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29 results for “box model”

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edi52/100

Great Bay Estuary, NH/ME, Box Model Water Chemistry, Flow, Precipitation, and Seagrass Coverage Data, 2008 - 2023.

This data repository contains compiled surface water (tributary and estuarine), wet deposition, and wastewater effluent chemistry, along with discharge, precipitation totals, and monthly effluent flows necessary for the completion of solute budgets for Great Bay, a subregion of Great Bay Estuary, NH/ME, USA. These datasets are part of on-going monitoring programs in the Great Bay Estuary and Lamprey River Hydrological Observatory. A subset of the monitoring data for the 2008 to 2023 period was compiled. The tributary and estuarine monitoring data were requested from the NH Department of Environmental Services Environmental Monitoring Database as part of the Tidal Tributary and Estuary Water Quality Monitoring Programs. The wet deposition chemistry record is maintained as part of the Lamprey River Hydrologic Observatory. Wastewater effluent chemistry was downloaded from the EPA's Enforcement and Compliance History Online Database. The annual (1996 - 2023) seagrass coverage dataset for Great Bay Estuary reflects coverage of Zostera marina seagrass only and was compiled from annual monitoring reports. Mean daily instantaneous discharge data for the three tidal tributaries used in the load calculations are available from the USGS National Water Information System. Hourly precipitation volume data for the Durham, NH SSW station are available from the NCDC U.S. Climate Reference Network, with minor hourly gaps filled using the University of New Hampshire Durham weather station (https://www.weather.unh.edu).

openCC (other)Feb 2025View details →

zenodo40/100

3D model of a box-type structure under a small cairn near the Bear Trap in Northwest Greenland

This dataset consists of files that can be used to view a high-resolution 3D model of a box-type structure under a small cairn in the vicinity of ‘The Bear Trap’. The interior of the stone box appears to have been completely empty. Similar small stone box structures have been identified and discussed by Schedermann (e.g. 1990: 159) for Arctic Small Tool tradition (ASTt) sites on Skraeling Island and by McGee (1979) for Port Refuge in the Canadian High Arctic. Similar and equally enigmatic features have also been described by Knuth (1966/67: 203) for far north and northeast Greenland. They have been alternatively attributed to numerous PalaeoEskimo cultural complexes, but without dated materials from the site current attribution of the feature’s function, significance or date are not possible. The 3D model was created from 280 digital photographs that were processed usingAgisoft Metashape Pro v1.7. More information is provided in processing report and the readme file that accompanies this dataset.  The image survey was conducted as part of the Vaigat Iceberg-Microbial Oil Degradation and Archaeological Heritage Investigation (VIMOA) project, which was funded by the Danish Centre for Marine Research and supported by the Arctic Research Centre at Aarhus University, the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Proper permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provides an overview of the archaeological surveys conducted during the VIMOA project and Walsh et al. (in prep) provides further details specific to The Bear Trap and surrounding archaeological contexts.  Knuth, Egil. (1966/67) The ruins of the Musk Ox Highway. Folk 8-9: 191-219. McGee, Robert. (1979) The Palaeoeskimo occupations at Port Refuge, High Arctic Canada. National Museum of Man Mercury Series. Archaeological Survey of Canada Paper No. 92. Ottawa: National Museums of Canada. Schledermann, Peter. (1990) Crossroads to Greenland: 3000 years of prehistory in the Eastern High Arctic.Calgary: The Arctic Institute of North America of the University of Calgary. Walsh et al. (2020) The VIMOA project and archaeological heritage in the Nuussuaq Peninsula of north-west Greenland. Antiquity 94:e6 doi:10.15184/aqy.2019.230 Walsh, Matthew J., Daniel F. Carlson, Pelle Tejsner, and Steffen Thomsen. The Bear Trap: Reinvestigating a unique stone structure on the northwest tip of the Nuussuaq Peninsula, Greenland. Submitted to Arctic Anthropology

opencc-by-4.0Sep 2020View details →

zenodo40/100

Making the Most out of a Hydrological Model Dataset: Sensitivity Analyses to Open the Model Black-Box (data and code)

This is "data and code" repository for the Water Resources Research Article 2017WR020401 by Borgonovo et al. (2017): "Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box". Each sub-directory contains the Matlab or R scripts to reproduce all paper plots. Note, that the data of this repository (i.e. under ./data_input ) are identical to the data analysed by Rakovec et al. (2014). References: <ul> <li>Borgonovo, E., Lu, X., Plischke, E., Rakovec, O. and Hill, M. C. (2017), Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box. Water Resour. Res.. Accepted Author Manuscript. doi:10.1002/2017WR020767</li> <li>Rakovec, O., M. C. Hill, M. P. Clark, A. H. Weerts, A. J. Teuling, and R. Uijlenhoet (2014), Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resour. Res., 50, 409–426, doi:10.1002/2013WR014063.</li> </ul>

opencc-by-4.0Sep 2017View details →

zenodo40/100

Figure 3. The artefact as it exists as a black box-The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts

Consequently, the artefact as it exists as a black box can be represented according to the representation in Figure 3. It can be noticed that the artefact as it exists as a black box begins to interact with the environment. Two main categories of interfaces may be utilized by any artefact in order to interact with the environment: — Human Computer Interfaces (HCI); — Shared Resource Interfaces (SRI).

opencc-by-4.0Jan 2016View details →

zenodo36/100

Data and code for paper "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning"

Simulation results from the NOAA/SWPC Geospace model used in the paper Camporeale et al. (2020) "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning" published in J. Geophys. Res. (2020) MATLAB code is provided to train process the data and train the machine learning model and plot results. Manuscript available on <a href="https://arxiv.org/abs/1912.01038">https://arxiv.org/abs/1912.01038</a>

opencc-by-4.0Mar 2020View details →

zenodo36/100

Noah Mountain - Music Box 3D Model

This Music Box came off a shelf and was fun to model using photogrammetry. This very detailed music box is great to look at as well as spin and inspect. Can you find the 1 damaged animal on this mountain of doubled up creatures??? Asset Double is ready to model practically any real world asset/item that you can gain access to multiple sides Source: Objaverse 1.0 / Sketchfab

opencc-byJan 2022View details →

zenodo36/100

Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests – Replication Package

This is the replication package associated with the paper: Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests. We explain how to use it to reproduce the results reported in the paper. A maintainable version of this replication package is available on GitHub (<a href="https://github.com/uOttawa-Nanda-Lab/Flakify">https://github.com/uOttawa-Nanda-Lab/Flakify</a>). Flakify Test Smell Detector This is a step-by-step guideline to detect test smells in the source code of test cases and retain statements that match them. Requirements: <ul> <li>Eclipse IDE (the version we used was 2021-12)</li> <li>The libraries (the .jar files in the <code>lib\</code> directory)</li> </ul> Input Files: This is a list of input files that are required to accomplish this step: <ul> <li> dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv </li> <li> dataset/FlakeFlagger/FlakeFlagger_class_files/ </li> <li> dataset/IDoFT/IDoFT_filtered_dataset.csv </li> <li> dataset/IDoFT/IDoFT_class_files/ </li> </ul> The <code>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</code> and <code>dataset/IDoFT/IDoFT_filtered_dataset.csv</code> are used to obtain the label (flaky=1 or non-flaky=0) and project name for each test case parsed from <code>dataset/FlakeFlagger/FlakeFlagger_class_files/</code> and <code>dataset/IDoFT/IDoFT_class_files/</code>, respectively. Output Files: <ul> <li> dataset/FlakeFlagger/FlakeFlagger_dataset.csv </li> <li> dataset/FlakeFlagger/FlakeFlagger_test_cases_full_code/ </li> <li> dataset/FlakeFlagger/FlakeFlagger_test_cases_preprocessed_code/ </li> <li> dataset/IDoFT/IDoFT_dataset.csv </li> <li> dataset/IDoFT/IDoFT_test_cases_full_code/ </li> <li> dataset/IDoFT/IDoFT_test_cases_preprocessed_code/ </li> </ul>   Replicating the experiment To detect test smells and retain only code statements related to them, the <code>src/FlakifySmellsDetector.java</code> file should be compiled and run using the Eclipse IDE by having all the .jar files in the classpath. The pre-generated executable Jar file <code>src/FlakifySmellsDetector.jar</code> can be executed using the shell script <code>src/FlakifySmellsDetector.sh</code> after changing paths for each dataset as needed, using the following commands: <pre><code class="language-bash">bash FlakifySmellsDetector.sh FlakeFlagger bash FlakifySmellsDetector.sh IDoFT</code></pre> It will generate the dataset required to run Flakify's flaky test prediction model for the datasets given as input. The class file containing each of the test cases is then parsed to produce the corresponding full code and pre-processed code of the test case. The full and pre-processed source code of all test cases are also combined and saved in a CSV file, along with test smells found, project names, and labels.   Flakify Replication This is the guideline for replicating the experiments we used to evaluate Flakify for classifying test cases as flaky and non-flaky using both cross-validation and per-project validation. Requirements: This is a list of all required python packages: <ul> <li>python =3.8.5</li> <li>imbalanced_learn= 0.8.1</li> <li>numpy= 1.19.5</li> <li>pandas= 1.3.3</li> <li>transformer= 4.10.2</li> <li>torch=1.5.0</li> <li>scikit_learn= 0.22.1</li> </ul> Input Files: This is a list of input files that are required to accomplish this step: <ul> <li>dataset/FlakeFlagger/Flakify_FlakeFlagger_dataset.csv</li> <li>dataset/IDoFT/Flakify_IDoFT_dataset.csv</li> </ul> This file contains the full code and pre-processed code of the test cases in both FlakeFlagger and IDOFT datasets, along with their ground truth labels (flaky and non-flaky). Output File: <ul> <li> results/Flakify_cross_validation_results_on_FlakeFlagger_dataset.csv </li> <li> results/Flakify_per_project_results_on_FlakeFlagger_dataset.csv </li> <li> results/Flakify_model_trained_on_FlakeFlagger_dataset.pt </li> <li> results/Flakify_cross_validation_results_on_IDoFT_dataset.csv </li> <li> results/Flakify_per_project_results_on_IDoFT_dataset.csv </li> <li> results/Flakify_model_trained_on_IDoFT_dataset.pt </li> </ul>   Replicating Flakify experiments Cross-Validation To run the Flakify experiment using cross-validation on the two datasets, navigate to <code>src\</code> folder and run the following commands: <pre><code class="language-bash">bash Flakify_predictor_cross_validation.sh FlakeFlagger bash Flakify_predictor_cross_validation.sh IDoFT</code></pre> This will generate the classification results into <code>results/Flakify_cross_validation_results_on_FlakeFlagger_dataset.csv</code> and <code>results/Flakify_cross_validation_results_on_IDoFT_dataset.csv</code> for the cross-validation experiments on both datasets. It will also save the weights of the two models trained on the FlakeFlagger and IDoFT datasets into <code>results/Flakify_model_trained_on_FlakeFlagger_dataset.pt</code> and <code>results/Flakify_model_trained_on_IDoFT_dataset.pt</code>, respectively.   Per-project Validation To run the Flakify experiment using per-project validation on the two datasets, navigate to <code>src\</code> folder and run the following commands: <pre><code class="language-bash">bash Flakify_predictor_per_project.sh FlakeFlagger bash Flakify_predictor_per_project.sh IDoFT</code></pre> This will generate the classification results into <code>results/Flakify_per_project_results_on_FlakeFlagger_dataset.csv</code> and <code>results/Flakify_per_project_results_on_IDoFT_dataset.csv</code> for the whole per-project validation experiments on both datasets.   FlakeFlagger Replication This is the guideline for replicating the experiments we used to evaluate the two versions of FlakeFlagger, white-box and black-box, for classifying test cases as flaky and non-flaky using cross-validation on the FlakeFlagger dataset. Requirements: This is a list of all required python packages: <ul> <li>python =3.8.5</li> <li>imbalanced_learn= 0.8.1</li> <li>pandas= 1.3.3</li> <li>scikit_learn= 0.22.1</li> </ul> Input File: This is a list of input files that are required to accomplish this step: <ul> <li>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</li> <li>dataset/FlakeFlagger/FlakeFlaggerFeaturesTypes.csv</li> <li>dataset/FlakeFlagger/Information_gain_per_feature.csv</li> </ul> Output File: <ul> <li>results/FlakeFlagger_black-box_results.csv</li> <li>results/FlakeFlagger_white-box_results.csv</li> </ul>   Replicating FlakeFlagger experiments To run the FlakeFlagger experiments, navigate to <code>src\</code> folder and run the following command: <pre><code class="language-bash">bash FlakeFlagger_predictor.sh white-box bash FlakeFlagger_predictor.sh black-box</code></pre> This will generate the classification results into <code>results/FlakeFlagger_white-box_results.csv</code> and <code>results/FlakeFlagger_black-box_results.csv</code> for both white-box and black-box experiments, respectively.

opencc-by-4.0Aug 2022View details →

zenodo36/100

Additional raw video and pose estimation data of top view mouse behavior recordings (marble burying test, light-dark box, fear conditioning box) of acute and chronic stress models

This repository contains raw data for 296 different behavioral recordings of mice (marble burying test, light-dark box, fear conditioning box). These include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.

opencc-by-4.0May 2024View details →

zenodo36/100

Results for Several Simple Algorithms on the W-Model for Black-Box Discrete Optimization Benchmarking

The W-Model follows a layered approach, where each layer can either be omitted or introduce a different characteristic feature such as neutrality via redundancy, ruggedness and deceptiveness, epistasis, and multi-objectivity, in a tunable way. The model problem is defined over bit string representations, which allows for extracting some of its layers and stacking them on top of existing problems that use this representation, such as OneMax, the Maximum Satisfisiability or the Set Covering tasks, and the NK landscape. The ruggedness and deceptiveness layer can be stacked on top of any problem with integer-valued objectives. Here we provide some experimental results with several simple algorithms on the W-Model along with the algorithm and experiment executor implementation. The most recent version of the code can be found at <a href="http://www.github.com/thomasWeise/BBDOB_W_Model">http://www.github.com/thomasWeise/BBDOB_W_Model</a>, while the program used in the experiment are attached to this dataset. The implemented algorithms are <ul> <li>1-flip hill climber with restarts,</li> <li>2-flip hill climber with restarts,</li> <li>μ+λ Evolutionary Algorithms (for different values of μ, λ, and different crossover rates),</li> <li>μ+λ Evolutionary Algorithms with Frequency Fitness Assignment (FFA) (for different values of μ, λ, and different crossover rates),</li> <li>exhaustive enumerate (EE), and</li> <li>random sampling (RS)</li> </ul> These algorithms are applied to a range of different parametric setups of the W-Model for single-objective optimization and fixed-length bit string representations. The experiments are executed on a HP Z640 Work Station with 32 GB DDR4-2400 RAM and Intel Xeon E5-2609v4 CPU under Ubuntu Server Linux 16.04, Kernel 4.4.0-116-generic, and with Java 1.8.0_151. Each run was granted at most 1048576 function evaluations (FEs). The total amount of data collected is about 45 GB, with tar.xz compression down to about 1.2 GB. The text files containing the algorithm traces are fairly self-describing. Publications that describe the W-Model: <ul> <li>Thomas Weise and Zijun Wu. Difficult Features of Combinatorial Optimization Problems and the Tunable W-Model Benchmark Problem for Simulating them. In Black Box Discrete Optimization Benchmarking (<a href="http://iao.hfuu.edu.cn/bbdob-gecco18">BB-DOB</a>) Workshop of Companion Material Proceedings of the Genetic and Evolutionary Computation Conference (<a href="http://gecco-2018.sigevo.org/">GECCO 2018</a>), July 15th-19th 2018, Kyoto, Japan, ISBN: 978-1-4503-5764-7. ACM. doi:<a href="http://dx.doi.org/10.1145/3205651.3208240">10.1145/3205651.3208240</a> / <a href="http://github.com/thomasWeise/BBDOB_W_Model">source codes</a> [This paper contains some errata for the one below]</li> <li>Thomas Weise, Stefan Niemczyk, Hendrik Skubch, Roland Reichle, and Kurt Geihs. A Tunable Model for Multi-Objective, Epistatic, Rugged, and Neutral Fitness Landscapes. In Maarten Keijzer, Giuliano Antoniol, Clare Bates Congdon, Kalyanmoy Deb, Benjamin Doerr, Nikolaus Hansen, John H. Holmes, Gregory S. Hornby, Daniel Howard, James Kennedy, Sanjeev P. Kumar, Fernando G. Lobo, Julian Francis Miller, Jason H. Moore, Frank Neumann, Martin Pelikan, Jordan B. Pollack, Kumara Sastry, Kenneth Owen Stanley, Adrian Stoica, El-Ghazali, and Ingo Wegener, editors, Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO'08), pages 795-802, July 12-16, 2008, Renaissance Atlanta Hotel Downtown: Atlanta, GA, USA. ISBN: 978-1-60558-130-9, New York, NY, USA: ACM Press. doi:<a href="http://dx.doi.org/10.1145/1389095.1389252">10.1145/1389095.1389252</a> / <a href="http://iao.hfuu.edu.cn/images/publications/WNSRG2008ATMFMOERANFL.pdf">pdf</a> [This paper has some deficits fixed in the paper above]</li> </ul>

opencc-by-4.0May 2018View details →

zenodo36/100

LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models - Replication Package

LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models This is the replication package associated with the paper "LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models". Replication Package Contents: This replication package contains all the necessary data and code required to reproduce the results reported in the paper. We provide the results of the Fault Detection Rate (FDR), Total Minimization Time (MT), Time Saving Rate (TSR) , statistical tests for all the minimization budgets (i.e., 25%, 50%, and 75%), results for the preliminary study, results for UniXcoder/Cosine with preprocessed code on 16 projects. Data: We provide in the Data directory the data used in our experiments, which is the source code of test cases (Java test methods) of 17 projects collected from Defects4J. Code: We provide in the Code directory the code (Python) and bash files required to run the experiments and reproduce the results. Results: We provide in the Results directory the detailed results for our approach (called LTM). We also provide the summarized results of LTM and a baseline (ATM) for comparison purposes. Additional technical details about ATM can be found at https://zenodo.org/record/7455766. _________________________________ LTM's Similarity Measurement: The source code of this step is in the Code/LTM/Similarity directory. Requirements: To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the Code/LTM/Similarity/requirements.txt file should be installed, as follows: cd Code/LTM/Similarity pip install -r requirements.txt Input: <ul> <li>Data/LTM/TestMethods</li> </ul> Output: <ul> <li>Data/LTM/similarity_measurements</li> </ul> Running the experiment: To measure the similarity between all pairs of test cases, the following bash script should be executed: bash measure_similarity.sh The source code of test methods of each project in the Data/LTM/TestMethods is parsed to generate pairs of test cases. This steps includes test methods tokenization, test methods embeddings extraction and similarity calculation. Then, all similarity scores are stored in Data/LTM/similarity_measurements folder. Due to the large size of the calculated similarity scores (60 GB), they were not uploaded on Zenodo, but they can be available upon request. LTM's Test Suite Minimization: The source code of this step is in the Code/LTM/Search directory. Requirements: To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the Code/LTM/Search/requirements.txt file should be installed, as follows: cd Code/LTM/Search pip install -r requirements.txt Input: Data/LTM/similarity_measurements Output: Results/LTM/minimization_results Running the experiments: To minimize the test suite for each project version, the following bash script should be executed: bash minimize.sh The similarity scores of all test case pairs per project version are parsed by the search algorithm (Genetic Algorithm). Each experiment runs ten times using three minimization budgets (25%, 50%, and 75%). The results are stored in the Results/LTM/minimization_results directory. LTM's Evaluation: To evaluate the minimization results for each version and each project, the following bash script should be executed: cd Code/LTM/Evaluation bash evaluate_per_version.sh cd Code/LTM/Evaluation bash evaluate_per_project.sh This will evaluate the FDR, MT and TSR results for each version and each project for each minimization budget. These results are stored in the Results/LTM directory. Note that for each version, the FDR is either 1 or 0. For each project, the FDR ranges from 0 to 1.

opencc-by-4.0Jul 2024View details →

zenodo36/100

GCN-TFiLM: Modelling Black-box Audio Effects with Time-varying Feature Modulation

Dataset, checkpoints, evaluation, and supplementary material associated with the following publication: <a href="https://arxiv.org/abs/2211.00497">Modelling Black-box Audio Effects with Time-varying Feature Modulation</a> Citation: <pre><code>@inproceedings{comunita2023modelling, title={Modelling black-box audio effects with time-varying feature modulation}, author={Comunit{\`a}, Marco and Steinmetz, Christian J and Phan, Huy and Reiss, Joshua D}, booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={1--5}, year={2023}, organization={IEEE} }</code></pre>

opencc-by-4.0Oct 2022View details →

zenodo36/100

Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"

Model outputs associated with “Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"

opencc-by-4.0Apr 2023View details →

dryad36/100

Unpacking the "black box": improving ecological interpretation of regression based models

Aim Many tree species distribution models use black-box machine learning techniques that often neglect interpretative aspects and instead focus mainly on maximising predictive accuracy. In this study, we outline an interpretative modelling framework to gain better ecological insights while mapping abundance patterns of six North American species. Location Continental United States and Canada Methods We develop an innovative procedure using regression trees by stabilising variance and mapping dominant rules which we term 'optimized regression tree bagging for interpretation and mapping' (ORTBIM). We apply this technique to understand ecological features influencing the abundance patterns of three eastern (Pinus strobus, Acer saccharum, and Quercus montana), and three western (Picea engelmannii, Pinus ponderosa, and Pseudotsuga menziesii) tree species in North America. For these species, we assess and map the dominant climate-terrain interactions that partly determine abundance patterns in the eastern and western regions. In the process, we examine the role of varying responses and scales and explore finer-scale species climate-terrain niches and non-linear relationships. Results Our study emphasizes the prominent role of elevation and heat-moisture variables in the west and the greater importance of seasonal precipitation and seasonal temperature in the east. The abundance patterns under future climate (SSP5–8.5) show climate-terrain habitats shifting northward and westward into Canada and Alaska for the eastern species, and predominantly north-westward for the western species. Conclusion Our interpretative modelling framework can be used to gain a more comprehensive understanding of the abundance patterns across the full species range, to formulate better predictive models, and to facilitate improved management practices under climate change.

opencc-zeroApr 2023View details →

dryad36/100

Unpacking the "black box": improving ecological interpretation of regression based models

Open the record for dataset details and reuse information.

publicApr 2023View details →

zenodo32/100

The evaluation data and source codes of a new conceptual coupled Earth system model and the MOC box model.

The dataset contains the results of a conceptual Atmosphere-Ocean-Ice-Land coupled Earth system model and a MOC box model and the evaluation data of their.

opencc-by-4.0Jan 2024View details →

zenodo32/100

Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization -- Reproducibility Files

This repository contains the files to reproduce the results from the paper "Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization". Data Collection In this folder, all files used to generate the raw performance data for the set of algorithms are included, as well as the code used to generate the ELA features. For the performance, the packages 'ioh', 'nevergrad', 'modde' and 'modcma' are essential, while 'pflacco' is used for ELA. For all scripts in this folder, the number of parallel threads and the folders for reading function settings (included as 3 csv files in this folder) and storing data should be set before execution.  Note that the full performance data exceeds 50GB, so it is not included in this repository. Instead, the results of processing it (using the aocc_extraction script) are included in the 'auc_MABBOB' folder (spread across multiple csv-files, with a version using a different budget factor included as well). The ELA data is included as 'ELA' and 'ELA_BBOB' for the affine combinations and component functions respectively.   Data Processing, Analysis and Visualization The remaining reproducibility files can be found in the Reproducibility folder. Within this folder are several notebooks which handle various steps in the pipeline, starting with preprocessing the data collected in the previous steps. This results in some csv-files, which are also included for convenience. Afterwards, the remaining notebooks deal with correlation analysis, instance selection methods, and all included plots from the paper. To match the environment used during our execution of these scripts. a yml-file (to be used with conda or mamba) is available as well.

opencc-by-4.0Apr 2024View details →

zenodo32/100

Dataset of paper "Neural Network Modeling of Black Box Controls for Internal Combustion Engine Calibration"

These are the two training datasets for the Exhaust Temperature Model (ETM) and the Three-Way Catalyst Control Lambda Set Point (TWCC) Model, used in Black-Box Modeling, such as through Neural Networks. The datasets for each black box model are stored in XLSX files. Each dataset file contains ECU channel inputs, related Map parameters, and output. The ECU channel inputs and Map parameters are collected with Sobol methods. The output is generated by Hardware-in-the-Loop (HiL) corresponding to the related ECU channel inputs and Map parameters. The ETM model has four effective Maps, hence there are 32 corresponding shape-based algorithm parameters. Its output is the exhaust temperature. Its ECU channel inputs are as follows: CH_1:       Intake valve cut-off condition CH_2:       Thrust cut-off condition CH_3:       Cylinder equalization control work cycle injection enabled CH_4:       Ignition angle efficiency CH_5:       Relative level of reduction CH_6:       Lean engine lambda for cylinders on exhaust bank with lambda split CH_7:       Rich engine lambda for cylinders on exhaust bank with lambda split CH_8:       Exhaust gas mass flow CH_9:       Engine speed CH_10:     Relative air filling CH_11:     Material temperature CH_12:     Engine temperature   The TWCC model has one Map, hence there are 8 corresponding shape-based algorithm parameters. Its output is lambda set point. Its ECU channel inputs are as follows: CH_1:       Exhaust mass flow CH_2:       Trigger bit for inhibiting the purge CH_3:       Trigger bit for a rich mixture CH_4:       Trigger bit for empty catalyst CH_5:       Trigger bit for catalyst reset CH_6:       Oxygen storage level CH_7:       Desired air-fuel ratio

opencc-by-4.0Jun 2024View details →

zenodo32/100

Box model data and figure script in support of Nature Communications Comment

Contained within the zip file are model outputs from the Sonke et al. 2022 box model described in the associated manuscript. File names describe the different model runs. The Science_fig.jnl file is a pyferret script to plot the data. Model data from Azimrayat Andrews et al. 2024 are found by following this link: https://iopscience.iop.org/article/10.1088/1748-9326/ad472c

opencc-by-4.0Jul 2024View details →

zenodo32/100

Secondary organic aerosol formation from smoldering and flaming combustion of biomass: a box model parametrization based on volatility basis set

Here, we studied the aging of emissions from flaming and smoldering-dominated wood fires in three different residential stoves, across a wide range of aging temperatures (-10°C, 2°C and 15°C) and emission loads. Organic gases (OGs) acting as SOA precursors were monitored by a proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS), while the evolution 10 of the aerosol properties during aging in the smog chamber was monitored by a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). We developed a novel box model based on the volatility basis set (VBS) to determine the volatility distributions of the oxidation products from different precursor classes found in the emissions, grouped according to their emission pathways and SOA production rates.

opencc-by-4.0Jun 2019View details →

zenodo32/100

Deep Learning for Black-Box Modeling of Audio Effects

Accompanying audio samples for the paper: Martínez Ramírez M. A., Benetos, E. and Reiss J. D., “Deep Learning for Black-Box Modeling of Audio Effects” submitted to the Applied Sciences:  Acoustics and Vibration - Digital Audio Effects special issue, December, 2019. Dry and wet bass and guitar recordings. Dry notes are taken from the IDMT-SMT-Audio-Effects dataset. Author: Michael Stein (Fraunhofer IDMT) https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html Wet notes are recorded from the limiter and preamplifier of a Universal Audio 6176 Vintage Channel Strip and from the horn and woofer of a 145 Leslie speaker cabinet.

opencc-by-4.0Dec 2019View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record