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6,467 results for “process”
Figure 7 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 7. Vertical distribution of chlorophyll a concentration along the coastal area of the Sambia Peninsula on August 22, 2018.
Figure 8 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 8. Fragments of optical satellite images derived from OLCI Sentinel-2 on May 3, 2019 (a) and August 28, 2022 (b).
Figure 2 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 2. Study area and locations of station in the southeastern Baltic Sea (а) Conditional symbols: Yellow circles correspond to the stations conducted on August 22, 2018. Red circles correspond to the stations conducted on August 23, 2018. White circle is Wastewater Treatment Plant (WTP) on the northern coast of Sambia Peninsula. Amber Mining Plant (AC) is indicated as an asterisk on the western coast.
Figure 1 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 1. Phytoplankton patches on the surface of the southern Baltic Sea. Fragment of a color-synthesized image of the Baltic Sea surface in the visible range from OLCI-Sentinel-3 satellite scanner data of June 27, 2018.
Figure 6 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area
Figure 6. Phytoplankton biomass contribution in the upper 1 m layer (station 16 and 24 – integrated samples over euphotic depth).
Loss of multi-level 3D genome organization during breast cancer progression - Processed LAD files
<p>This entry contains the processed LAD files produced as part of the following study:<br><strong>Loss of multi-level 3D genome organization during breast cancer progression</strong></p>
Рис. 1–11. Mimela spp., имаго и ΔетаΛи строения. 1–6 – M. cupidinea sp. n., гоΛотип, самец; 7–8 – M. anopunctata, самец («Tenasserim»); 9 – M. nigrosellata, гоΛотип, самец (о. Ява, «Java coll. Nonfried»); 10–11 – M. gressitti comb. n., паратип, самка. 1, 10 – общий виΔ; 2, 11 – простернаΛьный отросток; 3 – переΔняя Λапка; 4, 7, 9 – эΔеагус, виΔ сбоку; 5, 8 – парамеры, виΔ сверху, 6 – парамеры и базаΛьная пΛастинка, виΔ снизу. Масштабные Λинейки: 1, 4–10 – 2 мм (Λинейка общая ΔΛя 4–9), 2 – 0.2 мм, 3 – 1 мм, 11 – 0.5 мм. Figs 1–11. Mimela spp., imagoes and details of structure. 1–6 – M. cupidinea sp. n., holotype, male; 7–8 – M. anopunctata, male ("Tenasserim"); 9 – M. nigrosellata, holotype, male ("Java coll. Nonfried"); 10–11 –M. gressitti comb. n., paratype, female.1, 10 – habitus; 2, 11 – prosternal process; 3 – fore protarsus; 4, 7, 9 – aedeagus, lateral view; 5, 8 – parameres, dorsal view; 6 – parameres and basal plate, ventral view. Scale bars: 1, 4–10 – 2 mm (common for 4–9), 2 – 0.2 mm, 3 – 1 mm, 11 – 0.5 mm. in A new species of Mimela Kirby, 1825 from Laos with comments on M. gressitti (Frey, 1970), comb. n. (Coleoptera: Scarabaeidae: Rutelinae)
Рис. 1–11. Mimela spp., имаго и ΔетаΛи строения. 1–6 – M. cupidinea sp. n., гоΛотип, самец; 7–8 – M. anopunctata, самец («Tenasserim»); 9 – M. nigrosellata, гоΛотип, самец (о. Ява, «Java coll. Nonfried»); 10–11 – M. gressitti comb. n., паратип, самка. 1, 10 – общий виΔ; 2, 11 – простернаΛьный отросток; 3 – переΔняя Λапка; 4, 7, 9 – эΔеагус, виΔ сбоку; 5, 8 – парамеры, виΔ сверху, 6 – парамеры и базаΛьная пΛастинка, виΔ снизу. Масштабные Λинейки: 1, 4–10 – 2 мм (Λинейка общая ΔΛя 4–9), 2 – 0.2 мм, 3 – 1 мм, 11 – 0.5 мм. Figs 1–11. Mimela spp., imagoes and details of structure. 1–6 – M. cupidinea sp. n., holotype, male; 7–8 – M. anopunctata, male ("Tenasserim"); 9 – M. nigrosellata, holotype, male ("Java coll. Nonfried"); 10–11 –M. gressitti comb. n., paratype, female.1, 10 – habitus; 2, 11 – prosternal process; 3 – fore protarsus; 4, 7, 9 – aedeagus, lateral view; 5, 8 – parameres, dorsal view; 6 – parameres and basal plate, ventral view. Scale bars: 1, 4–10 – 2 mm (common for 4–9), 2 – 0.2 mm, 3 – 1 mm, 11 – 0.5 mm.
Fig. 2 in Helminths of sigmodontine rodents in an agroforestry mosaic in the Brazilian Atlantic Forest: Patterns and processes of the metacommunity structure
Fig. 2. Ordinated matrices for the helminths metacommunity at Pratigi Environmental Protection Area, municipality of Igrapiúna, state of Bahia, northeast Brazil. A) Infracommunities and B) Component Communities.
Fig. 1 in Helminths of sigmodontine rodents in an agroforestry mosaic in the Brazilian Atlantic Forest: Patterns and processes of the metacommunity structure
Fig. 1. The bipartite network analysis illustrating the rodent–helminth association at Pratigi Environmental Protection Area, municipality of Igrapiúna, state of Bahia, northeast Brazil. The brackets separate the rodent tribes.
FIGURE 2. A flowchart showing the steps for sediment processing for efficient and effective ichthyolith isolation from a in Methods for isolation and quantification of microfossil fish teeth and elasmobranch dermal denticles (ichthyoliths) from marine sediments
FIGURE 2. A flowchart showing the steps for sediment processing for efficient and effective ichthyolith isolation from a variety of sediment types. Sediment types are in boxes, while processing steps are shown in ovals.
Dataset: Stimulus selection drives value-modulated somatosensory processing in superior colliculus
<p>Pluta Lab</p> <p>.mat data files used in the following paper</p> <p>Stimulus selection drives value-modulated somatosensory processing in superior colliculus </p> <p>Details can be found in readme.txt</p>
Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication <em>"Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses"</em>. </p> <p>It includes the output of 250 random model realizations of an aquifer thermal energy storage system in a thick productive aquifer (Case 1). It also includes the output of 500 random model realizations of an aquifer thermal energy storage system in a shallow alluvial aquifer (Case 2 part 1 and part 2).</p> <p>If there is interest in generating new output, the datset also includes the model input files for both cases.</p> <p>(Scripts to process the output data or to generate new output data can be found in the corresponding GitHub repository: https://github.com/lukatas/ATES_SensitivityAnalyses.git )</p>
Processed snRNAseq data from female Aedes aegypti antennal neurons
<p>Single-nucleus RNA sequencing data accompanying Adavi et al. 2024 <em>bioRxiv </em>preprint: https://doi.org/10.1101/2024.08.21.608847</p> <p>For analysis scripts see: https://github.com/mcbridelab/Adavi_2024_snRNAseqAaegAntennae</p> <p>For raw sequencing files see NCBI BioProject: PRJNA1138769</p>
Processed dataset for "Links between deforestation, conservation areas, and conservation funding in major deforestation regions of South America"
<p>Processed dataset used for the models in "Links between deforestation, conservation areas, and conservation funding in major deforestation regions of South America"</p> <p>Authors: Siyu Qin, Ana Buchadas, Patrick Meyfroidt, Yifan He, Arash Ghoddousi, Florian Pötzschner, Matthias Baumann, Tobias Kuemmerle</p> <p> </p> <p> </p>
Data for "Bulk and fracture process zone contribution to the rate-dependent adhesion amplification in viscoelastic broad-band materials"
<p>This dataset contains all the data generated for the publication</p> <p>[1] A. Maghami, Q. Wang, M. Tricarico et al., Bulk and fracture process zone contribution to the rate-dependent adhesion amplification in viscoelastic broad-band materials. Journal of the Mechanics and Physics of Solids (2024), doi:<br>https://doi.org/10.1016/j.jmps.2024.105844.</p> <p><br>The provided data are those which appear in the figures of Ref. [1]. Data are stored using informative named structures in a ".mat" file. The data are easily accessible through the Commercial Software MATLAB (© 1994-2023 The MathWorks, Inc.) or by using the free software GNU Octave. An exemplary code for loading and plotting the data contained in "data_Sphere_Viscoelastic_BroadBand.mat" is the following:</p> <p>%%%%%%%%%%%%%</p> <div> <div> <div> <div>clc</div> <div>clearvars</div> <div> </div> <div>close all</div> <div> </div> <div> </div> <div>% Create a new figure</div> <div>load('data_Sphere_Viscoelastic_BroadBand.mat');</div> <div> </div> <div> </div> <div>figure</div> <div> </div> <div>% Choose one of the following names:</div> <div>% Fig_3_a, Fig_3_b, Fig_3_c,</div> <div>% Fig_4, Fig_5_a, Fig_5_b, Fig_5_b_inset,</div> <div>% Fig_6, Fig_6_inset, Fig_7, Fig_7_inset,</div> <div>% Fig_8_a, Fig_8_b, Fig_10_a, Fig_10_b</div> <div> </div> <div>name=Fig_3_a;</div> <div>% Note: some plots in the manuscript are in log space.</div> <div> </div> <div>% Plot the data from the structure</div> <div> </div> <div> </div> <div>for i = 1:numel(name.x) hold on</div> <div> </div> <div>x_cell = name.x(i);</div> <div>y_cell = name.y(i);</div> <div>x = cell2mat(x_cell);</div> <div>y = cell2mat(y_cell);</div> <div> </div> <div>plot(x, y);</div> <div>end</div> <div> </div> <div> </div> <div> </div> </div> %%%%%%%%%%%%%</div> </div> <p>The code can be easily adapted for plotting the curves of all the figures contained in Ref. [1]. </p>
Exploring deep learning models for 4D-STEM-DPC data processing
<p>This repository contains scanning transmission electron microscopy data and processing files used in the journal publication <strong>"Exploring deep learning models for 4D-STEM-DPC data processing"</strong>. DOI: <a href="https://doi.org/10.1016/j.ultramic.2024.114058">10.1016/j.ultramic.2024.114058</a></p> <p><strong>Prerequisites</strong></p> <p>The scripts presented below require certain open-source Python packages to run. Library versions used to run the scripts are:</p> <ul> <li>hyperspy 1.7.1</li> <li>pyxem 0.14.2</li> <li>fpd 0.2.5</li> <li>pytorch 1.12.1 (cudatoolkit 11.6.0)</li> <li>jupyterlab 4.0.7</li> </ul> <p><strong>Data files</strong></p> <p>Three zipped folders are included. Two of them contain the training- and inference data for the neural networks, aptly named <em>training_data.zip</em> and <em>inference_data.zip</em>. PyTorch state dictionaries for trained models are included in the <em>models.zip</em> folder.</p> <p><strong>Processing scripts</strong></p> <p>All scripts are included in an IPython notebook format (.ipynb extension). The notebooks <em>Segmentation.ipynb</em> and <em>Regression.ipynb</em> contain the code for training and inference of the segmentation and regression models, respectively. The <em>Training_data_creation.ipynb<strong> </strong></em>notebook contains the code to preprocess the training data for both neural network models. The <em>Standard_algorithms.ipynb</em> notebook has the code for doing center of mass and edge filtering/disc detection algorithms for STEM-DPC processing.</p>
Data Access Made Easy: flexible, on the fly data standardization and processing (for research automatic weather stations)
<pre>Automatic Weather Stations (AWS) deployed in the context of research projects provide very valuable data thanks to the flexibility they offer in term of measured meteorological parameters, choice of sensors and quick deployment and redeployment. However this flexibility is a challenge in terms of metadata and data management. Traditional approaches based on networks of standard stations can not accommodate these needs and often no tools are available to manage these research AWS, leading to wasted data periods because of difficult data reuse, low reactivity in identifying potential measurement problems, and lack of metadata to document what happened. The Data Access Made Easy (DAME) effort is our answer to these challenges. At its core, it relies on the mature and flexible open source MeteoIO meteorological pre-processing library. It was originally developed as a flexible data processing engine for the needs of numerical models consuming meteorological data and further developed as a data standardization engine for the Global Cryosphere Watch (GCW) of the World Meteorological Organization (WMO). For each AWS, a single configuration file describes how to read and parse the data, defines a mapping between the available fields and a set of standardized names and provides relevant Attribute Conventions Dataset Discovery (ACDD) metadata fields, if necessary on a per input file basis. Low level data editing is also available, such as excluding a given sensor, swapping sensors or merging data from another AWS, for any given time period. Moreover an arbitrary number of filters can be applied on each meteorological parameter, restricted to specific time periods if required. This allows to describe the whole history of an AWS within a single configuration file and to deliver a single, consistent, standardized output file possibly spanning many years, many input data files and many changes both in format and available sensors. Finally, all configuration files are versionned in order to document their history. A web interface has been developed that allows data owners to manage the configuration files for their stations, refresh their data at regular intervals, inspect the data QA log files and allow on-demand data generation. The same interface allows other users to request data on-demand for any time period.<br><br>This presentation and software has received funding from the World Meteorological Organization under grant agreement No. 29539/2022-1.9 as well as the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101003472 (Arctic Passion). It has also been supported by the WSL/SLF over many years and projects.</pre>
The data for Radar Circular Polarization Ratio of Near-Earth Asteroids: Links to Spectral Taxonomy and Surface Processes
<p>README</p> <p>% =========================================================================<br>% Project: "Radar Circular Polarization Ratio of Near-Earth Asteroids: Links to <br>% Spectral Taxonomy and Surface Processes"<br>% Author: Edgard G. Rivera-Valentín<br>% Institution: Johns Hopkins University Applied Physics Laboratory<br>% Email: edgard.rivera-valentin@jhuapl.edu<br>% ORCID: 0000-0002-0786-7307<br>% Date: 18 September 2024<br>% =========================================================================</p> <p>% =========================================================================<br>% Licenses:<br>% Any software provided in this repository is licenced under the MIT License, detailed below and within <br>% this archive. <br>% Any data provided in this repository is licenced under Creative Commons Attribution 4.0 International, <br>% detailed within this archive. <br>%<br>% MIT License<br>%<br>% Copyright (c) 2024 The Johns Hopkins University Applied Physics Laboratory LLC<br>%<br>% Permission is hereby granted, free of charge, to any person obtaining a copy<br>% of this software, data, and associated documentation files (the "Software"), to deal<br>% in the Software without restriction, including without limitation the rights<br>% to use, copy, modify, merge, publish, distribute, sublicense, and/or sell<br>% copies of the Software, and to permit persons to whom the Software is<br>% furnished to do so, subject to the following conditions:<br>%<br>% The above copyright notice and this permission notice shall be included in<br>% all copies or substantial portions of the Software.<br>%<br>% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR<br>% IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,<br>% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE<br>% AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER<br>% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,<br>% OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN<br>% THE SOFTWARE.<br>%<br>% ADDITIONAL LICENSING INFORMATION:<br>% Any reuse of the figures and data provided in this repository must abide by <br>% the Copyright policy of the American Astronomical Society Journals:<br>% https://journals.aas.org/article-charges-and-copyright/#AAS_material<br>% =========================================================================</p> <p>% =========================================================================<br>% Description:<br>% This is a data repository for the manuscript published in the Planetary<br>% Science Journal:<br>% Title = Radar Circular Polarization Ratio of Near-Earth Asteroids: Links <br>% to Spectral Taxonomy and Surface Processes<br>% Please see the Zenodo metadata for detailed publicatoin information.<br>% This repository includes:<br>% (1) A csv file that has the table of data used in the published work. The<br>% table includes the data for each near-Earth asteroid used in the<br>% anlaysis. The header information includes: Asteroid Number, Designation<br>% or Name, Taxonomic Type (Following the Bus-DeMeo classification system),<br>% CPR (where CPR is circular polarization ratio), CPR uncertainty (where<br>% the uncertainty is the 1-sigma value), a (where a is semi-major axis in<br>% au), q (where q is the perihelion distance in au), Q (where Q is the<br>% aphelion distance in au), P (where P is the rotational period in hours),<br>% Hmag (where Hmag is the absolute magnitude), and Orbital Class. <br>% (2) A .mrt file that contains the same data as the csv file, but formatted<br>% according to the specifications of the machine-readable table format<br>% used by the AAS Journals. The .mrt version will also be published with<br>% the final PSJ article.<br>% (3) .mat files containing the output from the statistical modeling<br>% presented in the paper. These files are required to run the .m file<br>% included in this repository.<br>% (4) Make Figures.m, which is a Matlab code that remakes all the figures<br>% presented in the paper given the data in each of the .mat files. <br>% (5) The .png files for each of the figures presented in the paper. <br>% =========================================================================</p> <p>% =========================================================================<br>% File Formats:<br>% This archive includes various file formats. <br>% .csv file is a text file format that uses commas to separate values, and<br>% newlines to separate records. <br>% .mrt file is an ASCII byte-by-byte format that is documented here:<br>% https://journals.aas.org/mrt-overview/<br>% and readable by tools such as astropy, TOPCACT, etc. <br>% .m file is a simple text file used by Matlab, it can be opened by any<br>% text editor and executed by Matlab. <br>% .mat file is the file format used by MATLAB for saving data. It can be<br>% ready by other software, such as python, e.g., <br>% https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html<br>% .png file is short for Portable Network Graphic, which is a type of<br>% raster image file. <br>% =========================================================================</p>
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Data and Processing from "Carbon-centric dynamics of Earth's marine phytoplankton"
<div><strong>Brief Summary:</strong></div> <div>This documentation is for associated data and code for: </div> <div>A. Stoer, K. Fennel, Carbon-centric dynamics of Earth's marine phytoplankton. Proceedings of the National Academy of Sciences (2024).</div> <div> </div> <div>To cite this software and data, please use:</div> <div> <div>A. Stoer, K. Fennel, Data and processing from "Carbon-centric dynamics of Earth's marine phytoplankton". Zenodo. <a href="https://doi.org/10.5281/zenodo.10949682" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10949682</a>. Deposited 1 October 2024.</div> </div> <div> </div> <div><strong>List of folders and subfolders and what they contain:</strong></div> <div> <ol> <li>raw data: Contains raw data used in the analysis. This folder does not contain the satellite imagery, which will need to be downloaded from the NASA Ocean Color website (https://oceancolor.gsfc.nasa.gov/). <ol> <li>bgc-argo float data (subfolder): Includes Argo data from its original source or put into a similar Argo format</li> <li>global region data (subfolder): Includes data used to subset the Argo profiles into each 10deg lat region and basin.</li> <li>graff et al 2015 data (subfolder): Include the data digitized from Graff et al.'s Fig. 2.</li> </ol> </li> <li>processed data: data processing by this study (Stoer and Fennel, 2024) <ol> <li>processed bgc-argo data (subfolder): A binned processed file is present for each Argo float used in the analysis. Note these files include those describe in Table S1 (these are later processed in "3_stock_bloom_calc.py")</li> <li>processed satellite data (subfolder): includes a 10-deg latitude averaged for each satellite image processed (called "chl_sat_df_merged.csv"). This is later used to calculate a satellite chlorophyll-a climatology in "3_stock_bloom_calc.py".</li> <li>processed chla-irrad data (subfolder): includes the quality-controlled light diffuse attenuation data coupled with the chlorophyll-a fluorescence data to calculate slope factor corrections (the file is called "processed chla-irrad data.csv").</li> <li>processed topography data (subfolder): includes smoothed topography data (file named "ETOPO_2022_v1_60s_N90W180_surface_mod.tiff").</li> </ol> </li> <li>software: <ol> <li>0_ftp_argo_data_download.py: This program downloads the Argo data from the Global Data Assembly Center's FTP. Running this program will provide new Argo float profiles. However, there will be new floats and profiles present if downloaded. This will not match the historical record of Argo floats used in this analysis but could be useful for replicating this analysis when more data becomes available. The historical record of BGC-Argo floats are present in "/raw data/bgc-argo float data/" path. If you wish to downloaded other float data, see Gordon et al. (2020), Hamilton and Leidos (2017) and the data from the misclab website (https://misclab.umeoce.maine.edu/floats/).</li> <li>1_argo_data_processing.py: This program quality-controls and bins the biogeochemical data into a consistent format. This includes corrections and checks, like the spike/noise test or the non-photochemical quenching correction.</li> <li>2_sat_data_processing.py: this program processes the satellite data downloaded from the NASA Ocean Color website.</li> <li>3_stock_bloom_calc.py: this is the main program used to described the results of the study. The program takes the processed Argo data and groups it into regions and calculates slope factors, phytoplankton carbon & chlorophyll-a, global stocks, and bloom metrics.</li> <li>4_stock_calc_longhurst_province.py: This program repeats the global stocks calculations performed in "3_stock_bloom_calc.py" but bases the grouping on Longhurst Biogeochemical Provinces.</li> </ol> </li> </ol> </div> <div><strong>How to Replicate this Analysis:</strong></div> <div>Each program should be run in the order listed above. Path names where the data files have been downloaded will need to be updated in the code.</div> <div> </div> <div>To use the exact same Sprof files as used in the paper, skip running "0_ftp_argo_data_download.py" and start with "1_argo_data_processing.py" instead. Use the float data from the folder "bgc-argo float data". The program "0_ftp_argo_data_download.py" downloads the latest data from Argo database, so it is useful for updating the analysis. The program "1_argo_data_processing.py" may also be skipped to save time and the processed BGC-Argo float data may be used instead (see folder named "processed bgc-argo data"). </div> <div> </div> <div>Similarly, the program "2_sat_data_processing.py" may also be skipped, which otherwise can take multiple hours to process. The raw data is available from the NASA Ocean Color website (<a href="https://oceancolor.gsfc.nasa.gov/">https://oceancolor.gsfc.nasa.gov/</a>). The processed data from "2_sat_data_processing.py" is available so this step may be skipped to save time as well.</div> <div> </div> <div>The program "3_stock_bloom_calc.py" will require running "ocean_toolbox.py" (see below) in another tab. The portion of the program that involves QC for the irradiance profiles has been commented out to save processing time, and the pre-processed data used in the study has been linked instead (see folder "processed light data"). Similarly, pre-processed topography data is present in this repository. The original Earth Topography data can be accessed at <a href="https://www.ncei.noaa.gov/products/etopo-global-relief-model">https://www.ncei.noaa.gov/products/etopo-global-relief-model.</a></div> <p> </p> <p>A version of "3_stock_bloom_calc.py" using Longhurst provinces is available for exploring alternative groupings and their effects on stock calculations. See the program named "4_stock_calc_longhurst_province.py". You will need to download the Longhurst biogeochemical provinces from <a href="https://www.marineregions.org/">https://www.marineregions.org/</a>.</p> <p>To explore the effects of different slope factors, averaging methods, bbp spectral slopes, etc, the user will likely want to make changes to "3_stock_bloom_calc.py". Please do not hesitate to contact the correponding author (Adam Stoer) for guidance or questions.</p> <p><strong>ocean_toolbox.py:</strong></p> <p>import statsmodels.formula.api as smf<br>import os<br>import matplotlib.pyplot as plt<br>import numpy as np<br>from uncertainties import unumpy as unp<br>from scipy import stats</p> <p>def file_grab(root,find,start): #grabs files by file extensions and location<br> filelst = []<br> for subdir, dirs, files in os.walk(root):<br> for file in files:<br> filepath = subdir + os.sep + file<br> if filepath.endswith(find):<br> if filepath.startswith(start):<br> filelst.append(filepath)<br> return filelst</p> <p>def sep_bbp(data, name_z, name_chla, name_bbp):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_z: name of the depth variable in data<br> name_chla: name of the chlorophyll-a variable in data<br> name_bbp: name of the particle backscattering variable in data <br> <br> returns: the data variable with particle backscattering partitioned into <br> phytoplankton (bbpphy) and non-algal particle components (bbpnap).<br> '''<br> #name_chla = 'chla'<br> #name_z = 'depth'<br> #name_bbp = 'bbp470'<br> dcm = data[data.loc[:,name_chla]==data.loc[:,name_chla].max()][name_z].values[0] # Find depth of deep chla maximum<br> part_prof = data[(data.loc[:,name_bbp]<np.median(data.loc[:,name_bbp]))] # find median bbp of profile<br> <br> mod = smf.quantreg('bbp470 ~ ' + str(name_z), <br> part_prof).fit(q=0.01) # Find model to 1 percentile<br> y_pred = mod.predict(part_prof.loc[:,name_z]) # Create predicted bbp_nap<br> <br> part_prof.loc[:,'bbp_back'] = y_pred.values # Predicted bbp NAP from linear trend<br> z_lim = part_prof.loc[(part_prof.loc[:,'bbp_back'].div(part_prof.loc[:,name_bbp])>=1), name_z].min() <br> <br> # Find depth where bbp NAP and bbp intersect<br> data.loc[data[name_z]>=z_lim, 'bbp_back'] = data.loc[data[name_z]>=z_lim, name_bbp].tolist()<br> data.loc[data[name_z]<z_lim,'bbp_back'] = data.loc[data[name_z]==z_lim, name_bbp].values[0] #data.loc[data[name_z]<z_lim, name_z].mul(lr.slope).add(lr.intercept)<br> <br> <br> data.loc[:,'bbpphy'] = data.loc[:, name_bbp].sub(data.loc[:,'bbp_back']) # Subtract bbp NAP from bbp for bbp from phytoplankton<br> data.loc[(data['bbpphy']<0)|(data['depth']>z_lim),'bbpphy'] = 0 # Subtract bbp NAP from bbp for bbp from phytoplankton</p> <p> return data['bbpphy'], z_lim</p> <p>def bbp_to_cphy(bbp_data, sf):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_bbp: name of the particulate backscattering variable in data<br> name_bbp_err: name of particulate backscattering error variable in data<br> <br> returns: the data variable with particle backscattering [/m] converted into<br> phytoplankton carbon [mg/m^3].<br> '''<br> <br> cphy_data = bbp_data.mul(sf) </p> <p> return cphy_data</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.