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6,467 results for “process”
Data for Observation of the fastest chemical processes in the radiolysis of water
<p>These are the data underlying the figures in the manuscript entitled "Observation of the fastest chemical processes in the radiolysis of water". </p>
Predictive perception of self-generated movements: Commonalities and differences in the neural processing of tool and hand actions
<p>Dataset relative to the following publication:</p> <p>Pazen, M., Uhlmann, L., van Kemenade, B.M., Steinsträter, O., Straube, B., Kircher, T. Predictive perception of self-generated movements: Commonalities and differences in the neural processing of tool and hand actions. <em>NeuroImage</em>. DOI: <a href="https://doi.org/10.1016/j.neuroimage.2019.116309">10.1016/j.neuroimage.2019.116309</a></p> <p>Details are specified in the "readme.docx" file.</p>
Data set and data processing software of: Bacterial cell size modulation along the growth curve across nutrient conditions
<div>In Repository.zip it is possible to find the following folders:</div> <div> </div> <div>ImageProcess: Shows an example of the studied phtos, the segmentation mask obtained using Ilastik and the scripts used to estimate the cell dimensions.</div> <div> </div> <div>DataProcessing: Includes the raw data for cells size in all the studied conditions, a script showing the filtering and the data processing for plotting most of the figures of the article.</div> <div> </div> <div>CFUod: Includes the dataset of CFU and OD measurements studied in the article. The inered trends over different biological replica and the data processing for plotting the Figures in the main text. </div> <div> </div> <div> </div> <div>_______________________________________________________________</div> <div> </div> <div>ImageProces:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* IMAGES folder: Contains a 10 arbitrary folders of images, one for different OD conditions for the experiment of M9 + 0.25% CAS. Each image is a .tif file. The pixel size is 0.07 micrometers per pixel and they were obtained using bright field microscopy imaging. </div> <div> </div> <div>* SEG folder: Contains the masks for the same number of folders and photos equivalent photos in the IMAGES folder. Masks are also in .tif format.</div> <div> </div> <div>* "Dataset.csv": Is a typical dataset obtained from the images using the script of image processing. The data consists on the following columns:</div> <div>a. OD: Label of the OD measurement. Following experimental arbitrary notation, this number was the time in hours times 10. </div> <div>b. Photo: The label of the segmented photo.</div> <div>c. Area: Area of the segmenteated contour (squared micrometers).</div> <div>d. Len: Cell size length (Micrometers).</div> <div> </div> <div>* "ImageProcesing.ipynb": Jupyter notebook for procesing the images and their masks. The output is "Dataset.csv"</div> <div> </div> <div>____________________________________________________________________________________________________</div> <div> </div> <div> </div> <div>DataProcessing:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* RawData.csv: comma separated values file with the dimensions of different cells in for the studied conditions. The data consists on the following columns:</div> <div>a. Strain: Represents the experimental condition. It has the following values:</div> <div>M9= E.coli Growth in minimal M9</div> <div>M9cas25= E.coli in M9 + 0.25% Casaminoacids</div> <div>LBSS= E.coli in LB in steady growth</div> <div>SalLB= S. enterica in LB.</div> <div>SalM9=S. enterica in M9</div> <div>M9cas50= E.coli in M9 + 0.5% Casaminoacids</div> <div>LB2= E. coli in LB</div> <div>b. Photo: label for the studied photo.</div> <div>c. Time: Time in hours after resuspension.</div> <div>d. OD: Optical density of the studied population.</div> <div>e. Len: Cell length of the situdied contour (micrometers).</div> <div>f. Area: Projected area of the cell contour (squared micrometers).</div> <div>g. Area: Volume of the cell (cubic micrometers).</div> <div>h. SAV surface/volume ratio.</div> <div>i. Width: Cell width </div> <div>j. Aspect; Aspect ratio length/width</div> <div> </div> <div>*Stats.csv: Results of the statistical moments of cell size dimensions calculated from "Rawdata.csv" using "Plotter.ipynb". These data consists on the following columns:</div> <div> </div> <div>a. Time: Time (hours)</div> <div>b. OD: Optical density </div> <div>c. MnVol: Mean cell volume (cubic micrometers)</div> <div>d. MnVolErr: 95% confidence interval of the mean volume.</div> <div>e. CV2Vol: squared coefficient of variation of the volume.</div> <div>f. CV2VolErr: 95% confidence interval squared coefficient of variation of the volume.</div> <div>g. Mnw: Mean cell width (micrometers)</div> <div>h. MnwErr: 95% confidence interval of the mean width.</div> <div>i. CV2w: squared coefficient of variation of the cell width.</div> <div>j. CV2wErr: 95% confidence interval squared coefficient of variation of the width.</div> <div>k. MnLen: Mean cell length (micrometers)</div> <div>l. MnLenErr: 95% confidence interval of the mean length.</div> <div>m. CV2Len: squared coefficient of variation of the cell length.</div> <div>n. CV2LenErr: 95% confidence interval of the squared coefficient of variation of the cell length.</div> <div>o. Strain: Nutrient conditions</div> <div> </div> <div>*Ploter.ipnyb: Jupyter notebook which using "RawData.csv" calculates the moments in "Stats.csv" and plots most of the figures of the main article. </div> <div> </div> <div> </div> <div>__________________________________________________________________________________ </div> <div> </div> <div>CFUod: </div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* resultsOD.csv: OD values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the optical density</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the optical density.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the optical density for each experimental replica.</div> <div> </div> <div> </div> <div>* resultscfu.csv: cfu values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the cfu</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the cfu.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the cfu for each experimental replica.</div> <div> </div> <div> </div> <div>*ODGrowthRate.ipynb: jupyter notebook that uses "resultsOD.csv" and "resultscfu.csv" for plotting the ratio OD/cfu.</div> <div> </div> <div> </div> <div>Any question please ask cnieto@udel.edu</div> <div> </div> <div>Cesar Augusto Nieto Acuna</div> <div> </div> <div>Newark, Delaware, USA</div> <div> </div> <div>08/05/2024</div>
Raw data, processing, and simulation scripts for "Observation of Dynamic Nuclear Polarization Echoes"
<p>Data files and processing/plotting scripts for the first observation of "dynamic nuclear polarization echoes". Also a simulation script for a semi-quantitative quantum mechanical simulation of the spin dynamics.</p>
Wired for Stability: Evaluating Electrical Performance in Zinc Oxide-Modified Silver Nanowire Solution-Processed Transparent Electrode
<p>Characterisation dataset for “<span>Wired for Stability: Evaluating Electrical Performance in Zinc Oxide-Modified Silver Nanowire Solution-Processed Transparent Electrode</span>”, DOI:xx. Data for main and supporting figures provided as *.xlsx and *.txt files.</p>
Processed CODEX Datasets from - Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens
<p>This entry provides access to processed CODEX data files of three studies analyzed in the article "Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens". Details of datasets can be found in the Methods section of the article.</p> <p>For each dataset:</p> <ul> <li>A comma-separated values (CSV) file containing metadata of regions is included</li> <li>A zip file containing multiple CSV files is included: <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified and normalized for all cells in this region.</li> <li>`{region_id}.cell_types.csv`, a table containing two columns: "CELL_ID" and "CELL_TYPE". This table provides cell type annotations for all cells in this region.</li> <li>`{region_id}.cell_features.csv`, a table containing two columns: "CELL_ID" and "SIZE". This table provides morphology descriptors (only containing cell size for these studies) for all cells in this region.</li> </ul> </li> </ul> <p>These data files are also available through the Enable Medicine Public Study page: <a href="https://app.enablemedicine.com/portal/atlas-library/studies/92394a9f-6b48-4897-87de-999614952d94?sid=1168">https://app.enablemedicine.com/portal/atlas-library/studies/92394a9f-6b48-4897-87de-999614952d94?sid=1168</a>. Raw multiplexed immunofluorescence images will be accessible through the visualizer app of Enable Medicine Portal.</p> <p>Codes for this study are stored in <a href="https://gitlab.com/enable-medicine-public/space-gm">https://gitlab.com/enable-medicine-public/space-gm</a>. Please direct all further questions and/or issues to the gitlab repository or lead contact (A.E.T.).</p>
Computational Metabolomics - database files for raw data processing
<p>These files are used by an R-script that process LCMS data files in a project directory to detect peaks, make the aligned peak table and annotate peaks with molecular formulas. The workflow is optimized for human blood samples (n > 100). </p>
The data for "Periodic Coronal Rain Driven by Self-consistent Heating Process in a Radiative Magnetohydrodynamic Simulation"
<p>The volumetric heating rate along the coronal loop at all time steps in the work is provided as 'appendix_heating.h5'. The Python script for reading and plotting the data is provided as 'read_appendix_heating.py'. Note that the python module 'h5py' is necessary to read .h5 file.</p>
Post-processed dataset: An ocean memory approach for analysing decadal variability in the North Atlantic Ocean
<p>The dataset contains Python scripts and pre-processed data from selected CMIP6 models and observations. These were used to examine how the North Atlantic Oscillation affects decadal variations in the North Atlantic Ocean. An ocean memory approach, combining heat budget analysis with the Green's function method, is proposed.</p>
Magnetic particles (Fe3O4) magnify ion transfer processes at the electrified liquid-liquid interface. Case study: Levamisole detection.
<p>Data set for the publication Magnetic particles (Fe3O4) magnify ion transfer processes at the electrified liquid-liquid interface. Case study: Levamisole detection. </p>
Aerosol products presented in "ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications"
<p>ALICENET output products on aerosol optical and physical properties and vertical layering presented in “Bellini, A., Diémoz, H., Di Liberto, L., Gobbi, G. P., Bracci, A., Pasqualini, F., and Barnaba, F.: Alicenet – An Italian network of Automated Lidar-Ceilometers for 4D aerosol monitoring: infrastructure, data processing, and applications, AMT, https://doi.org/10.5194/egusphere-2024-730, 2024”.</p> <p>The aod*.txt files include the following information:</p> <p>- date: date in UTC<br>- AOD_ALICENET: AOD as retrieved by ALICENET at 1064 nm<br>- AOD_AERONET/SKYNET: AOD measured by a co-located photometer from AERONET/SKYNET (level 2) at 1020 nm<br>- AE: Angstrom Exponent from AERONET/SKYNET (level 2)</p> <p>The contiunous.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- continuous_aerosol_layer: Continous Aerosol Layer heights as retrieved by ALICENET</p> <p>The mixed.aerosol.layer.rome.txt file includes the following information:</p> <p>- date: date in CET<br>- mixed_aerosol_layer: Mixed Aerosol Layer heights as retrieved by ALICENET</p> <p>This work received partial financial support from the EC H2020 Project RI-URBANS (GA No 101036245), and benefited from work done within the Action PROBE (CA18235), supported by COST (European Cooperation in Science and Technology).</p>
Mismanagement and poor transparency in the European processed seafood supply revealed by DNA metabarcoding
<p>Raw Illumina MiSeq data in zipped FASTQ format related to the manuscript "Mismanagement and poor transparency in the European processed seafood supply revealed by DNA metabarcoding" (DOI: <a href="https://doi.org/10.1016/j.foodres.2024.114901">https://doi.org/10.1016/j.foodres.2024.114901</a>).</p> <p>Lorusso, L., Shum, P., Piredda, R., Mottola, A., Maiello, G., Cartledge, E. L., Neave E. F., & Di Pinto A., Mariani, S. (2024). Mismanagement and poor transparency in the European processed seafood supply revealed by DNA metabarcoding. Food Research International, <a href="https://doi.org/10.1016/j.foodres.2024.114901">https://doi.org/10.1016/j.foodres.2024.114901</a>.</p>
Neural pathways and computations that achieve stable contrast processing tuned to natural scenes
<h1>Gur et al. 2024 database</h1> <p>Source data of the paper Gür et al. 2024, “Neural pathways and computations that achieve stable contrast processing tuned to natural scenes”, Nature Communications. This work contains an analysis of post-receptor luminance gain in the Drosophila visual system, focusing on the circuitry and algorithms for implementation of rapid luminance gain control.</p> <p>All data can be analyzed using the code provided in the Github repository: <a href="https://github.com/silieslab/Gur-etal-2024">https://github.com/silieslab/Gur-etal-2024</a>. Please go to the “Readme” file in the repository for how to use the code.</p> <h2>Raw data</h2> <p>All raw data is located in the folder “raw_data”. "Readme" file located in the code repository will guide you on how to analyze all data.</p> <h2>Processed data</h2> <p>All processed data is located in the folder “processed_data”. "Readme" file located in the code repository will guide you on how to analyze all data.</p> <p>- 2p_imaging_python_pickle: Processed data stored as .pickle files. <br>- Dm12_Figure7_Mat_files: Processed data for Dm12 imaging and optogenetics experiments presented in Figure 7 stored as .mat files.<br>- EM_data: Processed data for EM analysis done in Figure 7.<br>- Figure 6 Tm9 flpSTOP: tdTomato expression data for Figure 6 Tm9 flpSTOP experiments.<br>- Figure S5 Tm1 flpSTOP: tdTomato expression data for FigureS5 Tm1 flpSTOP experiments.</p>
Investigating the Impact of Process Parameters on Bead Geometry in Laser Wire-Feed Metal Additive Manufacturing
<p>The Excel file is structured to provide a comprehensive overview of the design of experiments (DoE) for the research project titled <em>"Investigating the Impact of Process Parameters on Bead Geometry in Laser Wire-Feed Metal Additive Manufacturing"</em>, conducted under the BALSAM project. This research specifically contributes to deliverables D1.2 and D3.1.</p>
Identification of processes in Cu-ore heap leaching using Cu isotopes and leachate chemistry at Tschudi mine, northern Namibia - Supplementary data
<p>This is a supplementary dataset to the paper:</p> <p>Sracek O., Ettler V., Mihaljevič M., Kříbek B., Mapani B., Penížek V., Zádorová T., Vaněk A. (2024): Identification of processes in Cu-ore heap leaching using Cu isotopes and leachate chemistry at Tschudi mine, northern Namibia. <em>Hydrometallurgy</em> <strong>228</strong>, 106356.</p> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks. The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Post-processed SAM (System for Atmospheric Modeling) simulation output for "Tipping to an Aggregated State by Mesoscale Convective Systems"
<p>Statistics output files for all variables, for a select number of SAM (System for Atmospheric Modeling v. 6.11) simulation runs used in the study "Tipping to an Aggregated State by Mesoscale Convective Systems". The following simulations are included: DIU, OCEAN, DIU2OCEAN branch A1, DIU2OCEAN branch A2.</p>
Fracture toughness of mixed-mode anticracks in highly porous materials dataset and data processing
<blockquote> <div>This repository contains the code and datasets used in the data analysis for "Fracture toughness of mixed-mode anticracks in highly porous materials". The analysis is implemented in Python, using Jupyter Notebooks.</div> </blockquote> <h2>Contents</h2> <ul> <li><code>main.ipynb</code>: Jupyter notebook with the main data analysis workflow.</li> <li><code>energy.py</code>: Methods for the calculation of energy release rates.</li> <li><code>regression.py</code>: Methods for the regression analyses.</li> <li><code>visualization.py</code>: Methods for generating visualizations.</li> <li><code>df_mmft.pkl</code>: Pickled DataFrame with experimental data gathered in the present work.</li> <li><code>df_legacy.pkl</code>: Pickled DataFrame with literature data.</li> </ul> <h2>Prerequisites</h2> <ul> <li>To run the scripts and notebooks, you need:</li> <li>Python 3.12 or higher</li> <li>Jupyter Notebook or JupyterLab</li> <li>Libraries: <code>pandas</code>, <code>matplotlib</code>, <code>numpy</code>, <code>scipy</code>, <code>tqdm</code>, <code>uncertainties</code>, <code>weac</code></li> </ul> <h2>Setup</h2> <ol> <li>Download the zip file or clone this repository to your local machine.</li> <li>Ensure that Python and Jupyter are installed.</li> <li>Install required Python libraries using <code>pip install -r requirements.txt</code>.</li> </ol> <h2>Running the Analysis</h2> <ol> <li>Open the <code>main.ipynb</code> notebook in Jupyter Notebook or JupyterLab.</li> <li>Execute the cells in sequence to reproduce the analysis.</li> </ol> <h2>Data Description</h2> <div>The data included in this repository is encapsulated in two pickled DataFrame files, <code>df_mmft.pkl</code> and <code>df_legacy.pkl</code>, which contain experimental measurements and corresponding parameters. Below are the descriptions for each column in these DataFrames:</div> <h3><code>df_mmft.pkl</code></h3> <div>Includes data such as experiment identifiers, datetime, and physical measurements like slope inclination and critical cut lengths.</div> <ul> <li><code>exp_id</code>: Unique identifier for each experiment.</li> <li><code>datestring</code>: Date of the experiment as a string.</li> <li><code>datetime</code>: Timestamp of the experiment.</li> <li><code>bunker</code>: Field site of the experiment. Bunker IDs 1 and 2 correspond to field sites A and B, respectively.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h_sledge_top</code>: Distance from sample top surface to the sled in mm.</li> <li><code>h_wl_top</code>: Distance from sample top surface to weak layer in mm.</li> <li><code>h_wl_notch</code>: Distance from the notch root to the weak layer in mm.</li> <li><code>rc_right</code>: Critical cut length in mm, measured on the front side of the sample.</li> <li><code>rc_left</code>: Critical cut length in mm, measured on the back side of the sample.</li> <li><code>rc</code>: Mean of <code>rc_right</code> and <code>rc_left</code>.</li> <li><code>densities</code>: List of density measurements in kg/m^3 for each distinct slab layer of each sample.</li> <li><code>densities_mean</code>: Daily mean of <code>densities</code>.</li> <li><code>layers</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>layers_mean</code>: Daily mean of <code>layers</code>.</li> <li><code>surface_lineload</code>: Surface line load of added surface weights in N/mm.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>notes</code>: Additional notes regarding the experiment or observations.</li> <li><code>L</code>: Length of the slab–weak-layer assembly in mm.</li> </ul> <h3><code>df_legacy.pkl</code></h3> <div>Contains robustness data such as radii of curvature, slope inclination, and various geometrical measurements.</div> <ul> <li><code>#</code>: Record number.</li> <li><code>rc</code>: Critical cut length in mm.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h</code>: Slab height in mm.</li> <li><code>density</code>: Mean slab density in kg/m^3.</li> <li><code>L</code>: Lenght of the slab–weak-layer assembly in mm.</li> <li><code>collapse_height</code>: Weak-layer height reduction through collapse.</li> <li><code>layers_mean</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>surface_lineload</code>: Surface line load from added weights in N/mm.</li> </ul> <p>For more detailed information on the datasets, refer to the paper or the documentation provided within the Jupyter notebook.</p> <h2>License</h2> <div>This work is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</div> <p> </p> <div>You are free to:</div> <ul> <li><strong>Share</strong> — copy and redistribute the material in any medium or format</li> <li><strong>Adapt</strong> — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <div>Under the following terms:</div> <div> <ul> <li><strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</li> </ul> </div> <h2>Citation</h2> <div>Please cite the following paper if you use this analysis or the accompanying datasets:</div> <div> <ul> <li>Adam, V., Bergfeld, B., Weißgraeber, P. van Herwijnen, A., Rosendahl, P.L., Fracture toughness of mixed-mode anticracks in highly porous materials. <em>Nature Communincations</em> <strong>15</strong>, 7379 (2024). https://doi.org/10.1038/s41467-024-51491-7</li> </ul> </div>
Data bundle for powerd-data: A transparent and reproducible data processing pipeline for energy system modeling based on egon-data
<div> <p><strong>powerd-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. Is is a fork from the open-source tool <strong>egon-data</strong>. </p> <p>powerd-data and egon-data retrieve and process data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources, we provide a data bundle to be downloaded by egon-data.</p> <p>The following data sets are part of the available data bundle:</p> <ol> <li>district_heating_shares: <ul> <li>Assumed district heating share for all European countries in 2050</li> <li>Source: Own representation</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>egon_demandregio_cts_ind:<br> <ul> <li>Industrial and CTS demands per branch and NUTS3 region in Germany for the year 2050</li> <li>Source: egon-data, based on data from DemandRegio disaggregator tool</li> <li>License: Data license Germany – © FfE 2019, © Statistisches Bundesamt (Destatis), 2008-2017 – version 2.0</li> </ul> </li> <li>industrial_gas_demand: <ul> <li>This folder contains 5 files. The files CH4_for_industry_eGon100RE.json, CH4_for_industry_eGon2035.json, H2_for_industry_eGon100RE.json and H2_for_industry_eGon2035.json contain the industrial hourly demands for hydrogen and methane in NUTS3 resolution for the scenarios eGon100RE and eGon2035. The file region_corr.json provides information that make it possible to correlate each load to a geographical position.</li> <li>License: Attribution 4.0 International (CC BY 4.0) © FfE, eXtremOS Project</li> </ul> </li> </ol> <p> </p> </div>
Chromocenter image processing and data for "Volume buffering in multi-component phase separation"
<p>Contains image processing code and a csv of the image processing results for the images of chromocenters in mammalian cells for the paper "Volume buffering in multi-component phase separation".</p>
Processed model of a Computed Tomography scan of Roman window glass from Ephesos
<h1>Origin</h1> <p>The model is based on a tomography scan on an antique window glass fragment from Ephesos (today Efes, Turkey). The sample was contributed by the <em>Austrian Archaeological Institute</em>, Vienna, and has the inventory ID EVH12/1017/1322.</p> <p>The original scan data is published as</p> <p>Grobe and Schuetz (2021). Computed tomography scan of Roman window glass from Ephesos. <a href="https://doi.org/10.5281/zenodo.5651890">doi:10.5281/zenodo.5651890</a></p> <p>and is described in:</p> <p>Grobe, Noback, and Schuetz (2021). A model chain to simulate daylight in historic built environments. Presented at: <em>Widening Horizons - 27 Annual Meeting of the European Association of Archaeologists</em>, Kiel, Germany. <a href="https://doi.org/10.5281/zenodo.5495764">doi:10.5281/zenodo.5495764</a></p> <h1>Processing</h1> <p>The scan was processed to prepare its conversion into a simulation model, i.e.</p> <ul> <li>small isolated mesh components were deleted,</li> <li>disconnected and duplicate vertices and faces were removed,</li> <li>the outer surfaces were re-constructed by meshlab's implementation of the _Screened Poisson_ algorithm, and</li> <li>the resulting mesh was intersected with an exctruded rectangle.</li> </ul>
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.