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5,805 results for “Data model”
Data set of CA1 pyramidal cell models using an intact whole hippocampus preparationV2
<p>The frequency-current (f-I) profiles and an example of rebound firing of pyramidal cells are presented. Four .abf files contain the f-I curve data for the respective cell (as labelled PYR1, PYR2, PYR3 and PYR4 for Pyramidal cell 1, Pyramidal cell 2, Pyramidal cell 3 and Pyramidal cell 4). That is, they contain the cell's response to the application of a series of depolarizing current steps of 1 s duration while the cells are held in current clamp, as well as the current clamp data itself. Each recording is 2 s total. Amplitudes of the input were increased incrementally with step sizes of 10 pA for PYR1, PYR3, and PYR4, and a step size of 25 pA for PYR 2. PYR1 first spikes on the 5th of 30 steps with 38.7 pA of depolarizing input. PYR2 first spikes on the 3rd of 13 steps with 1.2 pA of input. PYR3 first spikes on the 7th of 34 steps with 62.0 pA of input, and PYR4 first spikes on the 7th of 30 steps with 12.1 pA of input. In the .abf file labelled PYR5_rebound, an example of rebound firing of a pyramidal cell following hyperpolarizing input is given. While the cell was held at -52 mV in current clamp, as series of 1 s hyperpolarizing steps (10 steps, 25 pA increments) were used to record the post- hyperpolarization rebound spiking. For the figure showing this rebound spiking (Figure 1), the first two hyperpolarizing steps and the respective firing are shown. For visualization purposes, the spike artifact in the current clamp input trace was removed and replaced with the mean current in the Figure.</p>
Experimental Data for: Research Perspective on Supporting Software Engineering via Physical 3D Models
<p>Experimental data for the experiment presented in the technical report 1507: "Research Perspective on Supporting Software Engineering via Physical 3D Models"</p>
Data from "Testing models of peripheral encoding using metamerism in an oddity paradigm"
<p>Raw data and stimuli from the experiments reported in Wallis, Bethge & Wichmann (under review). "Testing models of peripheral encoding using metamerism in an oddity paradigm". Journal of Vision.</p> <p>For the code, see http://doi.org/10.5281/zenodo.34218.</p> <p>Please consult the README file in the archive for detailed information on reproducing the results of the paper.</p> <p>Stimuli are modified from the "Judd" dataset (https://people.csail.mit.edu/tjudd/WherePeopleLook/index.html). The citation is </p> <p>Judd, T., Ehinger, K., Durand, F., & Torralba, A. (2009). Learning to predict where humans look. In <em>Computer Vision, 2009 IEEE 12th international conference on</em> (pp. 2106–2113). IEEE.</p> <p>We have been granted permission to reshare these images by Tilke Judd (thanks!).</p>
Supporting data: Structure-based Markov random field model for representing evolutionary constraints on functional sites
<p>Supporting data for the paper:</p> <p>Chan-Seok Jeong, Dongsup Kim. Structure-based Markov random field model for representing evolutionary constraints on functional sites. Submitted. (2015)</p> <p>See 'README' for a description of the contents.</p>
Modeling of the bacterial molecular chaperone GroEL using 3D EM data and cnmultifit
<p>These scripts demonstrate the use of IMP, MODELLER and Chimera in the modeling of the bacterial molecular chaperone GroEL. First, MODELLER is used to generate structures for the individual components in the GroEL complex. Then, IMP is used to fit these components together into the electron microscopy density map of the entire complex.</p>
Learning stochastic process-based models of dynamical systems from knowledge and data - Libraries, incomplete models and data
<p>The archive contains all libraries of domain knowledge, the incomplete models and the data used in the experiments described in the manuscript titled "Learning stochastic process-based models of dynamical systems from knowledge and data" pubilshed in BMC Systems Biology</p>
Model, configuration, data, and analysis scripts for The Evolution of Cooperation by the Hankshaw Effect
<p>Computational model, configuration files, result data, and analysis scripts for The Evolution of Cooperation by the Hankshaw Effect as published in Evolution (doi: 10.1111/evo.12928)</p>
Data and plotting scripts used in "High level implementation of geometric multigrid solvers for finite element problems: applications in atmospheric modelling"
<p>Raw performance data and plotting scripts used to generate the figures in the paper "High level implementation of geometric multigrid solvers for finite element problems: applications in atmospheric modelling"</p>
Testing sky brightness models against radial dependency: A dense two dimensional survey around the city of Madrid, Spain: SQM data
<p>We present a study of the night sky brightness around the extended<br /> metropolitan area of Madrid using Sky Quality Meter (SQM) photometers. The map is the first to cover the spatial distribution of the sky brightness in the<br /> center of the Iberian peninsula. These surveys are neccessary to test the light<br /> pollution models that predict night sky brightness as a function of the<br /> location and brightness of the sources of light pollution and the scattering of<br /> light in the atmosphere. We describe the data-retrieval methodology, which<br /> includes an automated procedure to measure from a moving vehicle in order to<br /> speed up the data collection, providing a denser and wider survey than previous<br /> works with similar time frames. We compare the night sky brightness map to the<br /> nocturnal radiance measured from space by the DMSP satellite. We find that i) a<br /> single source model is not enough to explain the radial evolution of the night<br /> sky brightness, despite the predominance of Madrid in size and population, and<br /> ii) that the orography of the region should be taken into account when deriving<br /> geo-specific models from general first-principles models. We show the tight<br /> relationship between these two luminance measures. This finding sets up an<br /> alternative roadmap to extended studies over the globe that will not require<br /> the local deployment of photometers or trained personnel.</p>
Data for publication: Autoadaptive motion modelling for MR-based respiratory motion estimation
<p>This repository contains four T1-weighted 2D MR slice datasets from multiple slice positions covering the entire thorax during free breathing and breath holds. The data was used to evaluate our novel autoadaptive respiratory motion model which we proposed in [1]. In particular, the datasets contain the following:</p> <ol> <li>Acquisition of all sagittal slice positions covering the thorax and one coronal slice position acquired during a breath hold.</li> <li>Results of registration between adjacent sagittal slice positions [control point displacements (cpp) and displacement fields (dfs)]</li> <li>40 dynamic acquisitions of each slice position also present in the breath-hold acquired during free breathing. </li> <li>Results of registration of the dynamic acquisitions to the respective breath-holds slices (cpp's and dfs's). </li> </ol> <p>The data is divided into 4 zip files, each containing the data of one volunteer. The folder structure for each is as follows:</p> <blockquote> <p>|-- bhs (breath hold data)<br /> | |-- images (images)<br /> | | |-- cor<br /> | | `-- sag<br /> | `-- mfs_slpos2slpos (registration results)<br /> | `-- sag<br /> `-- dyn (dynamic free-breathing data)<br /> |-- images (images)<br /> | |-- cor<br /> | `-- sag<br /> `-- mfs_tpos2tpos (registration results)<br /> |-- cor<br /> `-- sag</p> </blockquote> <p>Please, see our publication [1] for details on the acquisition sequence and registration used. </p> <p>--</p> <p>[1]: CF Baumgartner, C Kolbitsch, JR McClelland, D Rueckert, AP King, <em>Autoadaptive motion modelling for MR-based respiratory motion estimation</em>, Medical Image Analysis (2016), http://dx.doi.org/10.1016/j.media.2016.06.005</p>
Data for the paper "Addressing Uncertainties in Modelling Cumulative Impacts within Maritime Spatial Planning in the Adriatic and Ionian Region"
<p>Data for the paper "Addressing Uncertainties in Modelling Cumulative Impacts within Maritime Spatial Planning in the Adriatic and Ionian Region."</p>
Data and plotting scripts used in "High level implementation of geometric multigrid solvers for finite element problems: applications in atmospheric modelling"
<p>Raw performance data and plotting scripts used to generate the figures in the paper "High level implementation of geometric multigrid solvers for finite element problems: applications in atmospheric modelling"</p> <p>A previous version of this dataset (corresponding to an earlier revision of the paper) is available as https://doi.org/10.5281/zenodo.50533.</p>
Development of predictive models of the kinetics of a hydrogen abstraction reaction combining quantum-mechanical calculations and experimental data
<p>The files contain the electronic structure calculations for all the levels of theory tested in this work.</p>
Raw fMRI data from 12 rats used in the manuscript "Mapping of hemodynamic responses to the sensorymotor stimulation in a rodent model: a BOLD fMRI study" submitted to PlosOne for publication
<p>Raw data are from twelve male adult Wistar rats (Charles River Laboratories, Paris-France) weighing 300 ± 20g.</p> <p>Rats were initially anesthetized (induction) with 3% isoflurane and were maintained under 0,7-0.8% (sedation along with a muscle relaxation) during fMRI session. </p> <p>Each rat was submitted to two fMRI sessions: one with TE of 30 ms and and other with TE of 40 ms.</p> <p>For fMRI acquisition, electrodes were inserted subcutaneously in the palmar surface of the right hindpaw of each rat and electrical stimulation (current pulses with a 1.7 mA amplitude, 10 ms duration and 8 Hz frequency) was applied in a block-design starting with a resting period of 25s as a baseline followed by 25s stimulation, repeated 8 times.</p> <p>Ten 1-mm thick contiguous axial slices, from -6.36 mm to +2.64 mm to Bregma, were acquired with a two-shot gradient echo planar imaging (GE EPI) pulse sequence (2.56 cm2 FOV; 64x64 matrix size; a TR of 1000 ms; a flip angle of 50°) resulting in the pixel size of 0.4 mm.</p> <p>All imaging experiments were performed on a 4.7T Bruker (Biospec 47/40, Bruker, GmbH, Ettlingen,Germany) with a horizontal bore magnet equipped with a 12 cm gradient coil (Bruker BGA12, 400 mT/m) and interfaced to AVANCE III console. Two actively decoupled RF coils were used: a 7.2-cm diameter volume coil for transmission and a 2-cm diameter surface coil (Rapid Biomedical, Rimpar, Germany) positioned on the top of the animal's head for reception.</p>
Adjudicating between face-coding models with individual-face fMRI responses: Data and analysis software
<p>Computational model fits to human neuroimaging data. Please see included readme.txt file.</p>
Underlying data for Slimani et al. Identification of dominant hydrogeochemical processes for groundwaters in the Algerian Sahara supported by inverse modeling of chemical and isotopic data
<p>The data hereafter underlie the paper by Slimani et al. doi:10.5194/hess-20-1-2016,</p> <p>appeared to Hydrol. Earth Syst. Sci., 20, 1-23, 2016.</p> <p> </p> <p> </p> <p>1. File Tableaux_data.xls</p> <p> </p> <p>This is an Excel sheet file. It contains:</p> <p>- raw analytical data, mostly in mg/L;</p> <p>- data converted in mmol/L;</p> <p>- data corrected from the defect of cations - anions balance; the correction is made proportionally.</p> <p>- the previous data completed with logarithms of activities, computed by Phreeqc, </p> <p>for calcium, sulfate, carbonate and water; those data are used to plot equilibrium diagrams for calcite and gypsum (figure 6);</p> <p>- for Phreatic aquifer only, saturation indexes for halite, anhydrite, calcite, dolomite and gypsum, along with distance from south to north, used in figure 7.</p> <p>2. Directory Phreeqc_res</p> <p>Contains the input file with all samples from CI, CT and Phr in a single file, and the selected output file.</p> <p>All calculations were made with version phreeqc-3.1.2 and database sit.dat.</p> <p>3. Directory Inverse models</p> <p>This directory contains inverse models for computing transformations:</p> <p>- from CI (average) to CT (average):</p> <p>- from CT (average) to Phr (pole I, average);</p> <p>- from pure water to Phr (pole II, sample P036);</p> <p>- for mixing Phr pole I and II, and try to explain a sample typical of medium mixing ratio, sample P068.</p>
Data and trained word2vec model for ``Easy over Hard: A Case Study on Deep Learning''
<p>The data include: training and testing data pairs</p> <p>The word2vec model is pre-trained. </p> <p>More details, please refer to the paper</p>
Biological and environmental data for a study on transferability of statistical and machine learning models using North Sea Macrozoobenthos
<p>General</p> <p>Data documented here are not the product of our research but was scraped from various sources and processed - so no genuine reupload. This collection is a contribution to reproduceable reseach. All datasets are given in "RData" binary format</p> <p> </p> <p>Data description</p> <p>majornorthseabenthos </p> <p>This is macrozoobenthos data as data frame scraped from the GBIF repository (gbif.org). Species are Corbula gibba, Tellina fabula, Turritella communis, Euspira pulchella, Corystes cassive- launus, Upogebia deltaura, Lanice conchilega, Nephtys hombergii, Echinocardium cordatum, and Amphiura filiformis. data was postprocessed to have only single occurrence fon the approxinatel 1x1 km grid used for this study. Also, occurrences closer than 5 km close to shore were removed - including occurrences on land.</p> <p> </p> <p>Predictors</p> <p>A SpatialPixelsDataFrame in EPSG 4326 with five layers: Median grain size in micrometers, mud content in percent (both MUDAB database), water depth in meters above MSL (Weatherall et al, 2015), modelled average bottom shear stress from waves in N/sqrm (The Wamdi Group, 1988) and climatologival average winter bottom water temperature in deg. C (Stips et al, 2004).</p> <p> </p> <p> </p> <p>References</p> <p>Stips A, Bolding K, Pohlmann T, Burchard H (2004) Simulating the temporal and spatial dy- namics of the North Sea using the new model GETM (general estuarine transport model). Ocean Dynamics 54(2):266–283</p> <p>The Wamdi Group (1988) The WAM model-a third generation ocean wave prediction model. Journal of Physical Oceanography 18(12):1775–1810</p> <p>Weatherall P, Marks K, Jakobsson M, Schmitt T, Tani S, Arndt JE, Rovere M, Chayes D, Ferrini V, Wigley R (2015) A new digital bathymetric model of the world’s oceans. Earth and Space Science 2(8):331–345</p> <p> </p>
Data for "Fractal analysis of urban catchments and their representation in semi-distributed models: imperviousness and sewer system"
<p>The data set corresponds the data used in the paper : “Fractal analysis of urban catchments and their representation in semi-distributed models: imperviousness and sewer system”, published in 2017 in the Journal “Hydrology and Earth System Sciences” (http://www.hydrol-earth-syst-sci.net/).</p> <p>More precisely it corresponds to the matrices that are used in the fractal and multi-fractal analysis of the ten urban areas investigated in the paper.</p> <p> </p> <p>For each catchment, it is organised as follow:</p> <p>- catchment_name_conduit.asc : the matrix describing the sewer system.</p> <p>- catchment_name_OSM.asc : the matrix describing the impervious areas (roads and buildings) obtained via Open Street Map (www.openstreetmap.org)</p> <p>- catchment_name_OSM_house_only.asc : the matrix describing the “building” areas obtained via Open Street Map (www.openstreetmap.org)</p> <p>- catchment_name_imperviousness.asc : the matrix describing the representation of imperviousness in operational semi-distributed models.</p> <p> </p> <p>More details can be found in the paper.</p>
Supporting data for "Synthesizing long-term sea level rise projections - the MAGICC sea level model v2.0"
<p>This is supporting data and configuration information to reproduce results from the MAGICC sea level model (DOI: 10.5281/zenodo.572395) presented in Nauels et al. (2017), using version 7.0 beta of the simple climate carbon-cycle model MAGICC (Meinshausen et al. 2011). For a compiled or source code version of MAGICC including the sea level model (git hash: c5c4e05518ed99f2bd53e2c6e68d238bbd6f17ec), please contact alexander.nauels@climate-energy-college.org.</p> <p>MAGICC input data and CMIP5 reference datasets are provided as a zip-file.</p> <p><br> REFERENCE DATASETS</p> <p>For MAGICC version 7.0 beta, the ocean model has been updated to emulate CMIP5 ocean temperatures and thermal expansion. The calibration results shown in Nauels et al. (2017) are based on potential ocean temperature (thetao) and thermal expansion (zostoga) reference datasets that are provided the 'data' directory. Reference datasets for the other sea level components have to be requested from the authors of the corresponding studies (Marzeion et al. 2014, Fettweis et al. 2013, Nick et al. 2013, Ligtenberg 2013, Levermann 2014). All relevant CMIP5 MAGICC input (.IN) is provided in the 'run' directory.</p> <p><br> MAGICC MODEL CONFIGURATION</p> <p>To customize a MAGICC run, namelist entries have to be modified in the configuration file 'MAGCFG_USER.CFG'. In order to select a CMIP5 model specific MAGICC ocean calibration, the model setup has to be called with the 'FILE_TUNINGMODEL_XX' entry, e.g. 'OCNTUNE_CCSM4' for the CCSM4 model. Automatically, the corresponding initial ocean temperature profile and model specific ocean layer area fractions will be applied. For prescribing the respective surface air temperatures, the namelist flag 'CORE_PRESCRTEMP_APPLY' has to be set to 1, with the model specific temperature dataset defined by 'FILE_PRESCR_SURFACETEMP', e.g. 'CORE_PRESCRTEMP_CMIP5_CCSM4_RCP85.IN'. The following MAGICC namelist entries have to be adapted in order to fully reproduce CMIP5 consistent results presented in Nauels et al. (2017): </p> <p>[...]<br> e.g. FILE_TUNINGMODEL_1 = "OCNTUNE_CCSM4",<br> [...]<br> CORE_SWITCH_TEMPADJUST_OCN2ATM = 1,<br> CORE_SWITCH_OCN_TEMPPROFILE = 2,<br> CORE_SWITCH_OCN_AREAFACTOR = 1,<br> CORE_PRESCRTEMP_APPLY = 1,<br> e.g. FILE_PRESCR_SURFACETEMP = "CORE_PRESCRTEMP_CMIP5_CCSM4_RCP85.IN",<br> [...]<br> OUT_TEMPERATURE = 1,<br> OUT_TEMPOCEANLAYERS = 1,<br> OUT_SEALEVEL = 1,<br> OUT_PARAMETERS = 1,<br> [...]<br> OUT_ASCII_BINARY = "ASCII",<br> [...]</p> <p>MAGICC output is stored in the 'out' directory. Depending on the flag 'OUT_ASCII_BINARY', either ASCII or BINARY files are produced for the output parameters which are set to 1 in the namelist, e.g. 'OUT_SEALEVEL = 1'. </p> <p><br> MAGICC SEA LEVEL MODEL LICENSE</p> <p>This source code of the MAGICC sea level model is distributed under a Creative Commons Attribution-ShareAlike 4.0 license (https://creativecommons.org/licenses/by-sa/4.0/legalcode).</p> <p><br> MAGICC PARENT MODEL LICENSES</p> <p>The MAGICC executable is provided under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license (https://creativecommons.org/licenses/by-nc-sa/3.0/). The MAGICC source code is available under a separate license agreement. Any derivatives have to be fed back to the MAGICC developers, so that users of future MAGICC versions can have the benefit of applying the model alterations, enhancements etc. Furthermore, we would like you to provide feedback, bug reports and development suggestions.</p> <p><br> REFERENCES</p> <p>Fettweis, X., Franco, B., Tedesco, M., van Angelen, J. H., Lenaerts, J. T. M., van den Broeke, M. R., and Gallée, H.: Estimating the Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric climate model MAR, The Cryosphere, 7, 469–489, 2013.</p> <p>Levermann, A.,Winkelmann, R., Nowicki, S., Fastook, J. L., Frieler, K., Greve, R., Hellmer, H. H., Martin, M. A., Meinshausen, M., Mengel, M., Payne, A. J., Pollard, D., Sato, T., Timmermann, R., Wang, W. L., and Bindschadler, R. A.: Projecting Antarctic ice discharge using response functions from SeaRISE ice-sheet models, Earth Syst. Dynam., 5, 271–293, 2014.</p> <p>Ligtenberg, S. R. M., van de Berg, W. J., van den Broeke, M. R., Rae, J. G. L., and van Meijgaard, E.: Future surface mass balance of the Antarctic ice sheet and its influence on sea level change, simulated by a regional atmospheric climate model, Climate Dynamics, 41, 867–884, 2013.</p> <p>Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 1: Model description and calibration, Atmospheric Chemistry and Physics, 11, 1417–1456, 2011.</p> <p>Nauels, A., Meinshausen, M., Mengel, M., Lorbacher, K., and Wigley, T. M. L.: Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0, Geosci. Model Dev., 2017.</p> <p>Nick, F. M., Vieli, A., Andersen, M. L., Joughin, I., Payne, A., Edwards, T. L., Pattyn, F., and van de Wal, R. S. W.: Future sea-level rise from Greenland’s main outlet glaciers in a warming climate, Nature, 497, 235–238, 2013.</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.