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60 results for “surface code”
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Dataset and codes for 'Climatic control on seasonal variations of glacier surface velocity'
<p><strong>This repository contains the codes and processed data used to retrieve 10-day changes in glacier surface velocity over the Western Pamir.</strong></p> <p>The supp_CODES.zip contains all details and codes to use COSI-CORR (<a href="http://www.tectonics.caltech.edu/slip_history/spot_coseis/">http://www.tectonics.caltech.edu/slip_history/spot_coseis/</a>) to process a large batch of satellite images. The images can be downloaded directly via <a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a> or <a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu</a>. Please read the Methods and Data section of the associated manuscript for details.</p> <p> </p> <p>The Matrix_velocities.zip contains, for each of the 48 investigated glaciers, the DEM, X, Y (NANNI_2022_supp_glacier_centreline_DEM_XY_1px_30m_1.txt) as well as a matrix of n*m with m the distance along flow and n the number of time step over which the velocity is calculated (NANNI_2022_supp_glacier_centreline_vel_matrix_1px_30m_1.txt), ans the associated figure that show the multi year velocity changes together with the one year average and the along centreline profiles. An example is shown in the two figures for glacier 48 in the main repository.</p> <p> </p> <p>The NANNI_2022_supp_glacier_characteristics file contains the glacier characteristics (48*8), as shown in the associated figures.</p> <p> </p> <p>The NANNI_2022_supp_pickedpoints_migration_AUTUMN/SPRING contains the automatically picked points for the onset of the acceleration in Spring and Autmun for each glacier. The headers contains the information, and the files contains is shown in the associated figure.</p> <p>the temperature profiles used to calculate the Iso 0C are in NANNI_2022_supp_temp_perday_fedchenko_2400m</p> <p>The position of each 48 glacier is shown in the associated figure.</p> <p> </p> <p>You can also find the processed velocity fields (velocity magnitude) under the different path an row: p151r33.zip and p152r33.zip for Landsat8, T42SYJ.zip and T43SBD.zip for Sentinel 2. In these folder you will a find a .tif file names similar to:</p> <p><em>Working_cosicorr_windows_FCorr_16days_p152r33_159_175_AB_1101110_Filtered_correlations_p152r33_filtered_abs.tif</em></p> <p>The name of the files gives information about the time span used (16days), the path and raw (p152r33), the data of the slave in DOY from 2013 (159) and of the master (175).</p> <p>The Statistics.zip file contains for each path and row the associated DEM, glacier mask (RGI), median magnitude (ABS), median NS displacement (NS), median EW displacemnt (EW), with the associated median absolute deviation (MAD). The files containing 'bflt' corresponds to the values computed before the filtering procedure, and the one without, after the filtering procedure. </p> <p>The .tif files are not georeferenced, but are all projected on the same grid with a 30m square pixel size on a UTM 33 42N projection.</p> <p> </p> <p> </p> <p>Please contact me for any question.</p> <p> </p> <div class="notranslate"> </div>
Solute particle near a nanopore: influence of size and surface properties on the solvent-mediated forces: ftDFT code
<p>This is the complete ftDFT code used to generate the results reported in our Nov 2017 Nanoscale article "Solute particle near a nanopore: influence of size and surface properties on the solvent-mediated forces",DOI: 10.1039/C7NR07218J. </p>
Data for "Quantum error correction below the surface code threshold"
<p>Datasets of the surface code and repetition code memory experiments executed on Google's quantum processor.</p> <p>See README at the root of each zip archive for detailed description of each dataset.</p>
Near surface softening and healing in eastern Honshu associated with the 2011 Tohoku-Oki Earthquake: Research data and code
<p>The zip file named 'mainshock.zip' contains the Matlab codes and seismic data for reproducing the results of Figure 2.</p> <p>The Excel file named 'Source Data.xls' contains the raw data of Figures 3 and 4.</p>
Data and code for the publication "Tracing the horizontal transport of microplastics on rough surfaces"
<p><strong>Background</strong></p> <p>The data set contains images of fluorescent PMMA (Polymethyl methacrylate) particles that are moved by water on rough surfaces in an irrigation experiment. The experiments were done in the laboratory at the Institute of Geography, University of Cologne, Germany, in Septembre 2020. The images were taken with an sCMOS (advanced scientific complementary metal-oxide-semiconductor) high resolution pco.panda 4.2 camera (PCO AG, Kehlheim, Germany).</p> <p>The data set was analysed in the publication: Laermanns, H., Lehmann, M., Klee, M., Löder, M.G.J., Gekle, S. and Bogner, C., 2021, “Tracing the horizontal transport of microplastics on rough surfaces,” Microplastics and Nanoplastics, <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a></p> <p>Additionally to the data, this collection of files contains the Python and R scripts/notebooks used to analyse the images and create graphics for the publication. The code for the simulation of flow patterns can be obtained from the authors upon request.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p>Experimental parameters</p> <ul> <li> <p>Surface roughness: two levels, fine and course</p> </li> <li> <p>Inclination: 6 levels, 2.5°, 5°, 7.5°, 10°, 12.5° and 15°</p> </li> <li> <p>Irrigation: three levels, 4.8, 7.2 and 10.44 L/h</p> </li> <li> <p>Repetitions: three</p> </li> </ul> <p>More details on the experimental setup are given in the publication.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The folder <strong>images.zip</strong> contains the images. They are organized as follows:</p> <ul> <li><strong>Feinsand_10_Partikel</strong>: images of PMMA particles on the fine surface</li> <li><strong>Grobsand_10_Partikel</strong>: images of PMMA particles on the rough surface <ul> <li> <p>Both folders contain six subfolders <strong>_XX_Grad_Gefaelle</strong>, XX being 2_5, 5, 7_5, 10, 12_5, 15. These folders refer to inclinations of 2.5°, 5°, 7.5°, 10°, 12.5° and 15° of the rough surfaces, respectively.</p> </li> <li> <p>every folder _XX_Grad_Gefaelle contains three subfolders <strong>Fliessgeschwindigkeit_YY</strong>, with YY being 20mlx4, 30mlx4 and 43_5mlx4, the parameters of the peristaltic pump, corresponding to irrigation rates of 4.8, 7.2 or 10.44 L/h, respectively.</p> </li> <li> <p>every folder Fliessgeschwindigkeit_YY contains three subfolders <strong>Z_Durchgang</strong> with Z being 1, 2 or 3 corresponding to the tree repetitions of the experiment.</p> </li> </ul> </li> <li><strong>stained_flow_patterns</strong>: images of flow patterns of the fluorescent dye Nile Red (in methanol), an mp4 video and a text file with parameters to produce the video based on the images. The images were produced for the following experimental parameters: <ul> <li> <p><strong>Feinsand_2_5_Grad_20_ml</strong>: fine surface, inclined by 2.5° and irrigated with 7.2 L/h</p> </li> <li> <p><strong>Grobsand_7_5_Grad_20_ml</strong>: coarse surface, inclined by 7.5° and irrigated with 7.2 L/h</p> </li> </ul> </li> </ul> <p>The file <strong>experimental_data.csv</strong> links the concatenated folder names to experimental parameters.</p> <p> </p> <p><strong>Description of the code</strong></p> <p>The images were first processed in Python to locate the PMMA particles and calculate particle sizes. The Python code is located in the <strong>py_scripts.zip</strong> folder. It contains the following files:</p> <ul> <li> <p><strong>find_XYZ</strong>: locates PMMA particles. XYZ stands for different experimental parameters (see above). Scripts containing the string <strong>_problems</strong> locate PMMA particles for images with possible artefacts (smeared particles, residual light etc.). You need to uncomment the appropriate lines in the files to rerun the code because it was run piece by piece.</p> </li> <li> <p><strong>pickle_to_csv.py</strong>: converts pickle files to csv files</p> </li> <li> <p><strong>calculate_sizes.py</strong>: calculates the sizes of PMMA particles from the first image of each experiment</p> </li> <li> <p><strong>py_functions_new.py</strong>: contains custom functions</p> </li> </ul> <p>Further analysis run in a mixture of R and Pyhton in one working document (R Notebook):</p> <ul> <li> <p><strong>Analysis_with_loops.Rmd</strong>: tracking of the PMMA particles by PtrakPy version 0.4.2 (Allan et al. 2019). Python 3.8 (Python Software Foundation, <a href="https://www.python.org/">https://www.python.org/</a>) was called directly from R using the R package reticulate (<a href="https://rstudio.github.io/reticulate/">https://rstudio.github.io/reticulate/</a>) in RStudio (<a href="https://www.rstudio.com/">https://www.rstudio.com/</a>).</p> </li> <li> <p><strong>Analysis_for_paper.Rmd</strong>: R code for analysis of tracking, statistical analysis, plotting. We used the R version 4.0.3 (R Core Team 2020).</p> </li> <li> <p><strong>helper_function.R</strong>: contains custom R functions for the analysis</p> </li> </ul> <p> </p> <p><strong>Results</strong></p> <p>The file <strong>results.zip</strong> contains the folders:</p> <ul> <li> <p><strong>data</strong>: *.pickle files produced by Python containing the trajectories of PMMA particles</p> </li> <li> <p><strong>data_csv</strong>: *.pickle files converted to *.csv files</p> </li> <li> <p><strong>figures</strong>: figures produced by the code during the analysis, organized in different subfolders</p> </li> <li> <p><strong>RData</strong>: large computational results produced and saved during analysis</p> </li> <li> <p><strong>sizes_csv</strong>: *.csv files containing PMMA particle sizes and further morphological characteristics; produced during analysis</p> </li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors thank Julia Horn for support in the laboratory and Florian Steininger for technical assistance.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 391977956, SFB 1357, subprojects B04 and B06.</p> <p> </p> <p><strong>References</strong></p> <p>Allan, Dan, Casper van der Wel, Nathan Keim, Thomas A Caswell, Devin Wieker, Ruben Verweij, Chaz Reid, et al. 2019. <em>Soft-Matter/Trackpy: Trackpy V0.4.2</em> (version v0.4.2). Zenodo. <a href="https://doi.org/10.5281/zenodo.3492186">https://doi.org/10.5281/zenodo.3492186</a>.</p> <p>Laermanns, Hannes, Moritz Lehmann, Marcel Klee, Martin GJ Löder, Stephan Gekle, and Christina Bogner. 2021. “Tracing the Horizontal Transport of Microplastics on Rough Surfaces.” <em>Microplastics and Nanoplastics</em>. <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a>.</p> <p>R Core Team. 2020. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.</p>
Code for manuscript "Organic ligands in whale excrement support iron availability and reduce copper toxicity to the surface ocean" by Monreal et al.
<p>.zip file containing GitHub repository titled "ligands-in-whale-excrement" (<a href="https://github.com/patrickmon38/ligands-in-whale-excrement/tree/main">https://github.com/patrickmon38/ligands-in-whale-excrement/tree/main</a>)<br><br><strong>README from GitHub: </strong></p> <div> <h3>Code used to generate figures for the manuscript "Organic ligands in whale excrement support iron availability and reduce copper toxicity to the surface ocean" by Monreal et al. are found in this repository.</h3> </div> <div> <p>In press at Communications Earth & Environment</p> <p> </p> </div> <p>Most data (all except .mzXML data) called in code is from Github_Data_For_Whale_Ligand_Manuscript.xlsx in this repository.</p> <p> </p> <p>Mass spec data from .mzXML files are has been depositied and is available for download in the Mass Spectrometry Interactive Virtual Environment (MassIVE). LC-ESI-MS (Orbitrap) and LC-FT-ICR-MS raw data can be accessed there under MSV000094994 (doi:10.25345/C50000B5D) and MSV000094995 (doi:10.25345/C5V98034P), respectively.</p> <p> </p> <p>html output from Rmarkdown file can be viewed directly at <a href="https://html-preview.github.io/?url=https://github.com/patrickmon38/ligands-in-whale-excrement/blob/main/Figures_for_GitHub_Whale_Excrement_Manuscript.html" rel="nofollow">https://html-preview.github.io/?url=https://github.com/patrickmon38/ligands-in-whale-excrement/blob/main/Figures_for_GitHub_Whale_Excrement_Manuscript.html</a>.</p> <p>If trying to run Rmarkdown on your own system, you will need to download the .xlsx and .mzXML files and change paths accordingly.</p> <p>Co-authors of this manuscript:</p> <h5>Patrick J. Monreal (University of Washington)</h5> <h5>Matthew S. Savoca (Stanford University)</h5> <h5>Lydia Babcock-Adams (National High Magnetic Field Laboratory)</h5> <h5>Laura E. Moore (University of Washington)</h5> <h5>Angel Ruacho (Univesrity of Washington)</h5> <h5>Dylan Hull (University of Washington)</h5> <h5>Logan J. Pallin (Unversity of California, Santa Cruz)</h5> <h5>Ross C. Nichols (Unversity of California, Santa Cruz)</h5> <h5>John Calambokidis (Cascadia Research Collective)</h5> <h5>Joseph A. Resing (Unviersity of Washington/CICOES/NOAA)</h5> <h5>Ari S. Friedlaender (Unversity of California, Santa Cruz)</h5> <h5>Jeremy Goldbogen (Stanford University)</h5> <h5>Randelle M. Bundy (University of Washington)</h5> <p> </p> <div> </div> <div><strong>If there are issues or questions with the code, author for contact is Patrick Monreal (<a href="mailto:pmonreal@uw.edu">pmonreal@uw.edu</a>).</strong></div>
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (<a href="../records/6482346">https://zenodo.org/records/6482346</a>) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Datasets and code for Rapljenović et al. 2024: Adsorption of trace metals onto different plastics during long-term exposure in an estuarine environment: influence of time, stratified water column, and specific surface area
<p>This repository contains datasets and R code to reproduce results from Rapljenović et al. 2024: Adsorption of trace metals onto different plastics during long-term exposure in an estuarine environment: influence of time, stratified water column, and specific surface area.</p>
Response of convectively coupled Kelvin waves to surface temperature forcing in aquaplanet simulations: data and code
<p>This is the data and code used for a journal paper entitled "Response of convectively coupled Kelvin waves to surface temperature forcing in aquaplanet simulations", written by Mu-Ting Chien and Daehyun Kim in 2024. This paper is in minor revision in the Journal of Advances in Modeling Earth System. The submitted paper is here: (https://essopenarchive.org/doi/full/10.22541/essoar.171322728.86206700/v1).</p>
Data and Code For PhD Thesis- Making an Impression: An Assessment of the Role of Print Surfaces Within the Technological, Commercial, Intellectual and Cultural Trajectory of Book Illustration c.1780-c.1860.
<p><strong>Overview</strong></p> <p>These datasets support research found in the PhD Thesis entitled: Making an Impression: An Assessment of the Role of Print Surfaces Within the Technological, Commercial, Intellectual and Cultural Trajectory of Book Illustration c.1780-c.1860, submitted by William Finley. The data was provided by the British Library as part of a wider ambition to digitise millions of book illustrations (further details can be found here: <a href="https://github.com/BL-Labs/imagedirectory">https://github.com/BL-Labs/imagedirectory</a>). The main dataset contains 6063 rows and 108,784 illustrations. All of the data is held in comma-separated values (csv) files. The collection has been subdivided in order to interrogate the dataset further. All of the datasets have been interrogated using Rscript (R 3.4.2 El Capitan Build), the codes of which have been included in the dataset repository. </p> <p><strong>Guidance to Datasets</strong></p> <p>'Counts of Illustration by Size over Time'</p> <ul> <li>Title: Title of Book</li> <li>Author: Author of Book </li> <li>Pub_Place: Location the book was published</li> <li>Book_Id: British Library image identifier </li> <li>YEAR: Year the book was published</li> <li>Image_Count: Number of illustrations belonging to that book</li> <li>N: Number of illustrations according to the size of illustration</li> <li>IMAGE_TYPE: Size of Illustration </li> </ul> <p>'Density Graphs of the Position of Illustrations on the Page'</p> <ul> <li>X1: The X position in pixels of the top left of the image</li> <li>Y1: The Y position in pixels of the top left of the image</li> <li>X2: Width in pixels of the Image</li> <li>Y2: Height in pixels of the Image</li> <li>PERCENT_PAGE: Percentage of the page taken up by illustrations</li> <li>TYPE: Percentage Range of the Page taken up by illustrations </li> <li>VOL: Volume</li> </ul> <p>'Frequency of Illustrations Across First 100 Pages of the Book 1800-1850'</p> <ul> <li>Page: Page number </li> <li>X1: The X position in pixels of the top left of the image</li> <li>Y1: The Y position in pixels of the top left of the image</li> <li>X2: Width in pixels of the image</li> <li>Y2: Height in pixels of the image</li> <li>PERCENTPAGE: Percentage of the page occupied by illustration</li> <li>SIZE: Size category each illustration belongs to</li> </ul> <p>'Illustration Arrangement in Single Books and Editions'</p> <ul> <li>Book_ID: British Library image identifier</li> <li>Page_NO: Page Number Illustration is found on </li> <li>X: The X position in pixels of the top left of the image</li> <li>Y: The Y position in pixels of the top left of the image </li> <li>WIDTH: Width in pixels of the image</li> <li>HEIGHT: Height in pixels of the image</li> <li>IMAGE_SIZE: Percentage of the page occupied by illustration</li> </ul> <p>'Relative Frequency of Printing Methods Over Time'</p> <ul> <li>Publisher: Publisher of the book</li> <li>Title: Title of the book</li> <li>first_author: Author</li> <li>pub_place: Location the book was published</li> <li>book_identifier: British Library image identifier</li> <li>Year: Year the book was published </li> <li>COUNT_IMAGE: Number of illustrations found in a given book</li> <li>YEAR_BOOK_COUNT: Number of book published in a given year</li> <li>YEAR_IMAGE: Number of Illustrations found in books published in a given year</li> <li>AVERAGE_IMAGE: Average number of illustrations found in books published in a given year</li> <li>IMAGE_METHOD: Print method used to produce the illustration</li> <li>IMAGE_SIZE_COUNT: Number of illustrations printed in books in a given year according to its size</li> <li>IMAGE_SIZE: Categories of image sizes</li> </ul> <p><strong>Contact</strong></p> <p>Will Finley can be contacted via the following email. Information listed is accurate at the time of publication</p> <p>Email: wafinley1@sheffield.ac.uk</p> <p> </p> <p> </p>
National Weather Service Coded Surface Bulletins, 2003- (JSON format)
<p>This dataset contains the Coded Surface Bulletin dataset reformatted as JSON files. The Coded Surface Bulletin dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003.</p> <p>Each JSON file contains one top-level object corresponding to one bulletin. The top-level object is composed of name/value pairs with the names bulletinType, createDate, validDate, Highs, Lows, ColdFronts, WarmFronts, OccludedFronts, StationaryFronts, and Troughs. The name/value pairs for bulletinType, createDate, and validDate are always present. The other name/value pairs are only present if there is corresponding data. The value for bulletinType is either "LR" or "HR", for low-resolution or high-resolution, respectively. The values for createDate and validDate are UTC timestamp strings. If the bulletinType value is "LR", the longitudes and latitudes have 1° precision. If the bulletinType value is "HR", the longitudes and latitudes have 0.1° precision.</p> <p>The value associated with the name High in the top-level object is itself an object composed of three name value pairs that describe the geographic locations and surface air pressure levels for one or more high pressure centers. The names of the object elements are lats, lons, and pressures. The values for these are all arrays. For a given object, the arrays will all have the same size. The arrays contain latitudes in degrees, longitudes in degrees, and pressures in millibars. If the arrays contain N elements apiece, the object is describing N pressure centers. The object associated with the name Low in the top-level object is structured in the same way. It describes the geographic locations and surface air pressure levels for one or more low pressure centers.</p> <p>The ColdFronts, WarmFronts, StationaryFronts, OccludedFronts, and Troughs names in the top-level object, when present, have values that are arrays. In each case, the array is composed of one or more objects. Each object represents a front or trough of the given type. Each object is composed of three name/value pairs with the names lats, lons, and strength. The value for the name strength is a string that is one of "weak", "moderate", "strong", or "unstated". The values associated with the names lats and lons are arrays. This pair of arrays represent the vertices of a polyline describing the location of a frontal boundary or trough.</p> <p>The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the <a href="https://mesonet.agron.iastate.edu/">Iowa Environmental Mesonet</a>.</p>
National Weather Service Coded Surface Bulletins, 2003- (netCDF format)
<p>This dataset contains the Coded Surface Bulletin (CSB) dataset reformatted as <a href="https://www.unidata.ucar.edu/software/netcdf/docs/">netCDF-4</a> files. The CSB dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003.</p> <p>Each netCDF file contains one year of CSB fronts data represented as spatial map data grids. The times and geospatial locations for the data grid cells are also included. The front data is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded. The front polylines from the CSB dataset are rasterized into the appropriate data grids. Each file conforms to the <a href="http://cfconventions.org/">Climate and Forecast Metadata Conventions</a>.</p> <p>There are two large groupings of the CSB netCDF files. One group uses a data grid based on the <a href="https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr">North American Regional Reanalysis</a> (NARR) <a href="https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID221">grid</a>, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.</p> <p>The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.</p> <p>There are a number of files within each group. The files all follow the naming convention codsus_[masked]_<grid>_<subproduct>.nc, where [masked] indicates that the presence of the word <em>masked</em> is optional and <grid> is either <em>merra2-1deg</em> or <em>narr-96km</em>. The <subproduct> element is either the word <em>mask</em> or the sequence <n>wide_<year>, where <n> is the front width and <year> is the year for the data stored in the file.</p> <p>The codsus_<grid>_mask.nc file is a file containing a single data grid that delineates the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year. The WPC meteorologists don't attempt to provide equal levels of attention to every grid cell displayed on their workstations. The files of the form codsus_masked_<grid>_<n>wide_<year>.nc have all had the mask described above applied to exclude parts of fronts that extend past the envelope. The files of the form codsus_<grid>_<n>wide_<year>.nc have no masking applied.</p> <p>The <n>wide portion of the file names takes two forms—<em>1wide</em> and <em>3wide</em>. The fronts in the<em>1wide</em> files were rasterized by drawing the front polylines with a width of one grid cell. The fronts in the <em>3wide</em> files were rasterized by drawing the front polylines with a width of 3 grid cells.</p> <p>Within each grid group, there are five subsets of files:</p> <ul> <li>codsus_masked_<grid>_1wide_<year>.nc</li> <li>codsus_masked_<grid>_3wide_<year>.nc</li> <li>codsus_<grid>_1wide_<year>.nc</li> <li>codsus_<grid>_3wide_<year>.nc</li> <li>codsus_<grid>_mask.nc</li> </ul> <p>The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the <a href="https://mesonet.agron.iastate.edu/">Iowa Environmental Mesonet</a>.</p>
MITgcm simulations of sea level response to freshwater injected at the surface and at depth in southern high latitudes: Model output and analysis code
<p>Model output (netcdf) and python code (included in both py and ipynb formats) to create the figures in Eisenman et al. (2024).</p> <div> <p>See https://eisenman-group.github.io for further details.</p> </div>
Data and code to accompany 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record'
<p>Data and analysis code to accompany the manuscript 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record', published in <em>Oceanography and Marine Biology: An Annual Review </em>(<a href="https://doi.org/10.1201/9781003363873-2">https://doi.org/10.1201/9781003363873-2</a>). Data files include records of sea surface temperature (SST) collected in Pacific Grove, California, USA from January 20, 1919 to the end of 2020. The analysis code produces a continuous 100+ year record with adjustments made for time of day the data were collected, and filling gaps in the data set where necessary. </p> <p>This dataset makes use of an earlier 83-year version of the sea surface temperatures produced by Breaker et al. 2005 available at <a href="https://aquadocs.org/handle/1834/20890">https://aquadocs.org/handle/1834/20890</a>, with the data file available at <a href="https://purl.stanford.edu/rc833pc4972">https://purl.stanford.edu/rc833pc4972</a> </p>
Data supporting "Local Clustering Decoder: a fast and adaptive hardware decoder for the surface code"
<p>The data consists of a CSV file containing the raw performance data collected from running our decoder on a Xilinx Virtex Ultrascale+ VU19P FPGA. The Stim circuits that were used to create the samples are provided in a ZIP file. Our internal fork of Stim with support for leakage is needed to sample noise from the circuits.</p>
Replication data and code for "The Spatiotemporal Pattern of Surface Ozone and Its Impact on Agricultural Productivity in China"
<p>This dataset contains code and data to replicate the results for "The Spatiotemporal Pattern of Surface Ozone and Its Impact on Agricultural Productivity in China" by Xiaoguang Chen, Jing Gao, Luoye Chen, Madhu Khanna, Binlei Gong, and Maximilian Auffhammer. </p><p> </p>
Representing surface heterogeneity in land-atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime - E3SM SCM data and code
<p>This dataset contains post-processed E3SM single-column model output and code used to produce the figures in the manuscript that we are targeting Geoscientific Model Development to submit. </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.