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870 results for “boxing”
SherpaTT Orbiting Box
<p><em>This experiment with SherpaTT has been done in Morocco. SherpaTT orbits an object with the HCRU targeting the object. Stereo cameras are used to make a 3D reconstruction of the environment.</em></p>
Classifying Generated White-Box Tests: An Exploratory Study
<p>The data and analysis scripts for our paper entitled Classifying Generated White-Box Tests: An Exploratory Study</p>
LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models - Replication Package
<p>LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models</p> <p>This is the replication package associated with the paper "LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models".</p> <p><strong>Replication Package Contents:</strong></p> <p>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.</p> <p><strong>Data:</strong></p> <p>We provide in the <em><strong>Data</strong></em> directory the data used in our experiments, which is the source code of test cases (Java test methods) of 17 projects collected from Defects4J.</p> <p><strong>Code:</strong></p> <p>We provide in the<em> <strong>Code</strong> </em>directory the code (Python) and bash files required to run the experiments and reproduce the results.</p> <p><strong>Results:</strong></p> <p>We provide in the<em> <strong>Results </strong></em>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.</p> <p><strong>_________________________________</strong></p> <p><strong>LTM's Similarity Measurement:</strong></p> <p>The source code of this step is in the <strong><em>Code/LTM/Similarity</em></strong> directory.</p> <p><strong>Requirements:</strong></p> <p>To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the <em><strong>Code/LTM/Similarity/requirements.txt</strong></em> file should be installed, as follows:</p> <p>cd Code/LTM/Similarity</p> <p>pip install -r requirements.txt</p> <p><strong>Input:</strong></p> <ul> <li>Data/LTM/TestMethods</li> </ul> <p><strong>Output:</strong></p> <ul> <li>Data/LTM/similarity_measurements</li> </ul> <p><strong>Running the experiment:</strong></p> <p>To measure the similarity between all pairs of test cases, the following bash script should be executed:</p> <p>bash measure_similarity.sh</p> <p>The source code of test methods of each project in the <strong><em>Data/LTM/TestMethods</em></strong> 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 <em><strong>Data/LTM/similarity_measurements</strong></em> 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.</p> <p><strong>LTM's Test Suite Minimization:</strong></p> <p>The source code of this step is in the Code/LTM/Search directory.</p> <p><strong>Requirements:</strong></p> <p>To run this step, Python 3 is required (we used Python 3.10). Also, the required libraries in the <strong><em>Code/LTM/Search/requirements.txt</em></strong> file should be installed, as follows:</p> <p>cd Code/LTM/Search</p> <p>pip install -r requirements.txt</p> <p><strong>Input:</strong></p> <p>Data/LTM/similarity_measurements</p> <p><strong>Output:</strong></p> <p>Results/LTM/minimization_results</p> <p><strong>Running the experiments:</strong></p> <p>To minimize the test suite for each project version, the following bash script should be executed:</p> <p>bash minimize.sh</p> <p>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 <em><strong>Results/LTM/minimization_results</strong></em> directory.</p> <p><strong>LTM's Evaluation:</strong></p> <p>To evaluate the minimization results for each version and each project, the following bash script should be executed:</p> <p>cd Code/LTM/Evaluation</p> <p>bash evaluate_per_version.sh</p> <p>cd Code/LTM/Evaluation</p> <p>bash evaluate_per_project.sh</p> <p>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 <em><strong>Results/LTM</strong></em> directory.</p> <p>Note that for each version, the FDR is either 1 or 0. For each project, the FDR ranges from 0 to 1.</p>
An endothelial monolayer with leukocytes annotated for classification with bounding boxes for YOLO
<p>Movies of leukocyte TEM generated by Max Grönloh (Sanquin Research) were used as input for deep learning analysis with YOLO on the ZeroCostDL4Mic platform.</p> <p>Bounding boxes generated with Makesense.ai by Guusje Mouton.</p> <p>After training, an unseen dataset was analysed. For results see: 'Filmpje_Predictions_YOLO.gif'</p> <p>The result shows a large number of false negatives, which can potentially be improved by increasing the volume of training data.</p>
Expression and Purification of DNA-binding domain of T-box Transcription Factor TBXTA-c021
<p>A detailed protocol for the expression and purification of G177D variant of TBXT DNA-binding domain.</p>
Expression and Purification of Full-Length T-box Transcription Factor TBXTA-c027
<p>A detailed protocol for the expression and purification of G177D variant of biotinylated, full-length TBXT</p>
Supplementary Dataset for Trenner et al. 2022: Evolution and Functions of Plant U-box proteins (PUBs): From protein quality control to signalling
<p>This record contains additional information and a supplementary dataset of sequence alignment files and phylograms that support the publication:</p> <p>Trenner J, Monaghan J, Saeed B, Quint M, Shabek N, Trujillo M. 2022. Evolution and Functions of Plant U-box proteins (PUBs): From protein quality control to signalling. (submitted to Annual Review of Plant Biology)</p> <p>Included files:</p> <p><em>Additional information.pdf</em></p> <p>This file contains Supplementary Methods and References.</p> <p><br> <em>Supp_Fig1_Ubox_full_protein_maximum_likelihood_phylogram_linear.pdf</em></p> <p>A multiple protein sequence alignment by MAFFT of 1121 U-box protein sequences from 21 species and four outgroup sequences containing a RING finger domain (<em>A. thaliana</em> RBX1, <em>A. thaliana</em> RMA1, <em>S. cerevisiae</em> RAD18, <em>Homo sapiens</em> TRAF6 ) was used to infer a phylogenetic tree by maximum likelihood with IQ-TREE. The implemented ultrafast bootstrap approximation, set to 2000 bootstrap samples, was used for branch support. The consensus tree was annotated using iTOL.</p> <p> </p> <p><em>Supp_Fig2_Ubox_domain_maximum_likelihood_phylogram_linear.pdf</em></p> <p>In order to analyse specifically the evolution of the U-box domain, the U-box domain of the final 1121 U-box protein sequences as well as the RING finger domain of the four outgroup sequences were isolated and aligned by MAFFT. The U-box domain alignment was then used to infer a phylogenetic tree by maximum likelihood with IQ-TREE. The implemented ultrafast bootstrap approximation, set to 2000 bootstrap samples, was used for branch support. The consensus tree was annotated using iTOL. This tree was used to interpret the evolutionary history of U-box proteins in the main text.</p> <p> </p> <p><em>MAFFT_alignment _1121seqs_Ubox_full_protein_plus_4seqs_RING_outgroup.fasta</em></p> <p>Multiple protein sequences alignment by MAFFT of 1121 U-box full length protein sequences and four RING finger protein sequences, FASTA formatted.</p> <p> </p> <p><em>MAFFT_alignment _1121seqs_Ubox_domain_plus_4seqs_RING_outgroup.fasta</em></p> <p>Multiple protein sequences alignment by MAFFT of U-box domain sequences of 1121 U-box protein and four RING finger domain sequences of outgroup sequences, FASTA formatted.</p> <p> </p> <p><em>Ubox_protein_sequences_information.xlsx</em></p> <p>Spreadsheet with sequence ID information and source databases.</p>
GCN-TFiLM: Modelling Black-box Audio Effects with Time-varying Feature Modulation
<p>Dataset, checkpoints, evaluation, and supplementary material associated with the following publication:</p> <p><a href="https://arxiv.org/abs/2211.00497">Modelling Black-box Audio Effects with Time-varying Feature Modulation</a></p> <p><strong>Citation:</strong></p> <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> <p> </p>
Switching between Numerical Black-box Optimization Algorithms with Warm-starting Policies - Reproduction artifacts
<p># Reproducibility instructions</p> <p>This document details the steps to reproduce the results presented in the paper 'Chaining of Numerical Black-box Algorithms: Investigating the Impact of Warm-Starting and the Switching Point'</p> <p>## Algorithm code<br> The code for the individual algorithms and the switching routines is included in the 'dynas.zip' folder. </p> <p>## Data collection<br> To collect the performance data for the static algorithms, the file 'data_collection.py' can be used. This file runs all 5 static algorithm on the function suite used in the paper, using the 'ioh' package. To run it, please modify the 'ioh_dir' variable to a place where the data should be stored. <br> The resulting data is made available under the 'static.zip' file.</p> <p>## Processing code<br> The code which processes the performance data into the list of usecases is part of the 'Processing_R' notebook. <br> This results in a set of rds-files with the data in IOHanalyzer's internal format, and in a csv file ('Split_performances_relative.csv') with the selected usecases. </p> <p>This csv file can then be read in using the 'visualization' notebook, which can generate Figure 1 from the paper. This notebook can then be used to count the specific usecases, which is the data shown in Table 1. The csv-file containing the selected usecases ('usecases.csv') is also generated here.</p> <p>The 'usecases' csv file can be used in the 'Data_collection_switch' notebook to run the dynamic configurations. This results in the 'Dynamic.zip' data, which can again be read using IOHanalyzer in the 'Processing_R' notebook. This then generates several csv-files, for both the static and dynamic case, containing the per-run hitting times ('RT_samples.zip'). These are then used in the 'Visualization' notebook to generate figure 3 and 4, as well as table 2. </p> <p>## Countour plots<br> The code for generating Figure 5 is included in the end of the 'Visualization' notebook.</p> <p>## Performance comparison plot<br> Figure 6 has been generated using IOHanalyzer web-interface (iohanalyzer.liacs.nl), by uploading the files in 'cma_bfgs.zip'. </p> <p>## Switch point impact plot<br> Figure 7 has been generated using the 'split_experiment' notebook</p>
ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search – Replication Package
<p>This is the replication package associated with the paper "<em>ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search</em>" accepted at the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023) – Technical Track. Cite this paper using the following:</p> <p><em>@inproceedings{pan2023atm,<br> title={ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search},<br> author={Pan, Rongqi and Ghaleb, Taher A. and Briand, Lionel},<br> booktitle={Proceedings of the 45th IEEE/ACM International Conference on Software Engineering},<br> year={2023},<br> pages={1--12}<br> }</em></p> <p><strong>Replication Package Contents:</strong><br> The replication package contains all the necessary data and code required to reproduce the results reported in the paper. We also provide the results for other minimization budgets, and detailed <em>FDR,</em> execution time, and statistical test results. In addition, we provide the data and code required to reproduce the results of baselines techniques: FAST-R and random minimization.</p> <p><strong>Data:</strong><br> We provide in the <em><strong>Data</strong></em> directory the data used in our experiments, which is based on 16 projects from <a href="https://github.com/rjust/defects4j">Defects4J</a>, whose characteristics can be found in <em><strong>Data/subject_projects.csv</strong></em><em>.</em></p> <p><strong>Code:</strong><br> We provide in the <em><strong>Code</strong></em> directory the code and scripts (Java, Python, and Bash) required to run the experiments and reproduce the results.</p> <p><strong>Results:</strong><br> We provide in the <em><strong>Results</strong></em> directory the results for each technique independently, and also a summary of all results together for comparison purposes. The source code for this step is in the <em><strong>Code/ATM/CodeToAST</strong></em> directory. The source code for this step is in the <em><strong>Code/ATM/Similarity</strong></em> directory.</p> <p><strong>_________________________________</strong></p> <p><strong>ATM - Code to AST transformation:</strong></p> <p><strong>Requirements:</strong><br> * Eclipse IDE (we used 2021-12)<br> * The libraries (the <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/CodeToAST/lib</strong></em> directory)</p> <p><strong>Input:</strong><br> All zipped data files should be unzipped before running each step.<br> * Data/test_suites/all_test_cases.zip → Data/test_suites/all_test_cases<br> * Data/test_suites/changed_test_cases.zip → Data/test_suites/changed_test_cases<br> * Data/test_suites/relevant_test_cases.zip → Data/test_suites/relevant_test_cases</p> <p><strong>Output:</strong><br> * Data/ATM/ASTs/all_test_cases<br> * Data/ATM/ASTs/changed_test_cases</p> <p><strong>Running the experiment:</strong><br> To generate ASTS for all test cases in the project test suites, the <em><strong>Code/ATM/CodeToAST/src/CodeToAST.java</strong></em> file should be compiled and run using the Eclipse IDE by including all the required <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/CodeToAST/lib</strong></em> directory as part of the classpath. A bash script is provided along with a pre-generated <em><strong>.jar</strong></em> file in the <em><strong>Code/ATM/CodeToAST/bin</strong></em> directory to run this step, as follows:</p> <pre><code class="language-bash">cd Code/ATM/CodeToAST bash transform_code_to_ast.sh</code></pre> <p>Each test file in the <em><strong>Data/test_suites/all_test_cases</strong></em> and <em><strong>Data/test_suites/changed_test_cases</strong></em> directories is parsed to generate a corresponding AST for each test case method (saved in an XML format in <strong>Data/ATM/ASTs/all_test_cases</strong> and <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> for each project version)<br> <strong>_________________________________</strong></p> <p><strong>ATM - Similarity Measurement:</strong></p> <p><strong>Requirements:</strong><br> * Eclipse IDE (we used 2021-12)<br> * The libraries (the <em><strong>.jar</strong></em><strong> </strong>files in the <em><strong>Code/ATM/Similarity/lib</strong></em> directory)<br> <br> <strong>Input:</strong><br> * Data/test_suites/all_test_cases<br> * Data/test_suites/changed_test_cases<br> <br> <strong>Output:</strong><br> * Data/ATM/similarity_measurements<br> <br> <strong>Running the experiment:</strong><br> To measure the similarity between each pair of test cases, the <em><strong>Code/ATM/Similarity/src/SimilarityMeasurement.java</strong></em> file should be compiled and run using the Eclipse IDE by including all the required <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/Similarity/lib</strong></em> directory as part of the classpath. A bash script is provided along with a pre-generated <em><strong>.jar</strong></em> file in the <em><strong>Code/ATM/Similarity/bin</strong></em> directory to run this step, as follows:</p> <pre><code class="language-bash">cd Code/ATM/Similarity bash measure_similarity.sh</code></pre> <p>ASTs of each project in the <em><strong>Data/ATM/ASTs/all_test_cases</strong></em> and <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> directories are parsed to create pairs of ASTs containing one test case from the <em><strong>Data/ATM/ASTs/all_test_cases</strong></em> directory with another test case from the <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> directory (redundant pairs are discarded). Then, all similarity measurements are saved in the <em><strong>Data/ATM/similarity_measurements.zip</strong></em> file.<br> __________________________________________</p> <p><strong>Search-based Minimization Algorithms:</strong><br> The source code for this step is in the <em><strong>Code/ATM/Search</strong></em> directory.<br> <br> <strong>Requirements:</strong><br> To run this step, Python 3 is required (we used <em><strong>Python 3.10</strong></em>). Also, the libraries in the <strong>Code/AMT/Search/requirements.txt</strong> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/ATM/Search pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> * Data/ATM/similarity_measurements<br> <br> <strong>Output:</strong><br> * Results/ATM/minimization_results<br> <br> <strong>Running the experiment:</strong><br> To minimize the test suites in our dataset, the following bash script should be executed:</p> <pre><code class="language-bash">bash minimize.sh</code></pre> <p>All similarity measurements are parsed for each version of the projects, independently. Each version is run 10 times using three minimization budgets (25%, 50%, and 75%). Genetic Algorithm (GA) is run using four similarity measures, namely top-down, bottom-up, combined, and tree edit distance. NSGA-II is run using two combinations of similarity measures: top-down & bottom-up and combined & tree edit distance. The minimization results are generated in the <em><strong>Results/ATM/minimization_results</strong></em> directory.<br> __________________</p> <p><strong>Evaluate results:</strong><br> To evaluate and summarize the minimization results, run the following:</p> <pre><code class="language-bash">cd Code/ATM/Evaluation bash evaluate.sh</code></pre> <p>This will generate summarized <em>FDR</em> and execution time results (per-project and per-version) for each minimization budget, which can all be found in <strong>Results/ATM</strong>. In this replication package, we provide the final, merged <em>FDR</em> with execution time results.</p> <p><strong>_________________________________</strong></p> <p><strong>Running FAST-R experiments</strong><br> ATM was compared to <a href="https://github.com/ICSE19-FAST-R/FAST-R">FAST-R</a>, a state-of-the-art baseline, which is a set of test case minimization techniques called: <em>FAST++, FAST-CS, FAST-pw, and FAST-all</em>, which we adapted to our data and experimental setup.</p> <p><strong>Requirements:</strong><br> To run this step, Python 3.7 is required. Also, the libraries in the <em><strong>Code/FAST-R/requirements.txt</strong></em> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/FAST-R pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> * Data/FAST-R/test_methods<br> * Data/FAST-R/test_classes</p> <p><strong>Output:</strong><br> * Results/FAST-R/test_methods/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> * Results/FAST-R/test_classes/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> <br> To run FAST-R experiments, the following bash script should be executed:</p> <pre><code class="language-bash">bash fast_r.sh test_methods #method level bash fast_r.sh test_classes #class level</code></pre> <p>Results are generated in <em><strong>.csv</strong></em> files for each budget. For example, for the 50% budget, results are saved in <strong>FDR_and_Exec_Time_Results_50%_budget.csv</strong> in the <em><strong>Results/FAST-R/test_methods</strong></em> and <em><strong>Results/FAST-R/test_classes</strong></em> directories.</p> <p><strong>_________________________________</strong></p> <p><strong>Running the random minimization experiments</strong><br> ATM was also compared to random minimization as a standard baseline.</p> <p><strong>Requirements:</strong> To run this step, Python 3 is required (we used <em><strong>Python 3.10</strong></em>). Also, the libraries in the <em><strong>Code/RandomMinimization/requirements.txt</strong></em> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/RandomMinimization pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> <em>N/A</em></p> <p><strong>Output:</strong><br> * Results/RandomMinimization/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> <br> To run the random selection experiments, the following bash script should be executed:</p> <pre><code class="language-bash">bash random_minimization.sh</code></pre> <p>Results are generated in <em><strong>.csv</strong></em> files for each budget. For example, for the 50% budget, results are saved in <em><strong>FDR_and_Exec_Time_Results_50%_budget.csv</strong></em> in the <em><strong>Results/RandomMinimization</strong></em> directory.</p>
Dataset associated with article: Self-sufficient seismic boxes for monitoring glacier seismology in Greenland
<p>Dataset associated with article: Self-sufficient seismic boxes for monitoring glacier3 seismology in Greenland</p> <p>Contains:<br> - Seismic data of both SG-boxes and regular geophones from Gornergletscher fieldtest, 2021( Seismic_Data_Gorner_Fieldtest.zip) <br> --> SG-box data naming: GO"station_number"SG <br> --> Geophone data naming: GO"station_number"GP<br> <br> - Weather data Gornergletscher fieldtest from Monte Rosa, Meteo Swiss Weather station ( Weather_data_Gorner_Fieldtest_2021.zip) <br> --> 1hr wind averages <br> --> 1hr temperature averages<br> <br> - MSR logger data from SG-boxes from Gornergletscher fieldtest, 2021. Every 5 min these log battery power, tilt (along three axes, temperature and humidity inside the box and light strength on two sides of the SG-box. ( MSR_logger_data_SGboxes_Gorner_Fieldtest.zip )<br> <br> - Seismic data of SG box (Sensor code BSM) next to weather station first acquisition Greenland 2021 ( Seismic_Data_SG_Box_first_acquisition_Greenland_2021.zip) <br> --> .pri0 is East component, .pri1 is North component, .pri2 is Vertical component.<br> <br> - Weather data from weather station next to SG-box (sensor code BSM) during first acquisition Greenland 2021 ( Weather_station_data_Greenland_2021.zip) <br> --> The weather station logs a value every two hours.</p> <p> </p>
3D printing lifts the lid on black box instruments
<p>Within analytical chemistry, chemical instruments involve numerous interconnected parts working cohesively towards a specific functionality. However, these highly complex mechanisms cannot be fully depicted with a 2-dimensional textbook model or image, leading to confusion or misconceptions during the learning process. To address this comprehension gap, an array of chemical instrument components have been designed and fabricated with 3D printing to create a hands-on learning experience. The models developed were created in interlocking parts to allow disassembly and investigation of the inner workings of each instrument component. This work produced a series of teaching aids and dynamic models for common instruments including a quadrupole, quadrupole-ion-trap, orbitrap, FTICR mass spectrometer; a GC injection port, FID, and ECD detectors; an HPLC injector; an ICP torch and nebulizer; a Michaelson interferometer; a basic monochromator; and a shatterbox sample preparation device. These models were designed to be interactive and hand-held 1:1 scale models of commercial instrument components. Using these models, students' learning may be expanded from 2 to 3 dimensions; providing hands-on experience with modern laboratory instrumentation impossible with commercial instrumentation. Models were used in a small Analytical Chemistry lecture (<em>n</em> = 8) which demonstrated positive student reception, especially for models with kinematic relations to instrument functionality.</p>
Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Computing Star Discrepancies with Numerical Black-Box<br> Optimization Algorithms'. </p> <p>The following files are included:</p> <p>- TA.zip and DEM.zip: The code used for the TA and DEM algorithms respectively.</p> <p>- experiment_runner: Python file which was used to run the black-box optimization algorithms on the discrepancy problems from IOHexperimenter (requires package 'ioh', version 0.3.6 or higher). This generates data in IOH-format, which is included in 'raw_data.zip'</p> <p>- process_stardicr.R: R script which uses IOHanalyzer to extract the performance from the raw data into csv files for visualization. The resulting csvs are included in 'csv_with_pos' for the final results including the corresponding coordinates and 'csv_perf.zip', which contains the convergence information.</p> <p>- Found_Values: The discrepancy values found by TA and DEM, separated by sampler.</p> <p>- A csv file of the relative performance of each of the optimizers compared to the values found by TA is included in 'final_precision_table.csv'</p> <p>- Plot_StarDiscr: the python notebook used to generate all figures, except figure 3 which was created using the IOHanalyzer GUI (iohanalyzer.liacs.nl). The full dataset is available on the website under the source 'star_discrepancy'</p> <p>- Figures: some additional figures which were not included in the paper because of space constraints + higher quality versions of some of the landscape plots.</p>
SOLPS-ITER simulations of a vapour box design for the linear device Magnum-PSI
<p>Replication package for manuscript "SOLPS-ITER simulations of a vapour box design for the linear device Magnum-PSI" submitted to PPCF.</p>
XRF analysis of Gotlandic box brooch data set
<p>XRF analasis of Gotlandic box brooch, performed in order to evaluate the material, composition of alloy, and where there are different areas of silver. The information is valuable when conservating the object and cleaning up original surfaces. Results show that brooch is multi coloured with brass, bronze and silver.</p> <p>Raw data set in .rtx format. ARTAX soft ware will be needed to open files.</p>
Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"
<p>Model outputs associated with “Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"</p>
Unpacking the "black box": improving ecological interpretation of regression based models
<p><strong>Aim</strong><br>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.</p> <p><strong>Location</strong><br>Continental United States and Canada</p> <p><strong>Methods</strong><br>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 (<em>Pinus</em> <em>strobus</em>, <em>Acer</em> <em>saccharum</em>, and <em>Quercus</em> <em>montana</em>), and three western (<em>Picea</em> <em>engelmannii</em>, <em>Pinus</em> <em>ponderosa</em>, and <em>Pseudotsuga</em> <em>menziesii</em>) 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.</p> <p><strong>Results</strong><br>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.</p> <p><strong>Conclusion</strong><br>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.</p>
raw data for the article "Copper(I)-BOX Catalyzed Asymmetric 3-Component Reaction for the Synthesis of Trifluoromethylated Propargylic Ethers and Anilines"
<p>Raw NMR, HPLC, IR and MS data for the article "Copper(I)-BOX Catalyzed Asymmetric 3-Component Reaction for the Synthesis of Trifluoromethylated Propargylic Ethers and Anilines" published in Angewandte Chemie, DOI: </p> <p><a href="https://doi.org/10.1002/anie.202305776">https://doi.org/10.1002/anie.202305776</a> </p> <p>The number of the folders either correspond to compounds numbers in the article or the name of the folder is self-describing. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.</p>
Signal Box, Stanley Village
A signal box is the railway equivalent of today's air traffic control tower found at airports. The Trinidad Government Railways (TGR) once had 17 signal boxes at various locations across the rail network, operated by a staff of 66 signalmen. Today there is only one signal box left in Trinidad at a place now called Stanley village which is located about a quarter of a mile north east of the old sugar factory at Usine Sainte Madeleine (about five miles east of San Fernando). The signal box is popularly known, by those who know of it, as "Stanleyville Box" and it was built soon after the Cipero Tramway, established in 1849, was taken over by the Trinidad Government Railway in 1912. Located on the ground next to the box is an old iron lever once used to switch lines and signals. The old lever still bears the title of its UK manufacturing company, Tyer & Co London & Carlisle and date of installation, 1913. Text by Glen Beadon Photos by Sharon Burford Source: Objaverse 1.0 / Sketchfab
Old Wooden Box
Wooden Box low poly model (clear) <br> ⦁ Texture set - 4096x4096;<br> ⦁ Wooden Box - 2520 tris;<br> Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
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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.