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Additional Data: Mapping the Evolution of Computational Thinking in Education: A Bibliometrics Analysis of Scopus Database from 1987 to 2023
<p>The following is a selection of figures and tables from a bibliometric study that will be released later. The title of this study is Mapping the Evolution of Computational Thinking in Education: A Bibliometrics Analysis of Scopus Database from 1987 to 2023.</p> <p>In the online listing of the appendix, we will find three figures (Figure 5, Figure 6, and Figure 12) and three tables (Table 3, Table 4, and Table 4), also several references related to this research. It was important to us that the core of the study that is now being carried out not be diminished in any way, which is why we chose the photos and tables we did. This study was conceived and supported by the Indonesia Endowment Fund for Education (LPDP), which the Ministry of Finance administers in the Republic of Indonesia, to evaluate current trends and research problems in computational thinking for education. The Scopus database was used, and its range of coverage was from 1987 to 2023.</p> <p> </p>
Leveraging FSPMs for Unconventional Computing with Plants
<p>This software contains the code and presentation from the conference paper `` Comparing FSPMs using Unconventional Computing Methods'', presented at FSPM2023 in Berlin.</p> <p>The software to run the analysis can be found on [Github](https://github.com/opieters/fspm2023).</p> <p>The YAML-files (`hydroshoot_environment.yml`, `wheatfspm_environment.yml`) should be used to create the anaonda environments and reproduce the output CSV files. The files are also included for convenience (`hydroshoot.zip` and `WheatFspm.zip`). The source code is also included here in case the original repositories are no longer available on GitHub.</p> <p>The code for the grass leaf model is not yet available because the research paper has not yet been published.</p> <p>The input files (`*_meteo.csv`) is the input meteorological data. The output files all end with `_data.csv`. Import these into the `data` directory from the GitHub code and you should be able to reproduce the results.</p>
Dataset of "Challenging Point Scanning across Electron Microscopy and Optical Imaging using Computational Imaging"
<p>Dataset containing the jupyter notebook with codes for the simulation of the structured illumination patterns used for image reconstruction (the simulation parameters have been optimized to make sure that the patterns were almost identical to the experimental ones), the reconstruction algorithms. Moreover, there are three experimental dataset saved as txxt file, where each line contains the six biases applied to the electron modulator and the intensity measured by the single pixel detector that we used.</p>
Data from: Hydrodynamic analysis of bioinspired vortical cross-step filtration by computational modelling
<p><span><span>Research on the suspension-feeding apparatus of fishes has led recently to the identification of novel filtration mechanisms involving vortices. Structures inside fish mouths form a series of 'backward-facing steps' by protruding medially into the mouth cavity. In paddlefish and basking shark mouths, porous gill rakers lie inside 'slots' between the protruding branchial arches. Vortical flows inside the slots of physical models have been shown to be important for the filtration process, but the complex flow patterns have not been visualized fully. Here we resolve the three-dimensional hydrodynamics by computational fluid dynamics simulation of a simplified mouth cavity including realistic flow dynamics at the porous layer. We developed and validated a modelling protocol in ANSYS Fluent software that combines a porous media model and permeability direction vector mapping. We found that vortex shape and confinement to the medial side of the gill rakers result from flow resistance by the porous gill raker surfaces. Anteriorly directed vortical flow shears the porous layer in the centre of slots. Flow patterns also indicate that slot entrances should remain unblocked, except for the posterior-most slot. This new modelling approach will enable future design exploration of fish-inspired filters.</span></span></p>
Revisiting the historical scenario of a disease dissemination using genetic data and Approximate Bayesian Computation methodology: the case of Pseudocercospora fijiensis invasion in Africa
<p class="MsoNormal"><span>The reconstruction of geographic and demographic scenarios of dissemination for invasive pathogens of crops is a key step towards improving the management of emerging infectious diseases. Nowadays, the reconstruction of biological invasions typically uses the information of both genetic and historical information to test for different hypotheses of colonization. The Approximate Bayesian Computation framework and its recent Random Forest development (ABC-RF) have been successfully used in evolutionary biology to decipher multiple histories of biological invasions. Yet, for some organisms, typically plant pathogens, historical data may not be reliable notably because of the difficulty to identify the organism and the delay between the introduction and the first mention. We investigated the history of the invasion of Africa by the fungal pathogen of banana, <em>Pseudocercospora fijiensis</em>, by testing the historical hypothesis against other plausible hypotheses. We analysed the genetic structure of eight populations from six eastern and western African countries, using 20 microsatellite markers, and tested competing scenarios of population foundation using the ABC-RF methodology. We do find evidence for an invasion front consistent with the historical hypothesis, but also for the existence of another front never mentioned in historical records. We question the historical introduction point of the disease on the continent. Crucially, our results illustrate that even if ABC-RF inferences may sometimes fail to infer a single, well-supported scenario of invasion, they can be helpful in rejecting unlikely scenarios, which can prove much useful to shed light on disease dissemination routes.</span></p>
The Xpert Network and the Best Practices for Computational and Data-Intensive Research
<p>This video provides a brief overview of the Xpert Network and presents the best practices for professionals who support computational and data-intensive (CDI) research projects. The practices resulted from the Xpert Network activities, an initiative that brings together major NSF-funded projects for advanced cyberinfrastructure, national projects, and university teams that include individuals or groups of such professionals. Additionally, our recommendations are based on years of experience building multidisciplinary applications and teaching computing to scientists. These practices have proven effective in various CDI research settings and are easy to adopt by both research software engineers (RSEs) and domain scientists. You can find these best practices described on the following website: <a href="https://sites.udel.edu/xpert-cdi/resources/best-practices/">https://sites.udel.edu/xpert-cdi/resources/best-practices/</a>. We value your feedback on these practices and encourage you to read our paper for further insights: <a href="https://doi.org/10.1145/3491418.3530293">https://doi.org/10.1145/3491418.3530293</a>.</p>
Computer-rendered HDR and LDR 4k images database
<p>Realistic image computation mimics the natural process of acquiring pictures by simulating the physical interactions of light between all the objects, lights and cameras lying within a modelled 3D scene. This process is known as global illumination and was formalised by Kajiya with the following rendering Equation:<br> <span class="math-tex">\(\begin{equation} \label{eq:rendering_equation} L_o(x, \omega_o) = {L_e(x, \omega_o)} + \int_{\Omega}^{} {L_i(x, \omega_i)} \cdot f_r(x, \omega_i \rightarrow \omega_o) \cdot \cos \theta_i d\omega_i \end{equation}\)</span></p> <p>where:</p> <ul> <li> <span class="math-tex">\(L_o(x, \omega_o)\)</span> is the luminance traveling from point <span class="math-tex">\(x\)</span> in direction <span class="math-tex">\(\omega_o\)</span>;</li> <li><span class="math-tex">\(L_e(x, \omega_o)\)</span> is point <span class="math-tex">\(x\)</span> emitted luminance (it is null if point x does not lie on a ligth source surface);</li> <li>the integral represents the set of luminances <span class="math-tex">\(L_i\)</span>incident in <span class="math-tex">\(x \)</span> from the hemisphere of the directions <span class="math-tex">\(\Omega\)</span> and reflected in the direction <span class="math-tex">\(\omega_o\)</span>. The reflected luminances are weighted by the materials reflecting properties (bidirectionnal reflectance function <span class="math-tex">\(f_r(x, \omega_i \rightarrow \omega_o)\)</span>) and the cosinus of the incident angle.</li> </ul> <p>This equation cannot be analytically solved and Monte Carlo approaches are generally used to estimate the value of the pixels of the final image.</p> <p>This proposed dataset is composed of 32 points of view of photo realistics images with different level of samples (following the Monte Carlo approach) for each. Each image is 3840 × 2160 pixels in size. The most noisy image is of 2⁰ samples and the reference one (the most converged image obtained) is of 2²⁰ samples. The <a href="https://www.pbrt.org/index.html">pbrt</a> rendering engine (version 4) was used to generate these images.</p>
Training data for the "Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints"
<p>There are two ZIP-files consisting of small histological image tiles that have been used to detect and quantify distinct tissue textures and lymphocyte proportions from H&E-stained clear cell renal cell carcinoma (KIRC) digital tissue sections of the Cancer Genome Atlas (TCGA) image archive and the Helsinki dataset.</p> <p>The <strong>tissue_classification </strong>file contains 300x300px tissue texture image tiles (n=52,713) representing renal cancer (“cancer”; n=13,057, 24.8%); normal renal (“normal”; n=8,652, 16.4%); stromal (“stroma”; n= 5,460, 10.4%) including smooth muscle, fibrous stroma and blood vessels; red blood cells (“blood”; n=996, 1.9%); empty background (“empty”; n=16,026, 30.4%); and other textures including necrotic, torn and adipose tissue (“other”; n=8,522, 16.2%). Image tiles have been randomly selected from the TCGA-KIRC WSI and the Helsinki datasets.</p> <p>The <strong>binary_lymphocytes </strong>file contains mostly 256x256px-sized but also smaller image tiles of Low (n=20,092, 80.1%) or High (n=5,003, 19.9%) lymphocyte density (n=25,095). Image tiles have been randomly selected from the TCGA-KIRC WSI dataset.</p> <p>All accuracy of all annotations have been double-checked. However, the classification between multiple tissue textures or lymphocyte density can be sometimes ambiguous.</p> <p>The deep learning model parameters trained with the ResNet-18 infrastructure for (1) lymphocyte and (2) texture classification are named as (1) <strong>resnet18_binary_lymphocytes.pth</strong> and (2) <strong>resnet18_tissue_classification.pth</strong>. Codes and instructions to use these are found in <a href="https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis">https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis</a>.</p> <p> </p> <p>If you use either work, please cite the publication by Brummer O et al (1) AND the TCGA Research Network (2):<br><strong>(1) </strong><strong>Brummer, O., Pölönen, P., Mustjoki, S. <em>et al.</em> Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints. <em>Br J Cancer</em> 129, 683–695 (2023). </strong><a href="https://doi.org/10.1038/s41416-023-02329-4">https://doi.org/10.1038/s41416-023-02329-4</a></p> <p><strong>(2) The results shown here are in whole or part based upon data generated by the TCGA Research Network: </strong><strong><a href="https://www.cancer.gov/tcga">https://www.cancer.gov/tcga</a></strong><strong>.</strong></p>
Inputs and Outputs for paper The DESC Stellarator Code Suite Part I: Quick and accurate equilibria computations
<p>Contains the DESC and VMEC input and output files used in the paper, as well as the plotting scripts used to create the figures in the paper (Current with the <a href="https://arxiv.org/abs/2203.17173">arxiv Mar 31 2023 version</a>):</p> <p> </p> <p>3D equilibrium codes are vital for stellarator design and operation, and high-accuracy equilibria are also necessary for stability studies. This paper details comparisons of two 3D equilibrium codes, VMEC, which uses a steepest-descent algorithm to reach a minimum-energy plasma state, and DESC, which minimizes the MHD force error in real space directly. Accuracy as measured by final plasma energy and satisfaction of MHD force balance, as well as other metrics, will be presented for each code, along with the computation time. It is shown that DESC is able to achieve more accurate solutions, especially near-axis. DESC's global Fourier-Zernike basis also yields the solution everywhere in the plasma volume, not just on discrete flux surfaces. Further, DESC can compute the same accuracy solution as VMEC in an order of magnitude less time.</p> <p> </p> <p>Updated dataset for better readability 5-5-23</p>
Computational Screening of Supported Metal Oxide Nanoclusters for Methane Activation: Insights into Homolytic versus Heterolytic C-H Bond Dissociation
<p>Optimized geometries (in XYZ format) of methane activation over supported [M<sub>1</sub>OM<sub>2</sub>]<sup>2+</sup> complexes where M<sub>1</sub>, M<sub>2</sub> = Cu, Zn, Ni, Co, Fe, and Mn. 'SM' in the file name stands for spin multiplicity. Calculations were performed using Gaussian 16 and M06-L functional. The def2-SVP basis set was used for C, O, H, whereas the def2-TZVP basis set was employed for all metal elements. </p>
Dataset for "Light and Mass Transport Computations Guide the Fabrication of 3D-Structured TiO2 and Au/TiO2 Aerogel Photocatalysts for Efficient Hydrogen Production in the Gas Phase"
<p>This dataset is related to "Light and Mass Transport Computations Guide the Fabrication of 3D-Structured TiO<sub>2</sub> and Au/TiO<sub>2</sub> Aerogel Photocatalysts for Efficient Hydrogen Production in the Gas Phase" published in <em> Chemistry of Materials</em> <strong>2023</strong> <em>35</em> (10), 3849-3858.</p> <p>Each file contains the dataset for the respective Figure.</p> <p><strong>File 'Figure 1': </strong>Optical Photograph and SEM images of a 3D printed TiO<sub>2</sub> aerogel.</p> <p><strong>File 'Figure 2': </strong>The subdirectory <em>'absorbed'</em> contains data for the calculation of the light absorption of unstructured, sc-structured, and fcc-structured aerogels. A more detailed description is presented in the <em>'readme</em>' file. The subdirectory <em>'flux_time_resolved'</em> contains data for the calculation of the time-resolved flux in a fcc-structured aerogel. A more detailed description is presented in the readme file.</p> <p><strong>File 'Figure 3': </strong>Measured and calculated data of the pressure drop of unstructured, sc-structured, and fcc-structured aerogels. Images of the velocity profile. Images of simulated velocity profiles of an sc-structured aerogel without and with a surrounding wall. The simulations were performed in COMSOL.</p> <p><strong>File 'Figure 4': </strong>Data of the hydrogen evolution experiments.</p> <p><strong>File 'Figure SI1 and Table SI1': </strong>Data of nitrogen physisorption experiments. <em>'Figure_SI1-sample-identification'</em> contains a list to assign the dataset to the respective subfigures in Figure SI1. <em>'Table_SI1-sample-identification' </em>contains a list to assign the dataset to the respective entry in Table SI1.</p> <p><strong>File 'Figure SI2': </strong>Data of the hydrogen evolution experiments with a gas stream containing pure water and a water/methanol mixture, respectively.</p> <p><strong>File 'Figure SI3': </strong>Data of the UV cleaning experiment.</p> <p><strong>File 'Figure SI4': </strong>Chromatograms recorded during hydrogen evolution experiments to discuss the formation of side products.</p> <p><strong>File 'Figure SI5':</strong> Data of two consecutive hydrogen evolution experiments.</p> <p><strong>File 'Figure SI6': </strong>Data of the hydrogen evolution experiments for an fcc-structured and sc-structured TiO<sub>2</sub> aerogel of similar light absorption. Image of a simulated velocity profiles for an unstructured aerogel. The simulation were performed in COMSOL.</p> <p><strong>File 'Figure SI7': </strong>Data of an hydrogen evolution experiments of an fcc-structured TiO<sub>2</sub> aerogel for flow rates in a range of 1.25 to 20 mL min<sup>-1</sup>.</p> <p><strong>File 'Figure SI8': </strong>TEM/STEM images including EDX mapping of an Au/TiO<sub>2</sub> aerogel fragment.</p> <p><strong>File 'Figure SI9': </strong>Data of the hydrogen evolution, the irradiance of the LED, and the amount of water and methanol.</p> <p><strong>File 'Figure SI10': </strong>Attenuated total reflection infrared spectra of TiO<sub>2</sub> nanoparticle powder and aerogel after UV cleaning.</p> <p><strong>File 'Figure SI11': </strong>XRD pattern of TiO<sub>2</sub> nanoparticles and a reference of anatase TiO<sub>2</sub>.</p> <p><strong>File 'Figure SI12': </strong>Data of hydrogen evoltion for TiO<sub>2</sub> nanoparticle powders.</p> <p><strong>File 'Figure SI13': </strong>Transmission and reflectance spectra of a TiO<sub>2</sub> aerogel.</p> <p><strong>File 'Figure SI14': </strong>Calculated transmission and reflectance for an optical thickness and a scattering albedo in a range of 0 to 5 and 0 to 1, respectively. The data was calculated by solving the radiative transfer equation, as implemented in the DISORT algorithm. A more detailed description of the calculation and data processing is provided in the <em>'readme'</em> file. The code of the DISORT algorithm is provided in the <em>'DISORT'</em> subdirectory.</p> <p><strong>File 'Figure SI16': </strong>Data of the derived absorption and scattering coefficient.</p> <p><strong>File 'Figure SI17': </strong>Data of the light absorption and the scattering coefficient. The <em>'readme'</em> file contains a description of the data processing for the light absorption dataset.</p>
SynthRAD2023 Grand Challenge validation dataset: synthetizing computed tomography for radiotherapy
<p><strong>Version 1.1</strong>, updated on 2023-06-04 --> the task2_val.zip has been modified with a new cbct file for patient 2BA078.<br> <br> The dataset can be downloaded from <a href="https://doi.org/10.5281/zenodo.7260705">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.7868169">10.5281/zenodo.7868169</a> and a detailed description is offered at <a href="https://doi.org/10.5281/zenodo.7260704">https://doi.org/10.5281/zenodo.7260704</a> in the "synthRAD2023_dataset_description.pdf".</p> <p>The<strong> </strong>input of the<strong> validation datasets</strong> for Task1 is in Task1_val.zip, while for Task2 in Task2_val.zip. After unzipping, each Task is organized according to the following folder structure:</p> <p>Task1_val.zip/</p> <p>├── Task1</p> <p> ├── brain</p> <p> ├── 1Bxxxx</p> <p> ├── mr.nii.gz</p> <p> └── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 1_brain_val.xlsx</p> <p> ├── 1Bxxxx_val.png</p> <p> └── ... </p> <p> └── pelvis</p> <p> ├── 1Pxxxx</p> <p> ├── mr.nii.gz</p> <p> ├── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 1_pelvis_val.xlsx</p> <p> ├── 1Pxxxx_val.png</p> <p> └── ....</p> <p>Task2_val.zip/</p> <p>├──Task2</p> <p> ├── brain</p> <p> ├── 2Bxxxx</p> <p> ├── cbct.nii.gz</p> <p> └── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 2_brain_val.xlsx</p> <p> ├── 2Bxxxx_val.png</p> <p> └── ... </p> <p> └── pelvis</p> <p> ├── 2Pxxxx</p> <p> ├── cbct.nii.gz</p> <p> ├── mask.nii.gz</p> <p>├── ...</p> <p>└── overview</p> <p> ├── 2_pelvis_val.xlsx</p> <p> ├── 2Pxxxx_val.png</p> <p> └── ....</p> <p>Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below:</p> <p>[Task] [Anatomy] [Center] [PatientID]</p> <p>1 B A 001</p> <p>In each patient folder, two files can be found: </p> <ul> <li> <p>mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image</p> </li> <li> <p>mask.nii.gz: image containing a binary mask of the dilated patient outline </p> </li> </ul> <p>For each task and anatomy, an overview folder is provided which contains the following files:</p> <ul> <li> <p>[task]_[anatomy]_val.xlsx: This file contains information about the image acquisition protocol for each patient.</p> </li> <li> <p>[task][anatomy][center][PatientID]_val.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data.</p> </li> </ul> <p><strong>DATASET DESCRIPTION</strong></p> <p>This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks.</p> <p>Data was collected at 3 Dutch university medical centers:</p> <ul> <li> <p>Radboud University Medical Center;</p> </li> <li> <p>University Medical Center Utrecht;</p> </li> <li> <p>University Medical Center Groningen.</p> </li> </ul> <p>For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to.</p> <p>The following number of patients is available in the validation set.</p> <p><strong>Validation</strong></p> <table> <tbody> <tr> <td> </td> <td> <p><strong>Brain</strong></p> </td> <td> <p><strong>Pelvis</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Tota</strong>l</p> </td> </tr> <tr> <td> <p><strong>Task 1</strong></p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> <td> <p>20</p> </td> <td> <p>0</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> </tr> <tr> <td> <p><strong>Task 2</strong></p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> </tr> </tbody> </table> <p>In total, for all tasks and anatomies combined, 120 image pairs are available in this dataset. <strong>This repository only contains the validation data. </strong>The training data is provided at: h<a href="https://doi.org/10.5281/zenodo.7260704">ttps://doi.org/10.5281/zenodo.7260704</a>.</p> <p>All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure).</p> <p>Data was acquired with the following scanners:</p> <ul> <li> <p>Center A:</p> <ul> <li> <p>MRI: Philips Ingenia 1.5T/3.0T</p> </li> <li> <p>CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT</p> </li> <li> <p>CBCT: Elekta XVI</p> </li> </ul> </li> <li> <p>Center B:</p> <ul> <li> <p>MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T</p> </li> <li> <p>CT: Siemens SOMATOM Definition AS</p> </li> <li> <p>CBCT: IBA Proteus+ or Elekta XVI</p> </li> </ul> </li> <li> <p>Center C:</p> <ul> <li> <p>MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T</p> </li> <li> <p>CT: Philips Brilliance Big Bore</p> </li> <li> <p>CBCT: Elekta XVI</p> </li> </ul> </li> </ul> <p>For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast.</p> <p>For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT.</p> <p>The following pre-processing steps were performed on the data:</p> <ul> <li> <p>Conversion from dicom to compressed nifti (nii.gz)</p> </li> <li> <p>Rigid registration between CT and MR/CBCT</p> </li> <li> <p>Anonymization (face removal, only for brain patients)</p> </li> <li> <p>Patient outline segmentation (provided as a binary mask)</p> </li> <li> <p>Crop MR/CBCT, CT and mask to remove background and reduce file sizes</p> </li> </ul> <p>The code used to preprocess the images can be found at: <a href="https://github.com/SynthRAD2023/">https://github.com/SynthRAD2023/</a>. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics.</p> <p><strong>ETHICAL APPROVAL</strong></p> <p>Each institution received ethical approval from their internal review board/Medical Ethical committee:</p> <ul> <li> <p>UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”.</p> </li> <li> <p>UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”.</p> </li> <li> <p>Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”.</p> </li> </ul> <p><strong>CHALLENGE DESIGN</strong></p> <p>The overall challenge design can be found at <a href="https://doi.org/10.5281/zenodo.7746019">https://doi.org/10.5281/zenodo.7746019</a>.</p>
Dataset for the evaluation of student-level outcomes of a primary school Computer Science curricular reform
<p>Dataset for the evaluation of student-level outcomes of a primary school Computer Science curricular reform<br> =======================================================</p> <p>• If you publish material based on this dataset, please cite the following :</p> <p> • The Zenodo repository : Laila El-Hamamsy, Barbara Bruno, Jessica Dehler Zufferey, and Francesco Mondada (2023). Dataset for the evaluation of student-level outcomes of a primary school Computer Science curricular reform [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7489244</p> <p> • The associated peer reviewed article that will appear in the International Journal of STEM education : El-Hamamsy, L., Bruno, B., Audrin, C., Chevalier, M., Avry S., Dehler Zufferey, J., and Mondada, F. (2023). How are Primary School Computer Science Curricular Reforms Contributing to Equity? Impact on Student Learning, Perception of the Discipline, and Gender Gaps. arXiv, to appear in the International Journal of STEM Education. https://doi.org/10.48550/arXiv.2306.00820</p> <p>• License: This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY-4.0)</p> <p>• Creator: El-Hamamsy, L., Bruno, B., Dehler Zufferey, J., and Mondada, F.</p> <p>• Date: May 2nd 2023</p> <p>• Subject: Computer Science; Curricular Reform; Elementary Education; Learning Achievement; Computational Thinking; Perception Survey; Equity; Gender Gaps</p> <p>• Dataset format: CSV</p> <p>• Dataset collection: January 2021 to May 2022</p> <p>• Dataset size : < 100 kB</p> <p>• Dataset content : three excel files. We provide the detailed description of each of the files below. The original questions are available in the associated publication [1]. Please note that these datasets contain missing values due to students either not being present for all data collections or not having the associated teacher-related data.</p> <p>• Abbreviations :<br> - CS : Computer Science<br> - CT : Computational Thinking<br> - PD : Professional Development</p> <p>• Funding : This work was funded by the the NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543)</p> <p> </p> <p># References</p> <p>[1] El-Hamamsy, L., Bruno, B., Audrin, C., Chevalier, M., Avry S., Dehler Zufferey, J., and Mondada, F. (2023). How are Primary School Computer Science Curricular Reforms Contributing to Equity? Impact on Student Learning, Perception of the Discipline, and Gender Gaps. arXiv, to appear in the International Journal of STEM Education. https://doi.org/10.48550/arXiv.2306.00820</p>
Dataset for the article: Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment
<p>This open access repository houses the dataset utilized in the research article:</p><p>Pankiewicz, M. (2023). Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1413-1418). Vienna, Austria: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/222666</p><p>The repository includes these files:</p><p>"submissions.csv": This data file captures the evaluation results of programming assignments. It is organized by the following columns:</p><p> </p><ul><li>"user_id": The unique identifier for each student who submitted the assignment.</li><li>"task_id": The unique identifier for each task that received submissions.</li><li>"submission_seconds": The number of seconds since the first user accessed the initial task's description within the system.</li><li>"correct": The outcome of the evaluation (1 denotes correct; 0 denotes incorrect).</li><li>"subject": The specific subject matter that the task addresses.</li></ul><p> </p><p>"subjects.csv": This data file comprises the roster of subjects for which tasks have been assigned within the system. It includes these columns:</p><p> </p><ul><li>"subject_id": The unique identifier for each subject.</li><li>"subject": The actual name of the subject.</li></ul><p> </p>
Table2 computations
<p>This data set summarizes all the energy results entered in <strong>TableII </strong>of the manuscript entitled<em><strong> Electron Fields in Hydrogen</strong></em></p>
Mechanisms of simultaneous linear and nonlinear computations at the mammalian cone photoreceptor synapse
<p>Neurons enhance their computational power by combining linear and nonlinear transformations in extended dendritic trees. Rich, spatially distributed processing is rarely associated with individual synapses, but the cone photoreceptor synapse may be an exception. Graded voltages temporally modulate vesicle fusion at a cone's ~20 ribbon active zones. The transmitter then flows into a common, glia-free volume where bipolar cell dendrites are organized by type in successive tiers. Using super-resolution microscopy and tracking vesicle fusion and postsynaptic response at the quantal level in the thirteen-lined ground squirrel, <em>Ictidomys</em> <em>tridecemlineatus</em>, we show that certain bipolar cell types respond to individual fusion events in the stream while other types respond to degrees of locally coincident events, creating a gradient across tiers that are increasingly nonlinear. Nonlinearities emerge from a combination of factors specific to each bipolar cell type including diffusion distance, contact number, receptor affinity, and proximity to transporters. Complex computations related to feature detection begin within the first visual synapse.</p>
Associated data underlying the publication "Flipped Classroom Real-World Activities for Learning Open Computing Concepts"
<p>Various “open” concepts in Computing, such as open standards, open data, open licenses or system interoperability are becoming more important in the professional lives of software engineers. However, students usually do not receive a systematic education about these concepts ; rather they sporadically learn about a subset of these topics. This paper presents the revised version of the Open Computing course in the University of Zagreb, Faculty of Electrical Engineering and Computing (FER), which teaches a clear set of current topics focused on open data and correlating concepts. The new e-learning course is carried out using the “flipped classroom” educational method ; students construct their knowledge in a set of real-world mini-activities throughout the course, instead of passively learning from the official course resources. In this paper, we discuss our flipped classroom activities, their relation to revised Bloom’s taxonomy of educational objectives, and present the evaluation of the first course instance us- ing this model. Students’ feedback shows they welcome this change in approach, finding the course useful and interesting, and preferring this method to the traditional full lecture setting.</p>
CPT-1 pre-computed whole-proteome variant effect predictions and model source code
<p><strong>Cross-protein transfer learning for variant effect prediction</strong></p><p>This repository contains the variant effect predictions of CPT-1 for 18,602 human proteins, initially released with the manuscript "Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects". The proteins are split into three files.</p><p><i>CPT1_score_EVE_set.zip</i>: Proteins in the EVE set (<a href="https://www.nature.com/articles/s41586-021-04043-8">Frazer et al., 2021</a>)</p><p><i>CPT1_score_no_EVE_set_1.zip</i> & <i>CPT1_score_no_EVE_set_2.zip</i>: Proteins not in the EVE set. Predictions for these proteins use imputed values for features depending on the EVE MSA.</p><p>The protein names are UniProt gene names.</p><p>We also provide source code to train CPT-1 model and reproduce results in the manuscript :</p><p><i>source_code.zip </i>(corresponds to GitHub repository songlab-cal/CPT version as of Jul 12, 2023)</p><p> </p><p><strong>Citation</strong></p><p>Jagota, M.*, Ye, C.*, Albors, C., Rastogi, R., Koehl, A., Ioannidis, N., and Song, Y.S.†<br>"Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects", bioRxiv (2022)</p><p>*These authors contributed equally to this work.<br>†To whom correspondence should be addressed: <a href="mailto:yss@berkeley.edu">yss@berkeley.edu</a></p><p>DOI: <a href="https://doi.org/10.1101/2022.11.15.516532">https://doi.org/10.1101/2022.11.15.516532</a></p><p> </p>
Computational data on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR)
<p>This repository contains the data and files for the computational study on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR). <br> The repository is organized into different folders as described below:</p> <p>====================================================================================================<br> 1_Starting_structure<br> This folder contains the starting structures of the four binding modes obtained from the crystallographic study. These structures serve as the initial configurations for the Molecular Dynamics (MD) simulations.</p> <p>====================================================================================================<br> 2_mdp_files<br> This folder contains two subfolders:</p> <p>1_MD<br> The "1_MD" subfolder includes the MD simulation files in the mdp format. These files define the parameters and settings for running the MD simulations with Gromacs version 2019.3.</p> <p>2_US<br> The "2_US" subfolder includes the files required for performing Umbrella Sampling (US) simulations for each of the binding modes using Gromacs version 2021.3. Within each mode folder, you will find the following files:</p> <p>constraint: Position restraints files used in the Umbrella Sampling simulations.<br> pull: mdp files containing the settings for pulling in the US simulations.<br> us: mdp files used for the US simulations.</p> <p>====================================================================================================<br> 3_MD_results<br> This folder contains the results of the MD simulations. It includes the structure files (.gro) and trajectory files (.xtc) for each simulation. Due to the large size of the files, the solvent has been excluded, and the results are provided at every 1 nanosecond (ns) interval.</p> <p>====================================================================================================<br> 4_US_results<br> The "4_US_results" folder includes the trajectories of the US simulations for each of the binding modes. For each mode, two trajectories are provided.</p> <p>====================================================================================================<br> 5_pdb<br> This folder includes the pdb files of the simulated structures of the two binding modes after the equilibration step.</p> <p>====================================================================================================</p> <p>We acknowledge funding by the German Research Foundation (DFG) through the Emmy Noether Young Group Leader Programme (CK, project KO 5423/1-1), the Swedish e-Science Research Centre (SeRC, ML, PN), the Swedish Research Council (PN, Grant No. 2018-4343). Computing resources were provided by the Swedish National Infrastructure for Computing (SNIC).</p>
Data for paper "The Two Faces of AI in Green Mobile Computing: A Literature Review"
<p>This is the data associated with the literature review presented in the paper “The Two Faces of AI in Green Mobile Computing:<br> A Literature Review” accepted at SEAA 2023.</p>
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.