensation predicts variation in the capacity of evolution to generate morphological diversity in proximal and distal forelimb segments across more than </span>600 species of mammals. Distal elements not only exhibit greater shape diversity, but also show stronger within-element integration and, on average, faster evolutionary responses than intermediate and upper limb segments. Results are consistent with the hypothesis that late-developing distal bones display greater morphological variation than more proximal limb elements. However, the higher integration observed within the autopod deviates from such developmental predictions, suggesting that functional specialization plays an important role in driving within-element covariation. Proximal and distal limb segments also show different macroevolutionary patterns, albeit not showing a perfect proximo-distal gradient. The high disparity of the mammalian autopod, reported here, is consistent with <span>the higher potential of development to generate variation in more distal limb structures, as well as functional specialization of the distal elements.</span></p>
Intergenic RNAPII Atlas : input data
<p>This Zenodo record refers to the <strong>"input data" </strong>used in the manuscript titled "<a href="https://doi.org/10.1101/2023.03.24.534112">Characterising intergenic transcription at RNA polymerase II binding sites in normal and cancer tissues</a>" by de Langen <em>et al.</em> </p> <p>This Zenodo record allows to replicate the results presented in the manuscript, please refer to the instructions available on Github at <a href="https://github.com/benoitballester/Pol2Atlas">https://github.com/benoitballester/Pol2Atlas</a>. </p> <p>The results presented in the manuscript can be accessed at <a href="https://zenodo.org/record/7740073">https://zenodo.org/record/8091826</a>.</p> <p><strong>In short :</strong> </p> <ul> <li><strong>Data "in" :</strong> this record</li> <li><strong>Data "out"</strong> : <a href="https://zenodo.org/record/7740073">https://zenodo.org/record/8091826</a></li> <li><strong>Github Code</strong> : <a href="https://github.com/benoitballester/Pol2Atlas">https://github.com/benoitballester/Pol2Atlas</a></li> </ul> <pre><code class="language-bash"># uncompress the gz files $ cat repro_data.gz.part* | gunzip -c > repro_data.gz</code></pre> <p> </p>
Emission input data for WRF-CHMIERE
<p>Emission input data for WRF-CHIMERE in eastern China during 2017.</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)</p>
Input data for the case study reported in "DREAM: an R package for druggability evaluation of human complex diseases".
<p>The data included in this record constituted the input for the case study reported in the manuscript "DREAM: an R package for druggability evaluation of human complex diseases", by Antonio Federico, Michele Fratello, Alisa Pavel, Lena Möbus, Giusy del Giudice, Angela Serra, Dario Greco. The data derive from transcriptomics experiments executed on lesional skin from atopic dermatitis patients and unaffected skin counterparts. The data consists of two files in ".txt" format reporting gene expression data in tabular format, where on the rows are reported genes and on the columns are reported samples. The data is an aggregated and batch-corrected collection of datasets originally downloaded by Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). The file "GE_Mic_AD_Pamr_MAARS.txt" reports gene expression estimates of lesional skin of atopic dermatitis patients, while the file "GE_Mic_AD_Pamr_nl_MAARS.txt" reports gene expression estimates of non-lesional skin of atopic dermatitis patients.</p>
Input data, species level results and code accompanying paper: Drivers of the changing abundance of European birds at two spatial scales
<p>This repository contains the input data, species level results and code associated with the paper: <strong>Drivers of the changing abundance of European birds at two spatial scales. </strong></p>
Input data for Atollgen pipeline
<p>Input data for Atollgen pipeline</p> <p>Contains:</p> <ul> <li>Frozen island raw sources (atollgen database inputs)</li> <li>hmm database (integrase and mobility signatures coming from ConjScan and Pfam-A)</li> <li>categorisation metadata for each signature contained in the integrase and mobility database</li> <li>Frozen genomes sequences from the NCBI</li> <li>Frozen list of actinobacteria taxonomy ids</li> <li>Frozen defense-finder database</li> <li>Frozen cards</li> </ul> <p>Frozen data ensure reproducibility for the pipeline, but up-to-date data should give similar (yet not identical) results.</p>
Input and Output simulation data of the THOR GCM for the paper Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme
<p>The input and ouput simulation data of the THOR GCM for Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme</p> <p>Global circulation models (GCMs) play an important role in contemporary investigations of exoplanet atmospheres. Different GCMs evolve various sets of dynamical equations which can result in obtaining different atmospheric properties between models. In this study, we investigate the effect of different dynamical equation sets on the atmospheres of hot Jupiter exoplanets. We compare GCM simulations using the quasi-primitive dynamical equations (QHD) and the deep Navier-Stokes equations (NHD) in the GCM THOR. We utilise a two-stream non-grey "picket-fence" scheme to increase the realism of the radiative transfer scheme. We perform GCM simulations covering a wide parameter range grid of system parameters in the population of exoplanets. Our results show significant differences between simulations with the NHD and QHD equation sets at lower gravity, higher rotation rates or at higher irradiation temperatures. The parameter exploration shows the relevance of choosing dynamical equation sets dependent on system and planetary properties.Climate states of hot Jupiters seemed to be more diverse than previously thought. There are exceptions to prograde superrotation. Overall, our study shows the evolution of different climate states which arise just due to different selection of Navier-Stokes equations and approximations. We show the shortcomings of approximations in GCMs made for Earth, but used for non Earth-like planets.</p>
Input data for running a GWAS on penicillin resistance in Streptococcus pneumoniae
<p>Results from running the pyseer tutorial at https://pyseer.readthedocs.io/en/master/tutorial.html</p>
HeatResilientCity II - work package 2.3: Interactions between buildings and open space adaptation measures – Meteorological input data for building performance simulation
<p>This repository contains <strong>meteorological</strong> <strong>data</strong> from urban climate simulations that were carried out in districts of the cities of Dresden and Erfurt as part of the <a href="http://heatresilientcity.de/">HeatResilientCity II</a> project. The data was extracted at specific points (receptors) of the urban climate model. In addition to the data, a <strong>script </strong>is attached that can be utilized to generate a time series for IDA ICE building performance simulations using IceWeather.exe. Therefore, a Microsoft Windows operating system is required. To create a time series, simply use the function <em>createIdaIceInput()</em> at the end of the script <em>createTimeSeries.py</em>. Further explanations can be found at the beginning of the script. Information about the ENVI-met data used to create the IDA ICE input can be found in <em>README_RawENVImetOutput_DD.txt</em> and <em>README_RawENVImetOutput_EF.txt</em>.</p> <p>Some input <strong>data files have already been generated</strong><strong> </strong>and can be directly used for<strong> thermal building performance simulations with IDA ICE</strong>. These files can be found in the folder <em>0.3_Input_Timeseries (Climate) for IDA ICE</em>.</p> <p>The <strong>naming convention</strong> of the final input data files for IDA ICE is as follows:</p> <ul> <li>TOWN_SCENARIO_RECEPTOR_AVERAGING_INTERFACE_LATITUDE_LONGITUDE_VERSION</li> <li>TOWN: Choose between 'Erfurt' and 'Dresden'</li> <li>SCENARIO: See further information in <em>README_RawENVImetOutput_DD.txt</em> and <em>README_RawENVImetOutput_EF.txt</em></li> <li>RECEPTOR: Location in the modelled area (ENVI-met simulation) where data was extracted.</li> <li>AVERAGING: Information about averaging the hourly values of the urban climate simulation (see <em>createTimeSeries.py and READMEs)</em></li> <li>INTERFACE: Information on how single days were joined together (see <em>createTimeSeries.py</em>).</li> <li>LATITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function <em>setIceWeatherParams()</em> if you are using other cities/custom ENVI-met simulation data.</li> <li>LONGITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function <em>setIceWeatherParams()</em> if you are using other cities/custom ENVI-met simulation data.</li> <li>VERSION: The version number can be set in the script.</li> </ul> <p>Example: <em>Dresden_2y_A1_a_timeSeries_24-24_51.0468_13.6707_v11.prn</em></p> <p><strong>Folder overview:</strong></p> <ul> <li>The ENVI-met raw data is stored in <em>0.1_Input_RawENVImetOutput</em>.</li> <li>The script is stored in <em>0.2_Input_ScriptsToCreateTimeSeries</em>.</li> <li>The final datasets ready for simulation with IDA ICE are stored in <em>0.3_Input_Timeseries(Climate)ForIDAICE</em>. This folder also contains some weather data time series that have already been created and can be used for IDA ICE (subfolders Erfurt_v11 and Dresden_v11).</li> </ul>
Deep-SDMs in the open oceans - INPUT DATA
<p>This repository contains input files to train the Deep-SDM model described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a></p> <p>This deposit contains:</p> <p>1. Training data: CSV dataset + 38 subfolders with data for each species (named after its GBIF id)</p> <p>2. Prediction data:</p> <p>2.1. Global use case (solstices & equinoxes of 2021): CSV dataset + data folder</p> <p>2.2. Western Indian Ocean use case: CSV dataset + data folder</p> <p>3. <em>species.csv </em>contains the taxonomic name of each taxon, as well as its GBIF id.</p> <p>4. <em>stats.npy</em> contains normalization factors for the data files</p> <pre><code class="language-python">meds, perc1, perc99 = np.load("stats.npy") item = np.load(file)[:,:,:25] real_values = (perc99 - perc1) * item + perc1</code></pre> <p> </p> <p>Each of these elements can be downloaded separately by scrolling to the <em>Files</em> section.</p>
Input Data for TEMIR v1.0
<p>This dataset contains the necessary input data for the Terrestrial Ecosystem Model in R (TEMIR) version 1.0 (<a href="https://github.com/amospktai/TEMIR">https://github.com/amospktai/TEMIR</a>).</p>
Input and output data from simulations of 2D valves and 3D inflow-outflow model using particle methods
<p>Input and output data of open-source softwares for computational fluid dynamics simulation involving fluid-structure interaction.</p> <p> </p> <p><strong>Data from two studies</strong></p> <ol> <li>Verifications of the weakly-compressible smoothed particle hydrodynamics (WCSPH) method, open-source code <a href="https://www.sphinxsys.org">SPHinXsys</a>, when applied to the flow of idealized 2D valve models.</li> <li>Validations of inflow-outflow model in moving particle semi-implicit (MPS) method, open-source code <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow">PolyMPS</a>.</li> </ol> <p> </p> <p><strong>Folders and Files</strong></p> <p><strong>valve-2D.zip </strong>is the folder with data from the idealized models of vertical and curved 2D valves:</p> <ul> <li>Vertical valves with parameters provided in <a href="https://doi.org/10.1016/j.jcp.2010.08.005">Gil et al., 2010</a></li> <li>Curved valves with parameters provided in <a href="http://doi.org/10.1007/s00466-013-0890-3">Wick, 2014</a></li> <li>source files (.cpp): input data (physical and numerical parameters) for SPHinXsys</li> <li>text files: SPHinXsys (.dat) and Reference (.tsv) results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>inflow-outflow-3D.zip </strong>is the folder with data from the inflow-outflow model in MPS:</p> <ul> <li>Fluid physical properties of water <ul> <li><span>\(\rho=1000kg/m^3 , \,\, \nu=10^{-6}m/s^{-2}\)</span></li> </ul> </li> <li>Pipes of length <span>\(L=0.15m\)</span>: <ul> <li>circular section of diameter <span>\(D=0.1m\)</span>.</li> <li>square section of sides <span>\(S=0.1m\)</span>.</li> </ul> </li> <li>Constante pressure variation (<span>\(\Delta P = 30 \,\, or \,\, 50 \,\, Pa\)</span>) between inflow and outflow: <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L}, \\ \Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> </ul> <ul> <li>Sinusoidal pressure variation (<span>\(\Delta P =700Pa \,\, , \,\, T = 2.0s\)</span>) between inflow and outflow <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L} \sin \omega t \, \\ \omega = \frac{2\pi}{T} \\ Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> <li>input data (.json, .grid, .stl): physical properties, numerical parameters and geometries for PolyMPS can be found at <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow/input">https://github.com/rubensamarojr/polymps/tree/inOutflow/input</a></li> <li>text files (.txt): PolyMPS and OpenFOAM results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>References</strong></p> <p><a href="https://doi.org/10.1016/j.jcp.2010.08.005">A. J. Gil. The Immersed Structural Potential Method for haemodynamic applications. J. Comput. Phys., 229 (2010), pp. 8613-8641</a></p> <p><a href="https://doi.org/10.1007/s00466-013-0890-3">T. Wick. Flapping and contact FSI computations with the fluid–solid interface-tracking/interface-capturing technique and mesh adaptivity. Comput Mech 53, 29–43 (2014)</a></p> <p><a href="https://doi.org/10.1016/j.cma.2014.10.040">D. Kamensky, et al. An immersogeometric variational framework for fluid–structure interaction: Application to bioprosthetic heart valves Comput. Methods Appl. Mech. Engrg., 284 (2015), pp. 1005-1053</a></p> <p><a href="https://doi.org/10.1016/j.cma.2015.12.023">C. Kadapa et al. A fictitious domain/distributed Lagrange multiplier based fluid–structure interaction scheme with hierarchical B-Spline grids. Comput. Methods Appl. Mech. Engrg., 301 (2016), pp. 1-27</a></p> <p><a href="https://doi.org/10.1016/j.jcp.2015.10.015">Jie Liu. A second-order changing-connectivity ALE scheme and its application to FSI with large convection of fluids and near contact of structures. J. Comput. Phys., 304 (2016), pp. 308-423</a></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.