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1,773 results for “Supplementary data,”
Supplementary Data: Trade-Offs in Land-Based Carbon Removal Measures under 1.5°C and 2°C Futures
<p>This compressed dataset includes the queried CVS files from 16 GCAM data bases generated for the study titled "<strong>Trade-Offs in Land-Based Carbon Removal Measures under 1.5°C and 2°C Futures</strong>".</p> <p>The data sets provided here came from the GCAM model output. Please find the model and code information at the GitHub repo: <a href="https://github.com/realxinzhao/paper-LandBasedCDR-GCAM">realxinzhao/paper-LandBasedCDR-GCAM</a>.</p> <p>In addition, the data were used for generating results used in the paper. See more information at <a href="https://github.com/realxinzhao/paper-LandBasedCDR-DisplayItems">realxinzhao/paper-LandBasedCDR-DisplayItems</a>.</p>
Supplementary code and data for "Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks"
<p>Supplementary code and data for the conference article</p> <blockquote> <p>"Tackling universal properties of minimal trap spaces of boolean networks" Sara Riva, Jean-Marie Lagniez, Gustavo Magaña López, and Loïc Paulevé Proceedings of CMSB 2023, LCNS, Springer.<a href="https://doi.org/10.1007/978-3-031-42697-1_11"> https://doi.org/10.1007/978-3-031-42697-1_11</a></p> </blockquote> <p>Requirements</p> <ul> <li> <p>Python at least 3.9</p> </li> <li> <p><a href="https://github.com/bnediction/bonesis">bonesis</a> (v0.5.6) - <a href="https://doi.org/10.5281/zenodo.7984628">https://doi.org/10.5281/zenodo.7984628</a></p> <p>With pip:</p> <ul> <li><code>pip install bonesis==0.5.6</code></li> </ul> <p>With conda:</p> <ul> <li><code>conda install -c potassco -c colomoto bonesis=0.5.6</code></li> </ul> </li> <li> <p>for the synthesis: <a href="https://github.com/sybila/biodivine-aeon-py/">AEON.py</a></p> <p>With pip:</p> <ul> <li><a href="https://rustup.rs">https://rustup.rs</a></li> <li><code>pip install biodivine-aeon</code></li> </ul> <p>With conda</p> <ul> <li><code>conda install -c daemontus biodivine_aeon</code></li> </ul> </li> </ul> <p> </p> <p>Usage</p> <ul> <li> to execute the CEGAR approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_cegar.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>The first parameter is the .bnet file, the second one is the marker (in JSON), and the third is the maximum number k of components in a perturbation. To fix the marker, it is also possible to use a special node (ex: p) in the bnet file and require a marker '{"p":1}'. In the Moon22 dataset, information about uncontrollable components are available, one can use <code>--exclude '["Apoptosis","Growth_Arrest","Proliferation"]'</code>. If the marker is given as a component, it must be involed in the uncontrollable list.</p> <p>Moreover, the markers of the Moon22 dataset are contained in the bnet files in a special component <code>p</code>. Concerning the Trappist dataset, the possible markers are contained in files .markers, the user can choose between them.</p> <p>The dataset synthetic_bns contains random generated BNs. Also in this case, different possible markers are contained in files .markers.</p> <ul> <li>to execute the Complementary approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_complementary.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>Again, uncontrollable components can be specified.</p> <ul> <li>to execute the Enumeration & Filtering approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_trapspaces_naive.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>Again, uncontrollable components can be specified.</p> <ul> <li>to execute the CEGAR approach for a synthesis problem:</li> </ul> <pre><code>python synthesis.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.aeon '{"p":1}' </code></pre> <p>The first parameter is the prior knowledge graph in an .aeon file and the second one is the marker (in JSON). Another option is <code>--maxclause</code> which allow one to specify the maximum integer number of clauses authorised in local functions (by default is 128). The option <code>--no-canonic</code> leads to specify the criteria for the refinement of the under-approximation (3 for TS(y)|=M ; 1 for \exists y s.t. TS(y)!=TS(x) or 0 to obtain just a different candidate solution).</p>
Supplementary data for Moreira et al.
<p>μ-XANES Sulphur Speciation data</p> <p>Zircon trace elements data</p> <p>Zircon U-Pb-O-Hf dataset</p> <p>Python code</p>
supplementary data for Genetic Polymorphism on Olanzapine-Induced Adverse Drug Reaction in Schizophrenic Patients
<p>this file contain supplementary data including age gender .... for our research </p>
Supplementary material and data, Global strategy for the conservation and use of Vigna
<p>Supplementary material and data, Global strategy for the conservation and use of Vigna. Second version, after typos in the Read_me file were corrected. </p>
Mitocheck Data Supplementary Files
<p>This dataset contains all supplementary files related to the <em><a href="https://github.com/WayScience/mitocheck_data">mitocheck_data</a></em> repository that were too large to upload on GitHub.</p> <p>The compressed folders include extracted and normalized single-cell features from the following subsets of the original screen by MitoCheck Consortium:</p> <ul> <li>negative_control_data/ : Nuclei features from negative control cells transfected with scrambled siRNA.</li> <li>positive_control_data/ : Nuclei features from positive control cells transfected with siRNA targeting genes used during mitosis (INCENP, KIF11, COPB1).</li> <li>training_data/ : Nuclei features from cells manually labeled with a phenotypic class by MitoCheck Consortium.</li> </ul> <p>More details for the compressed folders are as follows:</p> <ul> <li>1.idr_streams/extracted_features/ : All <em>IDR_stream</em>-extracted single-cell features (CellProfiler, DeepProfiler, merged) for the data subsets of the original MitoCheck Consortium screen. More information can be found in <a href="https://github.com/WayScience/mitocheck_data/tree/main/1.idr_streams">mitocheck_data/1.idr_streams</a>.</li> <li>3.normalize_data/normalized_data/ : All <em>IDR_stream</em>-extracted single-cell features (CellProfiler, DeepProfiler, merged) normalized with <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html">sklearn.preprocessing.StandardScaler</a>. One StandardScaler was derived from the negative control features and applied to all data subsets. More information can be found in <a href="https://github.com/WayScience/mitocheck_data/tree/main/3.normalize_data">mitocheck_data/3.normalize_data</a>.</li> </ul> <p>The <a href="https://idr.openmicroscopy.org/">Image Data Resource (IDR)</a> hosts all data for the original screen by MitoCheck Consortium under accession idr0013 (screenA). Dr. Jean-Karim Heriche and Dr. Thomas Walter of the MitoCheck Consortium shared the manually-assigned phenotypic class labels used in training_data.</p>
Supplementary Data: An evaluation of the General Bathymetric Chart of the Ocean (GEBCO) in shoreline-crossing geomorphometric investigations of volcanic islands
<p>Supplementary data for the titular publication. Consists of .csv and .gpkg files with the results of geomorphometric parameter extraction of volcanic islands from shoreline-crossing DEMs of various resolution, the calculation of the deviation of these parameters depending on the source DEM, as well as basic statistical distribution of these parameters. See publication for more detailed information.</p>
Supplementary Fig. S8 source data
<p>Haplotype <em>variations</em> data used to produce Supplementary Figure S8.</p>
SSIMS-Flow paper: supplementary data
<p>Supplementary data in support of the SSIMS-Flow paper in Environmental Modeling and Software journal, containing:</p> <ol> <li>SSIMS-Flow and PIVlab data and project files,</li> <li>Additional showcase examples,</li> <li>Snapshot of the SSIMS-Flow version used in the paper.</li> </ol>
Nonlinear optical computing with disordered media -- Experimental Data for results in Supplementary Information
<p>Supplementary experimental data for nonlinear optical computing results with disordered media</p>
The Role of Flexible Geothermal Power in Decarbonized Electricity Systems: Supplementary Data
<p>This repository contains raw data related to the paper "The Role of Flexible Geothermal Power in Decarbonized Electricity Systems." 'EGS_Costing.xlsx' is an excel model used to determine the cost and performance of EGS resources based on input characteristics such as resource depth, temperature, and location. 'Reservoir_Simulations.zip' contains outputs from the ResFrac reservoir simulations used to calibrate the EGS optimization model. The 'PowerGenome.zip' contains the PowerGenome input and output files used to create non-EGS inputs for the GenX power systems model. 'GenX_Source_Code.zip' contains the source code for the modified version of GenX used in this study. Other compressed folders contain the input and output files for all GenX runs referenced in this study. </p>
Supplementary data for "Buoyant impact melts on ancient Mars"
<p>MELTS inputs and lookup tables used to determine melt fraction, composition, and density for impact partial melts on Mars.</p>
Detecting Transient Deformation at the Active Volcano Ol Doinyo Lengai in Tanzania with the TZVOLCANO Network: Supplementary software, data, model files
<p><span>These are supplementary data, code, and model files associated with the manuscript "</span><span>Detecting Transient Deformation at the Active Volcano Ol Doinyo Lengai in Tanzania with the TZVOLCANO Network<span>" in consideration for publication in the Geophysical Research Letters. tzvolcano_code_and_models.zip contains all necessary Targeted Projection Operator (TPO) software, input, and output files for the GNSS inversions presented in our manuscript necessary to reproduce the results. The TPO program is a Unix/Linux code developed by <span>Kang-Hyeun Ji working at the Korea Institute for Geoscience and Mineral Resources, Daejeon, South Korea. The source code is available in the supplementary Zenodo repository. We also include input and output model files for the USGS code dMODELS for reproducibility. Please see the README.txt file for more details.</span></span></span></p> <p><span>This study was funded by the US National Science Foundation grant number EAR-1943681 to Virginia Tech, internal university funds via Ardhi University, and Ministry of Science and ICT of Korea Basic Research Project GP2021-006 to the Korea Institute of Geosciences and Mineral Resources. We acknowledge and thank the EarthScope Consortium for archiving and making TZVOLCANO GNSS datasets freely available, supported by the National Science Foundation’s Seismological Facility for the Advancement of Geoscience (SAGE) Award under Cooperative Support Agreement EAR-1851048 and Geodetic Facility for the Advancement of Geoscience (GAGE) Award under NSF Cooperative Agreement EAR-1724794.</span></p>
Supplementary Data for "Sedimentary conditions drive modern pyrite burial flux to exceed oxidation"
<h2>Supplementary Data for "Sedimentary conditions drive modern pyrite burial flux to exceed oxidation"</h2> <p><strong><br>The Supplementary Data</strong> is divided into the following folders:</p> <ol> <li> <p><strong>Model Validation</strong><br>This folder contains a collection of downcore profiles used for model validation. The profiles were compiled from various datasets to assess the accuracy and performance of the model. This Folder also contains a Validation subfolder, where model-data fits for all profiles are shown. </p> </li> <li> <p><strong>Raw Data</strong><br>This folder includes all the original global datasets utilized in the study. These data were compiled from multiple sources and serve as the foundational input for the analysis presented in the publication.</p> </li> <li> <p><strong>Processed Data</strong><br>This folder contains the processed datasets, which include global products derived from the raw data. Additionally, it contains validation data that has been extracted and compiled for use in the study. Subfolders provide gridded global data used as model inputs and the corresponding gridded global outputs generated by the model. All gridded data is provided as comma-delimited <code>.txt</code> files.</p> </li> </ol> <p><strong>Data Structure and Files</strong></p> <p>Within the processed data, you will find the following key files:</p> <ul> <li> <p><strong><code>latitude.txt</code></strong><br>Contains gridded latitude values.</p> </li> <li> <p><strong><code>longitude.txt</code></strong><br>Contains gridded longitude values.</p> </li> <li> <p><strong>Variable Files (e.g., <code>pyrite_burial_rate.txt</code>):</strong><br>Each variable you wish to work with or display is stored in its own file.</p> </li> <li> <p><strong>Output Grids: <br></strong>pyrite burial rate is <strong><code>pyrite_burial_rate.txt</code> </strong>in g cm⁻² y⁻¹<br>pyrite isotopic composition is <strong><code>delta_pyrite.txt</code> </strong>in ‰ (permil)<br>pyrite formation depth is <strong><code>z_max.txt</code> </strong>in cm <br>total depth-integrated pyrite content is <strong><code>total_mols_pyrite.txt</code></strong> in mol cm⁻².</p> </li> </ul> <p>When working with the data, you need to combine the <code>lat</code> and <code>long</code> grids with the variable file of interest to visualize or analyze the dataset.</p> <h2>Working with the Data in MATLAB</h2> <p>If you are using MATLAB, you can display the data using the <code>geoshow</code> function. For example, to display the pyrite burial rate:</p> <ol> <li> <p><strong>Read the Data:</strong></p> <div> <div><code>latitude = read('latitude.txt'); </code></div> <div><code>longitude = read('longitude.txt'); </code></div> <div><code>data = read('pyrite_burial_rate.txt'); </code></div> </div> </li> <li> <p><strong>Display the Data on a World Map:</strong></p> <div> <div> </div> <div><code>worldmap world </code></div> <div><code>geoshow(latitude, longitude, data, 'DisplayType', 'texturemap')</code></div> <div><code>set(gca,'ColorScale','log')</code></div> <div> </div> <div>Alternatively, you can use the provided <code>plot_global_data.m</code> function located in the <code>/plotting_functions</code> <a href="https://zenodo.org/uploads/14808279">Model</a> folder. </div> <div> </div> </div> </li> </ol> <h2>Working with the Data in Python</h2> <p>If you are using Python, you can work with the gridded data as follows:<code> </code></p> <ol> <li> <p><strong>Read the Data:</strong></p> <div> <div><code>import pandas as pd </code></div> <div><code>import numpy as np </code></div> <div><code>import matplotlib.pyplot as plt </code></div> <div><code>import cartopy.crs as ccrs</code></div> <div><code>from matplotlib.colors import LogNorm</code></div> <div> </div> <div><code>latitude = pd.read_csv('GlobalGridsOutput/latitude.txt', delimiter=',', header=None).values </code></div> <div><code>longitude = pd.read_csv('GlobalGridsOutput/longitude.txt', delimiter=',', header=None).values </code></div> <div><code>data = pd.read_csv('GlobalGridsOutput/pyrite_burial_rate.txt', delimiter=',', header=None).values </code></div> </div> <p> </p> </li> <li> <p><strong>Plotting the Data:</strong><br>You can plot the data on a world map using <code>cartopy</code>:</p> <div> <div> </div> <div><code>fig = plt.figure(figsize=(10, 5)) </code></div> <div><code>ax = plt.axes(projection=ccrs.PlateCarree()) </code></div> <div><code>ax.coastlines() </code></div> <div><code>mesh = ax.pcolormesh(longitude, latitude, data, transform=ccrs.PlateCarree(), cmap='viridis') </code></div> <div><code>plt.colorbar(mesh, ax=ax, orientation='vertical', label='Pyrite Burial Rate') </code></div> <div><code> plt.show() </code></div> </div> </li> </ol> <div> <div> </div> </div> <p><br><br></p>
Supplementary data to the paper: Causal association between serum thyroid-stimulating hormone and obesity: A bidirectional Mendelian randomization study
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Data from: Supplementary stocking selects for domesticated genotypes
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Supplementary data from: Phylogeny and macroevolution of a “dead clade walking”: a systematic revision of the Paragaricocrinidae (Crinoidea)
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Supplementary Data for: Life history in primate teeth is revealed by changes in major and minor element concentrations measured via field-emission SEM-EDS analysis
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Data from: Individual variation in winter supplementary food consumption and its consequences for reproduction in wild birds.
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Supplementary data from: Decoupling of morphological disparity and taxonomic diversity during the end-Permian mass extinction
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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.