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764 results for “Reproducibility”
Data from: Reproducibility of the Quantification of Reversible Wall Interactions in VOC Sampling Lines
<p>Dataset from the following publication (<a href="https://doi.org/10.3390/atmos12020280">https://doi.org/10.3390/atmos12020280</a>). In the paper, a method to quantify the amount of substance segregated by reversible interactions on sampling lines is proposed. The areic amount of a VOC (Acetone) interacting with the pipe is measured for a commercial test pipe (Sulfinert®) as the amount of substance per unit area of the internal surface of the test pipe segregated from the flowing gas mixture. The areic amount is function of numerical integrals estimated under different conditions and reproducibility is evaluated. The data used to estimate the integrals described in this work is organised in folders. Each folder correspond to a sample. Sample information is available on Table 3 of the paper.</p>
Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination
<p>Submissions for the Tractostorm 2 Project [1] from our collaborators (raters) are available for new analysis.<br> Contains regions of interest (ROIs) as well as resulting bundles. Segmentations were performed with MI-Brain [2] (<a href="https://github.com/imeka/mi-brain">MI-Brain</a>)</p> <p>Initial data is the same as in the initial <a href="https://zenodo.org/record/2547025#.YRV2S3VKiUk">Tractostorm Project</a> [3]<br> Contains the data as sent to collaborators and the written document containing the dissection protocol in detail.</p> <p>[1] Rheault, Francois, et al. "Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination." <em>Human Brain Mapping</em> (2022).<br> [2] Rheault, Francois, et al. "MI-Brain, a software to handle tractograms and perform interactive virtual dissection." <em>Proceedings of the ISMRM Diffusion study group workshop, Lisbon</em>. 2016.<br> [3] Rheault, Francois, et al. "Tractostorm: The what, why, and how of tractography dissection reproducibility." <em>Human brain mapping</em> 41.7 (2020): 1859-1874.</p> <p>Data Organization:<br> The 5 HCP subjects were duplicated 4 times each.<br> 193441 -> A111, B218, C317, D418<br> 219231 -> A127, B228, C320, D426<br> 286650 -> A136, B237, C338, D436<br> 486759 -> A149, B246, C344, D443<br> 615441 -> A156, B252, C359, D450<br> <br> Bundles can be segmented automatically using the <a href="https://github.com/scilus/scilpy">scilpy</a> toolbox.<br> scil_filter_tractogram.py ${INPUT} ${OUTPUT} ${OPTIONS}</p> <ul> <li>${INPUT} would be the whole brain tractogram of an HCP subject in data_to_segment.zip</li> <li>${OUTPUT} would be the bundle filename (preferably .trk format)</li> <li>${OPTIONS} would be the sequence of ROIs to apply, one for each bundle. <ul> <li><strong>CC</strong>: '--drawn_roi CENTRAL_CC.nii.gz any include --drawn_roi LOWER_AXIAL_LIM.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any exclude --drawn_roi PRE_C_L.nii.gz any exclude --drawn_roi POST_C_R.nii.gz any exclude --drawn_roi PRE_C_R.nii.gz any exclude'</li> <li><strong>AF_L</strong>: '--drawn_roi CENTRAL_CS_L.nii.gz any include --drawn_roi MEDIAL_SAGITTAL_LIM.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any include --drawn_roi PRE_C_L.nii.gz any include --drawn_roi TEMPORAL_ENTRY.nii.gz any include --drawn_roi TEMPORAL_STEM.nii.gz any exclude'</li> <li><strong>PYT_L</strong>: '--drawn_roi IC_L.nii.gz any include --drawn_roi MO_L.nii.gz any include --drawn_roi MB_L.nii.gz any include --drawn_roi MO_L_NOT.nii.gz any exclude --drawn_roi MID_SAGITTAL_PLANE.nii.gz any exclude --drawn_roi POST_C_L.nii.gz any exclude --drawn_roi PRE_C_L.nii.gz any exclude'</li> </ul> </li> </ul>
Including Data Management in Research Culture Increases the Reproducibility of Scientific Results
<p><strong>General Information:</strong></p> <p>This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended. </p> <p>The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268<br> The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30</p> <p>This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data.</p> <p><strong>Run the code:</strong></p> <p>To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt". </p> <p>Alternatively, you can import the .ipyab file in the colab [2] and run it. </p> <p><br> [1]. https://www.anaconda.com/<br> [2]. https://research.google.com/colaboratory/<br> </p>
Dynamic FRET example videos related to "Mars, a molecule archive suite for reproducible analysis and reporting of single-molecule properties from bioimages"
<p>Videos of dynamic switching between iso-I and iso-II conformations of a holiday junction at 50 mM Magnesium resulting in high and low FRET from Cy3 and Alexa647 labels positioned on the arms. Holiday junctions are surface immobilized through a biotin attachment and imaged using TIRF microscopy. The camera sensor is split using a dual view so that the acceptor emission is on the top and the donor emission is on the bottom. Videos from each position are provided as compressed zip files containing a sequence of tif files and associated metadata text file. Image sequences were collected using Micro-Manager 2.0 using ALEX or alternating laser excitation with alternating 637 and 532 pulses separated as two different channels. Beam profile images are provided for 637 and 532 excitation allowing for correction of the non-uniform beam profiles. The following 2D affine transformation matrix can be used to transform from the top acceptor emission region to the bottom donor emission region during processing.</p> <p>Affine 2D transformation from top to bottom: (m00, m01, m02, m10, m11, m12), (1.00276, 0.000208, 1.01236, 0.000267, 1.00312, 507.21025)</p> <p>A detailed image processing workflow for this dataset using Mars can be found under the example section at <a href="https://duderstadt-lab.github.io/mars-docs/">https://duderstadt-lab.github.io/mars-docs/</a> or directly at <a href="https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/">https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/</a></p>
Reproducible and Attributable Materials Science Workflows
<p>This set includes the deidentified data, reproducible analysis and research report of the project on Reproducible and Attributable Materials Science Workflows.</p>
Preprocessed dataset required to reproduce Supplementary Figure S6VW
<p>These files are required to reproduce the supplementary figure S6VW of the paper.</p> <p>The DataOverview_SF6VW provides Animal_IDs and recording dates.</p> <p>For each Animal_ID in the DataOverview this dataset contains:</p> <p>1) The pre-processed photometry data of the CoT session (filename: mouseID_date_smoothed_signal.npy) </p> <p>2) A dataframe containing all relevant behavioural events and time stamps of the CoT session (filename: mouseID_date_restructured_data.pkl)</p> <p>3) The pre-processed photometry data of the random-white-noise ('RWN') (filename: mouseID_date_RWN_smoothed_signal.npy) </p> <p>4) A dataframe containing all relevant events and time stamps of the RWN session (filename: mouseID_date_RWN_restructured_data.pkl)</p> <p>The RWN was recorded right after the CoT session. In this case the CoT session contained a small subset of trials with large rewards and omissions ('LRO'). These trials are excluded in the analysis of the APE signal. </p>
Participant survey for the article: More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education
<p>The artcile More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education concerns the introduction of a new course format in the Master Program in Biostatistics at the University of Zurich. This data set contains the results fo a survey among the participants in this new course.</p> <p>Sepcifically it contains the answers of 22 participants to the following questions:</p> <p>1) Did you use the following concepts or tools since you took STA472? <br>Good practice for...</p> <p>... spreadsheets<br>... file and folder organization<br>... version control<br>... dynamic reporting<br>... LaTeX<br>... presentation slide design <br>... oral presentations<br>... designing graphs<br>... designing tables<br>... structure for manuscript<br>... logic of a paragraph<br>... writing style<br>... writing R functions<br>... using unit tests<br>... setting up simulations<br>... code styling<br>... writing vectorized code<br>... writing parallelized code<br>... containerizing code</p> <p>Answers are in the scale: never since, rarely, sometimes, often, frequently, I do not know</p> <p>2) If you used the above concepts at least rarely, did the training of STA472 help you?</p> <p>Good paractice for...</p> <p>... spreadsheets<br>... file and folder organization<br>... version control<br>... dynamic reporting<br>... LaTeX<br>... presentation slide design <br>... oral presentations<br>... designing graphs<br>... designing tables<br>... structure for manuscript<br>... logic of a paragraph<br>... writing style<br>... writing R functions<br>... using unit tests<br>... setting up simulations<br>... code styling<br>... writing vectorized code<br>... writing parallelized code<br>... containerizing code</p> <p>Answers are in the scale: Not really Somewhat Definitively I do not know I do not use this concept</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Churfirsten GeoTIFF
<p>Multiscale elevation models centered on Churfirsten, Switzerland</p> <p>Resolutions: 0.5, 2, 5, 10, 15, 30, 60, 120, 250, 500, 1,000, and 2,000 meters, 3,000 × 2,500 height samples each</p> <p>File format: GeoTIFF</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning (datasets)
<p>The train/validation/test sets used in the study "<strong>A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning</strong>".</p> <p>Preprint:<a href="https://arxiv.org/abs/1908.06851"> https://arxiv.org/abs/1908.06851</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/8911792">https://ieeexplore.ieee.org/document/8911792</a></p> <p> </p> <p>The dataset used to create these sets was published in:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The full dataset is available here:</p> <pre><a href="https://doi.org/10.5281/zenodo.1212478">https://doi.org/10.5281/zenodo.1212478</a> </pre> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen; and Weyn, Maarten.</p> <p> </p>
Tractostorm: Rater reproducibility assessment in tractography dissection of the pyramidal tract
<p>Segmentation of the 13 experts and 11 non-experts for the Tractostorm project. Contains the original dataset each participants received to perform their tasks.</p> <p>The tractography file format is *.trk and the image file format is *.nii.gz</p>
A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN (datasets)
<p>The train/validation/test sets used in the study "<strong>A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN</strong>".</p> <p>Preprint: <a href="https://arxiv.org/abs/1908.05085">https://arxiv.org/abs/1908.05085</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/8970177">https://ieeexplore.ieee.org/document/8970177</a></p> <p> </p> <p>The dataset used to create these sets was published in:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The full dataset is available here:</p> <pre><a href="https://doi.org/10.5281/zenodo.1212478">https://doi.org/10.5281/zenodo.1212478</a> </pre> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen and Weyn, Maarten.</p>
Data to reproduce figures in "Tropical thermocline helps power Pacific equatorial upwelling"
<p>A set of netcdf include results of the energetics in the Pacific STC region. </p> <p>A jupyter notebook uses all those dataset to reproduce the main plots in the paper. Code used to compute the energetics can be found within the 'Tailleux' class inside this module: https://github.com/inciente/EastPac/blob/main/KE_tools.py</p> <p>Please feel free to reach out if you're trying to use the data, or apply the energetics framework to your own simulations.</p>
ARTE (Article Reproducibility Template & Environment) workflow folder structure
<p>Illustration of a suggested folder and file system for editing articles that are <strong>dynamic and reproducible</strong>.</p> <p>The <a title="TIER Protocol 4.0 Site" href="https://www.projecttier.org/tier-protocol/protocol-4-0/" target="_blank" rel="noopener"><strong>TIER Protocol 4.0</strong></a> served as the foundation for this proposal, which was intended to be modified in <strong>Quarto using Rstudio</strong>, for instance, then published on <a title="Article Template Exemple" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener"><strong>GitHub Pages</strong></a>.</p> <p>An example of its deployment is provided here: <a title="Article Template Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">https://phdpablo.github.io/article-template/</a></p> <p>According to <a title="Original Article" href="https://periodicos.ufpe.br/revistas/index.php/politicahoje/article/view/245776" target="_blank" rel="noopener">Domingos and Batista's (2021)</a> suggested content (files) recommendations, the figure illustrates the design of the proposal while accounting for the three primary <strong>TIER Protocol 4.0</strong> folders (Data, Scripts, and Output). The proposal for the <strong>TIER Protocol 4.0</strong> was modified to take into account that the research narrative would be edited in a <a title="Site Quarto" href="https://quarto.org/docs/guide/" target="_blank" rel="noopener"><strong>dynamic Quarto-type document</strong></a> (*.qmd), published on <a title="Article Template Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">GitHub Pages</a> (docs/) following Rstudio rendering, and potentially even produce a .pdf file of the article; <strong>*.qmd </strong> denotes a collection of <strong>Quarto files </strong>containing the standard sections of a scientific article (Introduction, Theoretical Framework, Methods, etc.); the other folders indicated in the root (adm, docker, renv) are for project management and for controlling dependencies and the environment, if adopted by the researcher.</p> <p>Visit the project repository at <a title="GitHub Repository" href="https://github.com/phdpablo/article-template/" target="_blank" rel="noopener">https://github.com/phdpablo/article-template/</a> for additional details. Visit <a title="OSF" href="https://osf.io/njdq5/" target="_blank" rel="noopener">https://osf.io/njdq5/</a> to view the template on OSF.</p>
Calculation of Ferrite Core Losses with Arbitrary Waveforms using the Composite Waveform Hypothesis: Reproducibility Dataset
<p><strong>Paper</strong></p> <p>This package contains the datasets used in the following paper:</p> <ul> <li><strong>Calculation of Ferrite Core Losses with Arbitrary Waveforms using the Composite Waveform Hypothesis</strong></li> <li><strong>Thomas Guillod, Jenna S. Lee, Haoran Li, Shukai Wang, Minjie Chen, and Charles R. Sullivan</strong></li> <li><strong><a href="https://doi.org/10.1109/APEC43580.2023.10131348">https://doi.org/10.1109/APEC43580.2023.10131348</a></strong></li> <li><strong>IEEE APEC 2023, Orlando, Florida, USA</strong></li> </ul> <p><strong>Datasets</strong></p> <p>The EPCOS TDK N87 datasets used in this paper are part of the MagNet initiative (<a href="http://mag-net.princeton.edu">https://mag-net.princeton.edu</a>). MagNet is an openly available large-scale dataset including measurements of several core materials under various operating conditions. MagNet is a joint project between Princeton University, Dartmouth College, and Plexim GmbH.</p> <p>This package includes the two datasets used in the paper:</p> <ul> <li>"N87_ambient_temperature" - Loss dataset for EPCOS TDK N87 at ambient temperature (measured on a R22.1X13.7X7.9, 2022-02-01).</li> <li>"N87_variable_temperature" - Loss dataset for EPCOS TDK N87 at different temperatures (measured on a R34.0X20.5X12.5, 2022-07-14).</li> </ul> <p>It should be noted that the datasets contains more measurements than used in the paper:</p> <ul> <li>The measurements where the iGCC can be evaluated without extrapolation are used in the paper.</li> <li>The measurements where the iGCC require an extrapolation of the loss data are not used in the paper.</li> <li>A flag in the dataset indicates in which category a measurement belongs.</li> </ul> <p>More details about the measurement setup can be found on the MagNet website (<a href="http://mag-net.princeton.edu">https://mag-net.princeton.edu</a>).</p> <p>More details about the iGCC method can be found on GitHub (<a href="https://github.com/otvam/magnet_webinar_eqn_models">https://github.com/otvam/magnet_webinar_eqn_models</a>).</p> <p><strong>File Formats</strong></p> <p>The datasets are available in three different formats:</p> <ul> <li>CSV (text files).</li> <li>MATLAB tables (MAT v7.3 binary files, exported with MATLAB 2021a).</li> <li>Pandas dataframes (HDF5 binary files, exported with Python 3.10.6 and Pandas 1.3.5).</li> </ul> <p>The file "dataset_metadata.csv" contains the description of the different variables.<br>The file "test_matlab.m" is a MATLAB test file for loading the MATLAB tables.<br>The file "test_python.py" is a Python test file for loading the Pandas dataframes.</p>
Data to reproduce the results: Statistical power of spatial earthquake forecast tests
<p>We provide data needed to reproduce the figures from the publication titled "Statistical power of spatial earthquake forecast tests".</p>
A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?
<p>Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials.<br> In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram.<br> We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties.<br> This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions.<br> An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties.<br> These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process.<br> The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.</p>
Automatically Reproducing Timing-Dependent Flaky-Test Failures
<p>This artifact contains the source code for FlakeRake, a tool for automatically reproducing timing-dependent flaky-test failures. It also includes raw and processed results produced in the evaluation of FlakeRake</p> <p> </p> <p>Contents:</p> <p> </p> <p>Timing-related APIs that FlakeRake considers adding sleeps at: timing-related-apis</p> <p>Anonymized code for FlakeRake (not runnable in its anonymized state, but included for reference; we will publicly release the non-anonymized code under an open source license pending double-blind review): flakerake.tgz</p> <p>Failure messages extracted from the FlakeFlagger dataset: 10k_reruns_failures_by_test.csv.gz </p> <p>Output from running isolated reruns on each flaky test in the FlakeFlager dataset: 10k_isolated_reruns_all_results.csv.gz (all test results summarized into a CSV), 10k_isolated_reruns_failures_by_test.csv.gz (CSV including just test failures, including failure messages), 10k_isolated_reruns_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven)</p> <p>Output from running the FlakeFlagger replication study (non-isolated 10k reruns):flakeFlaggerReplResults.csv.gz (all test results summarized into a CSV), 10k_reruns_failures_by_test.csv.gz (CSV including just failures, including failure messages), flakeFlaggerRepl_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven - this file is markedly larger than the 10k isolated reruns results because we ran *all* tests in this experiment, whereas the 10k isolated rerun experiment only re-ran the tests that were known to be flaky from the FlakeFlagger dataset).</p> <p>Output from running FlakeRake on each flaky test in the FlakeFlagger dataset:</p> <p>For bisection mode: results-bis.tgz</p> <p>For one-by-one mode: results-obo.tgz</p> <p>Scripts used to execute FlakeRake using an HPC cluster: execution-scripts.tgz<br> Scripts used to execute rerun experiments using an HPC cluster: flakeFlaggerReplScripts.tgz<br> Scripts used to parse the "raw" maven test result XML files in this artifact into the CSV files contained in this artifact: parseSurefireXMLs.tgz </p> <p>Output from running FlakeRake in “reproduction” mode, attempting to reproduce each of the failures that matched the FlakeFlagger dataset (collected for bisection mode only): results-repro-bis.tgz</p> <p>Analysis of timing-dependent API calls in the failure inducing configurations that matched FlakeFlagger failures: bis-sleepyline.cause-to-matched-fail-configs-found.csv</p>
Reproducibility package for Using root economics traits to predict biotic plant soil-feedbacks
<p>Using root economics space to predict biotic plant soil-feedbacks presents a novel framework linking below ground ecological theory to plant soil feedback effects. We show how to calculate root functional distance and location of two plant species in root economics space and how these measures can help to predict the strength and direction of the plant soil feedback between them. </p> <p>Contains data and scripts to reproduce analysis and figures for the manuscript (https://github.com/ggpmrutten/linkingRES-PSF)</p>
Data and scripts for reproducing "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer", submitted to Flow, Turbulence and Combustion.</p>
Data, code and software to reproduce the article entitled "Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model"
<p>This is the data, code and software to reproduce the article entitled " Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model". Here is a summary of the paper:</p> <p>The growing demand for sustainable agriculture is raising interest in intercropping for its multiple potential benefits to avoid or limit the use of chemical inputs or increase the production per surface unit. Predicting the existence and magnitude of those benefits remains a challenge given the numerous interactions between interspecific plant-plant relationships, their environment and the agricultural practices. Soil-crop models are critical in understanding these interactions in dynamics during the whole growing season, but few models are capable of accurately simulating intercropping systems.</p> <p>In this study, we propose a set of simple and generic formalisms for simulating key interactions in intercropping systems that can be readily included into existing dynamic crop models. This requires simulating important processes such as development, light interception, plant growth, N and water balance, and yield formation in response to management practices, soil conditions, and climate. These formalisms were integrated into the STICS soil-crop model and evaluated using observed data of intercropping systems of cereal and legumes mixtures, including Faba bean-Wheat, Pea-Barley, Sunflower-Soybean, and Wheat-Pea mixtures. We demonstrate that the proposed formalisms provide a comprehensive simulation of soil-plant interactions in various types of bispecific intercrops. The model was found consistent and generic under a range of spring and winter intercrops (nRMSE = 25% for maximum leaf area index, 23% for shoot biomass at harvest, and 18% for yield).</p> <p>This is the first time a complete set of formalisms has been developed and published for simulating intercropping systems and integrated into a soil-crop model. With its emphasis on being generic, sufficiently accurate, simple, and easy to parameterize, STICS is well-suited to help researchers designing <em>in silico</em> the agroecological transition by virtually pre-screening sustainable, manageable intercrop systems adapted to local conditions.</p> <p> </p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.