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5,805 results for “Data model”
Data from: Fluid-kinetic model of a propulsive magnetic nozzle
<p># Data from: Fluid-kinetic model of a propulsive magnetic nozzle</p> <p> </p> <p>- Authors: Mario Merino, Judit Nuez, Eduardo Ahedo</p> <p>- Contact email: mario.merino@uc3m.es</p> <p>- Date: 2021-10-08</p> <p>- Keywords: magnetic nozzle, plasma propulsion, electrodeless plasma thrusters, kinetic model, collisionless electron cooling, magnetic thrust</p> <p>- Version: 1.0.0</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.5557592</p> <p>- License: This dataset is made available under the [Open Data Commons Attribution License](http://opendatacommons.org/licenses/by/1.0/)</p> <p> </p> <p>## Abstract</p> <p> </p> <p>This dataset contains the magnetic nozzle fluid-kinetic simulation results used to prepare:</p> <p> </p> <p>_[Mario Merino, Judit Nuez, Eduardo Ahedo, "Fluid-kinetic model of a propulsive magnetic nozzle", Plasma Sources Science and Technology](https://doi.org/10.1088/1361-6595/ac2a0b)._</p> <p> </p> <p>## Dataset description</p> <p> </p> <p>The simulations have been prepared combining two open source codes:</p> <p>[Akiles](10.5281/zenodo.1098432) and [Fumagno](10.5281/zenodo.593787).</p> <p>The model and the simulation cases are explained in the accompanying paper (https://doi.org/10.1088/1361-6595/ac2a0b).</p> <p> </p> <p>## Data files</p> <p> </p> <p>The datafiles are in standard Matlab .mat format. A recent version of [Matlab](https://www.mathworks.com/products/matlab.html) (2018a or newer) is needed to read these files .</p> <p> </p> <p>Datafiles are subdivided into two groups (1D and 2D).</p> <p> </p> <p>In the 1D group, simulations for the first part of the paper are contained. These are simulations along a single (1D) magnetic line. There are 7 files:</p> <p>1. line_J0.mat</p> <p>2. line_phiinfty5.mat</p> <p>3. line_phiinfty6.mat</p> <p>4. line_phiinfty7.mat</p> <p>5. line_phiinfty8.mat</p> <p>6. line_phiinfty9.mat</p> <p>7. line_phiinfty10.mat </p> <p>Each of these files has an identical structure, with the following Matlab variables in them. All variables are normalized as explained in the paper:</p> <p>* h: a vector containing the value of B (magnetic field strength) at each point in the magnetic line</p> <p>* phi: a vector containing the value of phi (electric potential) at each point in the magnetic line</p> <p>* electrons: a structure with all the moments and all the properties of the electrons</p> <p>* ions: a structure with all the moments and all the properties of the ions</p> <p> </p> <p>In the 2D group, simulations for the second part of the paper are contained. These are 2D simulations. A total of 5 files exist, corresponding to each simulation case in the paper:</p> <p>1. F.mat</p> <p>2. PHID.mat</p> <p>3. PHII.mat</p> <p>4. TD.mat</p> <p>5. TI.mat</p> <p>Each of these files has an identical structure, with the following Matlab variables in them. All variables are normalized as explained in the paper:</p> <p>* Z,R: position of points</p> <p>* B,ALPHA,KAPPA: magnetic field strength, angle, and curvature. B_B0 is B normalized with the upstream value on each line.</p> <p>* PHI, EZ, ER: electric potential and field components</p> <p>* J, J0: current density, and the integral current in the magnetic nozzle</p> <p>* N, N1, N2, N4: density of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* TE, TE1, TE2, TE4: average temperature of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* TPARE, TPARE1, TPARE2, TPARE4: parallel temperature of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* TPERE, TPERE1, TPERE2, TPERE4: perpendicular temperature of the full electron population and subpopulations 1 (free), 2 (reflected), 4 (doubly-trapped)</p> <p>* UE, UE1, UI: velocity of electrons, free electrons, ions</p> <p> </p> <p>## Citation</p> <p> </p> <p>Works using this dataset or any part of it in any form shall cite it as follows.</p> <p> </p> <p>The preferred means of citation is to reference the publication associated to this dataset, of DOI 10.1088/1361-6595/ac2a0b.</p> <p> </p> <p>Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.5557592.</p> <p> </p> <p>## Acknowledgments</p> <p> </p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p> </p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466). </p>
Data set of paper Model-Driven System-Performance Engineering for Cyber-Physical Systems
<p>This data set contains the raw and processed data of the paper <em>Model-Driven System-Performance Engineering for Cyber-Physical Systems</em>, published in the proceedings of ESWEEK’21.</p>
Populations of local direction-selective cells encode global motion patterns generated by self-motion. Data, Code and Model.
<p>Directional tuning of the population of local motion detectors T4/T5 in the visual system of the fruit fly <em>Drosophila melanogaster</em>. Direction tuning and receptive field location was measured by recording responses to visual stimuli containing dark or bright edges/stripes moving into 8 directions. All provided MATLAB scripts were used to analyze and illustrate data show in the manuscript 'Populations of local direction-selective cells encode global motion patterns generated by self-motion.'</p> <p>All data were obtained using <em>in vivo </em>two photon microscopy. Image time series were preprocessed using SIMA python software for motion alignment and further processed using custom written matlab or python code.</p> <p>Please find all relevant information to use the code in the README file.</p>
Spatiotemporal analysis of plankton drivers in the Belgian part of the North Sea. Data, scripts and model output
This archive contains the input data, R scripts and final results of a mechanistic model that uses near real-time data from the Belgian Part of the North Sea (2011-2017) to quantify the relative contributions of the bottom-up and top-down drivers in phytoplankton dynamics. Input data are zooplankton and phytoplankton abundances, nutrients, Sea Surface Temperature (SST), photosynthetically active radiation (PAR); from the LifeWatch data and infrastructure, funded by Research Foundation - Flanders (FWO). Water temperature data for one of the locations was obtained from Flemish Banks Monitoring Network at https://meetnetvlaamsebanken.be/. The R scripts are presented in a R Markdown file that can be executed in the Blue-Cloud Zoo and Phytoplankton EOV products Vlab at https://blue-cloud.d4science.org/web/zoo-phytoplankton_eov, operated by D4Science.org, www.d4science.org (Assante et al., 2019).
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>
Data for replication of the publication: Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach
<p>20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.</p>
Ray Tracing-Based Delay Model for Compensating Gravitational Deformations of VLBI Radio Telescopes (Data Set)
<p>The precision and the reliability of very long baseline interferometry (VLBI) depend on several factors. Apart from fabrication discrepancies or meteorological effects, gravity-induced deformations of the receiving unit of VLBI radio telescopes are identified as a crucial error source biasing VLBI products and obtained results such as the scale of a realized global geodetic reference frame. Gravity-induced deformations are systematical errors and yield signal path variations (SPVs). In 1988, Clark and Thomsen derived a VLBI delay model, which was adopted by the International VLBI Service for Geodesy and Astrometry (IVS) to reduce these systematic errors. However, the model parametrizes the SPV by a linear substitute function and considers only deformations acting rotationally symmetrically. The aim of this investigation is to derive the signal path variations of a legacy radio telescope and a modern broadband VGOS-specified radio telescope and to study the effect of nonrotationally symmetric deformation patterns. For that purpose, SPVs are obtained from a nonlinear spatial ray tracing approach. For the first time, a tilt and a displacement of the subreflector perpendicular to the optical axis of the feed unit is taken into account. The results prove the commonly used VLBI delay model as a suitable first-order delay model to reduce gravity-induced deformations.</p>
Model data used for the paper: "Heat extremes driven by amplification of phase-locked circumglobal waves forced by topography in an idealized atmospheric model"
<p>Atmospheric model output to reproduce the results of the study: "<strong>Heat Extremes Driven by Amplification of Phase-Locked Circumglobal Waves Forced by Topography in an Idealized Atmospheric Model" </strong>published in Geophysical Research letters.<br> <br> Authors: B. Jiménez-Esteve. K. Kornhuber and D. I.V. Domeisen <br> <br> DOI: <a href="https://doi.org/10.1029/2021GL096337">https://doi.org/10.1029/2021GL096337</a><br> <br> For more information about the model setup and the design of the experiments please refer to the above publication.</p>
Data, code and supplementary material for "A data integration framework for spatial interpolation of temperature observations using climate model data"
<p>Each zipped file contains code and data to reproduce the results in the paper and supplementary material. The Cyprus folder contains also the files to run the model, as well as the associated results. The Morocco folder only contains the results and the code used to manipulate it. </p>
Simulations for pre-industrial climate using EC-Earth3-LR model — selected data for a study on AMOC
<p>A long-term control simulation of pre-industrial period (1850 CE) climates were performed by the EC-Earth3-LR climate model with a horizontal resolution of ~1.125°. The dataset contains selected output data from the simulations.</p> <p>In total, a 2000-year long control simulation was made, which has pre-industrial orbital boundary conditions, initialized by a pre-run steady restart file (the output of approximately 500-year pre-industrial control simulation). This dataset is used to investigate internal climate variability without external forcing changes under pre-industrial climate conditions.</p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Cao et al. (2022).</p> <p><strong>Model configuration</strong><br> Time periods: Pre-Industrial (2000-year time slice)<br> ESM configuration: EC-Earth3-LR<br> Horizontal resolution: ~1.125° (~125 km)</p> <p><strong>Available data</strong><br> Annual mean data for standard oceanographic and meteorological variables.</p>
Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data from two monitoring stations Bertha Ganter – Fort McKay and Barge Landing for 20 August 2013 to 2 September 2013. This data was used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125) for model output and observational data comparisons. The same data can be accessed and downloaded from "<a href="https://wbea.org/historical-monitoring-data/">https://wbea.org/historical-monitoring-data/</a>".</p>
Datastes for NeuroDAVIS: A neural network model for data visualization
<p>These are the datasets used in the work NeuroDAVIS: A neural network model for data visualization.</p>
Terrestrial laser scanning data Wytham Woods: individual trees and quantitative structure models (QSMs)
<p>This dataset was used for the analysis of the following publication:<br> <em>Laser scanning reveals potential underestimation of biomass carbon in temperate forest. Calders, K, Verbeeck, V, Burt, A, Origo, N, Nightingale, J, Malhi, Y, Wilkes, P, Raumonen, P, Bunce, R G H and Disney, M. Ecological Solutions and Evidence (accepted)</em></p> <p><strong>Any use of this dataset should cite the paper above </strong>(Creative Commons Attribution 4.0 International Public License).</p> <p>Contact: kim.calders@ugent.be</p> <p> </p> <p>================================================<br> Dataset<br> ================================================</p> <p><strong>General</strong>: <br> TLS data were collected in leaf-off conditions during late November 2015 - January 2016. Windy days were avoided to ensure data quality. We used a RIEGL VZ-400 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH). The instrument has a beam divergence of 0.35 mrad and operates in the infrared (wavelength 1550 nm) with a range up to 350 m. The pulse repetition rate for each scan was 300 kHz, the minimum range was 0.5 m and the angular sampling resolution was 0.04°. This resulted in 22,500,000 outgoing pulses for a single scan, resulting in a beam diameter of 2.45 cm and beam spacing of 3.5 cm at 50 m (for example). The azimuth angle range was 0-360° and the zenith angle range was 30-130°. Therefore an additional scan was acquired at each scan location with the scanner tilted at 90° from the vertical to complete sampling of the full hemisphere at each location. Scans were done in a larger 6 ha area using an approximate 20 m × 20 m grid, to ensure the best possible data quality within our 1.4 ha study area. Trees which had at least more than half of their stem at tree diameter 1.3 m inside the boundaries of the study area were included</p> <p>[ Note that this dataset contains 876 individual trees, but after applying the boundary conditions, 835 trees within the study area were used in the analysis of the paper >> see TLS_Inventory.ipynb]</p> <p>Full details of the methods to segment individual trees and generate the QSMs can be found in the paper <em>Calders et al. Ecological Solutions and Evidence.</em></p> <p><strong>Tree ID:</strong><br> Tree IDs can have numbers only or numbers + letters. A number only means this was a base with one stem. A number + letter means individual trees (split below 1.3m), that share a common tree base.</p> <p><strong>Datasets:</strong><br> 1) DATA_clouds_txt & DATA_clouds_ply: Individually segmented trees in *txt and *ply format. File naming is [tree_id].*txt or [tree_ply].*tx</p> <p>2) DATA_QSM_opt: optimised QSMs using TreeQSM v2.0 (https://github.com/InverseTampere/TreeQSM). File naming is [tree_id]-[dmin0]-[rcov0]-[nmin0]-[dmin]-[rcov]-[nmin]-[lcyl]-[NoGround]-[iteration].mat </p> <p>3) Raw scan data can be found here: http://dx.doi.org/10.5285/ed9156e1697343e4ad82e83ed550e345</p> <p> </p> <p>================================================<br> Paper analysis<br> ================================================</p> <p>We have provided all scripts (analysis_and_figures) that were used to:</p> <p>1 ) analyse the data (TLS_Inventory.ipynb):<br> ----- Analysis of point clouds and QSMs using TLS_Inventory.py.ipynb > tls_summary.csv (#876 trees)<br> ----- Link with census &1.4ha > trees_summary.csv (#835 trees)</p> <p>2) generate the paper figures:<br> ----- various *.R and *.ipynb scripts in the main folder and /allometriesTLS/</p> <p> </p> <p>================================================<br> Funding<br> ================================================</p> <p>The TLS fieldwork was funded through the Metrology for Earth Observation and Climate project (MetEOC-2), grant number ENV55 within the European Metrology Research Programme (EMRP). The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union. Funds for purchase of the UCL RIEGL VZ-400 instrument was provided by the UK NERC National Centre for Earth Observation (NCEO) and UCL Geography. The census of the forest plot was supported by an ERC Advanced Investigator Grant to Yadvinder Malhi (GEM-TRAIT, grant number 321131).</p>
Data for: Modeling the transition of death assemblages through the mixed layer predicts a downcore increase in time averaging
<p>Understanding how time averaging changes during the burial is essential for using Holocene and Anthropocene cores to analyze ecosystem change, given the many ways in which the time averaging affects biodiversity measures. Here, we use transition-rate matrices to explore how time averaging changes downcore when shells transit through a taphonomically-complex mixed layer into permanently-buried historical layers: this is a null model, without any temporal changes in rates of sedimentation or bioturbation, to contrast with downcore patterns that might be produced by human activity. Assuming stochastic burial and exhumation movements of shells between increments within the mixed layer and stochastic disintegration within increments, almost all combinations of net sedimentation, mixing, and disintegration produce a downcore increase in time averaging (interquartile range, IQR), typically associated with a decrease in kurtosis and skewness and with a shift from right-skewed to symmetrical age distributions. A downcore increase in time averaging is a null expectation wherever bioturbation generates an internally-structured mixed layer (i.e., a surface well-mixed layer is underlain by an incompletely-mixed layer), so that shells are mixed throughout the entire mixed layer at slower rate than they are buried below it by sedimentation. This downcore trend created by mixing is further amplified by the downcore decline in disintegration rate. Using data from the southern California shelf, we find that transition-rate matrices accurately reproduce the downcore changes in IQR, skewness, and kurtosis observed in sediment cores. The right-skewed distributions typical of surface death assemblages – the focus of most actualistic research – might be fossilized under exceptional conditions of episodic anoxia or sudden burial. However, such right-skewed assemblages will not typically transfer into subsurface historical layers and thus will be geologically transient. The deep-time fossil record will be dominated instead by more time-averaged assemblages with weakly skewed age distributions that form in the lower parts of the mixed layer.</p>
Data for: Occupancy–detection models with museum specimen data: Promise and pitfalls
<p>Historical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed in order to enable robust inferences. Occupancy-detection models, a relatively new and powerful suite of statistical methods, are a potentially promising avenue because they can account for changes in collection effort through space and time.</p> <p>We use simulated datasets to identify how and when patterns in data and/or modelling decisions can bias inference. We focus primarily on the consequences of contrasting methodological approaches for dealing with species' ranges and inferring species' non-detections in both space and time. </p> <p>We find that not all datasets are suitable for occupancy-detection analysis but, under the right conditions (namely, datasets that are broken into more time periods for occupancy inference and that contain a high fraction of community-wide collections, or collection events that focus on communities of organisms), models can accurately estimate trends. Finally, we present a case-study on eastern North American odonates where we calculate long-term trends of occupancy by using our most robust workflow. </p> <p>These results indicate that occupancy-detection models are a suitable framework for some research cases and expand the suite of available tools for macroecological analysis available to researchers, especially where structured datasets are unavailable.</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>
single-cell RNAseq data (data set 1) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset1) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CRC samples downloaded from the GEO website (<strong>GSE81861). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
Data for: Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction (Part 1/2)
<p>Magnetic Resonance Imaging measurement data used in our paper about "Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>)</p>
Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling
<p>The digital twin has recently become a popular topic in research related to manufacturing, such as Industry 4.0, the industrial internet of things, and cyber-physical systems. In addition, digital twins are the focus of several research areas: construction, urban management, digital transformation of the economy, medicine, virtual reality, software testing, and others. The concept is not yet fully defined, its scope seems unlimited, and the topic is relatively new; all this can present a barrier to research. The main goal of this paper is to develop a proper methodology for visualizing the digital-twin science landscape using modern bibliometric tools, text-mining and topic-modelling, based on machine learning models—Latent Dirichlet Allocation (LDA) and BERTopic (Bidirectional Encoder Representations from Transformers). The scope of the study includes 8693 publications on the topic selected from the Scopus database, published between January 1993 and September 2022. Keyword co-occurrence analysis and topic-modelling indicate that studies on digital twins are still in the early stage of development. At the same time, the core of the topic is growing, and some topic clusters are emerging. More than 100 topics can be identified; the most popular and fastest-growing topic is ‘digital twins of industrial robots, production lines and objects.’ Further efforts are needed to verify the proposed methodology, which can be achieved by analyzing other research fields.</p>
The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data
<p>In our work, we implemented the ABC-model and could show that one assay for measuring the openness of enhancers is sufficient. Further, we propose a generalised calculation of the ABC-score, which describes enhancer activity in a gene-specific manner, and which includes all TSS, without requiring any additional data. We combined our implementation of the ABC-score with an approach to quantify TF binding affinity into STARE: a framework to derive TF affinities to genes. STARE was also designed for potential application on single-cell data. You can find the code in our <a href="https://github.com/schulzlab/stare">GitHub repository</a> and more details in our <a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtad062">publication</a>.</p> <p>We provide the data for the validation of our ABC-implementation on two CRISPR-screens. We also provide the results of our analysis of single-cell data of the human heart with STARE. All data is in hg19.</p> <p>Content:</p> <ul> <li>CRISPRi_screens: One file for each CRISPRi-screen with interactions that were used to plot precision-recall curves, containing columns for different ABC scoring versions.</li> <li>Enformer: Similar to the CRISPRi_screens, but containing columns for different calculations for Enformer's predicted expression change upon in silico mutagenesis of the enhancer region.</li> <li>K562_CandidateEnhancer: K562 enhancer with the 4th column for enhancer activity, one file for each activity representation that was measured.</li> <li>K562_ABC_Predictions: Regular ABC-scores and generalised ABC-scores for each activity measurement. The files contain all scored interactions for a 10MB window, without any cut-off. We also included the results of the implementation of the ABC-score of Fulco et al. (2019).</li> <li>STARE_Hocker_*: Whole STARE output for human heart single-cell data, one for regular ABC, generalised ABC, generalised ABC with average Hi-C matrix and one based on co-accessibility analysis. All approaches were run with a 5 MB window (except for GeneralisedABC500kb), the ABC-based runs with a score cut-off of 0.02. Each folder contains two subdirectories, one for the ABC-scoring and one for the Gene-TF affinity matrices. The 'ABC_output' also contains a GeneInfo file for each cell type, summarising different attributes per gene.</li> <li>INVOKE_Hocker_*: Folder with the input and output of INVOKE (see https://github.com/schulzlab/tepic), based on the STARE runs. CS genes stands for cell type-specific genes, defined as genes with a z-score across cell types of ≥ 2 and TPM ≥ 0.5. The INVOKE commands were as follows: <ul> <li>Rscript INVOKE.R --dataDir=<TF-Gene matrix> --outDir=<out_path> --response=Expression --regularization=E --performance=TRUE --outerCV=10 --seed=1234</li> </ul> </li> </ul> <p>Importantly, the results are based on data from the following publications:</p> <ul> <li>CRISPRi-screens: <ul> <li>Gasperini, Molly, Andrew J. Hill, José L. McFaline-Figueroa, Beth Martin, Seungsoo Kim, Melissa D. Zhang, Dana Jackson, et al. “A Genome-Wide Framework for Mapping Gene Regulation via Cellular Genetic Screens.” <em>Cell</em> 176, no. 1–2 (January 2019): 377-390.e19. https://doi.org/10.1016/j.cell.2018.11.029.</li> <li> <p>Schraivogel, Daniel, Andreas R. Gschwind, Jennifer H. Milbank, Daniel R. Leonce, Petra Jakob, Lukas Mathur, Jan O. Korbel, Christoph A. Merten, Lars Velten, and Lars M. Steinmetz. “Targeted Perturb-Seq Enables Genome-Scale Genetic Screens in Single Cells.” <em>Nature Methods</em> 17, no. 6 (June 2020): 629–35. https://doi.org/10.1038/s41592-020-0837-5.</p> </li> <li> <p>Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” <em>Nature Genetics</em> 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</p> </li> </ul> </li> <li>Enformer model: Avsec, Žiga, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, and David R. Kelley. “Effective Gene Expression Prediction from Sequence by Integrating Long-Range Interactions.” <em>Nature Methods</em> 18, no. 10 (October 2021): 1196–1203. https://doi.org/10.1038/s41592-021-01252-x.</li> <li>K562 predictions and average Hi-C matrix: Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” <em>Nature Genetics</em> 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</li> <li>Hi-C matrix for K562 predictions: Rao, S. et al. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell, 159(7), 1665–1680</li> <li>STARE and INVOKE runs: Hocker, J. D. et al. (2021). Cardiac cell type–specific gene regulatory programs and disease risk association. Science Advances, 7(20), eabf1444</li> <li>H3K27ac HiChIP for STARE runs: Anene-Nzelu, C. G. et al. (2020). Assigning Distal Genomic Enhancers to Cardiac Disease–Causing Genes. Circulation, 142(9), 910–912</li> <li>INVOKE software: Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction Schmidt et al., Nucleic Acids Research 2016; doi: 10.1093/nar/gkw1061</li> </ul> <p> </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.