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575 results for “Modeling Codes”
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>
Data and code from: Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data
<p>We use open source human gut microbiome data to learn a microbial "language" model by adapting techniques from Natural Language Processing (NLP). Our microbial "language" model is trained in a self-supervised fashion (i.e., without additional external labels) to capture the interactions among different microbial species and the common compositional patterns in microbial communities. The learned model produces contextualized taxa representations that allow a single bacteria species to be represented differently according to the specific microbial environment it appears in. The model further provides a sample representation by collectively interpreting different bacteria species in the sample and their interactions as a whole. We show that, compared to baseline representations, our sample representation consistently leads to improved performance for multiple prediction tasks including predicting Irritable Bowel Disease (IBD) and diet patterns. Coupled with a simple ensemble strategy, it produces a highly robust IBD prediction model that generalizes well to microbiome data independently collected from different populations with substantial distribution shift.</p> <p>We visualize the contextualized taxa representations and find that they exhibit meaningful phylum-level structure, despite never exposing the model to such a signal. Finally, we apply an interpretation method to highlight bacterial species that are particularly influential in driving our model's predictions for IBD.</p>
Data & code repository for the article "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"
<p>This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". </p> <p>In detail the following data sources have been included:</p> <ul> <li>the relevant code and supporting data (code_to_upload.zip and supporting_data.zip);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results.zip);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results.zip);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures.zip)</li> </ul> </li> </ul>
Source Code—Motorized chain models of the ideal chromosome
<p>Code and data for reproducing the findings in "Motorized chain models of the ideal chromosome" by Zhiyu Cao & Peter G Wolynes</p> <p>We carried out hybrid Brownian-Gillespie simulations of the polymer dynamics for the swimming motors. The programs can run in parallel on Cuda software. We use periodic boundary conditions without considering the self-avoiding and excluded volume effect. In addition, we solve the self-consistent equation by an iterative method.</p>
Figure 11: Coding and decoding Cartesian coordinates of geometrical points-Brain Functors: A mathematical model of intentional perception and action
<p>The simplest form of a brain "functor" is just a two-way representation or coding system that constructs and implements a set of codes. Given some set of objects, it is encoded using some isomorphic set of representations or codes for the objects, and then given an instance of the code, it is decoded to determine the object. Coordinatizing is a form of coding. The geometrical plane is a collection of points, and the Cartesian coordinate system represents each point P by a pair (xP, yP) of coordinates. Given a point P , the "coordinate" function selects the coordinates (xP, yP) of the point which is the recognized or coded output, and given the coordinates or code for a point (xP, yP) as an input, the "plot" function designates the point.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 7. The HTML source (partial) code, integrating the X3D model
<p>To integrate the model into a web page, a model conversion to X3D format and an X3DOM output under the form of an HTML5 encoded webpage (Figure 7) were needed. Instant Reality distribution provides a command line transcoding tool, named Avalon Optimizer (aopt), that was used to convert a VRML format (wrl extension) of the model to X3D. </p>
R code for modeling indexical fields using MSTs
<p>R code for modeling indexical fields using Minimum Spanning Trees</p>
SLDeep: Statement-Level Software Defect Prediction Using Deep-Learning Models on Static Code Features
<p>Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing activities on more suspicious portions. Consequently, high-quality software is released with less time and effort. The current SDP techniques however work at coarse-grained units, such as a module or a class, putting some burden on the developers to locate the fault. To address this issue, we propose Statement-Level software defect prediction using Deep-learning model (SLDeep). To reify our proposal, we defined a suite of 32 statement-level metrics, such as the number of binary and unary operators used in a statement. Then, we applied as learning model, long short-term memory (LSTM). The significance of SLDeep for intelligent and expert systems is that it demonstrates a novel use of deep-learning models to the solution of a practical problem faced by software developers. We conducted experiments using more than 100,000 C/C++ programs within the Code4Bench. The programs total 2,356,458 lines of code with 292,064 faulty lines. The benchmark comprises diverse set of programs and versions, written by thousands of developers. Therefore, it tends to give a model that can be used for cross-project SDP. In the experiments, our trained model could successfully classify the unseen data with average performance measures 0.945, 0.971, and 0.976 in terms of recall, precision, and accuracy, respectively. These experimental results suggest that SLDeep is effective for statement-level SDP. The impact of this work is twofold. Working at statement-level further alleviates developer’s burden in pinpointing the fault locations. Second, cross-project feature of SLDeep helps defect prediction research become more industrially-viable</p> <p>for more information visit <a href="https://github.com/sldeep/SLDeep">https://github.com/sldeep/SLDeep</a></p>
Modelling environmental suitability of sorghum, wheat and maize in Europe under climate change (Code & data)
<p><span>Wheat and maize play an important role as crops for human consumption and animal feed in Europe. To guarantee food security and the stability of the agricultural sector in Europe, it is crucial to determine how climate change will impact the environmental suitability and thus the potential geographic distribution of these crops. Sorghum, a crop that originates in Africa, has seen a recent increase in cultivation in Europe. Due to its tolerance to more extreme climate conditions and its versatility of use, it might inherit a high potential as an alternative crop. </span></p> <p><span>Occurrence data of sorghum, wheat and maize as well as several environmental variables were used as input data for an ensemble modelling approach that averages machine learning models for species distribution modelling (SDM). CHELSA served as a source for present bioclimatic conditions and future climate scenarios, namely SSP126 and SSP370 for the period 2041-2070, and HSWD supplied soil variables, since both climate and soil influence crop development. A set of models was evaluated to select the best performing models for the ensemble modelling. The ensemble models were extrapolated to the future scenarios to predict geographic shifts of suitable cultivation areas due to climate change and analyze sorghum’s potential as an alternative crop. </span></p> <p><span>Under the climate scenarios, the three crops saw a shift of suitability in Europe with losses in Southern Europe and expansions of suitable environmental conditions in the northeast of Europe. Sorghum was the crop with the highest potential to replace maize and wheat in Southern Europe in areas where they lose suitability under climate change. Therefore, sorghum confirmed its function as an alternative crop. It also was the crop that benefits consistently from climate change, growing its total suitable area in Europe under both climate scenarios. Maize loses total suitable area in Europe in both climate scenarios but kept the highest amount of total suitable area in Europe in all projected time periods. </span></p> <p><span>The outcome of this study is of high importance for European farmers and policy makers as it enables them to apply effective adaptation and mitigation strategies that will support crop production under future climate conditions. <br></span></p>
Assets (code, scripts and datasets) for the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks"
<p>This dataset contains all relevant software and data related to the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks", submitted to AGU journals.</p>
Runoff from Greenland's Firn Area - Why do MODIS, RCMs and a Firn Model disagree? - Code and Data
<h1>README – Overview code and data for obtaining MODIS runoff limits and comparison to and between MAR and RACMO / IMAU-FDM</h1> <p> </p> <h2>Basic workflow</h2> <p>Table 1 lists the projects, the pieces of code in the projects and the key output of each piece of code. This serves at illustrating the basic workflow. More detailed information on required input is provided below. The README files of the various projects provide details on how to use the code.</p> <p> </p> <p><strong><em>Table 1:</em></strong><em> This <strong>table might not display properly, please refer to the file _README_overview.pdf.</strong> General project overview. The projects and code are shown in the order they are intended to be used. In italic are parts of code that were not used for Machguth et al. (in review) but instead for Machguth et al. (2022). They might not be fully compatible anymore with runoff limits Y<sub>r</sub> calculated along flowline-polygons.</em></p> <table> <tbody> <tr> <td> <p><strong>Project</strong></p> </td> <td> <p><strong>Code</strong></p> </td> <td> <p><strong>Output</strong></p> </td> </tr> <tr> <td> <p>flowlines</p> </td> <td> <p>crop_gdalwarp.py</p> </td> <td> <p>DEM, cropped and reprojected to required size and grid (identical to the MODIS files)</p> </td> </tr> <tr> <td> <p>flowline_seedpoints.py</p> </td> <td> <p>Seedpoints to calculate flowlines</p> </td> </tr> <tr> <td> <p>flowline.py</p> </td> <td> <p>Flowlines and flowline- polygons</p> </td> </tr> <tr> <td> <p>MODIS_Greenland</p> </td> <td> <p>MODIS_array_filter.py</p> </td> <td> <p>Daily MODIS grids filtered for outliers</p> </td> </tr> <tr> <td> <p>MODIS_stddev_spatial.py</p> </td> <td> <p>Daily grids of MODIS spatial standard deviation</p> </td> </tr> <tr> <td> <p>MODIS_mean_stddev.py</p> </td> <td> <p>Greenland-wide map of background MODIS spatial standard deviation</p> </td> </tr> <tr> <td> <p>MODIS_NDWI_tiff_to_nc.py</p> </td> <td> <p>MODIS NDWI converted from tiff to netCDF</p> </td> </tr> <tr> <td> <p>MODIS_find_slush_limit.py</p> </td> <td> <p>Table of all detected daily <em>Y<sub>r</sub></em></p> </td> </tr> <tr> <td> <p>Greenland_RCM_analysis_prep</p> </td> <td> <p>modis_vs_mar.py</p> </td> <td> <p>Tables of daily MAR <em>Y<sub>r</sub></em> for all flowline-polygons</p> </td> </tr> <tr> <td> <p>modis_vs_racmo.py</p> </td> <td> <p>Table of daily RACMO <em>Y<sub>r</sub></em> for all flowline-polygons</p> </td> </tr> <tr> <td> <p>MODIS_Greenland_analysis</p> </td> <td> <p>MODIS_A_SLmax.py</p> </td> <td> <p>Calculates annual maxima of the Yr, written into table. C<em>reates plots visualizing MODIS Y<sub>r</sub> spatial and temporal distribution – only used in Machguth et al. (2022)</em></p> </td> </tr> <tr> <td> <p><em>MODIS_A_SLanalyis.py</em></p> </td> <td> <p><em>Plots that visualize progression and forcing behind MODIS Y<sub>r</sub> – only used in Machguth et al. (2022)</em></p> </td> </tr> <tr> <td> <p>MODIS_comp_RCM_maxYs.py</p> </td> <td> <p>For all of Greenland: Plots that compare RCM and MODIS max<em>Y<sub>r</sub></em></p> </td> </tr> <tr> <td> <p>MODIS_comp_RCM_Ys.py</p> </td> <td> <p>For all of Greenland: Plots that compare RCM and MODIS daily <em>Y<sub>r</sub></em></p> </td> </tr> <tr> <td> <p>Greenland_RCM_analysis</p> </td> <td> <p>RCM_analysis_comp_Ktransect.py</p> </td> <td> <p>For the K-transect: Plots/tables that compare RACMO/FDM and MAR Yr as well as various RCM parameters</p> </td> </tr> <tr> <td> <p>Jupyter notebooks</p> </td> <td> <p>K-transect_MAR_Greenland-wide_to_K-transect.ipynb</p> </td> <td> <p>Writes MAR .nc files that contain data only for the K-Transect</p> </td> </tr> <tr> <td> <p>K-transect_RACMO-MAR_depth-data-plot.ipynb</p> </td> <td> <p>For the K-transect: Plots to compare depth information of MAR and RACMO/FDM</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <h2>Input and output data</h2> <p>Unless mentioned otherwise, all input data required is provided in this repository. Input files for one piece of code are at the same time the output of a piece of code that needs to be run before.</p> <p><em>Note 1:</em> due to space constraints only the so called “test” data set for runoff limit detection is provided. The full input data to Greenland-wide runoff limit detection are too large in volume but can be obtained from the authors.</p> <p>Note 2: While we only provide the test data for the MODIS runoff limit detection, we provide the full output of the Greenland-wide runoff limit detection (these data are small in volume).</p> <h3>Input to flowlines/crop_gdalwarp.py</h3> <p>arcticdem_mosaic_100m_v30_greenland_icesheet_geoidCorr.tif</p> <h3> </h3> <h3>Input to <em>flowlines/flowline_seedpoints.py</em></h3> <p>seedline_v2.1.shp</p> <h3> </h3> <h3>Input to <em>flowlines/flowline.py</em></h3> <p>è In the case of using the test data, no seed file exists. Do not provide one, seed points will be calculated along a north-south line as specified.</p> <p>greenland_vel_mosaic200_2015-2018_vx_v02-composite-crop.tif</p> <p>greenland_vel_mosaic200_2015-2018_vy_v02-composite-crop.tif</p> <p>dem_test_gapfilled.tif</p> <p><em>or for Greenland-wide:</em></p> <p>greenland_vel_mosaic500_2015-2018_vx_v02-composite-crop.tif</p> <p>greenland_vel_mosaic500_2015-2018_vy_v02-composite-crop.tif</p> <p>arcticdem_mosaic_500m_v30_greenland_icesheet_geoidCorr_GapFilled.tif</p> <p>seedpoints_v3.4.shp</p> <h3> </h3> <h3>Input to <em>MODIS_Greenland/MODIS_array_filter.py</em></h3> <p>/sat_modis_proc_GR_l1test/*</p> <p> </p> <h3>Input to <em>MODIS_Greenland/MODIS_stddev_spatial.py</em></h3> <p>/sat_modis_proc_test_l2/*</p> <p> </p> <h3>Input to <em>MODIS_Greenland/MODIS_mean_stddev.py</em></h3> <p>/sat_modis_proc_test_l3/*</p> <p>dem_test_gapfilled.tif</p> <p> </p> <h3>Input to <em>MODIS_Greenland/ MODIS_NDWI_tiff_to_nc.py</em></h3> <p>è Input data were computed directly on Google Earth Engine, they have not been preserved, only output data exist</p> <p> </p> <h3>Input to <em>MODIS_Greenland/MODIS_find_slush_limit.py</em></h3> <p>/sat_modis_proc_test_l3/*</p> <p>/sat_modis_proc_test_l2_NDWI/*</p> <p>mask_greenland_icesheet/dem_test_gapfilled.tif</p> <p>Ys_polygons__test_W20km.shp</p> <p>flowlines__test_W20km.shp</p> <p>test_MOD10A1.l3.v4_yrs2000-2021_doy126-136_stddev_median.tif</p> <p> </p> <h3>Input to <em>Greenland_RCM_analysis_prep/modis_vs_mar.py</em></h3> <p>/flash/tedstona/MARv.HorstRCMStudy_20240624/*</p> <p>what is the DEM, probably the same as used elsewhere?</p> <p>flowline Polygons</p> <p>/flash/tedstona/_list_PolyIDs.xlsx</p> <p>Why output still to 'MAR-v3.12.1-rlim-slush.nc'? Probably simply not changed without any effect?</p> <h3> </h3> <h3>Input to <em>Greenland_RCM_analysis_prep/modis_vs_racmo.py</em></h3> <p>/flash/tedstona/RACMO/1km/runoff/*</p> <p>arcticdem_mosaic_500m_v30_greenland_icesheet_GeoidCorr_GapFilled_RACMO1km.tif</p> <p>racmo_polys.nc</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_A_SLmax.py</em></h3> <p>_test_slush-limit_output_table.xlsx</p> <p><em>or the Greenland-wide output data:</em></p> <p>_GR_slush-limit_output_table.xlsx</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_A_SLanalyis.py</em></h3> <p>KAN_U_hourly_v3_fewer_columns.xlsx</p> <p>KAN_M_hourly_v3_fewer_columns.xlsx</p> <p>(selected_SL_years_and_stripes_20km.xlsx: <em>not found and was also only used in Machguth et al., 2022)</em></p> <p>__test_table_complete_annual_max_SL.xlsx</p> <p>__test_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p><em>or the Greenland-wide output data:</em></p> <p>__GR_table_complete_annual_max_SL.xlsx</p> <p>__GR_test_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_comp_RCM_maxYs.py</em></h3> <p>è This file has not been tested whether it also works with the “test” data</p> <p>RACMO2.3p2_ERA5_3h_FGRN055.1km-rlim-RUa1mm.xlsx</p> <p>MAR-v.20240624-rlim-RUa1mm.xlsx</p> <p>_stripes_with_aquifers.xlsx</p> <p>__test_table_simple_annual_max_SL.xlsx</p> <p>__test_table_complete_annual_max_SL.xlsx</p> <p><em>or the Greenland-wide output data:</em></p> <p>__GR_table_simple_annual_max_SL.xlsx</p> <p>__GR_table_complete_annual_max_SL.xlsx</p> <p> </p> <h3>Input to <em>MODIS_Greenland_analysis/MODIS_comp_RCM_Ys.py</em></h3> <p>è This file has not been tested whether it also works with the “test” data</p> <p>__GR_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p>flowlines_daily_rlims_RACMO_1mmEvents_10mmAnnual_2000_2021.xlsx</p> <p>flowlines_daily_rlims_MAR-v.20240624_1mmEvents_10mmAnnual_2000_2021.xlsx</p> <p> </p> <h3>Input to <em>Greenland_RCM_analysis/RCM_analysis_comp_Ktransect.py</em></h3> <p>è Certain data sets (indicated below) are not provided as they would exceed the available space in the repository. They can be obtained at no conditions from the authors</p> <p>è This file has not been tested whether it would also works with the “test” data</p> <p><em>Too large: /FDM_Greenland-wide/* </em></p> <p><em>Too large: /RACMO-FDM_K-Transect_updated/* </em></p> <p><em>Too large: /RACMO_Greenland-wide/* </em></p> <p><em>Too large: /MARv3.14_K-transect_continuous/*</em></p> <p><em>Too large: FGRN055_Masks.nc</em></p> <p>__GR_slush-limit_output_table_OnlyValidEntries.xlsx</p> <p>__GR_table_simple_annual_max_SL.xlsx</p> <p>__GR_table_complete_annual_max_SL.xlsx</p> <p> </p> <h3>Input to <em>_notebooks\K-transect_MAR_Greenland-wide_to_K-transect.ipynb</em></h3> <p> </p> <h3>Input to <em>_notebooks\K-transect_RACMO-MAR_depth-data-plot.ipynb</em></h3> <p> </p> <h2>Output data</h2> <p>The basic output files of the runoff limit detection for all of Greenland are:</p> <p>_GR_slush-limit_output_table.xlsx (written by MODIS_Greenland/MODIS_find_slush_limit.py)</p> <p>__GR_slush-limit_output_table_OnlyValidEntries.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__GR_table_simple_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__GR_table_complete_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p> </p> <p>The basic output files of the runoff limit detection for the “test” data set are:</p> <p>_test_slush-limit_output_table.xlsx (written by MODIS_Greenland/MODIS_find_slush_limit.py)</p> <p>__test_slush-limit_output_table_OnlyValidEntries.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__test_table_simple_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p>__test_table_complete_annual_max_SL.xlsx (by MODIS_Greenland_analysis/MODIS_A_SLmax.py)</p> <p> </p> <p> </p> <p> </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 for FishMIP global marine ecosystem model ensemble projections summarised by countries and territories and other selected marine spatial regions.
<p>R code to extract and create data tables and summary plots of FishMIP mean ensemble projections provided are for percentage change in "exploitable fish biomass", which is a proxy for the biomass available to fisheries, consisting of marine animals spanning the size range 10 g to 100 kg: this is typically dominated by fish, but is also inclusive of other animals such as crustaceans and cephalopods.</p> <p>This release contains scripts and summary data for producing figures in Part A of the following report:</p> <p>Blanchard, J.L., Novaglio, C., eds. (2024). Climate change risks to marine ecosystems and fisheries: Future projections from the Fisheries and Marine Ecosystems Model Intercomparison Project. FAO Fisheries and Aquaculture Technical Paper No. 707. Rome, FAO.</p> <p>Please refer to the above report to cite and for more information.</p> <p>The summary data are here:</p> <p>https://github.com/Fish-MIP/FAO_Report/blob/main/data/table_stats_formatted_admin_full.csv</p> <p>Where the column 'spatial_scale' refers to the type of aggregation:</p> <p>FAO_area = High Sea areas grouped by FAO Major Fishing Areas</p> <p>countries = Exclusive Economic Zones</p> <p>countries_admin = Exclusive Economic Zones results aggregated into Administrative Countries</p> <p>Please note that these results can also be visualised and downloaded from our shiny app: https://rstudio.global-ecosystem-model.cloud.edu.au/shiny/FAO_report_shiny/</p> <p> </p>
Paranal Airglow Line And Continuum Emission (PALACE) model: data and code of v1.0
<p>This data and code release is related to the article "PALACE v1.0: Paranal Airglow Line And Continuum Emission model", which has been published in Geoscientific Model Development, Vol. 18, 2025 (https://doi.org/10.5194/gmd-18-4353-2025).<br>The release consists of three .zip files:<br>PMD.zip: 436 ASCII files with data that were used to build and evaluate the model. More details in PMD/pmd_README.txt.<br>PALACE.zip: Python/Cython code for the calculation of the model. More details in PALACE/README.txt.<br>test.zip: Test output of the code for the default parameters (.dat file in alternative ASCII format).</p>
Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
Conformance Assessment of Architectural Design Decisions on API Endpoint Designs Derived from Domain Models: Dataset and Code
<p>This is the dataset and related code artifact for the article "Conformance Assessment of Architectural Design Decisions on API Endpoint Designs Derived from Domain Models".</p> <p><strong>Abstract of the article:</strong></p> <p>Domain-driven design (DDD) is commonly used, especially in enterprise systems, to design microservices. A crucial aspects of microservice design is API design, which includes the design of API endpoints. In particular, we studied link mapping, API operation design, and resource segregation as API endpoint design issues that are linked to domain model design. Based on Architectural Design Decisions (ADD) studied in a prior empirical study on the interrelation of DDD and APIs, we suggest a new approach for the automated assessment of conformance to ADD options. This approach aims to support the continuous analysis of API endpoint designs. The approach suggests automated detectors to detect ADD options selected in a given API endpoint design, as well as an assessment scoring scheme based on our empirical results. For evaluation of our results, we first manually created a ground truth for 12 cases in a multi-case study, and then compared the results of our automated detectors to the ground truth for each of those cases.</p>
Data and code for 'Influence of cross-correlation on the modelled uncertainty in stress–strain behavior of soft clays'
<p>This dataset contains data and code used in the research work for the manuscript "Influence of cross-correlation on the modelled uncertainty in stress–strain behavior of soft clays". The study considered two case studies, Haarajoki clay and Suurpelto clay. Two settlement calculation methods were used: compression index method and Janbu (tangential stiffness) method. In addition, clay database FI-CLAY/14/856 was extended and used to study cross-correlations between compressibility paramaters at different clay sites. Version 2 of FI-CLAY/14/856 is provided, including some other updates and corrections also.</p> <p>The Monte Carlo simulation with Gaussian copula was implemented with Python in Jupyter Notebook environment. In addition to data and code, supplementary figures are also provided. The contents of the dataset-folder are briefly described below:</p> <ul> <li>1_Data_Oedometer_test <ul> <li>Oedometer test data for Haarajoki clay and Suurpelto clay: <ul> <li>Data tables that include the clay specimen identifications, index properties, and oedometer test results (.xlsx)</li> <li>Oedometer raw data files that include all the available stress-strain measurements of both constant-rate-of-strain and incrementally loaded odometer tests (.xlsx)</li> </ul> </li> <li>Extended clay database FI-CLAY/14/856 (version 2) (.xlsx)</li> </ul> </li> <li>2_Code_Jupyter_Notebooks <ul> <li>Python code used to run the Monte Carlo simulations and to create the results figures (.ipynb)</li> <li>Readme-file (.txt)</li> </ul> </li> <li>3_Figures_Online_Supplement <ul> <li>Scatterplots with histograms that show the simulated compressibility parameters in each case (.pdf)</li> </ul> </li> </ul> <p> </p> <p>More information on database FI-CLAY/14/856 can be found from the original article (https://www.tandfonline.com/doi/full/10.1080/17499518.2020.1864410) and 304dB datbase compilation by TC304 (http://140.112.12.21/issmge/tc304.htm).</p> <p> </p>
Code and data associated with Christiansen et al. 2021 "Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing"
<p>All code and data input and output files (except reference genome and raw sequencing data) needed to reproduce the results of Christiansen et al. 2021 as released on <a href="https://github.com/notothen/radpilot">https://github.com/notothen/radpilot</a> alongside journal publication. See published paper:</p> <p>Christiansen, H., Heindler, F.M., Hellemans, B. <em>et al.</em> Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing. <em>BMC Genomics</em> <strong>22, </strong>625 (2021). <a href="https://doi.org/10.1186/s12864-021-07917-3">https://doi.org/10.1186/s12864-021-07917-3</a></p>
Statistical analysis code for output from a model used to simulate foot-and-mouth disease dynamics in the United Kingdom
<p>Epidemics can sometimes be managed through reductions of host density, such as social distancing for human diseases, reducing plant density through cultural and genetic means, and host culling for epizootics. These approaches allow for a certain density of hosts to remain within a targeted area. By contrast, total ring depopulation is often used as a management strategy for emerging infectious diseases in livestock. In this study, we explore the trade-offs of a density-based culling strategy to determine if fewer livestock farms can be culled within rings while maintaining a decrease in disease transmission. To do so, we evaluated a farm-density-based ring culling strategy to control foot-and-mouth disease (FMD) in the United Kingdom. This strategy may allow for some farms within rings around infected premises (IPs) to escape depopulation, with the aim to prevent over-culling during outbreaks. Using a spatially-explicit, stochastic, state-transition simulation algorithm originally developed by Keeling et al. 2001 to model FMD spread in the United Kingdom, we simulated this reduced-farm-density, or "target density" strategy. We modeled FMD disease spread in four counties in the UK (Aberdeenshire, Cumbria, Devon, and North Yorkshire) that have different farm demographies. We ran 740,000 simulations in a full-factorial analysis of epidemic impact measurements (i.e. culled animals, culled farms, epidemic length) and cull strategy parameters (i.e. target farm density, daily farm cull capacity, cull radius). We found that all of the cull strategy parameters were drivers of epidemic impact. We found that outbreaks in Cumbria had higher epidemic impacts and were more likely to take off compared with other counties with more outbreaks being likely to take off in Cumbria. Most importantly, in all counties, our proposed target density strategy was more effective at combatting FMD compared with traditional 'total ring depopulation' when considering average culled animals and culled farms. The differences in epidemic impact between the counties are likely driven by farm demography, especially differences in cattle and farm density. This target density strategy can be applied to many different systems, including other livestock and agricultural systems, to reduce host density as opposed to over-culling hosts.</p>
Code and data archive to accompany "A derivative-free optimisation method for global ocean biogeochemical models", Oliver et. al. 2021
<p>This archive is to accompany the article:</p> <p>A derivative-free optimisation method for global ocean biogeochemical models,<br> Sophy Oliver, Coralia Cartis, Iris Kriest, Simon Tett, and Samar Khatiwala.</p> <p>The optimisation framework used in this study can be found here: https://doi.org/10.5281/zenodo.5517610</p> <p>The original source code of MOPS were from the Supplement of Kriest et al. (2017).<br> The most recent TMM source code is available at https://github.com/samarkhatiwala/tmm.</p> <p>In this archive:</p> <p>Supplement/Configurations/OxfordMOPS_Configs contains:<br> - ReadOnlyFiles (Files and Code specifically used to run the global ocean biogeochemical model MOPS model with<br> the Transport Matrix Method, which have been edited to differ from the versions downloaded from the sources above.)<br> - RunCode (runscripts to run the MOPS model with the TMM)<br> - TWIN_Configs (JSON files required by each optimisation experiment carried out).</p> <p>Supplement/OxfordMOPS_EXP contains data for each iteration of all optimisation experiments carried out.</p> <p>Supplement/OPTCLIMSO_PlottingScripts contains MATLAB plotting scripts used to create results figures of these experiments.</p>
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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.